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Shirzadi S, Dadgostar M, Hosseinzadeh H, Einalou Z. Dynamics of frontal cortex functional connectivity during cognitive tasks: insights from fNIRS analysis in the Dual n-back Paradigm. Cogn Process 2025:10.1007/s10339-025-01275-8. [PMID: 40354005 DOI: 10.1007/s10339-025-01275-8] [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: 09/26/2024] [Accepted: 04/11/2025] [Indexed: 05/14/2025]
Abstract
The human brain operates as a complex network, and understanding its functional connectivity is a core challenge in neuroscience. Functional near-infrared spectroscopy (fNIRS) offers a non-invasive, portable method for studying brain activity and connectivity, providing valuable insights into the brain's network dynamics. In this study, we used fNIRS to examine the functional connectivity of the human brain during the Dual n-back task, a cognitive challenge that varies in memory load (0-back, 1-back, and 2-back). Data were collected from 24 channels in the frontal cortex and pre-processed with discrete wavelet transform. Functional connectivity matrices for each task level were calculated using correlation analysis, and graph theory metrics such as clustering coefficient and local and global efficiency were assessed. Statistical comparisons (t-tests and ANOVA) revealed significant differences in these metrics across memory load levels, with higher memory loads leading to altered brain connectivity patterns (p < 0.05 for clustering coefficient and local efficiency, p < 0.04 for global efficiency). These findings suggest that as cognitive demand increases, the functional connectivity of the brain's frontal network changes, reflecting the dynamic nature of brain activity during complex tasks. This research highlights the potential of fNIRS for exploring brain network functions and has broader implications for understanding cognitive processes and developing neurocognitive diagnostics and interventions.
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Affiliation(s)
- Sima Shirzadi
- Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Mehrdad Dadgostar
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Hamidreza Hosseinzadeh
- Department of Electrical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Zahra Einalou
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
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Khodadadi Arpanahi S, Hamidpour S, Ghasvarian Jahromi K. Binary and weighted network analysis and its applications to functional connectivity in subjective memory complaints: A resting-state fMRI approach. Ageing Res Rev 2025; 106:102688. [PMID: 39947486 DOI: 10.1016/j.arr.2025.102688] [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: 12/08/2024] [Revised: 12/31/2024] [Accepted: 02/08/2025] [Indexed: 02/27/2025]
Abstract
INTRODUCTION Despite normal cognitive abilities, subjective memory complaints (SMC) are common in older adults and are linked to mild memory impairment. SMC may be a sign of subtle cognitive decline and underlying pathological changes, according to research; however, there is not enough data to support the use of resting-state functional connectivity to identify early changes in the brain network before cognitive symptoms manifest. MATERIALS AND METHODS In this study, the topological structure and regional connectivity of the brain functional network in SMC individuals were analyzed using graph theoretical analysis in both weighted and binarized network models, alongside healthy controls. Resting-state functional magnetic resonance imaging data was collected from 24 SMCs and 39 cognitively normal people. Analysis of both binary and weighted graph theory was done using the Dosenbach Atlas as a basis based on area under curves (AUCs) for the graph network parameters, which comprised of six node metrics and nine global measures. We then performed group comparisons using statistical analyses based on Network-Based Statistics functional connectomes. Finally, the relationship between global graph measures and cognition was examined using neuropsychological tests such as the Mini-Mental State Examination (MMSE) and the Alzheimer Disease Assessment Scale (ADAS score). RESULTS The topologic properties of brain functional connectomes at both global and nodal levels were tested. The SMC patients showed increased functional connectivity in clustering coefficient global (P < 0.00001), global efficiency (P < 0.00001), and normalized characteristic path length or Lambda (P < 0.00001), while there was decreased functional connectivity in Modularity (P < 0.04542), characteristic path length (0.00001), and small-worldness or Sigma (P < 0.00001) in binary networks model. In contrast, SMC patients only exhibited decreased functional connectivity in Assortativity identified by weighted networks model. Furthermore, some brain regions located in the default mode network, sensorimotor, occipital, and cingulo-opercular network in SMC patients showed altered nodal centralities. No significant correlation was found between global metrics and MMSE scores in both groups using binary metrics. However, in cognitively normal individuals, negative correlation was observed with weighted metrics in global and local efficiency and Lambda. While In SMC patients, a significant positive correlation was found between ADAS scores and local efficiency in both binary and weighted metrics. CONCLUSION The findings suggest that functional impairments in SMC patients might be associated with disruptions in the global and regional topological organization of the brain's functional connectome, offering new and significant insights into the pathophysiological mechanisms underlying SMC.
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Yang Y, Li Y. Deep learning models as learners for EEG-based functional brain networks . J Neural Eng 2025;22:026005. [PMID: 40009886 DOI: 10.1088/1741-2552/adba8c] [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: 10/22/2024] [Accepted: 02/26/2025] [Indexed: 02/28/2025]
Abstract
Objective.Functional brain network (FBN) methods are commonly integrated with deep learning (DL) models for EEG analysis. Typically, an FBN is constructed to extract features from EEG data, which are then fed into a DL model for further analysis. Beyond this two-step approach, there is potential to embed FBN construction directly within DL models as a feature extraction module, enabling the models to learn EEG representations end-to-end while incorporating insights from FBNs. However, a critical prerequisite is whether DL models can effectively learn the FBN construction process.Approach.To address this, we propose using DL models to learn FBN matrices derived from EEG data. The ability of DL models to accurately reproduce these matrices would validate their capacity to learn the FBN construction process. This approach is tested on two publicly available EEG datasets, utilizing seven DL models to learn four representative FBN matrices. Model performance is assessed through mean squared error (MSE), Pearson correlation coefficient (Corr), and concordance correlation coefficient (CCC) between predicted and actual matrices.Main results.The results show that DL models achieve low MSE and relatively high Corr and CCC values when learning the Coherence network. Visualizations of predicted and error matrices reveal that while DL models capture the general structure of all four FBNs, certain regions remain difficult to model accurately. Additionally, a pairedt-test comparing global efficiency and nodal degree between predicted and actual networks indicates that most predicted networks significantly differ from the actual networks (p<0.05).Significance.These findings suggest that while DL models can learn the connectivity relationships of certain FBNs, they struggle to capture the intrinsic topological structures. This highlights the irreplaceability of traditional FBN methods in EEG analysis and underscores the need for hybrid strategies that combine FBN methods with DL models for a more comprehensive analysis.
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Affiliation(s)
- Yuxuan Yang
- School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, People's Republic of China
| | - Yanli Li
- Division of Image Processing, Department of Radiology, Leiden University Medical Center (LUMC), 2300 RC Leiden, The Netherlands
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Khan AF, Saleh N, Smith ZA. The Brain's Aging Resting State Functional Connectivity. J Integr Neurosci 2025; 24:25041. [PMID: 39862002 DOI: 10.31083/jin25041] [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: 05/30/2024] [Revised: 07/29/2024] [Accepted: 08/09/2024] [Indexed: 01/27/2025] Open
Abstract
Resting state networks (RSNs) of the brain are characterized as correlated spontaneous time-varying fluctuations in the absence of goal-directed tasks. These networks can be local or large-scale spanning the brain. The study of the spatiotemporal properties of such networks has helped understand the brain's fundamental functional organization under healthy and diseased states. As we age, these spatiotemporal properties change. Moreover, RSNs exhibit neural plasticity to compensate for the loss of cognitive functions. This narrative review aims to summarize current knowledge from functional magnetic resonance imaging (fMRI) studies on age-related alterations in RSNs. Underlying mechanisms influencing such changes are discussed. Methodological challenges and future directions are also addressed. By providing an overview of the current state of knowledge in this field, this review aims to guide future research endeavors aimed at promoting healthy brain aging and developing effective interventions for age-related cognitive impairment and neurodegenerative diseases.
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Affiliation(s)
- Ali F Khan
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Nada Saleh
- Graduate College, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Zachary A Smith
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
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Khodadadi Arpanahi S, Hamidpour S, Ghasvarian Jahromi K. Mapping Alzheimer's disease stages toward it's progression: A comprehensive cross-sectional and longitudinal study using resting-state fMRI and graph theory. Ageing Res Rev 2025; 103:102590. [PMID: 39566740 DOI: 10.1016/j.arr.2024.102590] [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: 10/08/2024] [Accepted: 11/16/2024] [Indexed: 11/22/2024]
Abstract
INTRODUCTION Functional brain connectivity of resting-state networks varies as Alzheimer's disease (AD) progresses. However, our understanding of the dynamic longitudinal changes that occur in the brain over the course of AD is sometimes contradictory and lacking. MATERIALS AND METHODS In this study, we analyzed whole-brain networks connectivity using longitudinal resting-state fMRI data from 132 participants from ADNI dataset. The cohort was divided into four groups: 20 AD, 35 CN, 46 Early MCI, and 31 Late MCI Cross-sectional analyses were conducted at baseline and follow-up (approximately two years apart), with longitudinal changes assessed within and between groups. RESULTS Cross-sectional analyses revealed that all groups differed significantly from AD in global network properties at both time points, with EMCI also showing disrupted topological metrics compared to CN. Longitudinal analyses highlighted notable changes in small-worldness (σ), global clustering coefficient (Cp), and normalized characteristic path length (λ) across groups. Both EMCI and LMCI groups showed significant alterations in global efficiency (Eglob), Cp, and σ over time. Pairwise comparisons also revealed significant interaction effects, particularly between CN-EMCI and CN-AD groups. All groups showed notable changes in σ, λ, and Cp, according to within-group longitudinal changes. Furthermore, distinct changes in Eglob over time were observed in the LMCI and EMCI groups. Almost all subnetwork attributes demonstrated significant changes between patients at various phases in both time intervals. CONCLUSION Our findings emphasize significant connectivity alterations across all groups at both baseline and follow-up, with longitudinal analyses underscoring the progression of these changes. Graph theory metrics provide valuable insights into the transition from normal cognition to AD, potentially serving as biomarkers for disease progression.
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Chen RB, Li XT, Huang X. Topological Organization of the Brain Network in Patients with Primary Angle-closure Glaucoma Through Graph Theory Analysis. Brain Topogr 2024; 37:1171-1185. [PMID: 38822211 DOI: 10.1007/s10548-024-01060-4] [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: 03/30/2024] [Accepted: 05/29/2024] [Indexed: 06/02/2024]
Abstract
Primary angle-closure glaucoma (PACG) is a sight-threatening eye condition that leads to irreversible blindness. While past neuroimaging research has identified abnormal brain function in PACG patients, the relationship between PACG and alterations in brain functional networks has yet to be explored. This study seeks to examine the influence of PACG on brain networks, aiming to advance knowledge of its neurobiological processes for better diagnostic and therapeutic approaches utilizing graph theory analysis. A cohort of 44 primary angle-closure glaucoma (PACG) patients and 44 healthy controls participated in this study. Functional brain networks were constructed using fMRI data and the Automated Anatomical Labeling 90 template. Subsequently, graph theory analysis was employed to evaluate global metrics, nodal metrics, modular organization, and network-based statistics (NBS), enabling a comparative analysis between PACG patients and the control group. The analysis of global metrics, including small-worldness and network efficiency, did not exhibit significant differences between the two groups. However, PACG patients displayed elevated nodal metrics, such as centrality and efficiency, in the left frontal superior medial, right frontal superior medial, and right posterior central brain regions, along with reduced values in the right temporal superior gyrus region compared to healthy controls. Furthermore, Module 5 showed notable disparities in intra-module connectivity, while Module 1 demonstrated substantial differences in inter-module connectivity with both Module 7 and Module 8. Noteworthy, the NBS analysis unveiled a significantly altered network when comparing the PACG and healthy control groups. The study proposes that PACG patients demonstrate variations in nodal metrics and modularity within functional brain networks, particularly affecting the prefrontal, occipital, and temporal lobes, along with cerebellar regions. However, an analysis of global metrics suggests that the overall connectivity patterns of the entire brain network remain unaltered in PACG patients. These results have the potential to serve as early diagnostic and differential markers for PACG, and interventions focusing on brain regions with high degree centrality and nodal efficiency could aid in optimizing therapeutic approaches.
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Affiliation(s)
- Ri-Bo Chen
- Department of Radiology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, Jiangxi, China
| | - Xiao-Tong Li
- Queen Mary School, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Xin Huang
- Department of Ophthalmology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, No 152, Ai Guo Road, Dong Hu District, Nanchang, 330006, Jiangxi, China.
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Zhao Y, Huang Y, Liu Z, Zhou Y. The architecture of functional brain network modulated by driving under train running noise exposure. PLoS One 2024; 19:e0306729. [PMID: 39146301 PMCID: PMC11326564 DOI: 10.1371/journal.pone.0306729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 06/22/2024] [Indexed: 08/17/2024] Open
Abstract
A noisy environment can considerably impact drivers' attention and fatigue, endangering driving safety. Consequently, this study designed a simulated driving experimental scenario to analyse the effects of noise generated during urban rail transit train operation on drivers' functional brain networks. The experiment recruited 16 participants, and the simulated driving scenario was conducted at noise levels of 50, 60, 70, and 80 dB. Functional connectivity between all electrode pairs across various frequency bands was evaluated using the weighted phase lag index (WPLI), and a brain network based on this was constructed. Graph theoretic analysis employed network global efficiency, degree, and clustering coefficient as metrics. Significant increases in the WPLI values of theta and alpha frequency bands were observed in high noise environments (70 dB, 80 dB), as well as enhanced brain synchronisation. Furthermore, concerning the topological metrics of brain networks, it was observed that the global efficiency of brain networks in theta and alpha frequency ranges, as well as the node degree and clustering coefficients, experienced substantial growth in high noise environments (70 dB, 80 dB) as opposed to 50 dB and 60 dB. This finding indicates that high-noise environments impact the reorganisation of functional brain networks, leading to a preference for network structures with improved global efficiency. Such findings may improve our understanding of the neural mechanisms of driving under noise exposure, and thus potentially reduce road accidents to some extent.
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Affiliation(s)
- Yashuai Zhao
- School of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai, P.R. China
| | - Yuanchun Huang
- School of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai, P.R. China
| | - Zhigang Liu
- School of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai, P.R. China
| | - Yifan Zhou
- School of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai, P.R. China
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Tsai ML, Wang CC, Wang AYD, Lee FC, Chang H, Liu YL, Wong TT, Peng SJ. Antiseizure Medications Normalize Electroencephalographic Functional Connectivity and Power in Children With Benign Epilepsy With Centrotemporal Spikes. Pediatr Neurol 2024; 156:41-50. [PMID: 38729071 DOI: 10.1016/j.pediatrneurol.2024.03.015] [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: 06/17/2023] [Revised: 02/12/2024] [Accepted: 03/17/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND The decision to treat children with benign epilepsy with centrotemporal spikes (BECTS) using antiseizure medications (ASM) is controversial. Our goal is to compare the effect of ASM treatment on the alteration of electroencephalographic (EEG) functional connectivity and power across four frequency bands in children with BECTS. METHODS Children with BECTS with two-year follow-up were retrospectively divided into ASM versus non-ASM groups. The network properties of the EEGs as based on network-based statistic and graph theory were evaluated by the following indices: global efficiency, clustering coefficient, betweenness centrality, and nodal strength in four frequency bands (delta, theta, alpha, and beta). EEG power including absolute power (AP) and relative power (RP) was analyzed in four frequency bands. RESULTS In children with BECTS with ASM treatment, there was no significant change in EEG connectivity across all bands before and after two years of ASM. In children with BECTS without ASM treatment, there was a significant increase of global efficiency, clustering coefficient, and nodal strength but not the betweenness centrality in the delta band after two years of follow-up. A decrease in AP in the delta and theta bands and a decrease in RP in the theta band were found in the ASM group after two years of treatment. CONCLUSIONS Our results suggest that ASM may play a role in modulating the development of increasing overall brain connectivity and in downregulating overt synaptic activity, but not intrinsic focal connectivity, in the early years of BECTS. The changes in the EEG power indicate that ASM significantly normalized slow-wave band power.
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Affiliation(s)
- Min-Lan Tsai
- Division of Pediatric Neurology, Department of Pediatrics, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan; Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chuang-Chin Wang
- School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Andy Yu-Der Wang
- Department of Neurology, University of California San Francisco, San Francisco, CA
| | - Feng-Chin Lee
- Division of Pediatric Neurology, Department of Pediatrics, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan; Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Hsi Chang
- Division of Pediatric Neurology, Department of Pediatrics, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan; Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yen-Lin Liu
- Division of Pediatric Neurology, Department of Pediatrics, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan; Department of Pediatrics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Tai-Tong Wong
- Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Division of Pediatric Neurosurgery, Department of Neurosurgery, Taipei Medical University Hospital, Taipei, Taiwan; Neuroscience Research Center, Taipei Medical University Hospital, Taipei, Taiwan
| | - Syu-Jyun Peng
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan.
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Wang S, Li T, He H, Li Y. Dynamical changes of interaction across functional brain communities during propofol-induced sedation. Cereb Cortex 2024; 34:bhae263. [PMID: 38918077 DOI: 10.1093/cercor/bhae263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 05/28/2024] [Accepted: 06/06/2024] [Indexed: 06/27/2024] Open
Abstract
It is crucial to understand how anesthetics disrupt information transmission within the whole-brain network and its hub structure to gain insight into the network-level mechanisms underlying propofol-induced sedation. However, the influence of propofol on functional integration, segregation, and community structure of whole-brain networks were still unclear. We recruited 12 healthy subjects and acquired resting-state functional magnetic resonance imaging data during 5 different propofol-induced effect-site concentrations (CEs): 0, 0.5, 1.0, 1.5, and 2.0 μg/ml. We constructed whole-brain functional networks for each subject under different conditions and identify community structures. Subsequently, we calculated the global and local topological properties of whole-brain network to investigate the alterations in functional integration and segregation with deepening propofol sedation. Additionally, we assessed the alteration of key nodes within the whole-brain community structure at each effect-site concentrations level. We found that global participation was significantly increased at high effect-site concentrations, which was mediated by bilateral postcentral gyrus. Meanwhile, connector hubs appeared and were located in posterior cingulate cortex and precentral gyrus at high effect-site concentrations. Finally, nodal participation coefficients of connector hubs were closely associated to the level of sedation. These findings provide valuable insights into the relationship between increasing propofol dosage and enhanced functional interaction within the whole-brain networks.
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Affiliation(s)
- Shengpei Wang
- Laboratory of Brain Atlas and Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Rd, Haidian District, Beijing 100190, PR China
- Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, Chinese Academy of Sciences, No. 95 Zhongguancun East Rd, Haidian District, Beijing 100190, PR China
| | - Tianzuo Li
- Department of Anesthesiology, Beijing Shijitan Hospital, Capital Medical University, No. 10 Yangfangdian Tieyi Rd, Haidian District, Beijing 100038, PR China
| | - Huiguang He
- Laboratory of Brain Atlas and Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Rd, Haidian District, Beijing 100190, PR China
- Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, Chinese Academy of Sciences, No. 95 Zhongguancun East Rd, Haidian District, Beijing 100190, PR China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 1 Yanqihu East Road, Huairou District, Beijing 101408, PR China
| | - Yun Li
- Department of Anesthesiology, Beijing Tiantan Hospital, Capital Medical University, No. 119, South Fourth Ring West Road, Fengtai District, Beijing 100070, PR China
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Rigoni I, Vorderwülbecke BJ, Carboni M, Roehri N, Spinelli L, Tononi G, Seeck M, Perogamvros L, Vulliémoz S. Network alterations in temporal lobe epilepsy during non-rapid eye movement sleep and wakefulness. Clin Neurophysiol 2024; 159:56-65. [PMID: 38335766 DOI: 10.1016/j.clinph.2024.01.003] [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: 08/09/2023] [Revised: 12/18/2023] [Accepted: 01/02/2024] [Indexed: 02/12/2024]
Abstract
OBJECTIVE Investigate sleep and temporal lobe epilepsy (TLE) effects on brain networks derived from electroencephalography (EEG). METHODS High-density EEG was recorded during non-rapid eye movement (NREM) sleep stage 2 (N2) and wakefulness in 23 patients and healthy controls (HC). Epochs without epileptic discharges were source-reconstructed in 72 brain regions and connectivity was estimated. We calculated network integration and segregation at global (global efficiency, GE; average clustering coefficient, avgCC) and hemispheric level. These were compared between groups across frequency bands and correlated with the individual proportion of wakefulness- or sleep-related seizures. RESULTS At the global level, patients had higher delta GE, delta avgCC and theta avgCC than controls, irrespective of the vigilance state. During wakefulness, theta GE of patients was higher than controls and, for patients, theta GE during wakefulness was higher than during N2. Wake-to-sleep differences in TLE were notable only in the ipsilateral hemisphere. Only measures from wakefulness recordings correlated with the proportion of wakefulness- or sleep-related seizures. CONCLUSIONS TLE network alterations are more prominent during wakefulness and at lower frequencies. Increased integration and segregation suggest a pathological 'small world' configuration with a possible inhibitory role. SIGNIFICANCE Network alterations in TLE occur and are easier to detect during wakefulness.
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Affiliation(s)
- I Rigoni
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland.
| | - B J Vorderwülbecke
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland; Epilepsy-Center Berlin-Brandenburg, Department of Neurology with Experimental Neurology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - M Carboni
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland
| | - N Roehri
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland
| | - L Spinelli
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland
| | - G Tononi
- Department of Psychiatry, University of Wisconsin, Madison, WI, USA
| | - M Seeck
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland
| | - L Perogamvros
- Center for Sleep Medicine, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
| | - S Vulliémoz
- EEG and Epilepsy Unit, University Hospital and Faculty of Medicine of Geneva, University of Geneva, Switzerland
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Tuhin KH, Nobi A, Sadique MJ, Rakib MI, Lee JW. Effect of network size on comparing different stock networks. PLoS One 2023; 18:e0288733. [PMID: 38096247 PMCID: PMC10721020 DOI: 10.1371/journal.pone.0288733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 07/03/2023] [Indexed: 12/17/2023] Open
Abstract
We analyzed complex networks generated by the threshold method in the Korean and Indian stock markets during the non-crisis period of 2004 and the crisis period of 2008, while varying the size of the system. To create the stock network, we randomly selected N stock indices from the market and constructed the network based on cross-correlation among the time series of stock prices. We computed the average shortest path length L and average clustering coefficient C for several ensembles of generated stock networks and found that both metrics are influenced by network size. Since L and C are affected by network size N, a direct comparison of graph measures between stock networks with different numbers of nodes could lead to erroneous conclusions. However, we observed that the dependency of network measures on N is significantly reduced when comparing larger networks with normalized shortest path lengths. Additionally, we discovered that the effect of network size on network measures during the crisis period is almost negligible compared to the non-crisis periods.
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Affiliation(s)
- Kamrul Hasan Tuhin
- Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
| | - Ashadun Nobi
- Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
| | - Md. Jafar Sadique
- Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
| | - Mahmudul Islam Rakib
- Department of Computer Science and Telecommunication Engineering, Noakhali Science and Technology University, Noakhali, Bangladesh
| | - Jae Woo Lee
- Department of Physics, Inha University, Incheon, Republic of Korea
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12
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Lizotte S, Young JG, Allard A. Hypergraph reconstruction from uncertain pairwise observations. Sci Rep 2023; 13:21364. [PMID: 38049512 PMCID: PMC10695935 DOI: 10.1038/s41598-023-48081-w] [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: 04/22/2023] [Accepted: 11/22/2023] [Indexed: 12/06/2023] Open
Abstract
The network reconstruction task aims to estimate a complex system's structure from various data sources such as time series, snapshots, or interaction counts. Recent work has examined this problem in networks whose relationships involve precisely two entities-the pairwise case. Here, using Bayesian inference, we investigate the general problem of reconstructing a network in which higher-order interactions are also present. We study a minimal example of this problem, focusing on the case of hypergraphs with interactions between pairs and triplets of vertices, measured imperfectly and indirectly. We derive a Metropolis-Hastings-within-Gibbs algorithm for this model to highlight the unique challenges that come with estimating higher-order models. We show that this approach tends to reconstruct empirical and synthetic networks more accurately than an equivalent graph model without higher-order interactions.
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Affiliation(s)
- Simon Lizotte
- Département de Physique, de génie Physique et d'optique, Université Laval, Québec, G1V 0A6, Canada
- Centre Interdisciplinaire en Modélisation Mathématique, Université Laval, Québec, G1V 0A6, Canada
| | - Jean-Gabriel Young
- Département de Physique, de génie Physique et d'optique, Université Laval, Québec, G1V 0A6, Canada
- Department of Mathematics and Statistics, University of Vermont, Burlington, VT, 05405, USA
- Vermont Complex Systems Center, University of Vermont, Burlington, VT, 05405, USA
| | - Antoine Allard
- Département de Physique, de génie Physique et d'optique, Université Laval, Québec, G1V 0A6, Canada.
- Centre Interdisciplinaire en Modélisation Mathématique, Université Laval, Québec, G1V 0A6, Canada.
- Vermont Complex Systems Center, University of Vermont, Burlington, VT, 05405, USA.
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Yuasa K, Hirosawa T, Soma D, Furutani N, Kameya M, Sano M, Kitamura K, Ueda M, Kikuchi M. Eyes-state-dependent alterations of magnetoencephalographic connectivity associated with delayed recall in Alzheimer's disease via graph theory approach. Front Psychiatry 2023; 14:1272120. [PMID: 37941968 PMCID: PMC10628524 DOI: 10.3389/fpsyt.2023.1272120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/10/2023] [Indexed: 11/10/2023] Open
Abstract
IntroductionAlzheimer’s disease (AD) is a neurodegenerative disorder characterized by memory impairment and cognitive decline. Electroencephalography (EEG) and magnetoencephalography (MEG) studies using graph theory show altered “Small-Worldness (SW)” properties in AD. This study aimed to investigate whether eye-state-dependent alterations in SW differ between patients with AD and healthy controls, considering the symptoms of AD.MethodsNineteen patients with AD and 24 healthy controls underwent MEG under different conditions (eyes-open [EO] and eyes-closed [EC]) and the Wechsler Memory Scale-Revised (WMS-R) with delayed recall. After the signal sources were mapped onto the Desikan–Killiany brain atlas, the statistical connectivity of five frequency bands (delta, theta, alpha, beta, and gamma) was calculated using the phase lag index (PLI), and binary graphs for each frequency band were constructed based on the PLI. Next, we measured SW as a graph metric and evaluated three points: the impact of AD and experimental conditions on SW, the association between SW and delayed recall, and changes in SW across experimental conditions correlated with delayed recall.ResultsSW in the gamma band was significantly lower in patients with AD (z = −2.16, p = 0.031), but the experimental conditions did not exhibit a significant effect in any frequency band. Next, in the AD group, higher scores on delayed recall correlated with diminished SW across delta, alpha, and beta bands in the EO condition. Finally, delayed recall scores significantly predicted relative differences in the SW group in the alpha band (t = −2.98, p = 0.009).DiscussionGiven that network studies could corroborate the results of previous power spectrum studies, our findings contribute to a multifaceted understanding of functional brain networks in AD, emphasizing that the SW properties of these networks change according to disease status, cognitive function, and experimental conditions.
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Affiliation(s)
- Keigo Yuasa
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Tetsu Hirosawa
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Daiki Soma
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Naoki Furutani
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Masafumi Kameya
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Masuhiko Sano
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Koji Kitamura
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Minehisa Ueda
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Mitsuru Kikuchi
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
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Lan C, Chan RHM. Comparing the Graphical Features of Simple Artificial Neural Networks and Cortical Development. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082639 DOI: 10.1109/embc40787.2023.10340927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Brain development is characterized by changes in connections and information processing complexity. These changes inspire the training process of artificial neural network (ANN), which requires adjusting the neuron weights and biases to enhance efficiency in performing a specific task. In this work, we found affinities in the ratio of positive and negative weights in simple ANNs during training with that of excitatory and inhibitory synapses in the cortex. Additionally, we present a graphical representation of simple ANNs formed by pruning unimportant weights and aligning neurons and connections of different layers. Our findings suggest a strong relationship between the accuracy of simple neural network and graphical representation features, with graphical features at the inflection point resembling the graphical representation of the cortex.
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15
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Wang H, Wen H, Li J, Chen Q, Li S, Wang Z. Disrupted topological organization of white matter structural networks in high myopia patients revealed by diffusion kurtosis imaging and tractography. Front Neurosci 2023; 17:1158928. [PMID: 37425009 PMCID: PMC10324656 DOI: 10.3389/fnins.2023.1158928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 06/06/2023] [Indexed: 07/11/2023] Open
Abstract
Introduction High myopia (HM) is a public health issue that can lead to severe visual impairment. Previous studies have exhibited widespread white matter (WM) integrity damage in HM patients. However, how these WM damages are topologically related, and the network-level structural disruptions underlying HM has not been fully defined. We aimed to assess the alterations of brain WM structural networks in HM patients using diffusion kurtosis imaging (DKI) and tractography in the present study. Methods Individual whole-brain and ROI-level WM networks were constructed using DKI tractography in 30 HM patients and 33 healthy controls. Graph theory analysis was then applied to explore the altered global and regional network topological properties. Pearson correlations between regional properties and disease duration in the HM group were also assessed. Results For global topology, although both groups showed a small-world network organization, HM patients exhibited significant decreased local efficiency and clustering coefficient compared with controls. For regional topology, HM patients and controls showed highly similar hub distributions, except for three additional hub regions in HM patients including left insula, anterior cingulate and paracingulate gyri (ACG), and median cingulate and paracingulate gyri (DCG). In addition, HM patients showed significantly altered nodal betweenness centrality (BC) mainly in the bilateral inferior occipital gyrus (IOG), left superior occipital gyrus (SOG), caudate nucleus, rolandic operculum and right putamen, pallidum, and gyrus rectus compared with controls. Intriguingly, the nodal BC of left IOG was negatively correlated with disease duration in HM patients. Discussion Our findings suggest that HM exhibited alterations in WM structural networks as indicated by decreased local specialization. This study may advance the current understanding of the pathophysiological mechanisms underlying HM.
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Affiliation(s)
- Huihui Wang
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Hongwei Wen
- Key Laboratory of Cognition and Personality (Ministry of Education), Faculty of Psychology, Southwest University, Chongqing, China
| | - Jing Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Qian Chen
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Shanshan Li
- Department of Ophthalmology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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Bessadok A, Mahjoub MA, Rekik I. Graph Neural Networks in Network Neuroscience. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023; 45:5833-5848. [PMID: 36155474 DOI: 10.1109/tpami.2022.3209686] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain connectivities were developed to create a comprehensive road map of neuronal activities in the human brain -namely brain graph. Relying on its non-euclidean data type, graph neural network (GNN) provides a clever way of learning the deep graph structure and it is rapidly becoming the state-of-the-art leading to enhanced performance in various network neuroscience tasks. Here we review current GNN-based methods, highlighting the ways that they have been used in several applications related to brain graphs such as missing brain graph synthesis and disease classification. We conclude by charting a path toward a better application of GNN models in network neuroscience field for neurological disorder diagnosis and population graph integration. The list of papers cited in our work is available at https://github.com/basiralab/GNNs-in-Network-Neuroscience.
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Kida T, Tanaka E, Kakigi R, Inui K. Brain-wide network analysis of resting-state neuromagnetic data. Hum Brain Mapp 2023; 44:3519-3540. [PMID: 36988453 DOI: 10.1002/hbm.26295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 03/16/2023] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
The present study performed a brain-wide network analysis of resting-state magnetoencephalograms recorded from 53 healthy participants to visualize elaborate brain maps of phase- and amplitude-derived graph-theory metrics at different frequencies. To achieve this, we conducted a vertex-wise computation of threshold-independent graph metrics by combining proportional thresholding and a conjunction analysis and applied them to a correlation analysis of age and brain networks. Source power showed a frequency-dependent cortical distribution. Threshold-independent graph metrics derived from phase- and amplitude-based connectivity showed similar or different distributions depending on frequency. Vertex-wise age-brain correlation maps revealed that source power at the beta band and the amplitude-based degree at the alpha band changed with age in local regions. The present results indicate that a brain-wide analysis of neuromagnetic data has the potential to reveal neurophysiological network features in the human brain in a resting state.
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Affiliation(s)
- Tetsuo Kida
- Higher Brain Function Unit, Department of Functioning and Disability, Institute for Developmental Research, Aichi Developmental Disability Center, Kasugai, Japan
- Department of Functioning and Disability, Institute for Developmental Research, Aichi Developmental Disability Center, Kasugai, Japan
- Department of Integrative Physiology, National Institute for Physiological Sciences, Okazaki, Japan
- Section of Brain Function Information, Supportive Center for Brain Research, National Institute for Physiological Sciences, Okazaki, Japan
| | - Emi Tanaka
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan
| | - Ryusuke Kakigi
- Department of Integrative Physiology, National Institute for Physiological Sciences, Okazaki, Japan
| | - Koji Inui
- Department of Functioning and Disability, Institute for Developmental Research, Aichi Developmental Disability Center, Kasugai, Japan
- Department of Integrative Physiology, National Institute for Physiological Sciences, Okazaki, Japan
- Section of Brain Function Information, Supportive Center for Brain Research, National Institute for Physiological Sciences, Okazaki, Japan
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18
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Liu M, Liu B, Ye Z, Wu D. Bibliometric analysis of electroencephalogram research in mild cognitive impairment from 2005 to 2022. Front Neurosci 2023; 17:1128851. [PMID: 37021134 PMCID: PMC10067679 DOI: 10.3389/fnins.2023.1128851] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/06/2023] [Indexed: 03/22/2023] Open
Abstract
BackgroundElectroencephalogram (EEG), one of the most commonly used non-invasive neurophysiological examination techniques, advanced rapidly between 2005 and 2022, particularly when it was used for the diagnosis and prognosis of mild cognitive impairment (MCI). This study used a bibliometric approach to synthesize the knowledge structure and cutting-edge hotspots of EEG application in the MCI.MethodsRelated publications in the Web of Science Core Collection (WosCC) were retrieved from inception to 30 September 2022. CiteSpace, VOSviewer, and HistCite software were employed to perform bibliographic and visualization analyses.ResultsBetween 2005 and 2022, 2,905 studies related to the application of EEG in MCI were investigated. The United States had the highest number of publications and was at the top of the list of international collaborations. In terms of total number of articles, IRCCS San Raffaele Pisana ranked first among institutions. The Clinical Neurophysiology published the greatest number of articles. The author with the highest citations was Babiloni C. In descending order of frequency, keywords with the highest frequency were “EEG,” “mild cognitive impairment,” and “Alzheimer’s disease”.ConclusionThe application of EEG in MCI was investigated using bibliographic analysis. The research emphasis has shifted from examining local brain lesions with EEG to neural network mechanisms. The paradigm of big data and intelligent analysis is becoming more relevant in EEG analytical methods. The use of EEG to link MCI to other related neurological disorders, and to evaluate new targets for diagnosis and treatment, has become a new research trend. The above-mentioned findings have implications in the future research on the application of EEG in MCI.
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Affiliation(s)
- Mingrui Liu
- Department of Rehabilitation, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Baohu Liu
- Department of Rehabilitation, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Zelin Ye
- Department of Cardiovascular, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Dongyu Wu
- Department of Rehabilitation, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- *Correspondence: Dongyu Wu,
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Sang F, Xu K, Chen Y. Brain Network Organization and Aging. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1419:99-108. [PMID: 37418209 DOI: 10.1007/978-981-99-1627-6_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
Despite recent substantial progress in neuroscience, the mechanisms and principles of the complex structure, functions, and the relationship between the brain and cognitive functions have not been fully understood. The modeling method of brain network can provide a new perspective for neuroscience research, and it is possible to provide new solutions to the related research problems. On this basis, the researchers define the concept of human brain connectome to highlight and emphasize the importance of network modeling methods in neuroscience. For example, using diffusion-weighted magnetic resonance imaging (dMRI) technology and fiber tractography methods, a white matter connection network of the whole brain can be constructed. From the perspective of brain function, functional magnetic resonance imaging (fMRI) data can build the brain functional connection network. A structural covariation modeling method is used to obtain a brain structure covariation network, and it appears to reflect developmental coordination or synchronized maturation between areas of the brain. In addition, network modeling and analysis methods can also be applied to other types of image data, such as positron emission tomography (PET), electroencephalogram (EEG), and magnetoencephalography (MEG). This chapter mainly reviews the research progress of researchers on brain structure, function, and other aspects at the network level in recent years.
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Affiliation(s)
- Feng Sang
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China
| | - Kai Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China
| | - Yaojing Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China.
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China.
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Teng C, Wang M, Wang W, Ma J, Jia M, Wu M, Luo Y, Wang Y, Zhang Y, Xu J. Abnormal Properties of Cortical Functional Brain Network in Major Depressive Disorder: Graph Theory Analysis Based on Electroencephalography-Source Estimates. Neuroscience 2022; 506:80-90. [PMID: 36272697 DOI: 10.1016/j.neuroscience.2022.10.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 10/04/2022] [Accepted: 10/11/2022] [Indexed: 11/05/2022]
Abstract
Studies of scalp electroencephalography (EEG) had shown altered topological organization of functional brain networks in patients with major depressive disorder (MDD). However, most previous EEG-based network analyses were performed at sensor level, while the interpretation of obtained results was not straightforward due to volume conduction effect. To reduce the impact of this defect, the whole cortical functional brain networks of MDD patients were studied during resting state based on EEG-source estimates in this paper. First, scalp EEG signals were recorded from 19 patients with MDD and 20 normal controls under resting eyes-closed state, and cortical neural signals were estimated by using sLORETA method. Then, the correntropy coefficient of wavelet packet coefficients was performed to calculate functional connectivity (FC) matrices in four different frequency bands: δ, θ, α, β, respectively. Afterwards, topological properties of brain networks were analyzed by graph theory approaches. The results showed that the global FC strength of MDD patients was significantly higher than that of healthy subjects in α band. Also, it was found that MDD patients have abnormally increased clustering coefficient and local efficiency in both α and β bands compared to normal people. Furthermore, patients with MDD exhibited increased nodal clustering coefficients in the left lingual gryus and left precuneus in α band. In addition, β band global clustering coefficient was positively correlated with the scores of depression severity. Therefore, the findings indicated the cortical functional brain networks in MDD patients were disruptions, which suggested it would be one of potential causes of depression.
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Affiliation(s)
- Chaolin Teng
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China; Department of Aerospace Medicine, The Air Force Medical University, Xi'an, Shaanxi 710068, PR China
| | - Mengwei Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China
| | - Wei Wang
- Department of Psychiatry, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, PR China
| | - Jin Ma
- Department of Aerospace Medicine, The Air Force Medical University, Xi'an, Shaanxi 710068, PR China
| | - Min Jia
- Department of Psychiatry, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, PR China
| | - Min Wu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China
| | - Yuanyuan Luo
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China; Department of Psychology, Xi'an Mental Health Center, Xi'an, Shaanxi 710061, PR China
| | - Yu Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China
| | - Yiyang Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China
| | - Jin Xu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi 710049, PR China.
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Spatiotemporal EEG Dynamics of Prospective Memory in Ageing and Mild Cognitive Impairment. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10075-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Abstract
Prospective memory (PM, the memory of future intentions) is one of the first complaints of those that develop dementia-related disease. Little is known about the neurophysiology of PM in ageing and those with mild cognitive impairment (MCI). By using a novel artificial neural network to investigate the spatial and temporal features of PM related brain activity, new insights can be uncovered. Young adults (n = 30), healthy older adults (n = 39) and older adults with MCI (n = 27) completed a working memory and two PM (perceptual, conceptual) tasks. Time-locked electroencephalographic potentials (ERPs) from 128-electrodes were analysed using a brain-inspired spiking neural network (SNN) architecture. Local and global connectivity from the SNNs was then evaluated. SNNs outperformed other machine learning methods in classification of brain activity between younger, older and older adults with MCI. SNNs trained using PM related brain activity had better classification accuracy than working memory related brain activity. In general, younger adults exhibited greater local cluster connectivity compared to both older adult groups. Older adults with MCI demonstrated decreased global connectivity in response to working memory and perceptual PM tasks but increased connectivity in the conceptual PM models relative to younger and healthy older adults. SNNs can provide a useful method for differentiating between those with and without MCI. Using brain activity related to PM in combination with SNNs may provide a sensitive biomarker for detecting cognitive decline. Cognitively demanding tasks may increase the amount connectivity in older adults with MCI as a means of compensation.
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22
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Functional Network: A Novel Framework for Interpretability of Deep Neural Networks. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.11.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Srivastava P, Fotiadis P, Parkes L, Bassett DS. The expanding horizons of network neuroscience: From description to prediction and control. Neuroimage 2022; 258:119250. [PMID: 35659996 PMCID: PMC11164099 DOI: 10.1016/j.neuroimage.2022.119250] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 04/15/2022] [Accepted: 04/25/2022] [Indexed: 01/11/2023] Open
Abstract
The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.
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Affiliation(s)
- Pragya Srivastava
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Panagiotis Fotiadis
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neuroscience, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA; Santa Fe Institute, Santa Fe NM 87501, USA.
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Rodríguez-Méndez DA, San-Juan D, Hallett M, Antonopoulos CG, López-Reynoso E, Lara-Ramírez R. A new model for freedom of movement using connectomic analysis. PeerJ 2022; 10:e13602. [PMID: 35975236 PMCID: PMC9375968 DOI: 10.7717/peerj.13602] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 05/26/2022] [Indexed: 01/17/2023] Open
Abstract
The problem of whether we can execute free acts or not is central in philosophical thought, and it has been studied by numerous scholars throughout the centuries. Recently, neurosciences have entered this topic contributing new data and insights into the neuroanatomical basis of cognitive processes. With the advent of connectomics, a more refined landscape of brain connectivity can be analysed at an unprecedented level of detail. Here, we identify the connectivity network involved in the movement process from a connectomics point of view, from its motivation through its execution until the sense of agency develops. We constructed a "volitional network" using data derived from the Brainnetome Atlas database considering areas involved in volitional processes as known in the literature. We divided this process into eight processes and used Graph Theory to measure several structural properties of the network. Our results show that the volitional network is small-world and that it contains four communities. Nodes of the right hemisphere are contained in three of these communities whereas nodes of the left hemisphere only in two. Centrality measures indicate the nucleus accumbens is one of the most connected nodes in the network. Extensive connectivity is observed in all processes except in Decision (to move) and modulation of Agency, which might correlate with a mismatch mechanism for perception of Agency.
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Affiliation(s)
| | - Daniel San-Juan
- Epilepsy Clinic, Instituto Nacional de Neurología y Neurocirugía, Mexico City, Mexico
| | - Mark Hallett
- Human Motor Control Section, Medical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD, United States of America
| | - Chris G. Antonopoulos
- Department of Mathematical Sciences, University of Essex, Wivenhoe Park, United Kingdom
| | - Erick López-Reynoso
- Facultad de Ciencias, Universidad Autónoma del Estado de México, Toluca, Estado de México, México
| | - Ricardo Lara-Ramírez
- Centro de Investigación en Ciencias Biológicas Aplicadas, Universidad Autónoma del Estado de México, Toluca, Estado de México, México
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Tan B, Yan J, Zhang J, Jin Z, Li L. Aberrant Whole-Brain Resting-State Functional Connectivity Architecture in Obsessive-Compulsive Disorder: An EEG Study. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1887-1897. [PMID: 35786557 DOI: 10.1109/tnsre.2022.3187966] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Obsessive-compulsive disorder (OCD) is a common neuropsychiatric disorder characterized by intrusive thoughts (obsessions) and repetitive behaviors (compulsions), and few studies have assessed the whole-brain functional connectivity architecture of OCD with electroencephalogram (EEG) during different resting states. Graph theory and network-based statistics (NBS) were employed to examine the neural synchronization and the whole-brain functional connectivity (FC) based on the phase-locking value (PLV) of OCD patients and healthy controls (HCs) during eyes-closed (EC) and eyes-open (EO) states. Compared with HCs, OCD patients exhibited not only decreased global synchronization in terms of phase synchrony but also aberrant global topological properties (decreased average shortest path lengths and normalized shortest path lengths together with increased global efficiencies and normalized clustering coefficients) together with inhibited intra-hemispheric and interhemispheric FCs during rest, which suggested an imbalance between functional integration and segregation of brain networks for OCD patients. Meanwhile, OCD patients had increased global efficiencies and normalized clustering coefficients, but decreased average clustering coefficients and normalized shortest path lengths together with significantly decreased FCs in the alpha band from EC to EO states, which suggested a dynamic switch between highly integrated (EC state) and highly specialized (EO state) modes of information processing. Moreover, the decreased FCs of OCD patients showed obvious hemispheric asymmetry within or between groups during EC and EO states, which might serve as a potential biomarker to classify OCD patients from HCs.
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26
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Liu L, Ren J, Li Z, Yang C. A review of MEG dynamic brain network research. Proc Inst Mech Eng H 2022; 236:763-774. [PMID: 35465768 DOI: 10.1177/09544119221092503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state networks. As a non-invasive electrophysiological data with high temporal and spatial resolution, magnetoencephalogram (MEG) can provide rich information for the analysis of dynamic functional brain networks. In this review, the development of MEG brain network was summarized. Several analysis methods such as sliding window, Hidden Markov model, and time-frequency based methods used in MEG dynamic brain network studies were discussed. Finally, the current research about multi-modal brain network analysis and their applications with MEG neurophysiology, which are prospected to be one of the research directions in the future, were concluded.
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Affiliation(s)
- Lu Liu
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Jiechuan Ren
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhimei Li
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chunlan Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
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27
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Lv K, Cao X, Wang R, Du P, Fu J, Geng D, Zhang J. Neuroplasticity of Glioma Patients: Brain Structure and Topological Network. Front Neurol 2022; 13:871613. [PMID: 35645982 PMCID: PMC9136300 DOI: 10.3389/fneur.2022.871613] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/26/2022] [Indexed: 11/19/2022] Open
Abstract
Glioma is the most common primary malignant brain tumor in adults. It accounts for about 75% of such tumors and occurs more commonly in men. The incidence rate has been increasing in the past 30 years. Moreover, the 5-year overall survival rate of glioma patients is < 35%. Different locations, grades, and molecular characteristics of gliomas can lead to different behavioral deficits and prognosis, which are closely related to patients' quality of life and associated with neuroplasticity. Some advanced magnetic resonance imaging (MRI) technologies can explore the neuroplasticity of structural, topological, biochemical metabolism, and related mechanisms, which may contribute to the improvement of prognosis and function in glioma patients. In this review, we summarized the studies conducted on structural and topological plasticity of glioma patients through different MRI technologies and discussed future research directions. Previous studies have found that glioma itself and related functional impairments can lead to structural and topological plasticity using multimodal MRI. However, neuroplasticity caused by highly heterogeneous gliomas is not fully understood, and should be further explored through multimodal MRI. In addition, the individualized prediction of functional prognosis of glioma patients from the functional level based on machine learning (ML) is promising. These approaches and the introduction of ML can further shed light on the neuroplasticity and related mechanism of the brain, which will be helpful for management of glioma patients.
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Affiliation(s)
- Kun Lv
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xin Cao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Reasearch, Shanghai, China
- Institute of Intelligent Imaging Phenomics, International Human Phenome Institutes (Shanghai), Shanghai, China
| | - Rong Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Reasearch, Shanghai, China
- Institute of Intelligent Imaging Phenomics, International Human Phenome Institutes (Shanghai), Shanghai, China
| | - Peng Du
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Junyan Fu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Reasearch, Shanghai, China
- Institute of Intelligent Imaging Phenomics, International Human Phenome Institutes (Shanghai), Shanghai, China
- *Correspondence: Daoying Geng
| | - Jun Zhang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Reasearch, Shanghai, China
- Institute of Intelligent Imaging Phenomics, International Human Phenome Institutes (Shanghai), Shanghai, China
- Jun Zhang
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28
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Aung T, Tenney JR, Bagić AI. Contributions of Magnetoencephalography to Understanding Mechanisms of Generalized Epilepsies: Blurring the Boundary Between Focal and Generalized Epilepsies? Front Neurol 2022; 13:831546. [PMID: 35572923 PMCID: PMC9092024 DOI: 10.3389/fneur.2022.831546] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 03/08/2022] [Indexed: 12/31/2022] Open
Abstract
According to the latest operational 2017 ILAE classification of epileptic seizures, the generalized epileptic seizure is still conceptualized as "originating at some point within and rapidly engaging, bilaterally distributed networks." In contrast, the focal epileptic seizure is defined as "originating within networks limited to one hemisphere." Hence, one of the main concepts of "generalized" and "focal" epilepsy comes from EEG descriptions before the era of source localization, and a presumed simultaneous bilateral onset and bi-synchrony of epileptiform discharges remains a hallmark for generalized seizures. Current literature on the pathophysiology of generalized epilepsy supports the concept of a cortical epileptogenic focus triggering rapidly generalized epileptic discharges involving intact corticothalamic and corticocortical networks, known as the cortical focus theory. Likewise, focal epilepsy with rich connectivity can give rise to generalized spike and wave discharges resulting from widespread bilateral synchronization. Therefore, making this key distinction between generalized and focal epilepsy may be challenging in some cases, and for the first time, a combined generalized and focal epilepsy is categorized in the 2017 ILAE classification. Nevertheless, treatment options, such as the choice of antiseizure medications or surgical treatment, are the reason behind the importance of accurate epilepsy classification. Over the past several decades, plentiful scientific research on the pathophysiology of generalized epilepsy has been conducted using non-invasive neuroimaging and postprocessing of the electromagnetic neural signal by measuring the spatiotemporal and interhemispheric latency of bi-synchronous or generalized epileptiform discharges as well as network analysis to identify diagnostic and prognostic biomarkers for accurate diagnosis of the two major types of epilepsy. Among all the advanced techniques, magnetoencephalography (MEG) and multiple other methods provide excellent temporal and spatial resolution, inherently suited to analyzing and visualizing the propagation of generalized EEG activities. This article aims to provide a comprehensive literature review of recent innovations in MEG methodology using source localization and network analysis techniques that contributed to the literature of idiopathic generalized epilepsy in terms of pathophysiology and clinical prognosis, thus further blurring the boundary between focal and generalized epilepsy.
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Affiliation(s)
- Thandar Aung
- Department of Neurology, University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, United States
| | - Jeffrey R. Tenney
- Division of Neurology, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Anto I. Bagić
- Department of Neurology, University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, United States
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29
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Griffiths JD, Bastiaens SP, Kaboodvand N. Whole-Brain Modelling: Past, Present, and Future. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:313-355. [DOI: 10.1007/978-3-030-89439-9_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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30
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Functional ultrasound imaging: A useful tool for functional connectomics? Neuroimage 2021; 245:118722. [PMID: 34800662 DOI: 10.1016/j.neuroimage.2021.118722] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 09/15/2021] [Accepted: 11/10/2021] [Indexed: 12/28/2022] Open
Abstract
Functional ultrasound (fUS) is a hemodynamic-based functional neuroimaging technique, primarily used in animal models, that combines a high spatiotemporal resolution, a large field of view, and compatibility with behavior. These assets make fUS especially suited to interrogating brain activity at the systems level. In this review, we describe the technical capabilities offered by fUS and discuss how this technique can contribute to the field of functional connectomics. First, fUS can be used to study intrinsic functional connectivity, namely patterns of correlated activity between brain regions. In this area, fUS has made the most impact by following connectivity changes in disease models, across behavioral states, or dynamically. Second, fUS can also be used to map brain-wide pathways associated with an external event. For example, fUS has helped obtain finer descriptions of several sensory systems, and uncover new pathways implicated in specific behaviors. Additionally, combining fUS with direct circuit manipulations such as optogenetics is an attractive way to map the brain-wide connections of defined neuronal populations. Finally, technological improvements and the application of new analytical tools promise to boost fUS capabilities. As brain coverage and the range of behavioral contexts that can be addressed with fUS keep on increasing, we believe that fUS-guided connectomics will only expand in the future. In this regard, we consider the incorporation of fUS into multimodal studies combining diverse techniques and behavioral tasks to be the most promising research avenue.
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31
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Chu SH, Parhi KK, Westlund Schreiner M, Lenglet C, Mueller BA, Klimes-Dougan B, Cullen KR. Effect of SSRIs on Resting-State Functional Brain Networks in Adolescents with Major Depressive Disorder. J Clin Med 2021; 10:jcm10194322. [PMID: 34640340 PMCID: PMC8509847 DOI: 10.3390/jcm10194322] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 08/27/2021] [Accepted: 09/14/2021] [Indexed: 01/03/2023] Open
Abstract
Investigation of brain changes in functional connectivity and functional network topology from receiving 8-week selective serotonin reuptake inhibitor (SSRI) treatments is conducted in 12 unmedicated adolescents with major depressive disorder (MDD) by using wavelet-filtered resting-state functional magnetic resonance imaging (fMRI). Changes are observed in frontal-limbic, temporal, and default mode networks. In particular, topological analysis shows, at the global scale and in the 0.12–0.25 Hz band, that the normalized clustering coefficient and smallworldness of brain networks decreased after treatment. Regional changes in clustering coefficient and efficiency were observed in the bilateral caudal middle frontal gyrus, rostral middle frontal gyrus, superior temporal gyrus, left pars triangularis, putamen, and right superior frontal gyrus. Furthermore, changes of nodal centrality and changes of connectivity associated with these frontal and temporal regions confirm the global topological alternations. Moreover, frequency dependence is observed from FDR-controlled subnetworks for the limbic-cortical connectivity change. In the high-frequency band, the altered connections involve mostly frontal regions, while the altered connections in the low-frequency bands spread to parietal and temporal areas. Due to the limitation of small sample sizes and lack of placebo control, these preliminary findings require confirmation with future work using larger samples. Confirmation of biomarkers associated with treatment could suggest potential avenues for clinical applications such as tracking treatment response and neurobiologically informed treatment optimization.
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Affiliation(s)
- Shu-Hsien Chu
- Department of Electrical & Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA; (S.-H.C.); (K.K.P.); (C.L.)
| | - Keshab K. Parhi
- Department of Electrical & Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA; (S.-H.C.); (K.K.P.); (C.L.)
| | - Melinda Westlund Schreiner
- Department of Psychiatry, Huntsman Mental Health Institute, University of Utah, Salt Lake City, UT 84108, USA;
| | - Christophe Lenglet
- Department of Electrical & Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA; (S.-H.C.); (K.K.P.); (C.L.)
- Department of Radiology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Bryon A. Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55454, USA;
| | | | - Kathryn R. Cullen
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55454, USA;
- Correspondence:
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32
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Blanken TF, Bathelt J, Deserno MK, Voge L, Borsboom D, Douw L. Connecting brain and behavior in clinical neuroscience: A network approach. Neurosci Biobehav Rev 2021; 130:81-90. [PMID: 34324918 DOI: 10.1016/j.neubiorev.2021.07.027] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/14/2021] [Accepted: 07/23/2021] [Indexed: 11/16/2022]
Abstract
In recent years, there has been an increase in applications of network science in many different fields. In clinical neuroscience and psychopathology, the developments and applications of network science have occurred mostly simultaneously, but without much collaboration between the two fields. The promise of integrating these network applications lies in a united framework to tackle one of the fundamental questions of our time: how to understand the link between brain and behavior. In the current overview, we bridge this gap by introducing conventions in both fields, highlighting similarities, and creating a common language that enables the exploitation of synergies. We provide research examples in autism research, as it accurately represents research lines in both network neuroscience and psychological networks. We integrate brain and behavior not only semantically, but also practically, by showcasing three methodological avenues that allow to combine networks of brain and behavioral data. As such, the current paper offers a stepping stone to further develop multi-modal networks and to integrate brain and behavior.
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Affiliation(s)
- Tessa F Blanken
- Department of Psychological Methods, University of Amsterdam, 1018 WT, Amsterdam, the Netherlands.
| | - Joe Bathelt
- Royal Holloway, University of London, Department of Psychology, Egham, Surrey, TW20 0EX, United Kingdom
| | - Marie K Deserno
- Max Planck Institute for Human Development, 14195, Berlin, Germany
| | - Lily Voge
- Department of Anatomy and Neurosciences, Amsterdam University Medical Centres, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HZ, Amsterdam, the Netherlands
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, 1018 WT, Amsterdam, the Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam University Medical Centres, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HZ, Amsterdam, the Netherlands; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusets General Hospital, Boston, MA, 02129, USA
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33
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Cui L, Tao S, Yin HC, Shen QQ, Wang Y, Zhu LN, Li XJ. Tai Chi Chuan Alters Brain Functional Network Plasticity and Promotes Cognitive Flexibility. Front Psychol 2021; 12:665419. [PMID: 34267705 PMCID: PMC8275936 DOI: 10.3389/fpsyg.2021.665419] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 06/01/2021] [Indexed: 01/17/2023] Open
Abstract
Objective: This study used resting-state functional magnetic resonance imaging to investigate the effects of 8 weeks of Tai Chi Chuan and general aerobic exercise on the topological parameters of brain functional networks, explored the advantages of Tai Chi Chuan for improving functional network plasticity and cognitive flexibility, and examined how changes in topological attributes of brain functional networks relate to cognitive flexibility. Methods: Thirty-six healthy adults were grouped into Tai Chi Chuan (Bafa Wubu of Tai Chi), general aerobic exercise (brisk walking), and control groups. All of the subjects underwent fMRI and behavioral assessment before and after the exercise intervention. Results: Tai Chi Chuan exercise significantly enhanced the clustering coefficient and local efficiency compared with general aerobic exercise. Regarding the nodal properties, Tai Chi Chuan significantly enhanced the nodal clustering coefficient of the bilateral olfactory cortex and left thalamus, significantly reduced the nodal clustering coefficient of the left inferior temporal gyrus, significantly improved the nodal efficiency of the right precuneus and bilateral posterior cingulate gyrus, and significantly improved the nodal local efficiency of the left thalamus and right olfactory cortex. Furthermore, the behavioral performance results demonstrated that cognitive flexibility was enhanced by Tai Chi Chuan. The change in the nodal clustering coefficient in the left thalamus induced by Tai Chi Chuan was a significant predictor of cognitive flexibility. Conclusion: These findings demonstrated that Tai Chi Chuan could promote brain functional specialization. Brain functional specialization enhanced by Tai Chi Chuan exercise was a predictor of greater cognitive flexibility.
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Affiliation(s)
- Lei Cui
- College of P.E. and Sports, Beijing Normal University, Beijing, China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Heng-Chan Yin
- College of P.E. and Sports, Beijing Normal University, Beijing, China
| | - Qi-Qi Shen
- College of P.E. and Sports, Beijing Normal University, Beijing, China
| | - Yuan Wang
- College of P.E. and Sports, Beijing Normal University, Beijing, China
| | - Li-Na Zhu
- College of P.E. and Sports, Beijing Normal University, Beijing, China
| | - Xiu-Juan Li
- PE Department, Renmin University of China, Beijing, China
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34
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Simpson S, Chen Y, Wellmeyer E, Smith LC, Aragon Montes B, George O, Kimbrough A. The Hidden Brain: Uncovering Previously Overlooked Brain Regions by Employing Novel Preclinical Unbiased Network Approaches. Front Syst Neurosci 2021; 15:595507. [PMID: 33967705 PMCID: PMC8097000 DOI: 10.3389/fnsys.2021.595507] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 03/26/2021] [Indexed: 12/18/2022] Open
Abstract
A large focus of modern neuroscience has revolved around preselected brain regions of interest based on prior studies. While there are reasons to focus on brain regions implicated in prior work, the result has been a biased assessment of brain function. Thus, many brain regions that may prove crucial in a wide range of neurobiological problems, including neurodegenerative diseases and neuropsychiatric disorders, have been neglected. Advances in neuroimaging and computational neuroscience have made it possible to make unbiased assessments of whole-brain function and identify previously overlooked regions of the brain. This review will discuss the tools that have been developed to advance neuroscience and network-based computational approaches used to further analyze the interconnectivity of the brain. Furthermore, it will survey examples of neural network approaches that assess connectivity in clinical (i.e., human) and preclinical (i.e., animal model) studies and discuss how preclinical studies of neurodegenerative diseases and neuropsychiatric disorders can greatly benefit from the unbiased nature of whole-brain imaging and network neuroscience.
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Affiliation(s)
- Sierra Simpson
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Yueyi Chen
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States.,Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, United States
| | - Emma Wellmeyer
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Lauren C Smith
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Brianna Aragon Montes
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Olivier George
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Adam Kimbrough
- Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, United States.,Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States.,Purdue Institute for Inflammation, Immunology, and Infectious Disease, West Lafayette, IN, United States
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35
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Ghulam-Jelani Z, Barrios-Martinez J, Eguiluz-Melendez A, Gomez R, Anania Y, Yeh FC. Redundancy circuits of the commissural pathways in human and rhesus macaque brains. Hum Brain Mapp 2021; 42:2250-2261. [PMID: 33559959 PMCID: PMC8046059 DOI: 10.1002/hbm.25363] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 01/06/2021] [Accepted: 01/21/2021] [Indexed: 12/25/2022] Open
Abstract
It has been hypothesized that the human brain has less redundancy than animals, but the structural evidence has not been identified to confirm this claim. Here, we report three redundancy circuits of the commissural pathways in primate brains, namely the orbitofrontal, temporal, and occipital redundancy circuits of the anterior commissure and corpus callosum. Each redundancy circuit has two distinctly separated routes connecting a common pair of cortical regions. We mapped their trajectories in human and rhesus macaque brains using individual and population‐averaged tractography. The dissection results confirmed the existence of these redundancy circuits connecting the orbitofrontal lobe, amygdala, and visual cortex. The volume analysis showed a significant reduction in the orbitofrontal and occipital redundancy circuits of the human brain, whereas the temporal redundancy circuit had a substantial organizational difference between the human and rhesus macaque. Our results support the hypothesis that the human brain has less redundancy in the commissural pathways than that of the rhesus macaque brain. Further studies are needed to explore its neuropathological implications.
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Affiliation(s)
- Zulfar Ghulam-Jelani
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA.,Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Jessica Barrios-Martinez
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Aldo Eguiluz-Melendez
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Ricardo Gomez
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Yury Anania
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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36
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Disrupted intrinsic functional brain topology in patients with major depressive disorder. Mol Psychiatry 2021; 26:7363-7371. [PMID: 34385597 PMCID: PMC8873016 DOI: 10.1038/s41380-021-01247-2] [Citation(s) in RCA: 128] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 07/15/2021] [Accepted: 07/23/2021] [Indexed: 02/06/2023]
Abstract
Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks.
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37
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Nauta IM, Kulik SD, Breedt LC, Eijlers AJ, Strijbis EM, Bertens D, Tewarie P, Hillebrand A, Stam CJ, Uitdehaag BM, Geurts JJ, Douw L, de Jong BA, Schoonheim MM. Functional brain network organization measured with magnetoencephalography predicts cognitive decline in multiple sclerosis. Mult Scler 2020; 27:1727-1737. [PMID: 33295249 PMCID: PMC8474326 DOI: 10.1177/1352458520977160] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
BACKGROUND Cognitive decline remains difficult to predict as structural brain damage cannot fully explain the extensive heterogeneity found between MS patients. OBJECTIVE To investigate whether functional brain network organization measured with magnetoencephalography (MEG) predicts cognitive decline in MS patients after 5 years and to explore its value beyond structural pathology. METHODS Resting-state MEG recordings, structural MRI, and neuropsychological assessments were analyzed of 146 MS patients, and 100 patients had a 5-year follow-up neuropsychological assessment. Network properties of the minimum spanning tree (i.e. backbone of the functional brain network) indicating network integration and overload were related to baseline and longitudinal cognition, correcting for structural damage. RESULTS A more integrated beta band network (i.e. smaller diameter) and a less integrated delta band network (i.e. lower leaf fraction) predicted cognitive decline after 5 years (Radj2=15%), independent of structural damage. Cross-sectional analyses showed that a less integrated network (e.g. lower tree hierarchy) related to worse cognition, independent of frequency band. CONCLUSIONS The level of functional brain network integration was an independent predictive marker of cognitive decline, in addition to the severity of structural damage. This work thereby indicates the promise of MEG-derived network measures in predicting disease progression in MS.
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Affiliation(s)
- Ilse M Nauta
- Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Shanna D Kulik
- Department of Anatomy & Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Lucas C Breedt
- Department of Anatomy & Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Anand Jc Eijlers
- Department of Anatomy & Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Eva Mm Strijbis
- Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands/Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Dirk Bertens
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands; Klimmendaal Rehabilitation Center, Arnhem, The Netherlands
| | - Prejaas Tewarie
- Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands/Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Bernard Mj Uitdehaag
- Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Jeroen Jg Geurts
- Department of Anatomy & Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Linda Douw
- Department of Anatomy & Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Brigit A de Jong
- Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Department of Anatomy & Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
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38
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Sangare A, Marchi A, Pruvost-Robieux E, Soufflet C, Crepon B, Ramdani C, Chassoux F, Turak B, Landre E, Gavaret M. The Effectiveness of Vagus Nerve Stimulation in Drug-Resistant Epilepsy Correlates with Vagus Nerve Stimulation-Induced Electroencephalography Desynchronization. Brain Connect 2020; 10:566-577. [PMID: 33073582 PMCID: PMC7757623 DOI: 10.1089/brain.2020.0798] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Introduction: VNS is an adjunctive neuromodulation therapy for patients with drug-refractory epilepsy. The antiseizure effect of VNS is thought to be related to a diffuse modulation of functional connectivity but remains to be confirmed. Aim: To investigate electroencephalographic (EEG) metrics of functional connectivity in patients with drug-refractory epilepsy treated by vagus nerve stimulation (VNS), between VNS-stimulated “ON” and nonstimulated “OFF” periods and between responder (R) and nonresponder (NR) patients. Methods: Scalp-EEG was performed for 35 patients treated by VNS, using 21 channels and 2 additional electrodes on the neck to detect the VNS stimulation. Patients were defined as VNS responders if a reduction of seizure frequency of ∼50% was documented. We analyzed the synchronization in EEG time series during “ON” and “OFF” periods of stimulation, using average phase lag index (PLI) in signal space and phase-locking value (PLV) between 10 sources. Based on graph theory, we computed brain network models and analyzed minimum spanning tree (MST) for responder and nonresponder patients. Results: Among 35 patients treated by VNS for a median time of 7 years (range 4 months to 22 years), 20 were R and 15 were NR. For responder patients, PLI during ON periods was significantly lower than that during OFF periods in delta (p = 0.009), theta (p = 0.02), and beta (p = 0.04) frequency bands. For nonresponder patients, there were no significant differences between ON and OFF periods. Moreover, variations of seizure frequency with VNS correlated with the PLI OFF/ON ratio in delta (p = 0.02), theta (p = 0.04), and beta (p = 0.03) frequency bands. Our results were confirmed using PLV in theta band (p < 0.05). No significant differences in MST were observed between R and NR patients. Conclusion: The correlation between VNS-induced interictal EEG time-series desynchronization and decrease in seizure frequency suggested that VNS therapeutic impact might be related to changes in interictal functional connectivity.
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Affiliation(s)
- Aude Sangare
- Neurophysiology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France
| | - Angela Marchi
- Neurophysiology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France
| | - Estelle Pruvost-Robieux
- Neurophysiology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France.,Université de Paris, Paris, France
| | - Christine Soufflet
- Neurophysiology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France
| | - Benoit Crepon
- Neurophysiology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France
| | - Céline Ramdani
- Institut de Recherche Biomédicale des Armées (IRBA), Paris, France
| | - Francine Chassoux
- Neurosurgery and Epileptology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France
| | - Baris Turak
- Neurosurgery and Epileptology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France
| | - Elisabeth Landre
- Neurosurgery and Epileptology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France
| | - Martine Gavaret
- Neurophysiology Department, GHU Paris Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France.,Université de Paris, Paris, France.,INSERM UMR 1266, IPNP, Paris, France
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39
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Tan C, Ceballos G, Kasabov N, Puthanmadam Subramaniyam N. FusionSense: Emotion Classification Using Feature Fusion of Multimodal Data and Deep Learning in a Brain-Inspired Spiking Neural Network. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5328. [PMID: 32957655 PMCID: PMC7571195 DOI: 10.3390/s20185328] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 09/04/2020] [Accepted: 09/11/2020] [Indexed: 01/22/2023]
Abstract
Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing. Several studies have utilized state of the art deep learning methods and combined physiological signals, such as the electrocardiogram (EEG), electroencephalogram (ECG), skin temperature, along with facial expressions, voice, posture to name a few, in order to classify emotions. Spiking neural networks (SNNs) represent the third generation of neural networks and employ biologically plausible models of neurons. SNNs have been shown to handle Spatio-temporal data, which is essentially the nature of the data encountered in emotion recognition problem, in an efficient manner. In this work, for the first time, we propose the application of SNNs in order to solve the emotion recognition problem with the multimodal dataset. Specifically, we use the NeuCube framework, which employs an evolving SNN architecture to classify emotional valence and evaluate the performance of our approach on the MAHNOB-HCI dataset. The multimodal data used in our work consists of facial expressions along with physiological signals such as ECG, skin temperature, skin conductance, respiration signal, mouth length, and pupil size. We perform classification under the Leave-One-Subject-Out (LOSO) cross-validation mode. Our results show that the proposed approach achieves an accuracy of 73.15% for classifying binary valence when applying feature-level fusion, which is comparable to other deep learning methods. We achieve this accuracy even without using EEG, which other deep learning methods have relied on to achieve this level of accuracy. In conclusion, we have demonstrated that the SNN can be successfully used for solving the emotion recognition problem with multimodal data and also provide directions for future research utilizing SNN for Affective computing. In addition to the good accuracy, the SNN recognition system is requires incrementally trainable on new data in an adaptive way. It only one pass training, which makes it suitable for practical and on-line applications. These features are not manifested in other methods for this problem.
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Affiliation(s)
- Clarence Tan
- Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New Zealand;
| | - Gerardo Ceballos
- School of Electrical Engineering, University of Los Andes, Merida 5101, Venezuela;
| | - Nikola Kasabov
- Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1010, New Zealand;
| | - Narayan Puthanmadam Subramaniyam
- Faculty of Medicine and Health Technology and BioMediTech Institute, Tampere University, 33520 Tampere, Finland;
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, 02150 Espoo, Finland
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40
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Smith LC, Kimbrough A. Leveraging Neural Networks in Preclinical Alcohol Research. Brain Sci 2020; 10:E578. [PMID: 32825739 PMCID: PMC7565429 DOI: 10.3390/brainsci10090578] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 08/17/2020] [Accepted: 08/18/2020] [Indexed: 12/25/2022] Open
Abstract
Alcohol use disorder is a pervasive healthcare issue with significant socioeconomic consequences. There is a plethora of neural imaging techniques available at the clinical and preclinical level, including magnetic resonance imaging and three-dimensional (3D) tissue imaging techniques. Network-based approaches can be applied to imaging data to create neural networks that model the functional and structural connectivity of the brain. These networks can be used to changes to brain-wide neural signaling caused by brain states associated with alcohol use. Neural networks can be further used to identify key brain regions or neural "hubs" involved in alcohol drinking. Here, we briefly review the current imaging and neurocircuit manipulation methods. Then, we discuss clinical and preclinical studies using network-based approaches related to substance use disorders and alcohol drinking. Finally, we discuss how preclinical 3D imaging in combination with network approaches can be applied alone and in combination with other approaches to better understand alcohol drinking.
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Affiliation(s)
- Lauren C. Smith
- Department of Psychiatry, School of Medicine, University of California San Diego, MC 0667, La Jolla, CA 92093, USA;
| | - Adam Kimbrough
- Department of Psychiatry, School of Medicine, University of California San Diego, MC 0667, La Jolla, CA 92093, USA;
- Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, 625 Harrison Street, West Lafayette, IN 47907, USA
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41
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Hein M, Lanquart JP, Loas G, Hubain P, Linkowski P. Alterations of neural network organization during REM sleep in women: implication for sex differences in vulnerability to mood disorders. Biol Sex Differ 2020; 11:22. [PMID: 32334638 PMCID: PMC7183628 DOI: 10.1186/s13293-020-00297-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 04/07/2020] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Sleep plays an important role in vulnerability to mood disorders. However, despite the existence of sex differences in vulnerability to mood disorders, no study has yet investigated the sex effect on sleep network organization and its potential involvement in vulnerability to mood disorders. The aim of our study was to empirically investigate the sex effect on network organization during REM and slow-wave sleep using the effective connectivity measured by Granger causality. METHODS Polysomnographic data from 44 healthy individuals (28 men and 16 women) recruited prospectively were analysed. To obtain the 19 × 19 connectivity matrix of all possible pairwise combinations of electrodes by Granger causality method from our EEG data, we used the Toolbox MVGC multivariate Granger causality. The computation of the network measures was realized by importing these connectivity matrices into EEGNET Toolbox. RESULTS In men and women, all small-world coefficients obtained are compatible with a small-world network organization during REM and slow-wave sleep. However, compared to men, women present greater small-world coefficients during REM sleep as well as for all EEG bands during this sleep stage, which indicates the presence of a small-world network organization less marked during REM sleep as well as for all EEG bands during this sleep stage in women. In addition, in women, these small-world coefficients during REM sleep as well as for all EEG bands during this sleep stage are positively correlated with the presence of subclinical symptoms of depression. CONCLUSIONS Thus, the highlighting of these sex differences in network organization during REM sleep indicates the presence of differences in the global and local processing of information during sleep between women and men. In addition, this small-world network organization less marked during REM sleep appears to be a marker of vulnerability to mood disorders specific to women, which opens up new perspectives in understanding sex differences in the occurrence of mood disorders.
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Affiliation(s)
- Matthieu Hein
- Erasme Hospital, Department of Psychiatry and Sleep Laboratory, Université libre de Bruxelles, ULB, Route de Lennik, 808, 1070 Anderlecht, Brussels, Belgium.
| | - Jean-Pol Lanquart
- Erasme Hospital, Department of Psychiatry and Sleep Laboratory, Université libre de Bruxelles, ULB, Route de Lennik, 808, 1070 Anderlecht, Brussels, Belgium
| | - Gwénolé Loas
- Erasme Hospital, Department of Psychiatry and Sleep Laboratory, Université libre de Bruxelles, ULB, Route de Lennik, 808, 1070 Anderlecht, Brussels, Belgium
| | - Philippe Hubain
- Erasme Hospital, Department of Psychiatry and Sleep Laboratory, Université libre de Bruxelles, ULB, Route de Lennik, 808, 1070 Anderlecht, Brussels, Belgium
| | - Paul Linkowski
- Erasme Hospital, Department of Psychiatry and Sleep Laboratory, Université libre de Bruxelles, ULB, Route de Lennik, 808, 1070 Anderlecht, Brussels, Belgium
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42
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Gaudet I, Hüsser A, Vannasing P, Gallagher A. Functional Brain Connectivity of Language Functions in Children Revealed by EEG and MEG: A Systematic Review. Front Hum Neurosci 2020; 14:62. [PMID: 32226367 PMCID: PMC7080982 DOI: 10.3389/fnhum.2020.00062] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 02/10/2020] [Indexed: 01/29/2023] Open
Abstract
The development of language functions is of great interest to neuroscientists, as these functions are among the fundamental capacities of human cognition. For many years, researchers aimed at identifying cerebral correlates of language abilities. More recently, the development of new data analysis tools has generated a shift toward the investigation of complex cerebral networks. In 2015, Weiss-Croft and Baldeweg published a very interesting systematic review on the development of functional language networks, explored through the use of functional magnetic resonance imaging (fMRI). Compared to fMRI and because of their excellent temporal resolution, magnetoencephalography (MEG) and electroencephalography (EEG) provide different and important information on brain activity. Both therefore constitute crucial neuroimaging techniques for the investigation of the maturation of functional language brain networks. The main objective of this systematic review is to provide a state of knowledge on the investigation of language-related cerebral networks in children, through the use of EEG and MEG, as well as a detailed portrait of relevant MEG and EEG data analysis methods used in that specific research context. To do so, we have summarized the results and systematically compared the methodological approach of 24 peer-reviewed EEG or MEG scientific studies that included healthy children and children with or at high risk of language disabilities, from birth up to 18 years of age. All included studies employed functional and effective connectivity measures, such as coherence, phase locking value, and Phase Slope Index, and did so using different experimental paradigms (e.g., at rest or during language-related tasks). This review will provide more insight into the use of EEG and MEG for the study of language networks in children, contribute to the current state of knowledge on the developmental path of functional connectivity in language networks during childhood and adolescence, and finally allow future studies to choose the most appropriate type of connectivity analysis.
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Affiliation(s)
- Isabelle Gaudet
- Laboratoire d'imagerie optique en neurodéveloppement (LIONLAB), Sainte-Justine University Hospital Research Center, Montréal, QC, Canada.,Department of Psychology, Université de Montréal, Montréal, QC, Canada
| | - Alejandra Hüsser
- Laboratoire d'imagerie optique en neurodéveloppement (LIONLAB), Sainte-Justine University Hospital Research Center, Montréal, QC, Canada.,Department of Psychology, Université de Montréal, Montréal, QC, Canada
| | - Phetsamone Vannasing
- Laboratoire d'imagerie optique en neurodéveloppement (LIONLAB), Sainte-Justine University Hospital Research Center, Montréal, QC, Canada
| | - Anne Gallagher
- Laboratoire d'imagerie optique en neurodéveloppement (LIONLAB), Sainte-Justine University Hospital Research Center, Montréal, QC, Canada.,Department of Psychology, Université de Montréal, Montréal, QC, Canada
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43
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Jo YT, Joo SW, Shon SH, Kim H, Kim Y, Lee J. Diagnosing schizophrenia with network analysis and a machine learning method. Int J Methods Psychiatr Res 2020; 29:e1818. [PMID: 32022360 PMCID: PMC7051840 DOI: 10.1002/mpr.1818] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 12/17/2019] [Accepted: 01/10/2020] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVE Schizophrenia is a chronic and debilitating neuropsychiatric disorder. It has been suggested that impaired brain connectivity underlies the pathophysiology of schizophrenia. Network analysis has thus recently emerged in the field of schizophrenia research. METHODS We investigated 48 schizophrenia patients and 24 healthy controls using network analysis and a machine learning method. A number of global and nodal network properties were estimated from graphs that were reconstructed using probabilistic brain tractography. These network properties were then compared between groups and used for machine learning to classify schizophrenia patients and healthy controls. RESULTS In classifying schizophrenia patients and healthy controls via network properties, the support vector machine, random forest, naïve Bayes, and gradient boosting machine learning models showed an encouraging level of performance. The overall connectivity was revealed as the most significant contributing feature to this classification among the global network properties. Among the nodal network properties, although the relative importance of each region of interest was not identical, there were still some patterns. CONCLUSION In conclusion, the possibility exists to classify schizophrenia patients and healthy controls using network properties, and we have found that there is a provisional pattern of involved brain regions among patients with schizophrenia.
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Affiliation(s)
- Young Tak Jo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sung Woo Joo
- Medical Corps, 1st fleet, Republic of Korea Navy, Donghae, Korea
| | - Seung-Hyun Shon
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Harin Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Yangsik Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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44
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Sadaghiani S, Wirsich J. Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches. Netw Neurosci 2020; 4:1-29. [PMID: 32043042 PMCID: PMC7006873 DOI: 10.1162/netn_a_00114] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 11/11/2019] [Indexed: 12/17/2022] Open
Abstract
The discovery of a stable, whole-brain functional connectivity organization that is largely independent of external events has drastically extended our view of human brain function. However, this discovery has been primarily based on functional magnetic resonance imaging (fMRI). The role of this whole-brain organization in fast oscillation-based connectivity as measured, for example, by electroencephalography (EEG) and magnetoencephalography (MEG) is only beginning to emerge. Here, we review studies of intrinsic connectivity and its whole-brain organization in EEG, MEG, and intracranial electrophysiology with a particular focus on direct comparisons to connectome studies in fMRI. Synthesizing this literature, we conclude that irrespective of temporal scale over four orders of magnitude, intrinsic neurophysiological connectivity shows spatial similarity to the connectivity organization commonly observed in fMRI. A shared structural connectivity basis and cross-frequency coupling are possible mechanisms contributing to this similarity. Acknowledging that a stable whole-brain organization governs long-range coupling across all timescales of neural processing motivates researchers to take "baseline" intrinsic connectivity into account when investigating brain-behavior associations, and further encourages more widespread exploration of functional connectomics approaches beyond fMRI by using EEG and MEG modalities.
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Affiliation(s)
- Sepideh Sadaghiani
- Psychology Department, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jonathan Wirsich
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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45
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Tavildar S, Mogen B, Zanos S, Seeman S, Perlmutter S, Fetz E, Ashrafi A. Inferring Cortical Connectivity from ECoG Signals Using Graph Signal Processing. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2019; 7:109349-109362. [PMID: 36883134 PMCID: PMC9988241 DOI: 10.1109/access.2019.2934490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
A novel method to characterize connectivity between sites in the cerebral cortex of primates is proposed in this paper. Connectivity graphs for two macaque monkeys are inferred from Electrocorticographic (ECoG) activity recorded while the animals were alert. The locations of ECoG electrodes are considered as nodes of the graph, the coefficients of the auto-regressive (AR) representation of the signals measured at each node are considered as the signal on the graph and the connectivity strengths between the nodes are considered as the edges of the graph. Maximization of the graph smoothness defined from the Laplacian quadratic form is used to infer the connectivity map (adjacency matrix of the graph). The cortical evoked potential (CEP) map was obtained by stimulating different electrodes and recording the evoked potentials at the other electrodes. The maps obtained by the graph inference and the traditional method of spectral coherence are compared with the CEP map. The results show that the proposed method provides a description of cortical connectivity that is more similar to the stimulation-based measures than spectral coherence. The results are also tested by the surrogate map analysis in which the CEP map is randomly permuted and the distribution of the errors is obtained. It is shown that error between the two maps is comfortably outside the surrogate map error distribution. This indicates that the similarity between the map calculated by the graph inference and the CEP map is statistically significant.
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Affiliation(s)
- Siddhi Tavildar
- Computational Science Research Center, San Diego State University, San Diego CA, USA
- Center for Neurotechnology, Seattle WA, USA
| | - Brian Mogen
- Center for Neurotechnology, Seattle WA, USA
- Department of Bioengineering, Univ of Washington, Seattle WA, USA
| | - Stavros Zanos
- Center for Neurotechnology, Seattle WA, USA
- WA National Primate Research Center, Univ of Washington, Seattle WA, USA
- Center for Bioelectronic Medicine, Feinstein Institute for Medical Research, Manhasset NY, USA
| | - Stephanie Seeman
- Center for Neurotechnology, Seattle WA, USA
- Dept. Physiology & Biophysics, University of Washington, Seattle WA, USA
| | - Steve Perlmutter
- Center for Neurotechnology, Seattle WA, USA
- WA National Primate Research Center, Univ of Washington, Seattle WA, USA
- Dept. Physiology & Biophysics, University of Washington, Seattle WA, USA
| | - Eberhard Fetz
- Center for Neurotechnology, Seattle WA, USA
- WA National Primate Research Center, Univ of Washington, Seattle WA, USA
- Dept. Physiology & Biophysics, University of Washington, Seattle WA, USA
| | - Ashkan Ashrafi
- Computational Science Research Center, San Diego State University, San Diego CA, USA
- Center for Neurotechnology, Seattle WA, USA
- Department of Electrical and Computer Engineering, San Diego State University, San Diego CA, USA
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46
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Shah P, Ashourvan A, Mikhail F, Pines A, Kini L, Oechsel K, Das SR, Stein JM, Shinohara RT, Bassett DS, Litt B, Davis KA. Characterizing the role of the structural connectome in seizure dynamics. Brain 2019; 142:1955-1972. [PMID: 31099821 PMCID: PMC6598625 DOI: 10.1093/brain/awz125] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 02/11/2019] [Accepted: 03/07/2019] [Indexed: 12/23/2022] Open
Abstract
How does the human brain's structural scaffold give rise to its intricate functional dynamics? This is a central question in translational neuroscience that is particularly relevant to epilepsy, a disorder affecting over 50 million subjects worldwide. Treatment for medication-resistant focal epilepsy is often structural-through surgery or laser ablation-but structural targets, particularly in patients without clear lesions, are largely based on functional mapping via intracranial EEG. Unfortunately, the relationship between structural and functional connectivity in the seizing brain is poorly understood. In this study, we quantify structure-function coupling, specifically between white matter connections and intracranial EEG, across pre-ictal and ictal periods in 45 seizures from nine patients with unilateral drug-resistant focal epilepsy. We use high angular resolution diffusion imaging (HARDI) tractography to construct structural connectivity networks and correlate these networks with time-varying broadband and frequency-specific functional networks derived from coregistered intracranial EEG. Across all frequency bands, we find significant increases in structure-function coupling from pre-ictal to ictal periods. We demonstrate that short-range structural connections are primarily responsible for this increase in coupling. Finally, we find that spatiotemporal patterns of structure-function coupling are highly stereotyped for each patient. These results suggest that seizures harness the underlying structural connectome as they propagate. Mapping the relationship between structural and functional connectivity in epilepsy may inform new therapies to halt seizure spread, and pave the way for targeted patient-specific interventions.
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Affiliation(s)
- Preya Shah
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Arian Ashourvan
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Fadi Mikhail
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Adam Pines
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lohith Kini
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Kelly Oechsel
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sandhitsu R Das
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joel M Stein
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Brian Litt
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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47
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Serra A, Galdi P, Pesce E, Fratello M, Trojsi F, Tedeschi G, Tagliaferri R, Esposito F. Strong-Weak Pruning for Brain Network Identification in Connectome-Wide Neuroimaging: Application to Amyotrophic Lateral Sclerosis Disease Stage Characterization. Int J Neural Syst 2019; 29:1950007. [PMID: 30929575 DOI: 10.1142/s0129065719500072] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Magnetic resonance imaging allows acquiring functional and structural connectivity data from which high-density whole-brain networks can be derived to carry out connectome-wide analyses in normal and clinical populations. Graph theory has been widely applied to investigate the modular structure of brain connections by using centrality measures to identify the "hub" of human connectomes, and community detection methods to delineate subnetworks associated with diverse cognitive and sensorimotor functions. These analyses typically rely on a preprocessing step (pruning) to reduce computational complexity and remove the weakest edges that are most likely affected by experimental noise. However, weak links may contain relevant information about brain connectivity, therefore, the identification of the optimal trade-off between retained and discarded edges is a subject of active research. We introduce a pruning algorithm to identify edges that carry the highest information content. The algorithm selects both strong edges (i.e. edges belonging to shortest paths) and weak edges that are topologically relevant in weakly connected subnetworks. The newly developed "strong-weak" pruning (SWP) algorithm was validated on simulated networks that mimic the structure of human brain networks. It was then applied for the analysis of a real dataset of subjects affected by amyotrophic lateral sclerosis (ALS), both at the early (ALS2) and late (ALS3) stage of the disease, and of healthy control subjects. SWP preprocessing allowed identifying statistically significant differences in the path length of networks between patients and healthy subjects. ALS patients showed a decrease of connectivity between frontal cortex to temporal cortex and parietal cortex and between temporal and occipital cortex. Moreover, degree of centrality measures revealed significantly different hub and centrality scores between patient subgroups. These findings suggest a widespread alteration of network topology in ALS associated with disease progression.
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Affiliation(s)
- Angela Serra
- *NeuRoNeLab, Department of Management and Innovation Systems, University of Salerno, Fisciano (Sa), 84084, Italy.,†Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Paola Galdi
- *NeuRoNeLab, Department of Management and Innovation Systems, University of Salerno, Fisciano (Sa), 84084, Italy.,‡MRC Centre for Reproductive Health, University of Edinburgh, EH16 4TJ Edinburgh, UK
| | - Emanuele Pesce
- *NeuRoNeLab, Department of Management and Innovation Systems, University of Salerno, Fisciano (Sa), 84084, Italy.,§International Digital Laboratory, WMG, University of Coventry, CV4 7AL, UK
| | - Michele Fratello
- ¶Department of Medical, Surgical, Neurological, Metabolic and Ageing Sciences, Università Degli Studi Della Campania "Luigi Vanvitelli", Napoli, 80138, Italy
| | - Francesca Trojsi
- ¶Department of Medical, Surgical, Neurological, Metabolic and Ageing Sciences, Università Degli Studi Della Campania "Luigi Vanvitelli", Napoli, 80138, Italy
| | - Gioacchino Tedeschi
- ¶Department of Medical, Surgical, Neurological, Metabolic and Ageing Sciences, Università Degli Studi Della Campania "Luigi Vanvitelli", Napoli, 80138, Italy
| | - Roberto Tagliaferri
- *NeuRoNeLab, Department of Management and Innovation Systems, University of Salerno, Fisciano (Sa), 84084, Italy
| | - Fabrizio Esposito
- ∥Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Baronissi (Sa), 84081, Italy
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48
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Graph Theoretical Framework of Brain Networks in Multiple Sclerosis: A Review of Concepts. Neuroscience 2019; 403:35-53. [DOI: 10.1016/j.neuroscience.2017.10.033] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 10/22/2017] [Accepted: 10/24/2017] [Indexed: 12/11/2022]
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49
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Duncan D, Vespa P, Pitkänen A, Braimah A, Lapinlampi N, Toga AW. Big data sharing and analysis to advance research in post-traumatic epilepsy. Neurobiol Dis 2019; 123:127-136. [PMID: 29864492 PMCID: PMC6274619 DOI: 10.1016/j.nbd.2018.05.026] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 05/24/2018] [Accepted: 05/31/2018] [Indexed: 11/26/2022] Open
Abstract
We describe the infrastructure and functionality for a centralized preclinical and clinical data repository and analytic platform to support importing heterogeneous multi-modal data, automatically and manually linking data across modalities and sites, and searching content. We have developed and applied innovative image and electrophysiology processing methods to identify candidate biomarkers from MRI, EEG, and multi-modal data. Based on heterogeneous biomarkers, we present novel analytic tools designed to study epileptogenesis in animal model and human with the goal of tracking the probability of developing epilepsy over time.
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Affiliation(s)
- Dominique Duncan
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
| | - Paul Vespa
- Division of Neurosurgery and Department of Neurology, University of California at Los Angeles School of Medicine, Los Angeles, CA, USA
| | - Asla Pitkänen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Adebayo Braimah
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Niina Lapinlampi
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
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Graph theoretical modeling of baby brain networks. Neuroimage 2019; 185:711-727. [DOI: 10.1016/j.neuroimage.2018.06.038] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 05/22/2018] [Accepted: 06/11/2018] [Indexed: 11/20/2022] Open
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