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1
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Zhang W, Qiu C, Lui S. Imaging Biomarker Studies of Antipsychotic-Naïve First-Episode Schizophrenia in China: Progress and Future Directions. Schizophr Bull 2025; 51:379-391. [PMID: 39841545 PMCID: PMC11908865 DOI: 10.1093/schbul/sbaf002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/24/2025]
Abstract
BACKGROUND AND HYPOTHESIS Identifying biomarkers at onset and specifying the progression over the early course of schizophrenia is critical for better understanding of illness pathophysiology and providing novel information relevant to illness prognosis and treatment selection. Studies of antipsychotic-naïve first-episode schizophrenia in China are making contributions to this goal. STUDY DESIGN A review was conducted for how antipsychotic-naïve first-episode patients were identified and studied, the investigated biological measures, with a focus on neuroimaging, and how they extend the understanding of schizophrenia regarding the illness-related brain abnormality, treatment effect characterization and outcome prediction, and subtype discovery and patient stratification, in comparison to findings from western populations. Finally, how biomarker studies should be conducted in the future was also discussed. STUDY RESULTS Gray matter reduction has been most robust within temporo-frontal regions and cerebellum, whereas altered brain function has been most pronounced in cerebello-cortical connections and default mode network, each might be related to long-standing illness alterations and acute physiological alterations at measurement. By studying untreated patients, the progressive alterations in temporal and frontal regions and enlargements in bilateral putamen were found more likely effects of illness, not just treatment. Some of these changes were found with potential to predict clinical outcomes and differentiate biologically patient subgroups. CONCLUSIONS Mostly with data-driven approaches, the studies from China are helping identify candidate imaging biomarkers in schizophrenia that are related to early-stage illness, treatment effects, and biological subgroup differentiation. Future work is needed to translate these biomarkers for clinical application.
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Affiliation(s)
- Wenjing Zhang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Changjian Qiu
- Mental Health Center, West China Hospital, Sichuan University, Sichuan Clinical Medical Research Center for Mental Disorders, Chengdu 610041, China
| | - Su Lui
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
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Tang B, Yao L, Strawn JR, Zhang W, Lui S. Neurostructural, Neurofunctional, and Clinical Features of Chronic, Untreated Schizophrenia: A Narrative Review. Schizophr Bull 2025; 51:366-378. [PMID: 39212651 PMCID: PMC11908860 DOI: 10.1093/schbul/sbae152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Studies of individuals with chronic, untreated schizophrenia (CUS) can provide important insights into the natural course of schizophrenia and how antipsychotic pharmacotherapy affects neurobiological aspects of illness course and progression. We systematically review 17 studies on the neuroimaging, cognitive, and epidemiological aspects of CUS individuals. These studies were conducted at the Shanghai Mental Health Center, Institute of Mental Health at Peking University, and Huaxi MR Research Center between 2013 and 2021. CUS is associated with cognitive impairment, severe symptoms, and specific demographic characteristics and is different significantly from those observed in antipsychotic-treated individuals. Furthermore, CUS individuals have neurostructural and neurofunctional alterations in frontal and temporal regions, corpus callosum, subcortical, and visual processing areas, as well as default-mode and somatomotor networks. As the disease progresses, significant structural deteriorations occur, such as accelerated cortical thinning in frontal and temporal lobes, greater reduction in fractional anisotropy in the genu of corpus callosum, and decline in nodal metrics of gray mater network in thalamus, correlating with worsening cognitive deficits and clinical outcomes. In addition, striatal hypertrophy also occurs, independent of antipsychotic treatment. Contrasting with the negative neurostructural and neurofunctional effects of short-term antipsychotic treatment, long-term therapy frequently results in significant improvements. It notably enhances white matter integrity and the functions of key subcortical regions such as the amygdala, hippocampus, and striatum, potentially improving cognitive functions. This narrative review highlights the progressive neurobiological sequelae of CUS, the importance of early detection, and long-term treatment of schizophrenia, particularly because treatment may attenuate neurobiological deterioration and improve clinical outcomes.
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Affiliation(s)
- Biqiu Tang
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry & Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH
| | - Li Yao
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Jeffrey R Strawn
- Department of Psychiatry & Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH
| | - Wenjing Zhang
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Su Lui
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
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3
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Cheng X, Yang Q, Zhang Y, Zhang M, Yu H, Ni P, Li X, Li M, Li T. The impact of the CACNB2 Rs11013860 polymorphism on grey matter volume and brain function in bipolar disorder. BMC Psychiatry 2025; 25:183. [PMID: 40016690 PMCID: PMC11866725 DOI: 10.1186/s12888-025-06611-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2024] [Accepted: 02/14/2025] [Indexed: 03/01/2025] Open
Abstract
BACKGROUND Recent genome-wide association studies have linked voltage-gated calcium channel genes to bipolar disorder (BD), in which CACNB2 gene rs11013860 is respectively reported. Less is known, though, about how precisely its polymorphism affects both the structure and function of the brain. METHODS 173 BD patients and 207 healthy controls (HCs) were underwent structural and functional magnetic resonance imaging scan and genotyped for CACNB2 rs11013860. Grey matter volume (GMV), regional homogeneity (ReHo) and degree centrality (DC) were used to examine the brain structure, functional activity and connectivity of these participants. RESULTS The emotional circuits in BD patients, such as cerebellum, insula, cingulate gyrus, fusiform gyrus, superior frontal gyrus, superior / middle temporal gyrus, middle occipital gyrus, lingual gyrus, precuneus, putamen, hippocampus and parahippocampal gyrus, were the main areas where GMV, ReHo, and DC differed from HCs. And the right anterior and posterior cerebellar lobes, parahippocampal gyrus as well as lingual gyrus showed an interaction between CACNB2 rs11013860 genotypes and diagnoses in GMV. In addition, there was a significant step-wise increase of GMV with decreased dosage of the A risk allele in HCs, but this pattern of relationship was absent in BD patients. No interaction between BD and CACNB2 rs11013860 was found in ReHo and DC. CONCLUSIONS These results suggest that the polymorphism of CACNB2 rs11013860 in BD patients may be associated with brain structural abnormalities in cerebellar, limbic system and other brain regions, perhaps contributing to the disease.
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Affiliation(s)
- Xiaofei Cheng
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| | - Qian Yang
- The Fourth People's Hospital of Haining, Jiaxing, China
| | - Yamin Zhang
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Mengmeng Zhang
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| | - Hua Yu
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Peiyan Ni
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xiaojing Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Mingli Li
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China.
| | - Tao Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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Li X, Zeng J, Liu N, Yang C, Tao B, Sun H, Gong Q, Zhang W, Li CSR, Lui S. Progressive alterations of resting-state hypothalamic dysconnectivity in schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 2024; 135:111127. [PMID: 39181307 DOI: 10.1016/j.pnpbp.2024.111127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 08/07/2024] [Accepted: 08/20/2024] [Indexed: 08/27/2024]
Abstract
BACKGROUND The hypothalamus may be involved in the pathogenesis of schizophrenia. Investigating hypothalamus dysfunction in schizophrenia and probing how it is related to symptoms and responds to antipsychotic medication is crucial for understanding the potential mechanism of hypothalamus dysfunction under the long-term illness. METHODS We recruited 216 patients with schizophrenia, including 140 antipsychotic-naïve first-episode patients (FES, including 44 patients with 1-year follow-up data), 76 chronically treated schizophrenia (CTS), and 210 healthy controls (HC). Hypothalamic seed-based functional connectivity (FC) was calculated and compared among the FES, CTS, and HC groups using analysis of covariance. Exploratory analysis was conducted between the FES patients at baseline and after 1-year follow-up. Significantly altered hypothalamic FCs were then related to clinical symptomology, while age- and illness-related regression analyses were also conducted and compared between diagnostic groups. RESULTS The FES patients showed decreased hypothalamic FCs with the midbrain and right thalamus, whereas the CTS patients showed more severe decreased hypothalamic FCs with the midbrain, right thalamus, left putamen, right caudate, and bilateral anterior cingulate cortex compared to HCs. These abnormalities were not correlated to the symptomology or illness duration, or not reversed by the antipsychotic treatment. Age-related hypothalamic FC decrease was also identified in the abovementioned regions, and a faster age-related decline of the hypothalamic FC was observed with the left putamen and bilateral anterior cingulate cortex. CONCLUSION Age-related hypothalamic FC decrease extends the functional alterations that characterize the neurodegenerative nature of schizophrenia. Future studies are required to further probe the hormonal or endocrinal underpinnings of such alterations and trace the precise progressive trajectories.
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Affiliation(s)
- Xing Li
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Jiaxin Zeng
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Naici Liu
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Chengmin Yang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Bo Tao
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Hui Sun
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Qiyong Gong
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Wenjing Zhang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
| | - Chiang-Shan R Li
- Departments of Psychiatry and of Neuroscience, Yale University School of Medicine, New Haven, CT, USA.
| | - Su Lui
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
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Di Stefano V, D’Angelo M, Monaco F, Vignapiano A, Martiadis V, Barone E, Fornaro M, Steardo L, Solmi M, Manchia M, Steardo L. Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry. Brain Sci 2024; 14:1196. [PMID: 39766395 PMCID: PMC11674252 DOI: 10.3390/brainsci14121196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2024] [Revised: 11/24/2024] [Accepted: 11/25/2024] [Indexed: 01/11/2025] Open
Abstract
Schizophrenia, a highly complex psychiatric disorder, presents significant challenges in diagnosis and treatment due to its multifaceted neurobiological underpinnings. Recent advancements in functional magnetic resonance imaging (fMRI) and artificial intelligence (AI) have revolutionized the understanding and management of this condition. This manuscript explores how the integration of these technologies has unveiled key insights into schizophrenia's structural and functional neural anomalies. fMRI research highlights disruptions in crucial brain regions like the prefrontal cortex and hippocampus, alongside impaired connectivity within networks such as the default mode network (DMN). These alterations correlate with the cognitive deficits and emotional dysregulation characteristic of schizophrenia. AI techniques, including machine learning (ML) and deep learning (DL), have enhanced the detection and analysis of these complex patterns, surpassing traditional methods in precision. Algorithms such as support vector machines (SVMs) and Vision Transformers (ViTs) have proven particularly effective in identifying biomarkers and aiding early diagnosis. Despite these advancements, challenges such as variability in methodologies and the disorder's heterogeneity persist, necessitating large-scale, collaborative studies for clinical translation. Moreover, ethical considerations surrounding data integrity, algorithmic transparency, and patient individuality must guide AI's integration into psychiatry. Looking ahead, AI-augmented fMRI holds promise for tailoring personalized interventions, addressing unique neural dysfunctions, and improving therapeutic outcomes for individuals with schizophrenia. This convergence of neuroimaging and computational innovation heralds a transformative era in precision psychiatry.
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Affiliation(s)
- Valeria Di Stefano
- Psychiatry Unit, Department of Health Sciences, University of Catanzaro Magna Graecia, 88100 Catanzaro, Italy; (V.D.S.); (L.S.J.)
| | - Martina D’Angelo
- Psychiatry Unit, Department of Health Sciences, University of Catanzaro Magna Graecia, 88100 Catanzaro, Italy; (V.D.S.); (L.S.J.)
| | - Francesco Monaco
- Department of Mental Health, Azienda Sanitaria Locale Salerno, 84125 Salerno, Italy; (F.M.); (A.V.)
- European Biomedical Research Institute of Salerno (EBRIS), 84125 Salerno, Italy
| | - Annarita Vignapiano
- Department of Mental Health, Azienda Sanitaria Locale Salerno, 84125 Salerno, Italy; (F.M.); (A.V.)
- European Biomedical Research Institute of Salerno (EBRIS), 84125 Salerno, Italy
| | - Vassilis Martiadis
- Department of Mental Health, Azienda Sanitaria Locale (ASL) Napoli 1 Centro, 80145 Naples, Italy;
| | - Eugenia Barone
- Department of Psychiatry, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy;
| | - Michele Fornaro
- Department of Neuroscience, Reproductive Science and Odontostomatology, University of Naples Federico II, 80138 Naples, Italy;
| | - Luca Steardo
- Department of Clinical Psychology, University Giustino Fortunato, 82100 Benevento, Italy;
- Department of Physiology and Pharmacology “Vittorio Erspamer”, SAPIENZA University of Rome, 00185 Rome, Italy
| | - Marco Solmi
- Department of Psychiatry, University of Ottawa, Ottawa, ON K1N 6N5, Canada;
- On Track: The Champlain First Episode Psychosis Program, Department of Mental Health, The Ottawa Hospital, Ottawa, ON K1H 8L6, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON K1N 6N5, Canada
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON K1N 6N5, Canada
- Department of Child and Adolescent Psychiatry, Charité-Universitätsmedizin, 10117 Berlin, Germany
| | - Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, 09124 Cagliari, Italy;
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, 09123 Cagliari, Italy
- Department of Pharmacology, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Luca Steardo
- Psychiatry Unit, Department of Health Sciences, University of Catanzaro Magna Graecia, 88100 Catanzaro, Italy; (V.D.S.); (L.S.J.)
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6
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Yao G, Luo J, Li J, Feng K, Liu P, Xu Y. Functional gradient dysfunction in drug-naïve first-episode schizophrenia and its correlation with specific transcriptional patterns and treatment predictions. Psychol Med 2024:1-13. [PMID: 39552400 DOI: 10.1017/s0033291724001739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2024]
Abstract
BACKGROUND First-episode schizophrenia (FES) is a progressive psychiatric disorder influenced by genetics, environmental factors, and brain function. The functional gradient deficits of drug-naïve FES and its relationship to gene expression profiles and treatment outcomes are unknown. METHODS In this study, we engaged a cohort of 116 FES and 100 healthy controls (HC), aged 7 to 30 years, including 15 FES over an 8-week antipsychotic medication regimen. Our examination focused on primary-to-transmodal alterations in voxel-based connection gradients in FES. Then, we employed network topology, Neurosynth, postmortem gene expression, and support vector regression to evaluate integration and segregation functions, meta-analytic cognitive terms, transcriptional patterns, and treatment predictions. RESULTS FES displayed diminished global connectome gradients (Cohen's d = 0.32-0.57) correlated with compensatory integration and segregation functions (Cohen's d = 0.31-0.36). Predominant alterations were observed in the default (67.6%) and sensorimotor (21.9%) network, related to high-order cognitive functions. Furthermore, we identified notable overlaps between partial least squares (PLS1) weighted genes and dysregulated genes in other psychiatric conditions. Genes linked with gradient alterations were enriched in synaptic signaling, neurodevelopment process, specific astrocytes, cortical layers (layer II and IV), and developmental phases from late/mid fetal to young adulthood. Additionally, the onset age influenced the severity of FES, with discernible differences in connection gradients between minor- and adult-FES. Moreover, the connectivity gradients of FES at baseline significantly predicted treatment outcomes. CONCLUSIONS These results offer significant theoretical foundations for elucidating the intricate interplay between macroscopic functional connection gradient changes and microscopic transcriptional patterns during the onset and progression of FES.
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Affiliation(s)
- Guanqun Yao
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, 030001, China
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Jing Luo
- School of Medicine, Tsinghua University, Beijing, 100084, China
- Department of Rheumatology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310000, China
| | - Jing Li
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, 030001, China
- College of Humanities and Social Science, Shanxi Medical University, Taiyuan, 030001, China
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Kun Feng
- School of Medicine, Tsinghua University, Beijing, 100084, China
- Department of Psychiatry, Yuquan Hospital, Tsinghua University, Beijing, 100040, China
| | - Pozi Liu
- School of Medicine, Tsinghua University, Beijing, 100084, China
- Department of Psychiatry, Yuquan Hospital, Tsinghua University, Beijing, 100040, China
| | - Yong Xu
- Department of Clinical Psychology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518031, China
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Fortier A, Dumais A, Boisvert M, Zouaoui I, Chung CF, Potvin S. Aberrant activity at rest of the associative striatum in schizophrenia: Meta-analyses of the amplitude of low frequency fluctuations. J Psychiatr Res 2024; 179:117-132. [PMID: 39284255 DOI: 10.1016/j.jpsychires.2024.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 08/28/2024] [Accepted: 09/09/2024] [Indexed: 11/05/2024]
Abstract
Schizophrenia is a severe psychiatric disorder associated with brain alterations at rest. Amplitude of low-frequency fluctuations (ALFF) and its fractional version (fALFF) have been widely used to investigate alterations in spontaneous brain activity in schizophrenia. However, results are still inconsistent. Furthermore, while these measurements are similar, they showed some differences, and no meta-analysis has been yet performed to compare them in schizophrenia. Thus, we conducted systematic research in five databases and in the grey literature to find articles investigating fALFF and/or ALFF alterations in schizophrenia. Two separate meta-analyses were performed using the SDM-PSI software to identify fALFF and ALFF alterations separately. Then, a conjunction analysis was conducted to determine congruent results between the two approaches. We found that patients with schizophrenia showed altered fALFF activity in the left insula/putamen, the right paracentral lobule and the left middle occipital gyrus compared to healthy individuals. Patients with schizophrenia exhibited ALFF alterations in the bilateral putamen, the bilateral caudate nucleus, the bilateral inferior frontal gyrus, the right precuneus, the right precentral gyrus, the left postcentral gyrus, the right posterior cingulate gyrus, compared to healthy controls. ALFF increased activity in the left putamen was higher in drug-naïve patients and was correlated with positive symptoms. The conjunction analysis revealed a spatial convergence between fALFF and ALFF studies in the left putamen. This left putamen cluster is part of the associative striatum. Its alteration in schizophrenia provides additional support to the influential aberrant salience hypothesis of psychosis.
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Affiliation(s)
- Alexandra Fortier
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montreal Quebec, Canada; Department of Psychiatry and Addiction, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Alexandre Dumais
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montreal Quebec, Canada; Department of Psychiatry and Addiction, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada; Philippe-Pinel National Institute of Legal Psychiatry, Montreal, Canada
| | - Mélanie Boisvert
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montreal Quebec, Canada; Department of Psychiatry and Addiction, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Inès Zouaoui
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montreal Quebec, Canada; Department of Psychiatry and Addiction, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Chen-Fang Chung
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montreal Quebec, Canada; Department of Psychiatry and Addiction, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Stéphane Potvin
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montreal Quebec, Canada; Department of Psychiatry and Addiction, Faculty of Medicine, University of Montreal, Montreal, Quebec, Canada.
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8
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Wei Y, Su W, Zhang T, Webler R, Tang X, Zheng Y, Tang Y, Xu L, Cui H, Zhu J, Qian Z, Ju M, Long B, Zhao J, Chen C, Zeng L, Zhang T, Wang J. Structural and functional abnormalities across clinical stages of psychosis: A multimodal neuroimaging investigation. Asian J Psychiatr 2024; 99:104153. [PMID: 39047353 DOI: 10.1016/j.ajp.2024.104153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 06/27/2024] [Accepted: 07/04/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND Structural and functional neurobiological abnormalities have been observed in schizophrenia. Previous studies have concentrated on specific illness stages, obscuring relationships between functional/structural changes and disorder progression. The present study aimed to quantify structural and functional abnormalities across different clinical stages using functional near-infrared spectroscopy (fNIRS) and structural magnetic resonance imaging (sMRI). METHODS Fifty-four participants with first-episode schizophrenia (FES), 120 with clinically high risk of psychosis (CHR), and 111 healthy controls (HCs) underwent functional near-infrared spectroscopy (fNIRS) to measure oxyhemoglobin (Oxy-Hb) during the verbal fluency task. Among them, 28FES, 64CHR and 55HC also finished sMRI. Oxy-Hb and gray matter volume (GMV) were compared among the three groups while controlling for covariates, including age, sex, years of education, and task performance. Mediation analysis was utilized to determine the mediating effect of GMV on Oxy-Hb and cognition. RESULTS Compared with the HC group, CHR and FES groups showed significantly reduced brain activity. However, there were no significant differences between the FES and CHR. Pronounced GMV increase in the right frontal pole area (F = 4.234, p = 0.016) was identified in the CHR and FES groups. Mediation analysis showed a significant mediation effect of the right frontal pole GMV between Channel 31 Oxy-Hb and processing speed (z = 2.105, p = 0.035) and attention/vigilance (z = 1.992, p = 0.046). CONCLUSIONS Brain activation and anatomical deficits were observed in different brain regions, suggesting that anatomical and functional abnormalities are dissociated in the early stages of psychosis. The relationship between neural activity and anatomy may reflect a specific pathophysiology related to cognitive deterioration in schizophrenia.
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Affiliation(s)
- Yanyan Wei
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Wenjun Su
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Tingyu Zhang
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China
| | - Ryan Webler
- Center for Brain Circuit Therapeutics, Brigham & Women's Hospital, Boston, MA, United States; Department of Psychiatry, Harvard Medical School, United States
| | - Xiaochen Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Yuchen Zheng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Huiru Cui
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Junjuan Zhu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Zhenying Qian
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Mingliang Ju
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Bin Long
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Jian Zhao
- Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Cheng Chen
- Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lingyun Zeng
- Department of Psychiatric Rehabilitation, Shenzhen Kangning Hospital, ShenZhen, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
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9
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Yu T, Pei WZ, Xu CY, Deng CC, Zhang XL. Identification of male schizophrenia patients using brain morphology based on machine learning algorithms. World J Psychiatry 2024; 14:804-811. [PMID: 38984327 PMCID: PMC11230103 DOI: 10.5498/wjp.v14.i6.804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 05/01/2024] [Accepted: 05/21/2024] [Indexed: 06/19/2024] Open
Abstract
BACKGROUND Schizophrenia is a severe psychiatric disease, and its prevalence is higher. However, diagnosis of early-stage schizophrenia is still considered a challenging task. AIM To employ brain morphological features and machine learning method to differentiate male individuals with schizophrenia from healthy controls. METHODS The least absolute shrinkage and selection operator and t tests were applied to select important features from structural magnetic resonance images as input features for classification. Four commonly used machine learning algorithms, the general linear model, random forest (RF), k-nearest neighbors, and support vector machine algorithms, were used to develop the classification models. The performance of the classification models was evaluated according to the area under the receiver operating characteristic curve (AUC). RESULTS A total of 8 important features with significant differences between groups were considered as input features for the establishment of classification models based on the four machine learning algorithms. Compared to other machine learning algorithms, RF yielded better performance in the discrimination of male schizophrenic individuals from healthy controls, with an AUC of 0.886. CONCLUSION Our research suggests that brain morphological features can be used to improve the early diagnosis of schizophrenia in male patients.
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Affiliation(s)
- Tao Yu
- Department of Clinical Nutrition, Hefei Fourth People’s Hospital, Hefei 230032, Anhui Province, China
| | - Wen-Zhi Pei
- Department of Psychiatry, Hefei Fourth People’s Hospital, Hefei 230032, Anhui Province, China
| | - Chun-Yuan Xu
- Department of Clinical Nutrition, Hefei Fourth People’s Hospital, Hefei 230032, Anhui Province, China
| | - Chen-Chen Deng
- Department of Gynaecology, Anhui Maternal and Child Health Hospital, Hefei 230032, Anhui Province, China
| | - Xu-Lai Zhang
- Department of Psychiatry, Hefei Fourth People’s Hospital, Hefei 230032, Anhui Province, China
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10
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Merola GP, Tarchi L, Saccaro LF, Delavari F, Piguet C, Van De Ville D, Castellini G, Ricca V. Transdiagnostic markers across the psychosis continuum: a systematic review and meta-analysis of resting state fMRI studies. Front Psychiatry 2024; 15:1378439. [PMID: 38895037 PMCID: PMC11184053 DOI: 10.3389/fpsyt.2024.1378439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 04/26/2024] [Indexed: 06/21/2024] Open
Abstract
Psychotic symptoms are among the most debilitating and challenging presentations of severe psychiatric diseases, such as schizophrenia, schizoaffective, and bipolar disorder. A pathophysiological understanding of intrinsic brain activity underlying psychosis is crucial to improve diagnosis and treatment. While a potential continuum along the psychotic spectrum has been recently described in neuroimaging studies, especially for what concerns absolute and relative amplitude of low-frequency fluctuations (ALFF and fALFF), these efforts have given heterogeneous results. A transdiagnostic meta-analysis of ALFF/fALFF in patients with psychosis compared to healthy controls is currently lacking. Therefore, in this pre-registered systematic review and meta-analysis PubMed, Scopus, and Embase were searched for articles comparing ALFF/fALFF between psychotic patients and healthy controls. A quantitative synthesis of differences in (f)ALFF between patients along the psychotic spectrum and healthy controls was performed with Seed-based d Mapping, adjusting for age, sex, duration of illness, clinical severity. All results were corrected for multiple comparisons by Family-Wise Error rates. While lower ALFF and fALFF were detected in patients with psychosis in comparison to controls, no specific finding survived correction for multiple comparisons. Lack of this correction might explain the discordant findings highlighted in previous literature. Other potential explanations include methodological issues, such as the lack of standardization in pre-processing or analytical procedures among studies. Future research on ALFF/fALFF differences for patients with psychosis should prioritize the replicability of individual studies. Systematic review registration https://osf.io/, identifier (ycqpz).
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Affiliation(s)
| | - Livio Tarchi
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Luigi F. Saccaro
- Psychiatry Department, Geneva University Hospital and Faculty of Medicine, Geneva University Hospital, Geneva, Switzerland
| | - Farnaz Delavari
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
| | - Camille Piguet
- Psychiatry Department, Geneva University Hospital and Faculty of Medicine, Geneva University Hospital, Geneva, Switzerland
- General Pediatric Division, Geneva University Hospital, Geneva, Switzerland
| | - Dimitri Van De Ville
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Giovanni Castellini
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Valdo Ricca
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
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11
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Maximo JO, Briend F, Armstrong WP, Kraguljac NV, Lahti AC. Higher-order functional brain networks and anterior cingulate glutamate + glutamine (Glx) in antipsychotic-naïve first episode psychosis patients. Transl Psychiatry 2024; 14:183. [PMID: 38600117 PMCID: PMC11006887 DOI: 10.1038/s41398-024-02854-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 02/07/2024] [Accepted: 02/26/2024] [Indexed: 04/12/2024] Open
Abstract
Human connectome studies have provided abundant data consistent with the hypothesis that functional dysconnectivity is predominant in psychosis spectrum disorders. Converging lines of evidence also suggest an interaction between dorsal anterior cingulate cortex (dACC) cortical glutamate with higher-order functional brain networks (FC) such as the default mode (DMN), dorsal attention (DAN), and executive control networks (ECN) in healthy controls (HC) and this mechanism may be impaired in psychosis. Data from 70 antipsychotic-medication naïve first-episode psychosis (FEP) and 52 HC were analyzed. 3T Proton magnetic resonance spectroscopy (1H-MRS) data were acquired from a voxel in the dACC and assessed correlations (positive FC) and anticorrelations (negative FC) of the DMN, DAN, and ECN. We then performed regressions to assess associations between glutamate + glutamine (Glx) with positive and negative FC of these same networks and compared them between groups. We found alterations in positive and negative FC in all networks (HC > FEP). A relationship between dACC Glx and positive and negative FC was found in both groups, but when comparing these relationships between groups, we found contrasting associations between these variables in FEP patients compared to HC. We demonstrated that both positive and negative FC in three higher-order resting state networks are already altered in antipsychotic-naïve FEP, underscoring the importance of also considering anticorrelations for optimal characterization of large-scale functional brain networks as these represent biological processes as well. Our data also adds to the growing body of evidence supporting the role of dACC cortical Glx as a mechanism underlying alterations in functional brain network connectivity. Overall, the implications for these findings are imperative as this particular mechanism may differ in untreated or chronic psychotic patients; therefore, understanding this mechanism prior to treatment could better inform clinicians.Clinical trial registration: Trajectories of Treatment Response as Window into the Heterogeneity of Psychosis: A Longitudinal Multimodal Imaging Study, NCT03442101 . Glutamate, Brain Connectivity and Duration of Untreated Psychosis (DUP), NCT02034253 .
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Affiliation(s)
- Jose O Maximo
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Frederic Briend
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
- UMR1253, iBrain, Université de Tours, Inserm, Tours, France
| | - William P Armstrong
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nina V Kraguljac
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA.
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12
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Liu W, Jiang X, Deng Z, Xie Y, Guo Y, Wu Y, Sun Q, Kong L, Wu F, Tang Y. Functional and structural alterations in different durations of untreated illness in the frontal and parietal lobe in major depressive disorder. Eur Arch Psychiatry Clin Neurosci 2024; 274:629-642. [PMID: 37542558 PMCID: PMC10995069 DOI: 10.1007/s00406-023-01625-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 05/22/2023] [Indexed: 08/07/2023]
Abstract
Major depressive disorder (MDD) is one of the most disabling illnesses that profoundly restricts psychosocial functions and impairs quality of life. However, the treatment rate of MDD is surprisingly low because the availability and acceptability of appropriate treatments are limited. Therefore, identifying whether and how treatment delay affects the brain and the initial time point of the alterations is imperative, but these changes have not been thoroughly explored. We investigated the functional and structural alterations of MDD for different durations of untreated illness (DUI) using regional homogeneity (ReHo) and voxel-based morphometry (VBM) with a sample of 125 treatment-naïve MDD patients and 100 healthy controls (HCs). The MDD patients were subgrouped based on the DUI, namely, DUI ≤ 1 M, 1 < DUI ≤ 6 M, 6 < DUI ≤ 12 M, and 12 < DUI ≤ 48 M. Subgroup comparison (MDD with different DUIs) was applied to compare ReHo and grey matter volume (GMV) extracted from clusters of regions with significant differences (the pooled MDD patients relative to HCs). Correlations and mediation effects were analysed to estimate the relationships between the functional and structural neuroimaging changes and clinical characteristics. MDD patients exhibited decreased ReHo in the left postcentral gyrus and precentral gyrus and reduced GMV in the left middle frontal gyrus and superior frontal gyrus relative to HCs. The initial functional abnormalities were detected after being untreated for 1 month, whereas this duration was 3 months for GMV reduction. Nevertheless, a transient increase in ReHo was observed after being untreated for 3 months. No significant differences were discovered between HCs and MDD patients with a DUI less than 1 month or among MDD patients with different DUIs in either ReHo or GMV. Longer DUI was related to reduced ReHo with GMV as mediator in MDD patients. We identified disassociated functional and anatomical alterations in treatment-naïve MDD patients at different time points in distinct brain regions at the early stage of the disease. Additionally, we also discovered that GMV mediated the relationship between a longer DUI and diminished ReHo in MDD patients, disclosing the latent deleterious and neuro-progressive implications of DUI on both the structure and function of the brain and indicating the necessity of early treatment of MDD.
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Affiliation(s)
- Wen Liu
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China
| | - Xiaowei Jiang
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China
- Department of Radiology, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China
| | - Zijing Deng
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China
| | - Yu Xie
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China
| | - Yingrui Guo
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China
| | - Yifan Wu
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China
| | - Qikun Sun
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China
| | - Lingtao Kong
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China
| | - Feng Wu
- Brain Function Research Section, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China
| | - Yanqing Tang
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China.
- Department of Gerontology, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, People's Republic of China.
- Department of Psychiatry and Geriatric Medicine, The First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, 110001, Liaoning, People's Republic of China.
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13
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Grosu C, Klauser P, Dwir D, Khadimallah I, Alemán-Gómez Y, Laaboub N, Piras M, Fournier M, Preisig M, Conus P, Draganski B, Eap CB. Associations between antipsychotics-induced weight gain and brain networks of impulsivity. Transl Psychiatry 2024; 14:162. [PMID: 38531873 DOI: 10.1038/s41398-024-02881-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 03/13/2024] [Accepted: 03/15/2024] [Indexed: 03/28/2024] Open
Abstract
Given the unpredictable rapid onset and ubiquitous consequences of weight gain induced by antipsychotics, there is a pressing need to get insights into the underlying processes at the brain system level that will allow stratification of "at risk" patients. The pathophysiological hypothesis at hand is focused on brain networks governing impulsivity that are modulated by neuro-inflammatory processes. To this aim, we investigated brain anatomy and functional connectivity in patients with early psychosis (median age: 23 years, IQR = 21-27) using anthropometric data and magnetic resonance imaging acquired one month to one year after initiation of AP medication. Our analyses included 19 patients with high and rapid weight gain (i.e., ≥5% from baseline weight after one month) and 23 patients with low weight gain (i.e., <5% from baseline weight after one month). We replicated our analyses in young (26 years, IQR = 22-33, N = 102) and middle-aged (56 years, IQR = 51-62, N = 875) healthy individuals from the general population. In early psychosis patients, higher weight gain was associated with poor impulse control score (β = 1.35; P = 0.03). Here, the observed brain differences comprised nodes of impulsivity networks - reduced frontal lobe grey matter volume (Pcorrected = 0.007) and higher striatal volume (Pcorrected = 0.048) paralleled by disruption of fronto-striatal functional connectivity (R = -0.32; P = 0.04). Weight gain was associated with the inflammatory biomarker plasminogen activator inhibitor-1 (β = 4.9, P = 0.002). There was no significant association between increased BMI or weight gain and brain anatomy characteristics in both cohorts of young and middle-aged healthy individuals. Our findings support the notion of weight gain in treated psychotic patients associated with poor impulse control, impulsivity-related brain networks and chronic inflammation.
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Affiliation(s)
- Claire Grosu
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland.
| | - Paul Klauser
- Service of Child and Adolescent Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Daniella Dwir
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Ines Khadimallah
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Yasser Alemán-Gómez
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
- Connectomics Lab, Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Nermine Laaboub
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Marianna Piras
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Margot Fournier
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Martin Preisig
- Psychiatric Epidemiology and Psychopathology Research Center, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Philippe Conus
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital, Prilly, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neuroscience - Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Chin B Eap
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland.
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland.
- Center for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva and University of Lausanne, Lausanne, Switzerland.
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14
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Kim JS, Hong SB, Park KW, Lee ATC. Psychotic Symptoms in Patients With Major Neurological Diseases. J Clin Neurol 2024; 20:153-165. [PMID: 38433485 PMCID: PMC10921039 DOI: 10.3988/jcn.2023.0501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 12/28/2023] [Accepted: 12/30/2023] [Indexed: 03/05/2024] Open
Abstract
Neurological diseases often manifest with neuropsychiatric symptoms such as depression, emotional incontinence, anger, apathy and fatigue. In addition, affected patients may also experience psychotic symptoms such as hallucinations and delusions. Various factors contribute to the development of psychotic symptoms, and the mechanisms of psychosis are similar, but still differ among various neurological diseases. Although psychotic symptoms are uncommon, and have been less well investigated, they may annoy patients and their families as well as impair the patients' quality of life and increase the caregiver burden. Therefore, we need to appropriately identify and treat these psychotic symptoms in patients with neurological diseases.
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Affiliation(s)
- Jong S Kim
- Department of Neurology, Gangneung Asan Hospital, University of Ulsan College of Medicine, Gangneung, Korea.
| | - Seung-Bong Hong
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Keun-Woo Park
- Department of Neurology, Korea University Anam Hospital, Seoul, Korea
| | - Allen T C Lee
- Department of Psychiatry, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China
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15
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Chaves C, Dursun SM, Tusconi M, Hallak JEC. Neuroinflammation and schizophrenia - is there a link? Front Psychiatry 2024; 15:1356975. [PMID: 38389990 PMCID: PMC10881867 DOI: 10.3389/fpsyt.2024.1356975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 01/30/2024] [Indexed: 02/24/2024] Open
Affiliation(s)
- Cristiano Chaves
- NeuroMood Lab, School of Medicine and Kingston Health Sciences Center (KHSC), Department of Psychiatry, Queen's University, Kingston, ON, Canada
- Department of Neuroscience and Behavior, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil
- National Institute for Translational Medicine (INCT-TM), CNPq, São Paulo, Brazil
| | - Serdar M Dursun
- National Institute for Translational Medicine (INCT-TM), CNPq, São Paulo, Brazil
- Department of Psychiatry (Neurochemical Research Unit) and Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| | - Massimo Tusconi
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
| | - Jaime E C Hallak
- Department of Neuroscience and Behavior, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil
- National Institute for Translational Medicine (INCT-TM), CNPq, São Paulo, Brazil
- Department of Psychiatry (Neurochemical Research Unit) and Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
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16
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Bayar Kapici O, Kapici Y, Tekın A, Şırık M. A novel diagnosis method for schizophrenia based on globus pallidus data. Psychiatry Res Neuroimaging 2023; 336:111732. [PMID: 37922672 DOI: 10.1016/j.pscychresns.2023.111732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/25/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023]
Abstract
This research aims to diagnose schizophrenia with machine learning-based algorithms. Bayesian neural network, logistic regression, decision tree, k-nearest neighbor, and gaussian kernel classification techniques are investigated to diagnose schizophrenia with data from 125 persons. This study showed that left lateral ventricles and left globus pallidus volumes and their percentages in the brain were significantly lower than HCs in FEP patients. Using brain volumes, we were able to diagnose FEP with an accuracy of 73.6 % via logistic regression and with an accuracy of 86.4 % using the SVM kernel classifier method. Therefore, brain volumes can be used to diagnose FEP with the SVM kernel classifier method.
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Affiliation(s)
- Olga Bayar Kapici
- Department of Radiology, Adıyaman Training and Research Hospital, Adıyaman, Turkey
| | - Yaşar Kapici
- Department of Psychiatry, Kahta State Hospital, Adıyaman, Turkey.
| | - Atilla Tekın
- Department of Psychiatry, Adıyaman University Faculty of Medicine, Adıyaman, Turkey
| | - Mehmet Şırık
- Department of Radiology, Adıyaman University Faculty of Medicine, Adıyaman, Turkey
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17
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Ruiz-Torras S, Gudayol-Ferré E, Fernández-Vazquez O, Cañete-Massé C, Peró-Cebollero M, Guàrdia-Olmos J. Hypoconnectivity networks in schizophrenia patients: A voxel-wise meta-analysis of Rs-fMRI. Int J Clin Health Psychol 2023; 23:100395. [PMID: 37533450 PMCID: PMC10392089 DOI: 10.1016/j.ijchp.2023.100395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 07/05/2023] [Indexed: 08/04/2023] Open
Abstract
In recent years several meta-analyses regarding resting-state functional connectivity in patients with schizophrenia have been published. The authors have used different data analysis techniques: regional homogeneity, seed-based data analysis, independent component analysis, and amplitude of low frequencies. Hence, we aim to perform a meta-analysis to identify connectivity networks with different activation patterns between people diagnosed with schizophrenia and healthy controls using voxel-wise analysis. METHOD We collected primary studies exploring whole brain connectivity by functional magnetic resonance imaging at rest in patients with schizophrenia compared with healthy controls. We identified 25 studies included high-quality studies that included 1285 patients with schizophrenia and 1279 healthy controls. RESULTS The results indicate hypoactivation in the right precentral gyrus and the left superior temporal gyrus of patients with schizophrenia compared with healthy controls. CONCLUSIONS These regions have been linked with some clinical symptoms usually present in Plea with schizophrenia, such as auditory verbal hallucinations, formal thought disorder, and the comprehension and production of gestures.
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Affiliation(s)
- Silvia Ruiz-Torras
- Clínica Psicològica de la Universitat de Barcelona, Fundació Josep Finestres, Universitat de Barcelona, Spain
| | | | | | - Cristina Cañete-Massé
- Facultat de Psicologia, Secció de Psicologia Quantitativa, Universitat de Barcelona, Spain
- UB Institute of Complex Systems, Universitat de Barcelona, Spain
| | - Maribel Peró-Cebollero
- Facultat de Psicologia, Secció de Psicologia Quantitativa, Universitat de Barcelona, Spain
- UB Institute of Complex Systems, Universitat de Barcelona, Spain
- Institute of Neuroscience, Universitat de Barcelona, Spain
| | - Joan Guàrdia-Olmos
- Facultat de Psicologia, Secció de Psicologia Quantitativa, Universitat de Barcelona, Spain
- UB Institute of Complex Systems, Universitat de Barcelona, Spain
- Institute of Neuroscience, Universitat de Barcelona, Spain
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18
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Li X, Liu Q, Chen Z, Li Y, Yang Y, Wang X, Guo X, Luo B, Zhang Y, Shi H, Zhang L, Su X, Shao M, Song M, Guo S, Fan L, Yue W, Li W, Lv L, Yang Y. Abnormalities of Regional Brain Activity in Patients With Schizophrenia: A Longitudinal Resting-State fMRI Study. Schizophr Bull 2023; 49:1336-1344. [PMID: 37083900 PMCID: PMC10483477 DOI: 10.1093/schbul/sbad054] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
BACKGROUND Evidence from functional and structural research suggests that abnormal brain activity plays an important role in the pathophysiology of schizophrenia (SZ). However, limited studies have focused on post-treatment changes, and current conclusions are inconsistent. STUDY DESIGN We recruited 104 SZ patients to have resting-state functional magnetic resonance imaging scans at baseline and 8 weeks of treatment with second-generation antipsychotics, along with baseline scanning of 86 healthy controls (HCs) for comparison purposes. Individual regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), and degree centrality values were calculated to evaluate the functional activity. The Positive and Negative Syndrome Scale (PANSS) and MATRICS Consensus Cognitive Battery were applied to measure psychiatric symptoms and cognitive impairment in SZ patients. RESULTS Compared with HCs at baseline, SZ patients had higher ALFF and ReHo values in the bilateral inferior temporal gyrus, inferior frontal gyrus, and lower ALFF and ReHo values in fusiform gyrus and precuneus. Following 8 weeks of treatment, ReHo was increased in right medial region of the superior frontal gyrus (SFGmed) and decreased in the left middle occipital gyrus and the left postcentral gyrus. Meanwhile, ReHo of the right SFGmed was increased after treatment in the response group (the reduction rate of PANSS ≥50%). Enhanced ALFF in the dorsolateral of SFG correlated with improvement in depressive factor score. CONCLUSIONS These findings provide novel evidence for the abnormal functional activity hypothesis of SZ, suggesting that abnormality of right SFGmed can be used as a biomarker of treatment response in SZ.
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Affiliation(s)
- Xue Li
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Qing Liu
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Zhaonian Chen
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Yalin Li
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Ying Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Xiujuan Wang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Xiaoge Guo
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Binbin Luo
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Yan Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Han Shi
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Luwen Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Xi Su
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Minglong Shao
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Meng Song
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Suqin Guo
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Lingzhong Fan
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Weihua Yue
- Institute of Mental Health, Peking University, Beijing, China
- Key Laboratory for Mental Health, Ministry of Health, Beijing, China
| | - Wenqiang Li
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
| | - Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
- International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China
- Henan Collaborative Innovation Center of Prevention and treatment of mental disorder, Xinxiang, China
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19
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Gao X, Yao L, Li F, Yang C, Zhu F, Gong Q, Lui S. The cortical hypogyrification pattern in antipsychotic-naive first-episode schizophrenia. Cereb Cortex 2023; 33:7619-7626. [PMID: 36916957 DOI: 10.1093/cercor/bhad065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/12/2023] [Accepted: 02/13/2023] [Indexed: 03/15/2023] Open
Abstract
Schizophrenia is thought to be a neurodevelopmental disease with high genetic heritability, and evidence from neuroimaging studies has consistently shown widespread cortical local gyrification index (LGI) alterations; however, genes accounting for LGI alterations in schizophrenia remain unknown. The present study examined the LGI alterations in first-episode antipsychotic-naive schizophrenia compared with controls (235 patients and 214 controls); transcription-neuroimaging association analysis was used to evaluate the relationship between LGI deficits and specific risk genes. The expression profiles of 232 schizophrenia risk genes were extracted from six donated normal brains from the Allen Human Brain Atlas database. The correlation between LGI alterations and clinical symptoms was also tested. We found lower LGI values involved in frontotemporal regions and limbic systems. Nonparametric correlation analysis showed that 83 risk genes correlated with the hypogyrification pattern in schizophrenia. These identified risk genes were functionally enriched for the development of the central nervous system. The LGI in the left superior temporal gyrus was negatively associated with Positive and Negative Syndrome Scale negative symptoms. In summary, the present study provides a set of risk genes possibly related to the hypogyrification pattern in antipsychotic-naive first-episode schizophrenia, which could help to unveil the neurobiological underpinnings of cortical impairments in early-stage schizophrenia.
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Affiliation(s)
- Xin Gao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Li Yao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Chengmin Yang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Fei Zhu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
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20
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Demjaha A, Galderisi S, Glenthøj B, Arango C, Mucci A, Lawrence A, O'Daly O, Kempton M, Ciufolini S, Baandrup L, Ebdrup BH, Rodriguez-Jimenez R, Diaz-Marsa M, Díaz-Caneja CM, Winter van Rossum I, Kahn R, Dazzan P, McGuire P. Negative symptoms in First-Episode Schizophrenia related to morphometric alterations in orbitofrontal and superior temporal cortex: the OPTiMiSE study. Psychol Med 2023; 53:3471-3479. [PMID: 35197142 PMCID: PMC10277764 DOI: 10.1017/s0033291722000010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 12/20/2021] [Accepted: 01/04/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Negative symptoms are one of the most incapacitating features of Schizophrenia but their pathophysiology remains unclear. They have been linked to alterations in grey matter in several brain regions, but findings have been inconsistent. This may reflect the investigation of relatively small patient samples, and the confounding effects of chronic illness and exposure to antipsychotic medication. We sought to address these issues by investigating concurrently grey matter volumes (GMV) and cortical thickness (CTh) in a large sample of antipsychotic-naïve or minimally treated patients with First-Episode Schizophrenia (FES). METHODS T1-weighted structural MRI brain scans were acquired from 180 antipsychotic-naïve or minimally treated patients recruited as part of the OPTiMiSE study. The sample was stratified into subgroups with (N = 88) or without (N = 92) Prominent Negative Symptoms (PMN), based on PANSS ratings at presentation. Regional GMV and CTh in the two groups were compared using Voxel-Based Morphometry (VBM) and FreeSurfer (FS). Between-group differences were corrected for multiple comparisons via Family-Wise Error (FWE) and Monte Carlo z-field simulation respectively at p < 0.05 (2-tailed). RESULTS The presence of PMN symptoms was associated with larger left inferior orbitofrontal volume (p = 0.03) and greater CTh in the left lateral orbitofrontal gyrus (p = 0.007), but reduced CTh in the left superior temporal gyrus (p = 0.009). CONCLUSIONS The findings highlight the role of orbitofrontal and temporal cortices in the pathogenesis of negative symptoms of Schizophrenia. As they were evident in generally untreated FEP patients, the results are unlikely to be related to effects of previous treatment or illness chronicity.
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Affiliation(s)
- Arsime Demjaha
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- National Institute for Health Research Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
| | - Silvana Galderisi
- Department of Psychiatry, University of Campania Luigi Vanvitelli, Caserta, Italy
| | - Birthe Glenthøj
- Faculty of Health and Medical Sciences, Department of Clinical Medicine, Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, Mental Health Center Glostrup, University of Copenhagen, Copenhagen, Denmark
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health. Hospital General Universitario Gregorio Marañón. IiSGM, CIBERSAM. School of Medicine, Universidad Complutense de Madrid, Madrid, Spain
| | - Armida Mucci
- Department of Psychiatry, University of Campania Luigi Vanvitelli, Caserta, Italy
| | - Andrew Lawrence
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- National Institute for Health Research Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
| | - Owen O'Daly
- Department of Neuroimaging, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Matthew Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- National Institute for Health Research Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
| | - Simone Ciufolini
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- National Institute for Health Research Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
| | - Lone Baandrup
- Faculty of Health and Medical Sciences, Department of Clinical Medicine, Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, Mental Health Center Glostrup, University of Copenhagen, Copenhagen, Denmark
| | - Bjørn H. Ebdrup
- Faculty of Health and Medical Sciences, Department of Clinical Medicine, Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, Mental Health Center Glostrup, University of Copenhagen, Copenhagen, Denmark
| | - Roberto Rodriguez-Jimenez
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health. Hospital General Universitario Gregorio Marañón. IiSGM, CIBERSAM. School of Medicine, Universidad Complutense de Madrid, Madrid, Spain
| | - Maria Diaz-Marsa
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health. Hospital General Universitario Gregorio Marañón. IiSGM, CIBERSAM. School of Medicine, Universidad Complutense de Madrid, Madrid, Spain
| | - Covadonga Martinez Díaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health. Hospital General Universitario Gregorio Marañón. IiSGM, CIBERSAM. School of Medicine, Universidad Complutense de Madrid, Madrid, Spain
| | | | - Rene Kahn
- Department of Psychiatry, Brain Center Rudolf Magnus, Utrecht, Netherlands
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- National Institute for Health Research Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- National Institute for Health Research Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK
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21
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Li M, Deng W, Li Y, Zhao L, Ma X, Yu H, Li X, Meng Y, Wang Q, Du X, Sham PC, Palaniyappan L, Li T. Ameliorative patterns of grey matter in patients with first-episode and treatment-naïve schizophrenia. Psychol Med 2023; 53:3500-3510. [PMID: 35164887 PMCID: PMC10277763 DOI: 10.1017/s0033291722000058] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 12/30/2021] [Accepted: 01/05/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND Grey matter (GM) reduction is a consistent observation in established late stages of schizophrenia, but patients in the untreated early stages of illness display an increase as well as a decrease in GM distribution relative to healthy controls (HC). The relative excess of GM may indicate putative compensatory responses, though to date its relevance is unclear. METHODS 343 first-episode treatment-naïve patients with schizophrenia (FES) and 342 HC were recruited. Multivariate source-based morphometry was performed to identify covarying 'networks' of grey matter concentration (GMC). Neurocognitive scores using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and symptom burden using the Positive and Negative Symptoms Scale (PANSS) were obtained. Bivariate linear relationships between GMC and cognition/symptoms were studied. RESULTS Compared to healthy subjects, FES had prominently lower GMC in two components; the first consists of the anterior insula, inferior frontal gyrus, anterior cingulate and the second component with the superior temporal gyrus, precuneus, inferior/superior parietal lobule, cuneus, and lingual gyrus. Higher GMC was seen in adjacent areas of the middle and superior temporal gyrus, middle frontal gyrus, inferior parietal cortex and putamen. Greater GMC of this component was associated with lower duration of untreated psychosis, less severe positive symptoms and better performance on cognitive tests. CONCLUSIONS In untreated stages of schizophrenia, both a distributed lower and higher GMC is observable. While the higher GMC is relatively modest, it occurs across frontoparietal, temporal and subcortical regions in association with reduced illness burden suggesting a compensatory role for higher GMC in the early stages of schizophrenia.
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Affiliation(s)
- Mingli Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Wei Deng
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yinfei Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaohong Ma
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Hua Yu
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaojing Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Yajing Meng
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Xiangdong Du
- Suzhou Psychiatry Hospital, Affiliated Guangji Hospital of Soochow University, Suzhou, 215137, Jiangsu, China
| | - Pak Chung Sham
- Centre for Genomic Sciences and State Key Laboratory in Cognitive and Brain Sciences, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Lena Palaniyappan
- Robarts Research Institute & The Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada
- Department of Psychiatry, University of Western Ontario, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
| | - Tao Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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22
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Chen Y, Cao H, Liu S, Zhang B, Zhao G, Zhang Z, Li S, Li H, Yu X, Deng H. Brain Structure Measurements Predict Individualized Treatment Outcome of 12-Week Antipsychotic Monotherapies in First-episode Schizophrenia. Schizophr Bull 2023; 49:697-705. [PMID: 37010371 PMCID: PMC10154710 DOI: 10.1093/schbul/sbad043] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/04/2023]
Abstract
BACKGROUND AND HYPOTHESIS Early prediction of treatment response to antipsychotics in schizophrenia remains a challenge in clinical practice. This study aimed to investigate if brain morphometries including gray matter volume and cortical thickness could serve as potential predictive biomarkers in first-episode schizophrenia. STUDY DESIGN Sixty-eight drug-naïve first-episode patients underwent baseline structural MRI scans and were subsequently randomized to receive a single antipsychotic throughout the first 12 weeks. Assessments for symptoms and social functioning were conducted by eight "core symptoms" selected from the Positive and Negative Syndrome Scale (PANSS-8) and the Personal and Social performance scale (PSP) multiple times during follow-ups. Treatment outcome was evaluated as subject-specific slope coefficients for PANSS-8 and PSP scores using linear mixed model. LASSO regression model were conducted to examine the performance of baseline gray matter volume and cortical thickness in prediction of individualized treatment outcome. STUDY RESULTS The study showed that individual brain morphometries at baseline, especially the orbitofrontal, temporal and parietal cortex, pallidum and amygdala, significantly predicted 12-week treatment outcome of PANSS-8 (r[predicted vs observed] = 0.49, P = .001) and PSP (r[predicted vs observed] = 0.40, P = .003) in first-episode schizophrenia. Moreover, the gray matter volume performed better than cortical thickness in the prediction the symptom changes (P = .034), while cortical thickness outperformed gray matter volume in the prediction of outcome of social functioning (P = .029). CONCLUSIONS These findings provide initial evidence that brain morphometry have potential to be used as prognostic predictors for antipsychotic response in patients, encouraging the future investigation of the translational value of these measures in precision psychiatry.
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Affiliation(s)
- Ying Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Hope Recovery and Rehabilitation Center, West China Hospital of Sichuan University, Chengdu, China
| | - Hengyi Cao
- Center for Psychiatric Neuroscience, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Shanming Liu
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Bo Zhang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | | | - Zhuoqiu Zhang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Shuiying Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Haiming Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Xin Yu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Hong Deng
- Hope Recovery and Rehabilitation Center, West China Hospital of Sichuan University, Chengdu, China
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
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23
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Wang M, Barker PB, Cascella NG, Coughlin JM, Nestadt G, Nucifora FC, Sedlak TW, Kelly A, Younes L, Geman D, Palaniyappan L, Sawa A, Yang K. Longitudinal changes in brain metabolites in healthy controls and patients with first episode psychosis: a 7-Tesla MRS study. Mol Psychiatry 2023; 28:2018-2029. [PMID: 36732587 PMCID: PMC10394114 DOI: 10.1038/s41380-023-01969-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 01/13/2023] [Accepted: 01/17/2023] [Indexed: 02/04/2023]
Abstract
Seven Tesla magnetic resonance spectroscopy (7T MRS) offers a precise measurement of metabolic levels in the human brain via a non-invasive approach. Studying longitudinal changes in brain metabolites could help evaluate the characteristics of disease over time. This approach may also shed light on how the age of study participants and duration of illness may influence these metabolites. This study used 7T MRS to investigate longitudinal patterns of brain metabolites in young adulthood in both healthy controls and patients. A four-year longitudinal cohort with 38 patients with first episode psychosis (onset within 2 years) and 48 healthy controls was used to examine 10 brain metabolites in 5 brain regions associated with the pathophysiology of psychosis in a comprehensive manner. Both patients and controls were found to have significant longitudinal reductions in glutamate in the anterior cingulate cortex (ACC). Only patients were found to have a significant decrease over time in γ-aminobutyric acid, N-acetyl aspartate, myo-inositol, total choline, and total creatine in the ACC. Together we highlight the ACC with dynamic changes in several metabolites in early-stage psychosis, in contrast to the other 4 brain regions that also are known to play roles in psychosis. Meanwhile, glutathione was uniquely found to have a near zero annual percentage change in both patients and controls in all 5 brain regions during a four-year follow-up in young adulthood. Given that a reduction of the glutathione in the ACC has been reported as a feature of treatment-refractory psychosis, this observation further supports the potential of glutathione as a biomarker for this subset of patients with psychosis.
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Affiliation(s)
- Min Wang
- Russell H Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Peter B Barker
- Russell H Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
| | - Nicola G Cascella
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jennifer M Coughlin
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Gerald Nestadt
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Frederick C Nucifora
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas W Sedlak
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alexandra Kelly
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Laurent Younes
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | - Donald Geman
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | - Lena Palaniyappan
- Robarts Research Institution, University of Western Ontario, London, ON, Canada
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Akira Sawa
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Mental Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Kun Yang
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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24
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Li Q, Xu X, Qian Y, Cai H, Zhao W, Zhu J, Yu Y. Resting-state brain functional alterations and their genetic mechanisms in drug-naive first-episode psychosis. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:13. [PMID: 36841861 PMCID: PMC9968350 DOI: 10.1038/s41537-023-00338-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 02/07/2023] [Indexed: 02/27/2023]
Abstract
Extensive research has established the presence of resting-state brain functional damage in psychosis. However, the genetic mechanisms of such disease phenotype are yet to be unveiled. We investigated resting-state brain functional alterations in patients with drug-naive first-episode psychosis (DFP) by performing a neuroimaging meta-analysis of 8 original studies comprising 500 patients and 469 controls. Combined with the Allen Human Brain Atlas, we further conducted transcriptome-neuroimaging spatial correlations to identify genes whose expression levels were linked to brain functional alterations in DFP, followed by a range of gene functional characteristic analyses. Meta-analysis revealed a mixture of increased and decreased brain function in widespread areas including the default-mode, visual, motor, striatal, and cerebellar systems in DFP. Moreover, these brain functional alterations were spatially associated with the expression of 1662 genes, which were enriched for molecular functions, cellular components, and biological processes of the cerebral cortex, as well as psychiatric disorders including schizophrenia. Specific expression analyses demonstrated that these genes were specifically expressed in the brain tissue, in cortical neurons and immune cells, and during nearly all developmental periods. Concurrently, the genes could construct a protein-protein interaction network supported by hub genes and were linked to multiple behavioral domains including emotion, attention, perception, and motor. Our findings provide empirical evidence for the notion that brain functional damage in DFP involves a complex interaction of polygenes with various functional characteristics.
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Affiliation(s)
- Qian Li
- grid.459419.4Department of Radiology, Chaohu Hospital of Anhui Medical University, 238000 Hefei, China ,grid.412679.f0000 0004 1771 3402Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022 Hefei, China ,Research Center of Clinical Medical Imaging, Anhui Province, 230032 Hefei, China ,Anhui Provincial Institute of Translational Medicine, 230032 Hefei, China
| | - Xiaotao Xu
- grid.412679.f0000 0004 1771 3402Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022 Hefei, China ,Research Center of Clinical Medical Imaging, Anhui Province, 230032 Hefei, China ,Anhui Provincial Institute of Translational Medicine, 230032 Hefei, China
| | - Yinfeng Qian
- grid.412679.f0000 0004 1771 3402Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022 Hefei, China ,Research Center of Clinical Medical Imaging, Anhui Province, 230032 Hefei, China ,Anhui Provincial Institute of Translational Medicine, 230032 Hefei, China
| | - Huanhuan Cai
- grid.412679.f0000 0004 1771 3402Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022 Hefei, China ,Research Center of Clinical Medical Imaging, Anhui Province, 230032 Hefei, China ,Anhui Provincial Institute of Translational Medicine, 230032 Hefei, China
| | - Wenming Zhao
- grid.412679.f0000 0004 1771 3402Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022 Hefei, China ,Research Center of Clinical Medical Imaging, Anhui Province, 230032 Hefei, China ,Anhui Provincial Institute of Translational Medicine, 230032 Hefei, China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, China. .,Research Center of Clinical Medical Imaging, Anhui Province, 230032, Hefei, China. .,Anhui Provincial Institute of Translational Medicine, 230032, Hefei, China.
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 230022, Hefei, China. .,Research Center of Clinical Medical Imaging, Anhui Province, 230032, Hefei, China. .,Anhui Provincial Institute of Translational Medicine, 230032, Hefei, China.
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25
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Tang B, Zhang W, Liu J, Deng S, Hu N, Li S, Zhao Y, Liu N, Zeng J, Cao H, Sweeney JA, Gong Q, Gu S, Lui S. Altered controllability of white matter networks and related brain function changes in first-episode drug-naive schizophrenia. Cereb Cortex 2023; 33:1527-1535. [PMID: 36790361 DOI: 10.1093/cercor/bhac421] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/25/2022] [Accepted: 09/26/2022] [Indexed: 11/06/2022] Open
Abstract
Understanding how structural connectivity alterations affect aberrant dynamic function using network control theory will provide new mechanistic insights into the pathophysiology of schizophrenia. The study included 140 drug-naive schizophrenia patients and 119 healthy controls (HCs). The average controllability (AC) quantifying capacity of brain regions/networks to shift the system into easy-to-reach states was calculated based on white matter connectivity and was compared between patients and HCs as well as functional network topological and dynamic properties. The correlation analysis between AC and duration of untreated psychosis (DUP) were conducted to characterize the controllability progression pattern without treatment effects. Relative to HCs, patients exhibited reduced AC in multiple nodes, mainly distributed in default mode network (DMN), visual network (VN), and subcortical regions, and increased AC in somatomotor network. These networks also had impaired functional topology and increased temporal variability in dynamic functional connectivity analysis. Longer DUP was related to greater reductions of AC in VN and DMN. The current study highlighted potential structural substrates underlying altered functional dynamics in schizophrenia, providing a novel understanding of the relationship of anatomic and functional network alterations.
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Affiliation(s)
- Biqiu Tang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Wenjing Zhang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Jiang Liu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006 Xiyuan Avenue, Chengdu 611731, China
| | - Shikuang Deng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006 Xiyuan Avenue, Chengdu 611731, China
| | - Na Hu
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Siyi Li
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Youjin Zhao
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Nian Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, No 1 Maoyuan South Road, Shunqing District, Nanchong 637000, China
| | - Jiaxin Zeng
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Hengyi Cao
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, 350 Community Drive, Manhasset, NY 11030, United States.,Division of Psychiatry Research, Zucker Hillside Hospital, 75-59 263rd Street, Glen Oaks, NY 11004, United States
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, 260 Stetson Street, Cincinnati, OH 45219, United States
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006 Xiyuan Avenue, Chengdu 611731, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
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26
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Sun H, Lui S, Huang X, Sweeney J, Gong Q. Effects of randomness in the development of machine learning models in neuroimaging studies of schizophrenia. Schizophr Res 2023; 252:253-261. [PMID: 36682316 DOI: 10.1016/j.schres.2023.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 11/29/2022] [Accepted: 01/07/2023] [Indexed: 01/21/2023]
Abstract
Numerous studies have used machine learning with neuroimaging data for identifying individuals with a schizophrenia diagnosis. However, inconsistent results have limited the ability of the psychiatric community to objectively judge and accept the value of this approach. One factor that has contributed to the inconsistency, but has long been ignored, is randomness in the practice of machine learning. This is manifest when executing the same machine learning pipeline multiple times on the same dataset but getting different results. In the current study, a dataset of anatomical MRI scans from 158 patients with first-episode medication-naïve schizophrenia and 166 matched controls was used to investigate the effect of randomness on classifier performance estimates under different algorithm complexity and data splitting ratios. The maximum discriminatory accuracy that could be reached was 62.6 % ± 4.7 % (43.5 %-79.3 %) obtained when using extra-trees classifiers without feature normalization. Regions contributing to discrimination were located at bilateral temporal lobes and right frontal lobe. The results show that randomness has a significant impact on the precision of model performance estimates, especially when the size of test set is small. Current neuroimaging feature engineering combined with machine learning still falls short of being able to make diagnoses in the clinical context, but has value in revealing patterns of regional brain alteration associated with the illness. The current results indicate that effects of randomness on model performance should be reported and considered in interpreting model utility and it is necessary to evaluate models on large test sets to obtain valid estimates of model performance.
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Affiliation(s)
- Huaiqiang Sun
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - John Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
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27
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Zoghbi AW, Lieberman JA, Girgis RR. The neurobiology of duration of untreated psychosis: a comprehensive review. Mol Psychiatry 2023; 28:168-190. [PMID: 35931757 PMCID: PMC10979514 DOI: 10.1038/s41380-022-01718-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 07/14/2022] [Accepted: 07/21/2022] [Indexed: 01/11/2023]
Abstract
Duration of untreated psychosis (DUP) is defined as the time from the onset of psychotic symptoms until the first treatment. Studies have shown that longer DUP is associated with poorer response rates to antipsychotic medications and impaired cognition, yet the neurobiologic correlates of DUP are poorly understood. Moreover, it has been hypothesized that untreated psychosis may be neurotoxic. Here, we conducted a comprehensive review of studies that have examined the neurobiology of DUP. Specifically, we included studies that evaluated DUP using a range of neurobiologic and imaging techniques and identified 83 articles that met inclusion and exclusion criteria. Overall, 27 out of the total 83 studies (32.5%) reported a significant neurobiological correlate with DUP. These results provide evidence against the notion of psychosis as structurally or functionally neurotoxic on a global scale and suggest that specific regions of the brain, such as temporal regions, may be more vulnerable to the effects of DUP. It is also possible that current methodologies lack the resolution needed to more accurately examine the effects of DUP on the brain, such as effects on synaptic density. Newer methodologies, such as MR scanners with stronger magnets, PET imaging with newer ligands capable of measuring subcellular structures (e.g., the PET ligand [11C]UCB-J) may be better able to capture these limited neuropathologic processes. Lastly, to ensure robust and replicable results, future studies of DUP should be adequately powered and specifically designed to test for the effects of DUP on localized brain structure and function with careful attention paid to potential confounds and methodological issues.
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Affiliation(s)
- Anthony W Zoghbi
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, 77030, USA.
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.
- Institute of Genomic Medicine, Columbia University Irving Medical Center, New York, NY, 10032, USA.
- Department of Psychiatry, Columbia University Irving Medical Center, New York State Psychiatric Institute, New York, NY, 10032, USA.
- Office of Mental Health, New York State Psychiatric Institute, New York, NY, 10032, USA.
| | - Jeffrey A Lieberman
- Department of Psychiatry, Columbia University Irving Medical Center, New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Ragy R Girgis
- Department of Psychiatry, Columbia University Irving Medical Center, New York State Psychiatric Institute, New York, NY, 10032, USA.
- Office of Mental Health, New York State Psychiatric Institute, New York, NY, 10032, USA.
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28
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Zeng J, Zhang W, Wu G, Wang X, Shah C, Li S, Xiao Y, Yao L, Cao H, Li Z, Sweeney JA, Lui S, Gong Q. Effects of Antipsychotic Medications and Illness Duration on Brain Features That Distinguish Schizophrenia Patients. Schizophr Bull 2022; 48:1354-1362. [PMID: 35925035 PMCID: PMC9673268 DOI: 10.1093/schbul/sbac094] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND HYPOTHESIS Previous studies have reported effects of antipsychotic treatment and illness duration on brain features. This study used a machine learning approach to examine whether these factors in aggregate impacted the utility of MRI features for differentiating individual schizophrenia patients from healthy controls. STUDY DESIGN This case-control study used patients with never-treated first-episode schizophrenia (FES, n = 179) and long-term ill schizophrenia (LTSZ, n = 30), with follow-up of the FES group after treatment (n = 71), a group of patients who had received long-term antipsychotic treatment (n = 93) and age and sex-matched healthy controls (n = 373) for each patient group. A multiple kernel learning classifier combining both structural and functional brain features was used to discriminate individual patients from controls. STUDY RESULTS MRI features differentiated untreated FES (0.73) and LTSZ (0.83) patients from healthy controls with moderate accuracy, but accuracy was significantly higher in antipsychotic-treated FES (0.94) and LTSZ (0.98) patients. Treatment was associated with significantly increased accuracy of case identification in both early course and long-term ill patients (both p < .001). Effects of illness duration, examined separately in treated and untreated patients, were less robust. CONCLUSIONS Our results demonstrate that initiation of antipsychotic treatment alters brain features in ways that further distinguish individual schizophrenia patients from healthy individuals, and have a modest effect of illness duration. Intrinsic illness-related brain alterations in untreated patients, regardless of illness duration, are not sufficiently robust for accurate identification of schizophrenia patients.
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Affiliation(s)
- Jiaxin Zeng
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Wenjing Zhang
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Guorong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China
| | - Xiaowan Wang
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China
| | - Chandan Shah
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Siyi Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Yuan Xiao
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Li Yao
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Hengyi Cao
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Center for Psychiatry Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Zhenlin Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - John A Sweeney
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Su Lui
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Qiyong Gong
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
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29
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Xiao S, Yang Z, Su T, Gong J, Huang L, Wang Y. Functional and structural brain abnormalities in posttraumatic stress disorder: A multimodal meta-analysis of neuroimaging studies. J Psychiatr Res 2022; 155:153-162. [PMID: 36029627 DOI: 10.1016/j.jpsychires.2022.08.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 08/09/2022] [Accepted: 08/15/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Numerous resting-state functional and structural studies have revealed that many brain regions are involved in the pathogenesis of posttraumatic stress disorder (PTSD), but their findings have been inconsistent. Moreover, there has no study explored the functional and structural alterations across languages in PTSD. METHODS A meta-analysis of whole-brain on the amplitude of low-frequency fluctuation (ALFF) and voxel-based morphometry (VBM) studies that explored alterations in the spontaneous functional brain activity and grey matter volume (GMV) in PTSD patients across languages by using the Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) software. RESULTS A total of 15 studies (19 datasets) comprising 577 PTSD patients and 499 HCs for ALFF, and 27 studies (31 datasets) comprising 539 PTSD patients and 693 HCs for VBM were included. Overall, PTSD patients across languages displayed decreased ALFF in the in the left amygdala. For VBM meta-analysis, PTSD patients across languages displayed reduced GMV in the bilateral anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC), striatum, insula, superior temporal gyrus, left postcentral gyrus, and occipital gyrus. CONCLUSIONS The multimodal meta-analysis suggest that PTSD patients showed similar pattern of aberrant resting-state functional brain activity and structure mainly in the amygdala, suggesting that structural deficits might underlie alterations in function. In addition, some regions exhibited only structural abnormalities in PTSD, including the ACC/mPFC, striatum, insula, primary visual, auditory and sensorimotor cortices. Moreover, consistent alterations in PTSD patients across languages may draw attention to the disparity in multi-cultural considerations in psychiatric research and further understanding the neurophysiopathology of PTSD.
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Affiliation(s)
- Shu Xiao
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China
| | - Zibin Yang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China
| | - Ting Su
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China
| | - Jiaying Gong
- Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China; Department of Radiology, Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510655, China
| | - Li Huang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China
| | - Ying Wang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China.
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30
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Gao Z, Xiao Y, Zhang Y, Zhu F, Tao B, Tang X, Lui S. Comparisons of resting-state brain activity between insomnia and schizophrenia: a coordinate-based meta-analysis. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:80. [PMID: 36207333 PMCID: PMC9547062 DOI: 10.1038/s41537-022-00291-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 09/19/2022] [Indexed: 11/05/2022]
Abstract
Growing evidence shows that insomnia is closely associated with schizophrenia (SCZ), but the neural mechanism under the association remains unclear. A direct comparison of the patterns of resting-state brain activities would help understand the above question. Using meta-analytic approach, 11 studies of insomnia vs. healthy controls (HC) and 39 studies of SCZ vs. HC were included to illuminate the common and distinct patterns between insomnia and SCZ. Results showed that SCZ and insomnia shared increased resting-state brain activities in frontolimbic structures including the right medial prefrontal gyrus (mPFC) and left parahippocampal gyrus. SCZ additionally revealed greater increased activities in subcortical areas including bilateral putamen, caudate and right insula and greater decreased activities in precentral gyrus and orbitofrontal gyrus. Our study reveals both shared and distinct activation patterns in SCZ and insomnia, which may provide novel insights for understanding the neural basis of the two disorders and enlighten the possibility of the development of treatment strategies for insomnia in SCZ in the future.
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Affiliation(s)
- Ziyang Gao
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Yuan Xiao
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Ye Zhang
- grid.412901.f0000 0004 1770 1022Sleep Medicine Center, Department of Respiratory and Critical Care Medicine, Mental Health Center, Translational Neuroscience Center, and State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Fei Zhu
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Bo Tao
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Xiangdong Tang
- grid.412901.f0000 0004 1770 1022Sleep Medicine Center, Department of Respiratory and Critical Care Medicine, Mental Health Center, Translational Neuroscience Center, and State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Su Lui
- grid.412901.f0000 0004 1770 1022Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
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31
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Shao T, Wang W, Hei G, Yang Y, Long Y, Wang X, Xiao J, Huang Y, Song X, Xu X, Gao S, Huang J, Wang Y, Zhao J, Wu R. Identifying and revealing different brain neural activities of cognitive subtypes in early course schizophrenia. Front Mol Neurosci 2022; 15:983995. [PMID: 36267704 PMCID: PMC9577612 DOI: 10.3389/fnmol.2022.983995] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/07/2022] [Indexed: 01/10/2023] Open
Abstract
Background Cognitive subtypes of schizophrenia may exhibit different neurobiological characteristics. This study aimed to reveal the underlying neurobiological features between cognitive subtypes in the early course of schizophrenia (ECS). According to prior studies, we hypothesized to identify 2–4 distinct cognitive subtypes. We further hypothesized that the subtype with relatively poorer cognitive function might have lower brain spontaneous neural activity than the subtype with relatively better cognitive function. Method Cognitive function was assessed by the MATRICS Consensus Cognitive Battery (MCCB). Resting-state functional magnetic resonance imaging scanning was conducted for each individual. There were 155 ECS individuals and 97 healthy controls (HCs) included in the subsequent analysis. Latent profile analysis (LPA) was used to identify the cognitive subtypes in ECS individuals, and amplitude of low-frequency fluctuations (ALFFs) was used to measure brain spontaneous neural activity in ECS individuals and HCs. Results LPA identified two cognitive subtypes in ECS individuals, containing a severely impaired subtype (SI, n = 63) and a moderately impaired subtype (MI, n = 92). Compared to HCs, ECS individuals exhibited significantly increased ALFF in the left caudate and bilateral thalamus and decreased ALFF in the bilateral medial prefrontal cortex and bilateral posterior cingulate cortex/precuneus (PCC/PCu). In ECS cognitive subtypes, SI showed significantly higher ALFF in the left precentral gyrus (PreCG) and lower ALFF in the left PCC/PCu than MI. Furthermore, ALFFs of left PreCG were negatively correlated with several MCCB cognitive domains in ECS individuals, while ALFF of left PCC/PCu presented opposite correlations. Conclusion Our findings suggest that differences in the brain spontaneous neural activity of PreCG and PCC/PCu might be the potential neurobiological features of the cognitive subtypes in ECS, which may deepen our understanding of the role of PreCG and PCC/PCu in the pathogenesis of cognitive impairment in schizophrenia.
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Affiliation(s)
- Tiannan Shao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Weiyan Wang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Gangrui Hei
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ye Yang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yujun Long
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xiaoyi Wang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Jingmei Xiao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yuyan Huang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xueqin Song
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xijia Xu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Shuzhan Gao
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Jing Huang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Ying Wang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Jingping Zhao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Renrong Wu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- *Correspondence: Renrong Wu
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32
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Luckhoff HK, Asmal L, Scheffler F, Phahladira L, Smit R, van den Heuvel L, Fouche JP, Seedat S, Emsley R, du Plessis S. Associations between BMI and brain structures involved in food intake regulation in first-episode schizophrenia spectrum disorders and healthy controls. J Psychiatr Res 2022; 152:250-259. [PMID: 35753245 DOI: 10.1016/j.jpsychires.2022.06.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 06/04/2022] [Accepted: 06/10/2022] [Indexed: 11/28/2022]
Abstract
Structural brain differences have been described in first-episode schizophrenia spectrum disorders (FES), and often overlap with those evident in the metabolic syndrome (MetS). We examined the associations between body mass index (BMI) and brain structures involved in food intake regulation in minimally treated FES patients (n = 117) compared to healthy controls (n = 117). The effects of FES diagnosis, BMI and their interactions on our selected prefrontal cortical thickness and subcortical gray matter volume regions of interest (ROIs) were investigated with hierarchical multivariate regressions, followed by post-hoc regressions for the individual ROIs. In a secondary analysis, we examined the relationships of other MetS risk factors and psychopathology with the brain ROIs. Both illness and BMI significantly predicted the grouped prefrontal cortical thickness ROIs, whereas only BMI predicted the grouped subcortical volume ROIs. For the individual ROIs, schizophrenia diagnosis predicted thinner left and right frontal pole and right lateral OFC thickness, and increased BMI predicted thinner left and right caudal ACC thickness. There were no significant main or interaction effects for diagnosis and BMI on any of the individual subcortical volume ROIs. Secondary analyses suggest associations between several brain ROIs and individual MetS risk factors, but not with psychopathology. Our findings indicate differential, independent effects for FES diagnosis and BMI on brain structures. Limited evidence suggests that the BMI effects are more prominent in FES. Exploratory analyses suggest associations between other MetS risk factors and some brain ROIs.
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Affiliation(s)
- H K Luckhoff
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, 7550, South Africa.
| | - L Asmal
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, 7550, South Africa
| | - F Scheffler
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, 7550, South Africa
| | - L Phahladira
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, 7550, South Africa
| | - R Smit
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, 7550, South Africa
| | - L van den Heuvel
- South African Medical Research Council, Stellenbosch University Genomics of Brain Disorders Research Unit, Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, 7550, South Africa
| | - J P Fouche
- South African Medical Research Council, Stellenbosch University Genomics of Brain Disorders Research Unit, Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, 7550, South Africa
| | - S Seedat
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, 7550, South Africa
| | - R Emsley
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, 7550, South Africa
| | - S du Plessis
- Department of Psychiatry, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, 7550, South Africa
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Qiu X, Zhang R, Wen L, Jiang F, Mao H, Yan W, Xie S, Pan X. Alterations in Spontaneous Brain Activity in Drug-Naïve First-Episode Schizophrenia: An Anatomical/Activation Likelihood Estimation Meta-Analysis. Psychiatry Investig 2022; 19:606-613. [PMID: 36059049 PMCID: PMC9441467 DOI: 10.30773/pi.2022.0074] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/21/2022] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE The etiology of schizophrenia is unknown and is associated with abnormal spontaneous brain activity. There are no consistent results regarding the change in spontaneous brain activity of people with schizophrenia. In this study, we determined the specific changes in the amplitude of low-frequency fluctuation/fractional amplitude of low-frequency fluctuation (ALFF/fALFF) and regional homogeneity (ReHo) in patients with drug-naïve first-episode schizophrenia (Dn-FES). METHODS A comprehensive search of databases such as PubMed, Web of Science, and Embase was conducted to find articles on resting-state functional magnetic resonance imaging using ALFF/fALFF and ReHo in schizophrenia patients compared to healthy controls (HCs) and then, anatomical/activation likelihood estimation was performed. RESULTS Eighteen eligible studies were included in this meta-analysis. Compared to the spontaneous brain activity of HCs, we found changes in spontaneous brain activity in Dn-FES based on these two methods, mainly including the frontal lobe, putamen, lateral globus pallidus, insula, cerebellum, and posterior cingulate cortex. CONCLUSION We found that widespread abnormalities of spontaneous brain activity occur in the early stages of the onset of schizophrenia and may provide a reference for the early intervention of schizophrenia.
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Affiliation(s)
- Xiaolei Qiu
- Department of Psychiatry, Jiangning District Second People's Hospital, Nanjing, China
| | - Rongrong Zhang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Lu Wen
- Department of Psychiatry, Jiangning District Second People's Hospital, Nanjing, China
| | - Fuli Jiang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Hongjun Mao
- Department of Psychiatry, Jiangning District Second People's Hospital, Nanjing, China
| | - Wei Yan
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shiping Xie
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xinming Pan
- Department of Psychiatry, Jiangning District Second People's Hospital, Nanjing, China
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Liu H, Hou H, Li F, Zheng R, Zhang Y, Cheng J, Han S. Structural and Functional Brain Changes in Patients With Classic Trigeminal Neuralgia: A Combination of Voxel-Based Morphometry and Resting-State Functional MRI Study. Front Neurosci 2022; 16:930765. [PMID: 35844235 PMCID: PMC9277055 DOI: 10.3389/fnins.2022.930765] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/06/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives Brain structural and functional abnormalities have been separately reported in patients with classic trigeminal neuralgia (CTN). However, whether and how the functional deficits are related to the structural alterations remains unclear. This study aims to investigate the anatomical and functional deficits in patients with CTN and explore their association. Methods A total of 34 patients with CTN and 29 healthy controls (HCs) with age- and gender-matched were recruited. All subjects underwent structural and resting-state functional magnetic resonance imaging (fMRI) scanning and neuropsychological assessments. Voxel-based morphometry (VBM) was applied to characterize the alterations of gray matter volume (GMV). The amplitude of low-frequency fluctuation (ALFF) method was used to evaluate regional intrinsic spontaneous neural activity. Further correlation analyses were performed between the structural and functional changes and neuropsychological assessments. Results Compared to the HCs, significantly reduced GMV was revealed in the right hippocampus, right fusiform gyrus (FFG), and temporal-parietal regions (the left superior/middle temporal gyrus, left operculo-insular gyrus, left inferior parietal lobule, and right inferior temporal gyrus) in patients with CTN. Increased functional activity measured by zALFF was observed mainly in the limbic system (the bilateral hippocampus and bilateral parahippocampal gyrus), bilateral FFG, basal ganglia system (the bilateral putamen, bilateral caudate, and right pallidum), left thalamus, left cerebellum, midbrain, and pons. Moreover, the right hippocampus and FFG were the overlapped regions with both functional and anatomical deficits. Furthermore, GMV in the right hippocampus was negatively correlated with pain intensity, anxiety, and depression. GMV in the right FFG was negatively correlated with illness duration. The zALFF value in the right FFG was positively correlated with anxiety. Conclusion Our results revealed concurrent structural and functional changes in patients with CTN, indicating that the CTN is a brain disorder with structural and functional abnormalities. Moreover, the overlapping structural and functional changes in the right hippocampus and FFG suggested that anatomical and functional changes might alter dependently in patients with CTN. These findings highlight the vital role of hippocampus and FFG in the pathophysiology of CTN.
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Affiliation(s)
- Hao Liu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Haiman Hou
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Fangfang Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ruiping Zheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
- *Correspondence: Yong Zhang,
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
- Jingliang Cheng,
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
- Shaoqiang Han,
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Soldevila-Matías P, Schoretsanitis G, Tordesillas-Gutierrez D, Cuesta MJ, de Filippis R, Ayesa-Arriola R, González-Vivas C, Setién-Suero E, Verdolini N, Sanjuán J, Radua J, Crespo-Facorro B. Neuroimaging correlates of insight in non-affective psychosis: A systematic review and meta-analysis. REVISTA DE PSIQUIATRIA Y SALUD MENTAL 2022; 15:117-133. [PMID: 35840278 DOI: 10.1016/j.rpsmen.2022.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 07/01/2021] [Indexed: 06/15/2023]
Abstract
OBJECTIVE Neurological correlates of impaired insight in non-affective psychosis remain unclear. This study aimed to review and meta-analyze the studies assessing the grey matter volumetric correlates of impaired insight in non-affective psychosis. METHODS This study consisted of a systematic review of 23 studies, and a meta-analysis with SDM-PSI of the 11 studies that were whole-brain and reported maps or peaks of correlation of studies investigating the grey matter volumetric correlates of insight assessments of non-affective psychosis, PubMed and OVID datasets were independently reviewed for articles reporting neuroimaging correlates of insight in non-affective psychosis. Quality assessment was realized following previous methodological approaches for the ABC quality assessment test of imaging studies, based on two main criteria: the statistical power and the multidimensional assessment of insight. Study peaks of correlation between grey matter volume and insight were used to recreate brain correlation maps. RESULTS A total of 418 records were identified through database searching. Of these records, twenty-three magnetic resonance imaging (MRI) studies that used different insight scales were included. The quality of the evidence was high in 11 studies, moderate in nine, and low in three. Patients with reduced insight showed decreases in the frontal, temporal (specifically in superior temporal gyrus), precuneus, cingulate, insula, and occipital lobes cortical grey matter volume. The meta-analysis indicated a positive correlation between grey matter volume and insight in the right insula (i.e., the smaller the grey matter, the lower the insight). CONCLUSION Several brain areas might be involved in impaired insight in patients with non-affective psychoses. The methodologies employed, such as the applied insight scales, may have contributed to the considerable discrepancies in the findings.
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Affiliation(s)
- Pau Soldevila-Matías
- Department of Basic Psychology, Faculty of Psychology, University of Valencia, Valencia, Spain; Research Institute of Clinic University Hospital of Valencia (INCLIVA), Valencia, Spain; National Reference Center for Psychosocial Care for People with Serious Mental Disorder (CREAP), Valencia, Spain
| | - Georgios Schoretsanitis
- The Zucker Hillside Hospital, Psychiatry Research, Northwell Health, Glen Oaks, New York, USA
| | - Diana Tordesillas-Gutierrez
- Marqués de Valdecilla University Hospital, Department of Radiology, IDIVAL, Santander, Spain; Marqués de Valdecilla University Hospital, Department of Psychiatry, School of Medicine, University of Cantabria, IDIVAL, Santander, Spain; CIBERSAM, Biomedical Research Network on Mental Health Area, Madrid, Spain.
| | - Manuel J Cuesta
- Department of Psychiatry, Complejo Hospitalario de Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Renato de Filippis
- The Zucker Hillside Hospital, Psychiatry Research, Northwell Health, Glen Oaks, New York, USA; Psychiatry Unit Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
| | - Rosa Ayesa-Arriola
- Marqués de Valdecilla University Hospital, Department of Radiology, IDIVAL, Santander, Spain; Marqués de Valdecilla University Hospital, Department of Psychiatry, School of Medicine, University of Cantabria, IDIVAL, Santander, Spain; CIBERSAM, Biomedical Research Network on Mental Health Area, Madrid, Spain
| | - Carlos González-Vivas
- Research Institute of Clinic University Hospital of Valencia (INCLIVA), Valencia, Spain
| | - Esther Setién-Suero
- Marqués de Valdecilla University Hospital, Department of Radiology, IDIVAL, Santander, Spain; Marqués de Valdecilla University Hospital, Department of Psychiatry, School of Medicine, University of Cantabria, IDIVAL, Santander, Spain; CIBERSAM, Biomedical Research Network on Mental Health Area, Madrid, Spain
| | - Norma Verdolini
- Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, 170 Villarroel Street, 12-0, 08036 Barcelona, Spain
| | - Julio Sanjuán
- Research Institute of Clinic University Hospital of Valencia (INCLIVA), Valencia, Spain; CIBERSAM, Biomedical Research Network on Mental Health Area, Madrid, Spain; Department of Psychiatric, University of Valencia, School of Medicine, Valencia, Spain
| | - Joaquim Radua
- CIBERSAM, Biomedical Research Network on Mental Health Area, Madrid, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centre for Psychiatric Research and Education, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Benedicto Crespo-Facorro
- Marqués de Valdecilla University Hospital, Department of Radiology, IDIVAL, Santander, Spain; Marqués de Valdecilla University Hospital, Department of Psychiatry, School of Medicine, University of Cantabria, IDIVAL, Santander, Spain; CIBERSAM, Biomedical Research Network on Mental Health Area, Madrid, Spain
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Gur RE. Considering alternatives to the schizophrenia construct. Schizophr Res 2022; 242:49-51. [PMID: 35033392 DOI: 10.1016/j.schres.2021.12.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 12/18/2021] [Accepted: 12/19/2021] [Indexed: 02/08/2023]
Affiliation(s)
- Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, United States of America.
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37
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Chen Y, Womer FY, Feng R, Zhang X, Zhang Y, Duan J, Chang M, Yin Z, Jiang X, Wei S, Wei Y, Tang Y, Wang F. A Real-World Observation of Antipsychotic Effects on Brain Volumes and Intrinsic Brain Activity in Schizophrenia. Front Neurosci 2022; 15:749316. [PMID: 35221884 PMCID: PMC8863862 DOI: 10.3389/fnins.2021.749316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022] Open
Abstract
BackgroundThe confounding effects of antipsychotics that led to the inconsistencies of neuroimaging findings have long been the barriers to understanding the pathophysiology of schizophrenia (SZ). Although it is widely accepted that antipsychotics can alleviate psychotic symptoms during the early most acute phase, the longer-term effects of antipsychotics on the brain have been unclear. This study aims to look at the susceptibility of different imaging measures to longer-term medicated status through real-world observation.MethodsWe compared gray matter volume (GMV) with amplitude of low-frequency fluctuations (ALFFs) in 89 medicated-schizophrenia (med-SZ), 81 unmedicated-schizophrenia (unmed-SZ), and 235 healthy controls (HC), and the differences were explored for relationships between imaging modalities and clinical variables. We also analyzed age-related effects on GMV and ALFF values in the two patient groups (med-SZ and unmed-SZ).ResultsMed-SZ demonstrated less GMV in the prefrontal cortex, temporal lobe, cingulate gyri, and left insula than unmed-SZ and HC (p < 0.05, family-wise error corrected). Additionally, GMV loss correlated with psychiatric symptom relief in all SZ. However, medicated status did not influence ALFF values: all SZ showed increased ALFF in the anterior cerebrum and decreased ALFF in posterior visual cortices compared with HC (p < 0.05, family-wise error corrected). Age-related GMV effects were seen in all regions, which showed group-level differences except fusiform gyrus. No significant correlation was found between ALFF values and psychiatric symptoms.ConclusionGMV loss appeared to be pronounced to longer-term antipsychotics, whereby imbalanced alterations in regional low-frequency fluctuations persisted unaffected by antipsychotic treatment. Our findings may help to understand the disease course of SZ and potentially identify a reliable neuroimaging feature for diagnosis.
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Affiliation(s)
- Yifan Chen
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Fay Y. Womer
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Ruiqi Feng
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Xizhe Zhang
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
- Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Yanbo Zhang
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
| | - Jia Duan
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Miao Chang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Zhiyang Yin
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Xiaowei Jiang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Shengnan Wei
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Yange Wei
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
- *Correspondence: Yanqing Tang,
| | - Fei Wang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
- Fei Wang,
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38
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Zhu Z, Wang Y, Lau WKW, Wei X, Liu Y, Huang R, Zhang R. Hyperconnectivity between the posterior cingulate and middle frontal and temporal gyrus in depression: Based on functional connectivity meta-analyses. Brain Imaging Behav 2022; 16:1538-1551. [PMID: 35088354 DOI: 10.1007/s11682-022-00628-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/30/2021] [Indexed: 12/18/2022]
Abstract
Disrupted whole-brain resting-state functional connectivity (RSFC) of the posterior cingulate (PCC) has been highlighted to associate with cognitive and affective dysfunction in major depressive disorder (MDD). However, prior findings showed certain inconsistency about the RSFC of the PCC in MDD. This study aims to investigate the aberrant RSFC of the PCC in MDD using anisotropic effect-size version of seed-based d mapping (AES-SDM). Web of Science and PubMed were searched for studies investigating PCC-based RSFC in MDD. A total of 17 studies, involving 804 patients and 724 healthy controls (HCs), fit our selection criteria. Additionally, to seek for the link between functional and structural differences, we did a meta-analysis on the studies in conjunction with voxel-based morphology (VBM) analysis. The PCC showed higher RSFC with the left middle temporal gyrus (MTG) and the right middle frontal gyrus (MFG), and lower RSFC with the left superior frontal gyrus (SFG) and the left precuneus in patients with MDD than HCs. Moreover, the meta-regression analysis revealed a negative correlation between the FC alteration of the right MFG with the PCC and depression severity. Notably, the left MTG and the left MFG demonstrated gray matter deviations in conjunction analysis. Our results indicated that the aberrant RSFC between the PCC and brain regions sub-serving cognitive control and emotional regulation in patients with MDD. And such functional alterations may have structural basis. These findings may underlie the mechanisms of deficits in cognitive control and emotional regulation of MDD.
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Affiliation(s)
- Ziqing Zhu
- Department of Psychology, School of Public Health, Southern Medical University, Room 202, Guangzhou, People's Republic of China.,Laboratory of Cognitive Control and Brain Healthy, School of Public Health, Southern Medical University, Guangzhou, People's Republic of China
| | - You Wang
- Department of Psychology, School of Public Health, Southern Medical University, Room 202, Guangzhou, People's Republic of China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, People's Republic of China
| | - Way K W Lau
- Department of Special Education and Counseling, The Education University of Hong Kong, Hong Kong, China
| | - Xinhua Wei
- Department of Radiology, Guangzhou First Affiliate Hospital, Guangzhou, China
| | - Yingjun Liu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, People's Republic of China
| | - Ruiwang Huang
- School of Psychology, South China Normal University, Guangzhou, China
| | - Ruibin Zhang
- Department of Psychology, School of Public Health, Southern Medical University, Room 202, Guangzhou, People's Republic of China. .,Laboratory of Cognitive Control and Brain Healthy, School of Public Health, Southern Medical University, Guangzhou, People's Republic of China. .,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, People's Republic of China.
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39
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Wang J, Ke P, Zang J, Wu F, Wu K. Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study. Front Neurosci 2022; 15:785595. [PMID: 35087373 PMCID: PMC8787107 DOI: 10.3389/fnins.2021.785595] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 12/01/2021] [Indexed: 12/12/2022] Open
Abstract
Interest in the application of machine learning (ML) techniques to multimodal magnetic resonance imaging (MRI) data for the diagnosis of schizophrenia (SZ) at the individual level is growing. However, a few studies have applied the features of structural and functional brain networks derived from multimodal MRI data to the discriminative analysis of SZ patients at different clinical stages. In this study, 205 normal controls (NCs), 61 first-episode drug-naive SZ (FESZ) patients, and 79 chronic SZ (CSZ) patients were recruited. We acquired their structural MRI, diffusion tensor imaging, and resting-state functional MRI data and constructed brain networks for each participant, including the gray matter network (GMN), white matter network (WMN), and functional brain network (FBN). We then calculated 3 nodal properties for each brain network, including degree centrality, nodal efficiency, and betweenness centrality. Two classifications (SZ vs. NC and FESZ vs. CSZ) were performed using five ML algorithms. We found that the SVM classifier with the input features of the combination of nodal properties of both the GMN and FBN achieved the best performance to discriminate SZ patients from NCs [accuracy, 81.2%; area under the receiver operating characteristic curve (AUC), 85.2%; p < 0.05]. Moreover, the SVM classifier with the input features of the combination of the nodal properties of both the GMN and WMN achieved the best performance to discriminate FESZ from CSZ patients (accuracy, 86.2%; AUC, 92.3%; p < 0.05). Furthermore, the brain areas in the subcortical/cerebellum network and the frontoparietal network showed significant importance in both classifications. Together, our findings provide new insights to understand the neuropathology of SZ and further highlight the potential advantages of multimodal network properties for identifying SZ patients at different clinical stages.
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Affiliation(s)
- Jing Wang
- School of Biomedical Engineering, Guangzhou Xinhua University, Guangzhou, China
| | - Pengfei Ke
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Jinyu Zang
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Fengchun Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- *Correspondence: Fengchun Wu,
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
- Key Laboratory of Biomedical Engineering of Guangdong Province, South China University of Technology, Guangzhou, China
- Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China
- Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- Kai Wu,
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40
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Kirschner M, Hodzic-Santor B, Antoniades M, Nenadic I, Kircher T, Krug A, Meller T, Grotegerd D, Fornito A, Arnatkeviciute A, Bellgrove MA, Tiego J, Dannlowski U, Koch K, Hülsmann C, Kugel H, Enneking V, Klug M, Leehr EJ, Böhnlein J, Gruber M, Mehler D, DeRosse P, Moyett A, Baune BT, Green M, Quidé Y, Pantelis C, Chan R, Wang Y, Ettinger U, Debbané M, Derome M, Gaser C, Besteher B, Diederen K, Spencer TJ, Fletcher P, Rössler W, Smigielski L, Kumari V, Premkumar P, Park HRP, Wiebels K, Lemmers-Jansen I, Gilleen J, Allen P, Kozhuharova P, Marsman JB, Lebedeva I, Tomyshev A, Mukhorina A, Kaiser S, Fett AK, Sommer I, Schuite-Koops S, Paquola C, Larivière S, Bernhardt B, Dagher A, Grant P, van Erp TGM, Turner JA, Thompson PM, Aleman A, Modinos G. Cortical and subcortical neuroanatomical signatures of schizotypy in 3004 individuals assessed in a worldwide ENIGMA study. Mol Psychiatry 2022; 27:1167-1176. [PMID: 34707236 PMCID: PMC9054674 DOI: 10.1038/s41380-021-01359-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/02/2021] [Accepted: 10/08/2021] [Indexed: 02/04/2023]
Abstract
Neuroanatomical abnormalities have been reported along a continuum from at-risk stages, including high schizotypy, to early and chronic psychosis. However, a comprehensive neuroanatomical mapping of schizotypy remains to be established. The authors conducted the first large-scale meta-analyses of cortical and subcortical morphometric patterns of schizotypy in healthy individuals, and compared these patterns with neuroanatomical abnormalities observed in major psychiatric disorders. The sample comprised 3004 unmedicated healthy individuals (12-68 years, 46.5% male) from 29 cohorts of the worldwide ENIGMA Schizotypy working group. Cortical and subcortical effect size maps with schizotypy scores were generated using standardized methods. Pattern similarities were assessed between the schizotypy-related cortical and subcortical maps and effect size maps from comparisons of schizophrenia (SZ), bipolar disorder (BD) and major depression (MDD) patients with controls. Thicker right medial orbitofrontal/ventromedial prefrontal cortex (mOFC/vmPFC) was associated with higher schizotypy scores (r = 0.067, pFDR = 0.02). The cortical thickness profile in schizotypy was positively correlated with cortical abnormalities in SZ (r = 0.285, pspin = 0.024), but not BD (r = 0.166, pspin = 0.205) or MDD (r = -0.274, pspin = 0.073). The schizotypy-related subcortical volume pattern was negatively correlated with subcortical abnormalities in SZ (rho = -0.690, pspin = 0.006), BD (rho = -0.672, pspin = 0.009), and MDD (rho = -0.692, pspin = 0.004). Comprehensive mapping of schizotypy-related brain morphometry in the general population revealed a significant relationship between higher schizotypy and thicker mOFC/vmPFC, in the absence of confounding effects due to antipsychotic medication or disease chronicity. The cortical pattern similarity between schizotypy and schizophrenia yields new insights into a dimensional neurobiological continuity across the extended psychosis phenotype.
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Affiliation(s)
- Matthias Kirschner
- grid.14709.3b0000 0004 1936 8649McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC Canada ,grid.7400.30000 0004 1937 0650Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Benazir Hodzic-Santor
- grid.14709.3b0000 0004 1936 8649McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC Canada
| | - Mathilde Antoniades
- grid.13097.3c0000 0001 2322 6764Department of Psychosis Studies, King’s College London, London, UK
| | - Igor Nenadic
- grid.10253.350000 0004 1936 9756University of Marburg, Marburg, Germany
| | - Tilo Kircher
- grid.10253.350000 0004 1936 9756University of Marburg, Marburg, Germany
| | - Axel Krug
- grid.10253.350000 0004 1936 9756University of Marburg, Marburg, Germany ,grid.10388.320000 0001 2240 3300Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Tina Meller
- grid.10253.350000 0004 1936 9756University of Marburg, Marburg, Germany
| | - Dominik Grotegerd
- grid.5949.10000 0001 2172 9288Department of Psychiatry, University of Münster, Münster, Germany
| | - Alex Fornito
- grid.1002.30000 0004 1936 7857Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Melbourne, VIC Australia
| | - Aurina Arnatkeviciute
- grid.1002.30000 0004 1936 7857Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Melbourne, VIC Australia
| | - Mark A. Bellgrove
- grid.1002.30000 0004 1936 7857Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Melbourne, VIC Australia
| | - Jeggan Tiego
- grid.1002.30000 0004 1936 7857Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Melbourne, VIC Australia
| | - Udo Dannlowski
- grid.5949.10000 0001 2172 9288Department of Psychiatry, University of Münster, Münster, Germany
| | - Katharina Koch
- grid.5949.10000 0001 2172 9288Department of Psychiatry, University of Münster, Münster, Germany
| | - Carina Hülsmann
- grid.5949.10000 0001 2172 9288Department of Psychiatry, University of Münster, Münster, Germany
| | - Harald Kugel
- grid.5949.10000 0001 2172 9288University Clinic for Radiology, University of Münster, Münster, Germany
| | - Verena Enneking
- grid.5949.10000 0001 2172 9288Department of Psychiatry, University of Münster, Münster, Germany
| | - Melissa Klug
- grid.5949.10000 0001 2172 9288Department of Psychiatry, University of Münster, Münster, Germany
| | - Elisabeth J. Leehr
- grid.5949.10000 0001 2172 9288Department of Psychiatry, University of Münster, Münster, Germany
| | - Joscha Böhnlein
- grid.5949.10000 0001 2172 9288Department of Psychiatry, University of Münster, Münster, Germany
| | - Marius Gruber
- grid.5949.10000 0001 2172 9288Department of Psychiatry, University of Münster, Münster, Germany
| | - David Mehler
- grid.5949.10000 0001 2172 9288Department of Psychiatry, University of Münster, Münster, Germany
| | - Pamela DeRosse
- grid.416477.70000 0001 2168 3646Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY USA ,grid.250903.d0000 0000 9566 0634The Feinstein Institutes for Medical Research, Center for Psychiatric Neuroscience, Manhasset, NY USA ,grid.512756.20000 0004 0370 4759Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY USA
| | - Ashley Moyett
- grid.416477.70000 0001 2168 3646Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY USA
| | - Bernhard T. Baune
- grid.5949.10000 0001 2172 9288Department of Psychiatry, University of Münster, Münster, Germany ,grid.1008.90000 0001 2179 088XDepartment of Psychiatry, Melbourne Medical School, University of Melbourne, Melbourne, VIC Australia
| | - Melissa Green
- grid.1005.40000 0004 4902 0432School of Psychiatry, University of New South Wales (UNSW), Sydney, NSW Australia ,grid.250407.40000 0000 8900 8842Neuroscience Research Australia (NeuRA), Randwick, NSW Australia
| | - Yann Quidé
- grid.1005.40000 0004 4902 0432School of Psychiatry, University of New South Wales (UNSW), Sydney, NSW Australia ,grid.250407.40000 0000 8900 8842Neuroscience Research Australia (NeuRA), Randwick, NSW Australia
| | - Christos Pantelis
- grid.1008.90000 0001 2179 088XMelbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, VIC Australia
| | - Raymond Chan
- grid.9227.e0000000119573309Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Yi Wang
- grid.9227.e0000000119573309Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Ulrich Ettinger
- grid.10388.320000 0001 2240 3300University of Bonn, Bonn, Germany
| | - Martin Debbané
- grid.8591.50000 0001 2322 4988University of Geneva, Geneva, Switzerland
| | - Melodie Derome
- grid.8591.50000 0001 2322 4988University of Geneva, Geneva, Switzerland
| | - Christian Gaser
- grid.275559.90000 0000 8517 6224Jena University Hospital, Jena, Germany
| | - Bianca Besteher
- grid.275559.90000 0000 8517 6224Jena University Hospital, Jena, Germany
| | - Kelly Diederen
- grid.13097.3c0000 0001 2322 6764Department of Psychosis Studies, King’s College London, London, UK
| | - Tom J. Spencer
- grid.13097.3c0000 0001 2322 6764Department of Psychosis Studies, King’s College London, London, UK
| | - Paul Fletcher
- grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Wulf Rössler
- grid.412004.30000 0004 0478 9977Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland ,grid.6363.00000 0001 2218 4662Department of Psychiatry and Psychotherapy, Charité University Medicine, Berlin, Germany ,grid.11899.380000 0004 1937 0722Institute of Psychiatry, School of Medicine, University of São Paulo, São Paulo, Brazil
| | - Lukasz Smigielski
- grid.412004.30000 0004 0478 9977Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Veena Kumari
- grid.7728.a0000 0001 0724 6933Brunel University London, Uxbridge, UK
| | - Preethi Premkumar
- grid.7728.a0000 0001 0724 6933Brunel University London, Uxbridge, UK
| | - Haeme R. P. Park
- grid.9654.e0000 0004 0372 3343School of Psychology, University of Auckland, Auckland, New Zealand
| | - Kristina Wiebels
- grid.9654.e0000 0004 0372 3343School of Psychology, University of Auckland, Auckland, New Zealand
| | | | - James Gilleen
- grid.13097.3c0000 0001 2322 6764Department of Psychosis Studies, King’s College London, London, UK ,grid.35349.380000 0001 0468 7274University of Roehampton, London, UK
| | - Paul Allen
- grid.35349.380000 0001 0468 7274University of Roehampton, London, UK
| | - Petya Kozhuharova
- grid.35349.380000 0001 0468 7274University of Roehampton, London, UK
| | - Jan-Bernard Marsman
- grid.4830.f0000 0004 0407 1981Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Irina Lebedeva
- grid.466467.10000 0004 0627 319XMental Health Research Center, Moscow, Russian Federation
| | - Alexander Tomyshev
- grid.466467.10000 0004 0627 319XMental Health Research Center, Moscow, Russian Federation
| | - Anna Mukhorina
- grid.466467.10000 0004 0627 319XMental Health Research Center, Moscow, Russian Federation
| | - Stefan Kaiser
- grid.150338.c0000 0001 0721 9812Department of Psychiatry, Geneva University Hospital, Geneva, Switzerland
| | - Anne-Kathrin Fett
- grid.13097.3c0000 0001 2322 6764Department of Psychosis Studies, King’s College London, London, UK ,grid.28577.3f0000 0004 1936 8497City, University London, London, UK
| | - Iris Sommer
- grid.4830.f0000 0004 0407 1981Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Sanne Schuite-Koops
- grid.4830.f0000 0004 0407 1981Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Casey Paquola
- grid.14709.3b0000 0004 1936 8649McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC Canada
| | - Sara Larivière
- grid.14709.3b0000 0004 1936 8649McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC Canada
| | - Boris Bernhardt
- grid.14709.3b0000 0004 1936 8649McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC Canada
| | - Alain Dagher
- grid.14709.3b0000 0004 1936 8649McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC Canada
| | - Phillip Grant
- grid.440934.e0000 0004 0593 1824Fresenius University of Applied Sciences, Frankfurt am Main, Germany
| | - Theo G. M. van Erp
- grid.266093.80000 0001 0668 7243Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA USA ,grid.266093.80000 0001 0668 7243Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA USA
| | - Jessica A. Turner
- grid.256304.60000 0004 1936 7400Imaging Genetics and Neuroinformatics Lab, Georgia State University, Atlanta, GA USA
| | - Paul M. Thompson
- grid.42505.360000 0001 2156 6853Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA USA
| | - André Aleman
- grid.4830.f0000 0004 0407 1981Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Gemma Modinos
- Department of Psychosis Studies, King's College London, London, UK. .,MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK.
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Yang B, Zhang W, Lencer R, Tao B, Tang B, Yang J, Li S, Zeng J, Cao H, Sweeney JA, Gong Q, Lui S. Grey matter connectome abnormalities and age-related effects in antipsychotic-naive schizophrenia. EBioMedicine 2021; 74:103749. [PMID: 34906839 PMCID: PMC8671864 DOI: 10.1016/j.ebiom.2021.103749] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 11/12/2021] [Accepted: 11/29/2021] [Indexed: 02/05/2023] Open
Abstract
Background Convergent evidence is increasing to indicate progressive brain abnormalities in schizophrenia. Knowing the brain network features over the illness course in schizophrenia, independent of effects of antipsychotic medications, would extend our sight on this question. Methods We recruited 237 antipsychotic-naive patients with schizophrenia range from 16 to 73 years old, and 254 healthy controls. High-resolution T1 weighted images were obtained with a 3.0T MR scanner. Grey matter networks were constructed individually based on the similarities of regional grey matter measurements. Network metrics were compared between patient groups and healthy controls, and regression analyses with age were conducted to determine potential differential rate of age-related changes between them. Findings Nodal centrality abnormalities were observed in patients with untreated schizophrenia, particularly in the central executive, default mode and salience networks. Accelerated age-related declines and illness duration-related declines were observed in global assortativity, and in nodal metrics of left superior temporal pole in schizophrenia patients. Although no significant intergroup differences in age-related regression were observed, the pattern of network metric alternation of left thalamus indicated higher nodal properties in early course patients, which decreased in long-term ill patients. Interpretations Global and nodal alterations in the grey matter connectome related to age and duration of illness in antipsychotic-naive patients, indicating potentially progressive network organizations mainly involving temporal regions and thalamus in schizophrenia independent from medication effects. Funding The National Natural Science Foundation of China, Sichuan Science and Technology Program, the Fundamental Research Funds for the Central Universities, Post-Doctor Research Project, West China Hospital, Sichuan University , the Science and Technology Project of the Health Planning Committee of Sichuan, Postdoctoral Interdisciplinary Research Project of Sichuan University and 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University.
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Affiliation(s)
- Beisheng Yang
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; Psychoradiology Research Unit of Chinese Academy of Medical Sciences, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Wenjing Zhang
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; Psychoradiology Research Unit of Chinese Academy of Medical Sciences, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Muenster, Germany
| | - Bo Tao
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; Psychoradiology Research Unit of Chinese Academy of Medical Sciences, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Biqiu Tang
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; Psychoradiology Research Unit of Chinese Academy of Medical Sciences, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Jing Yang
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; Psychoradiology Research Unit of Chinese Academy of Medical Sciences, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Siyi Li
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; Psychoradiology Research Unit of Chinese Academy of Medical Sciences, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Jiaxin Zeng
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; Psychoradiology Research Unit of Chinese Academy of Medical Sciences, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Hengyi Cao
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, United States
| | - John A Sweeney
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, OH, United States
| | - Qiyong Gong
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; Psychoradiology Research Unit of Chinese Academy of Medical Sciences, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.
| | - Su Lui
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China; Psychoradiology Research Unit of Chinese Academy of Medical Sciences, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.
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Wang Y, Jiang Y, Liu D, Zhang J, Yao D, Luo C, Wang J. Atypical Antipsychotics Mediate Dynamics of Intrinsic Brain Activity in Early-Stage Schizophrenia? A Preliminary Study. Psychiatry Investig 2021; 18:1205-1212. [PMID: 34965706 PMCID: PMC8721296 DOI: 10.30773/pi.2020.0418] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 09/24/2021] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVE Abnormalities of static brain activity have been reported in schizophrenia, but it remains to be clarified the temporal variability of intrinsic brain activities in schizophrenia and how atypical antipsychotics affect it. METHODS We employed a resting-state functional magnetic resonance imaging (rs-fMRI) and a sliding-window analysis of dynamic amplitude of low-frequency fluctuation (dALFF) to evaluate the dynamic brain activities in schizophrenia (SZ) patients before and after 8-week antipsychotic treatment. Twenty-six schizophrenia individuals and 26 matched healthy controls (HC) were included in this study. RESULTS Compared with HC, SZ showed stronger dALFF in the right inferior temporal gyrus (ITG.R) at baseline. After medication, the SZ group exhibited reduced dALFF in the right middle occipital gyrus (MOG.R) and increased dALFF in the left superior frontal gyrus (SFG.L), right middle frontal gyrus (MFG.R), and right inferior parietal lobule (IPL.R). Dynamic ALFF in IPL.R was found to significant negative correlate with the Scale for the Assessment of Negative Symptoms (SANS) scores at baseline. CONCLUSION Our results showed dynamic intrinsic brain activities altered in schizophrenia after short term antipsychotic treatment. The findings of this study support and expand the application of dALFF method in the study of the pathological mechanism in psychosis in the future.
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Affiliation(s)
- Yingchan Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuchao Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dengtang Liu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jianye Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, China.,Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China
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43
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Xiao Y, Liao W, Long Z, Tao B, Zhao Q, Luo C, Tamminga CA, Keshavan MS, Pearlson GD, Clementz BA, Gershon ES, Ivleva EI, Keedy SK, Biswal BB, Mechelli A, Lencer R, Sweeney JA, Lui S, Gong Q. Subtyping Schizophrenia Patients Based on Patterns of Structural Brain Alterations. Schizophr Bull 2021; 48:241-250. [PMID: 34508358 PMCID: PMC8781382 DOI: 10.1093/schbul/sbab110] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Schizophrenia is a complex and heterogeneous syndrome. Whether quantitative imaging biomarkers can identify discrete subgroups of patients as might be used to foster personalized medicine approaches for patient care remains unclear. Cross-sectional structural MR images of 163 never-treated first-episode schizophrenia patients (FES) and 133 chronically ill patients with midcourse schizophrenia from the Bipolar and Schizophrenia Network for Intermediate Phenotypes (B-SNIP) consortium and a total of 403 healthy controls were recruited. Morphometric measures (cortical thickness, surface area, and subcortical structures) were extracted for each subject and then the optimized subtyping results were obtained with nonsupervised cluster analysis. Three subgroups of patients defined by distinct patterns of regional cortical and subcortical morphometric features were identified in FES. A similar three subgroup pattern was identified in the independent dataset of patients from the multi-site B-SNIP consortium. Similarities of classification patterns across these two patient cohorts suggest that the 3-group typology is relatively stable over the course of illness. Cognitive functions were worse in subgroup 1 with midcourse schizophrenia than those in subgroup 3. These findings provide novel insight into distinct subgroups of patients with schizophrenia based on structural brain features. Findings of different cognitive functions among the subgroups support clinical differences in the MRI-defined illness subtypes. Regardless of clinical presentation and stage of illness, anatomic MR subgrouping biomarkers can separate neurobiologically distinct subgroups of schizophrenia patients, which represent an important and meaningful step forward in differentiating subtypes of patients for studies of illness neurobiology and potentially for clinical trials.
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Affiliation(s)
- Yuan Xiao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China,Department of Psychiatry, University of Münster, Münster, Germany
| | - Wei Liao
- Center for Information in BioMedicine, School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, Sichuan, China
| | - Zhiliang Long
- Center for Information in BioMedicine, School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, Sichuan, China
| | - Bo Tao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qiannan Zhao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Chunyan Luo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Carol A Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neurobiology, Yale University and Olin Neuropsychiatric Research Center, Hartford, CT, USA
| | - Brett A Clementz
- Department of Psychology, University of Georgia, Athens, GA, USA
| | - Elliot S Gershon
- Department of Psychiatry, University of Chicago, Chicago, IL, USA
| | - Elena I Ivleva
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Sarah K Keedy
- Department of Psychiatry, University of Chicago, Chicago, IL, USA
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Rebekka Lencer
- Department of Psychiatry, University of Münster, Münster, Germany
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China,To whom correspondence should be addressed; #37 GuoXue Xiang, Chengdu 610041, China; Tel: 86-28-85423960, Fax: 86-28-85423503; e-mail:
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
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Xie Y, Guan M, Wang Z, Ma Z, Wang H, Fang P, Yin H. rTMS Induces Brain Functional and Structural Alternations in Schizophrenia Patient With Auditory Verbal Hallucination. Front Neurosci 2021; 15:722894. [PMID: 34539338 PMCID: PMC8441019 DOI: 10.3389/fnins.2021.722894] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 08/12/2021] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Low-frequency transcranial magnetic stimulation (rTMS) over the left temporoparietal cortex reduces the auditory verbal hallucination (AVH) in schizophrenia. However, the underlying neural basis of the rTMS treatment effect for schizophrenia remains not well understood. This study investigates the rTMS induced brain functional and structural alternations and their associations with clinical as well as neurocognitive profiles in schizophrenia patients with AVH. METHODS Thirty schizophrenia patients with AVH and thirty-three matched healthy controls were enrolled. The patients were administered by 15 days of 1 Hz rTMS delivering to the left temporoparietal junction (TPJ) area. Clinical symptoms and neurocognitive measurements were assessed at pre- and post-rTMS treatment. The functional (amplitude of low-frequency fluctuation, ALFF) and structural (gray matter volume, GMV) alternations were compared, and they were then used to related to the clinical and neurocognitive measurements after rTMS treatment. RESULTS The results showed that the positive symptoms, including AVH, were relieved, and certain neurocognitive measurements, including visual learning (VisLearn) and verbal learning (VerbLearn), were improved after the rTMS treatment in the patient group. Furthermore, the rTMS treatment induced brain functional and structural alternations in patients, such as enhanced ALFF in the left superior frontal gyrus and larger GMV in the right inferior temporal cortex. The baseline ALFF and GMV values in certain brain areas (e.g., the inferior parietal lobule and superior temporal gyrus) could be associated with the clinical symptoms (e.g., positive symptoms) and neurocognitive performances (e.g., VerbLearn and VisLearn) after rTMS treatment in patients. CONCLUSION The low-frequency rTMS over the left TPJ area is an efficacious treatment for schizophrenia patients with AVH and could selectively modulate the neural basis underlying psychiatric symptoms and neurocognitive domains in schizophrenia.
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Affiliation(s)
- Yuanjun Xie
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Muzhen Guan
- Department of Mental Health, Xi’an Medical University, Xi’an, China
| | - Zhongheng Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Zhujing Ma
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi’an, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Peng Fang
- Department of Military Medical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi’an, China,*Correspondence: Peng Fang,
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi’an, China,Hong Yin,
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Zang J, Huang Y, Kong L, Lei B, Ke P, Li H, Zhou J, Xiong D, Li G, Chen J, Li X, Xiang Z, Ning Y, Wu F, Wu K. Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study. Front Neurosci 2021; 15:697168. [PMID: 34385901 PMCID: PMC8353157 DOI: 10.3389/fnins.2021.697168] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 07/07/2021] [Indexed: 11/24/2022] Open
Abstract
Recently, machine learning techniques have been widely applied in discriminative studies of schizophrenia (SZ) patients with multimodal magnetic resonance imaging (MRI); however, the effects of brain atlases and machine learning methods remain largely unknown. In this study, we collected MRI data for 61 first-episode SZ patients (FESZ), 79 chronic SZ patients (CSZ) and 205 normal controls (NC) and calculated 4 MRI measurements, including regional gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low-frequency fluctuation and degree centrality. We systematically analyzed the performance of two classifications (SZ vs NC; FESZ vs CSZ) based on the combinations of three brain atlases, five classifiers, two cross validation methods and 3 dimensionality reduction algorithms. Our results showed that the groupwise whole-brain atlas with 268 ROIs outperformed the other two brain atlases. In addition, the leave-one-out cross validation was the best cross validation method to select the best hyperparameter set, but the classification performances by different classifiers and dimensionality reduction algorithms were quite similar. Importantly, the contributions of input features to both classifications were higher with the GMV and ReHo features of brain regions in the prefrontal and temporal gyri. Furthermore, an ensemble learning method was performed to establish an integrated model, in which classification performance was improved. Taken together, these findings indicated the effects of these factors in constructing effective classifiers for psychiatric diseases and showed that the integrated model has the potential to improve the clinical diagnosis and treatment evaluation of SZ.
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Affiliation(s)
- Jinyu Zang
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Yuanyuan Huang
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
| | - Lingyin Kong
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Bingye Lei
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Pengfei Ke
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Hehua Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
| | - Jing Zhou
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Dongsheng Xiong
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Guixiang Li
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, China
| | - Jun Chen
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Zhiming Xiang
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China
- Department of Radiology, Panyu Central Hospital of Guangzhou, Guangzhou, China
| | - Yuping Ning
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
| | - Fengchun Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
| | - Kai Wu
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China
- National Engineering Research Center for Healthcare Devices, Guangzhou, China
- Key Laboratory of Biomedical Engineering of Guangdong Province, South China University of Technology, Guangzhou, China
- Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
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Soldevila-Matías P, Schoretsanitis G, Tordesillas-Gutierrez D, Cuesta MJ, de Filippis R, Ayesa-Arriola R, González-Vivas C, Setién-Suero E, Verdolini N, Sanjuán J, Radua J, Crespo-Facorro B. Neuroimaging correlates of insight in non-affective psychosis: A systematic review and meta-analysis. REVISTA DE PSIQUIATRIA Y SALUD MENTAL 2021; 15:S1888-9891(21)00067-7. [PMID: 34271162 DOI: 10.1016/j.rpsm.2021.07.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 06/28/2021] [Accepted: 07/01/2021] [Indexed: 12/22/2022]
Abstract
OBJECTIVE Neurological correlates of impaired insight in non-affective psychosis remain unclear. This study aimed to review and meta-analyze the studies assessing the grey matter volumetric correlates of impaired insight in non-affective psychosis. METHODS This study consisted of a systematic review of 23 studies, and a meta-analysis with SDM-PSI of the 11 studies that were whole-brain and reported maps or peaks of correlation of studies investigating the grey matter volumetric correlates of insight assessments of non-affective psychosis, PubMed and OVID datasets were independently reviewed for articles reporting neuroimaging correlates of insight in non-affective psychosis. Quality assessment was realized following previous methodological approaches for the ABC quality assessment test of imaging studies, based on two main criteria: the statistical power and the multidimensional assessment of insight. Study peaks of correlation between grey matter volume and insight were used to recreate brain correlation maps. RESULTS A total of 418 records were identified through database searching. Of these records, twenty-three magnetic resonance imaging (MRI) studies that used different insight scales were included. The quality of the evidence was high in 11 studies, moderate in nine, and low in three. Patients with reduced insight showed decreases in the frontal, temporal (specifically in superior temporal gyrus), precuneus, cingulate, insula, and occipital lobes cortical grey matter volume. The meta-analysis indicated a positive correlation between grey matter volume and insight in the right insula (i.e., the smaller the grey matter, the lower the insight). CONCLUSION Several brain areas might be involved in impaired insight in patients with non-affective psychoses. The methodologies employed, such as the applied insight scales, may have contributed to the considerable discrepancies in the findings.
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Affiliation(s)
- Pau Soldevila-Matías
- Department of Basic Psychology, Faculty of Psychology, University of Valencia, Valencia, Spain; Research Institute of Clinic University Hospital of Valencia (INCLIVA), Valencia, Spain; National Reference Center for Psychosocial Care for People with Serious Mental Disorder (CREAP), Valencia, Spain
| | - Georgios Schoretsanitis
- The Zucker Hillside Hospital, Psychiatry Research, Northwell Health, Glen Oaks, New York, USA
| | - Diana Tordesillas-Gutierrez
- Marqués de Valdecilla University Hospital, Department of Radiology, IDIVAL, Santander, Spain; Marqués de Valdecilla University Hospital, Department of Psychiatry, School of Medicine, University of Cantabria, IDIVAL, Santander, Spain; CIBERSAM, Biomedical Research Network on Mental Health Area, Madrid, Spain.
| | - Manuel J Cuesta
- Department of Psychiatry, Complejo Hospitalario de Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Renato de Filippis
- The Zucker Hillside Hospital, Psychiatry Research, Northwell Health, Glen Oaks, New York, USA; Psychiatry Unit Department of Health Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
| | - Rosa Ayesa-Arriola
- Marqués de Valdecilla University Hospital, Department of Radiology, IDIVAL, Santander, Spain; Marqués de Valdecilla University Hospital, Department of Psychiatry, School of Medicine, University of Cantabria, IDIVAL, Santander, Spain; CIBERSAM, Biomedical Research Network on Mental Health Area, Madrid, Spain
| | - Carlos González-Vivas
- Research Institute of Clinic University Hospital of Valencia (INCLIVA), Valencia, Spain
| | - Esther Setién-Suero
- Marqués de Valdecilla University Hospital, Department of Radiology, IDIVAL, Santander, Spain; Marqués de Valdecilla University Hospital, Department of Psychiatry, School of Medicine, University of Cantabria, IDIVAL, Santander, Spain; CIBERSAM, Biomedical Research Network on Mental Health Area, Madrid, Spain
| | - Norma Verdolini
- Bipolar and Depressive Disorders Unit, Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, 170 Villarroel Street, 12-0, 08036 Barcelona, Spain
| | - Julio Sanjuán
- Research Institute of Clinic University Hospital of Valencia (INCLIVA), Valencia, Spain; CIBERSAM, Biomedical Research Network on Mental Health Area, Madrid, Spain; Department of Psychiatric, University of Valencia, School of Medicine, Valencia, Spain
| | - Joaquim Radua
- CIBERSAM, Biomedical Research Network on Mental Health Area, Madrid, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Centre for Psychiatric Research and Education, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Benedicto Crespo-Facorro
- Marqués de Valdecilla University Hospital, Department of Radiology, IDIVAL, Santander, Spain; Marqués de Valdecilla University Hospital, Department of Psychiatry, School of Medicine, University of Cantabria, IDIVAL, Santander, Spain; CIBERSAM, Biomedical Research Network on Mental Health Area, Madrid, Spain
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Shi D, Li Y, Zhang H, Yao X, Wang S, Wang G, Ren K. Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging. DISEASE MARKERS 2021; 2021:9963824. [PMID: 34211615 PMCID: PMC8208855 DOI: 10.1155/2021/9963824] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/03/2021] [Indexed: 01/10/2023]
Abstract
Schizophrenia (SZ) is a severe psychiatric illness, and it affects around 1% of the general population; however, its reliable diagnosis is challenging. Functional MRI (fMRI) and structural MRI (sMRI) are useful techniques for investigating the functional and structural abnormalities of the human brain, and a growing number of studies have reported that multimodal brain data can improve diagnostic accuracy. Machine learning (ML) is widely used in the diagnosis of neuroscience and neuropsychiatry diseases, and it can obtain high accuracy. However, the conventional ML which concatenated the features into a longer feature vector could not be sufficiently effective to combine different features from different modalities. There are considerable controversies over the use of global signal regression (GSR), and few studies have explored the role of GSR in ML in diagnosing neurological diseases. The current study utilized fMRI and sMRI data to implement a new method named multimodal imaging and multilevel characterization with multiclassifier (M3) to classify SZs and healthy controls (HCs) and investigate the influence of GSR in SZ classification. We found that when we used Brainnetome 246 atlas and without performed GSR, our method obtained a classification accuracy of 83.49%, with a sensitivity of 68.69%, a specificity of 93.75%, and an AUC of 0.8491, respectively. We also got great classification performances with different processing methods (with/without GSR and different brain parcellation schemes). We found that the accuracy and specificity of the models without GSR were higher than that of the models with GSR. Our findings indicate that the M3 method is an effective tool to distinguish SZs from HCs, and it can identify discriminative regions to detect SZ to explore the neural mechanisms underlying SZ. The global signal may contain important neuronal information; it can improve the accuracy and specificity of SZ detection.
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Affiliation(s)
- Dafa Shi
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Yanfei Li
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Haoran Zhang
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Xiang Yao
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Siyuan Wang
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Guangsong Wang
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
| | - Ke Ren
- Department of Radiology, Xiang'an Hospital of Xiamen University, Xiamen 361002, China
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Abstract
Our current diagnostic methods for treatment planning in Psychiatry and Neurodevelopmental Disabilities leave room for improvement, and null results in clinical trials in these fields may be a result of insufficient tools for patient stratification. Great hope has been placed in novel technologies to improve clinical and trial outcomes, but we have yet to see a substantial change in clinical practice. As we examine attempts at biomarker validation within these fields, we find that it may be the diagnoses themselves that fall short. We now need to improve neuropsychiatric nosologies with a focus on validity based not solely on behavioral features, but on a synthesis that includes genetic and biological data as well. The eventual goal is diagnostic biomarkers and diagnoses themselves based on distinct mechanisms, but such an understanding of the causal relationship across levels of analysis is likely to be elusive for some time. Rather, we propose an approach in the near-term that deconstructs diagnosis into a series of independent, empiric and clinically relevant associations among a single, defined patient group, a single biomarker, a single intervention and a single clinical outcome. Incremental study across patient groups, interventions, outcomes and modalities will lead to a more interdigitated network of knowledge, and correlations in metrics across levels of analysis will eventually give way to the causal understanding that will allow for mechanistically based diagnoses.
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Haatveit B, Mørch-Johnsen L, Alnæs D, Engen MJ, Lyngstad SH, Færden A, Agartz I, Ueland T, Melle I. Divergent relationship between brain structure and cognitive functioning in patients with prominent negative symptomatology. Psychiatry Res Neuroimaging 2021; 307:111233. [PMID: 33340940 DOI: 10.1016/j.pscychresns.2020.111233] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 11/30/2020] [Accepted: 12/01/2020] [Indexed: 11/19/2022]
Abstract
Investigating commonalities in underlying pathology of cognitive dysfunction and negative symptoms in schizophrenia is important, as both are core features of the disorder and linked to brain structure abnormalities. We aimed to explore the relationship between cognition, negative symptoms and brain structure in schizophrenia. A total of 225 patients with Schizophrenia spectrum disorder and 283 healthy controls from the Norwegian Thematically Organized Psychosis (TOP) cohort were included in this study. Patients were categorized into four patient subgroups based on severity of negative symptoms: no-negative- (NNS), threshold-negative- (TNS), moderate-negative- (MNS), and prominent-negative (PNS) subgroups. MRI measures of brain volume, mean cortical thickness and surface area from pre-selected brain regions were tested for relationships with general cognitive ability and negative symptom subgroups. Positive associations were found between brain volume, thickness, surface area and cognition in the dorsolateral prefrontal cortex (DLPFC), orbitofrontal cortex (OFC), fusiform gyrus (FG) and the left anterior cingulate cortex (ACC), but with no differences between subgroups. In the PNS subgroup, cognition was conversely negatively associated with brain volume in the left ACC. These results indicate that patients with prominent negative symptoms have different associations between cognition and brain structure in the left ACC, which may point to abnormal neurodevelopment.
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Affiliation(s)
- Beathe Haatveit
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Lynn Mørch-Johnsen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry, Ostfold Hospital Trust, Graalum, Norway
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Magnus Johan Engen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Siv Hege Lyngstad
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ann Færden
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Acute Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, 0319 Oslo, Norway; Centre for Psychiatric Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Torill Ueland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Ingrid Melle
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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Yanagi M, Shirakawa O. Application of Near-Infrared Spectroscopy for Understanding Spontaneous Brain Activity During Resting State in Schizophrenia: A Mini Review. Front Psychiatry 2021; 12:704506. [PMID: 34475831 PMCID: PMC8407079 DOI: 10.3389/fpsyt.2021.704506] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 07/20/2021] [Indexed: 11/24/2022] Open
Abstract
Spontaneous brain activity occurs at rest, as represented by the default mode network. A resting paradigm is suitable for investigating brain function of patients with psychiatric diseases who may have difficulties adhering to goal-oriented tasks. Evidence accumulated in neuroimaging studies using functional magnetic resonance imaging has shown that the resting cerebral blood flow is impaired in psychiatric diseases. Near-infrared spectroscopy (NIRS), a simple neuroimaging modality, is an optimal tool for the resting paradigm, because it can offer a comfortable environment for measurement. Recent NIRS studies have demonstrated some promising data of altered resting activity in the prefrontal cortex of patients with schizophrenia, which may be exploited to develop further applications of NIRS in clinical psychiatry. Based on these findings, we emphasize the benefits of NIRS for assessing the prefrontal pathophysiology during the resting state and some methodological issues to be noted while analyzing cerebral blood flow using NIRS; moreover, we focus on interpreting these changes based on the complex nature of the spontaneous brain activity during resting state.
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Affiliation(s)
- Masaya Yanagi
- Department of Neuropsychiatry, Kindai University Faculty of Medicine, Osakasayama, Japan
| | - Osamu Shirakawa
- Department of Neuropsychiatry, Kindai University Faculty of Medicine, Osakasayama, Japan
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