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Schultz DH, Gansemer A, Allgood K, Gentz M, Secilmis L, Deldar Z, Savage CR, Ghazi Saidi L. Second language learning in older adults modulates Stroop task performance and brain activation. Front Aging Neurosci 2024; 16:1398015. [PMID: 39170898 PMCID: PMC11335563 DOI: 10.3389/fnagi.2024.1398015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 07/12/2024] [Indexed: 08/23/2024] Open
Abstract
Introduction Numerous studies have highlighted cognitive benefits in lifelong bilinguals during aging, manifesting as superior performance on cognitive tasks compared to monolingual counterparts. Yet, the cognitive impacts of acquiring a new language in older adulthood remain unexplored. In this study, we assessed both behavioral and fMRI responses during a Stroop task in older adults, pre- and post language-learning intervention. Methods A group of 41 participants (age:60-80) from a predominantly monolingual environment underwent a four-month online language course, selecting a new language of their preference. This intervention mandated engagement for 90 minutes a day, five days a week. Daily tracking was employed to monitor progress and retention. All participants completed a color-word Stroop task inside the scanner before and after the language instruction period. Results We found that performance on the Stroop task, as evidenced by accuracy and reaction time, improved following the language learning intervention. With the neuroimaging data, we observed significant differences in activity between congruent and incongruent trials in key regions in the prefrontal and parietal cortex. These results are consistent with previous reports using the Stroop paradigm. We also found that the amount of time participants spent with the language learning program was related to differential activity in these brain areas. Specifically, we found that people who spent more time with the language learning program showed a greater increase in differential activity between congruent and incongruent trials after the intervention relative to before. Discussion Future research is needed to determine the optimal parameters for language learning as an effective cognitive intervention for aging populations. We propose that with sufficient engagement, language learning can enhance specific domains of cognition such as the executive functions. These results extend the understanding of cognitive reserve and its augmentation through targeted interventions, setting a foundation for future investigations.
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Affiliation(s)
- Douglas H. Schultz
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, United States
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Alison Gansemer
- Department of Communication Disorders, College of Education, University of Nebraska at Kearney, Kearney, NE, United States
| | - Kiley Allgood
- Department of Communication Disorders, College of Education, University of Nebraska at Kearney, Kearney, NE, United States
| | - Mariah Gentz
- Department of Communication Disorders, College of Education, University of Nebraska at Kearney, Kearney, NE, United States
| | - Lauren Secilmis
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Zoha Deldar
- Department of Psychology, McGill University, Montreal, QC, Canada
| | - Cary R. Savage
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, United States
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Ladan Ghazi Saidi
- Center for Brain, Biology and Behavior, University of Nebraska-Lincoln, Lincoln, NE, United States
- Department of Communication Disorders, College of Education, University of Nebraska at Kearney, Kearney, NE, United States
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Drake DF, Derado G, Zhang L, Bowman FD. Neuroimaging statistical approaches for determining neural correlates of Alzheimer's disease via positron emission tomography imaging. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL STATISTICS 2023; 15:e1606. [PMID: 39655245 PMCID: PMC11626230 DOI: 10.1002/wics.1606] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 01/05/2023] [Indexed: 12/12/2024]
Abstract
Alzheimer's disease (AD) is a degenerative disorder involving significant memory loss and other cognitive deficits, manifesting as a progression from normal cognitive functioning to mild cognitive impairment to AD. The sooner an accurate diagnosis of probable AD is made, the easier it is to manage symptoms and plan for future therapy. Functional neuroimaging stands to be a useful tool in achieving early diagnosis. Among the many neuroimaging modalities, positron emission tomography (PET) provides direct regional assessment of, among others, brain metabolism, cerebral blood flow, amyloid deposition-all quantities of interest in the characterization of AD. However, there are analytic challenges in identifying early indicators of AD from these high-dimensional imaging data sets, and it is unclear whether early indicators of AD are more likely to emerge in localized patterns of brain activity or in patterns of correlation between distinct brain regions. Early PET-based analyses of AD focused on alterations in metabolic activity at the voxel-level or in anatomically defined regions of interest. Other approaches, including seed-voxel and multivariate techniques, seek to characterize metabolic connectivity by identifying other regions in the brain with similar patterns of activity across subjects. We briefly review various neuroimaging statistical approaches applied to determine changes in metabolic activity or metabolic connectivity associated with AD. We then present an approach that provides a unified statistical framework for addressing both metabolic activity and connectivity. Specifically, we apply a Bayesian spatial hierarchical framework to longitudinal metabolic PET scans from the Alzheimer's Disease Neuroimaging Initiative.
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Affiliation(s)
- Daniel F. Drake
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Gordana Derado
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Lijun Zhang
- Department of Population and Quantitative Health Science, Case Western Reserve University, Cleveland, Ohio, USA
| | - F. DuBois Bowman
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
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Characteristic patterns of inter- and intra-hemispheric metabolic connectivity in patients with stable and progressive mild cognitive impairment and Alzheimer's disease. Sci Rep 2018; 8:13807. [PMID: 30218083 PMCID: PMC6138637 DOI: 10.1038/s41598-018-31794-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 08/23/2018] [Indexed: 12/15/2022] Open
Abstract
The change in hypometabolism affects the regional links in the brain network. Here, to understand the underlying brain metabolic network deficits during the early stage and disease evolution of AD (Alzheimer disease), we applied correlation analysis to identify the metabolic connectivity patterns using 18F-FDG PET data for NC (normal control), sMCI (stable MCI), pMCI (progressive MCI) and AD, and explore the inter- and intra-hemispheric connectivity between anatomically-defined brain regions. Regions extracted from 90 anatomical structures were used to construct the matrix for measuring the inter- and intra-hemispheric connectivity. The brain connectivity patterns from the metabolic network show a decreasing trend of inter- and intra-hemispheric connections for NC, sMCI, pMCI and AD. Connection of temporal to the frontal or occipital regions is a characteristic pattern for conversion of NC to MCI, and the density of links in the parietal-occipital network is a differential pattern between sMCI and pMCI. The reduction pattern of inter and intra-hemispheric brain connectivity in the metabolic network depends on the disease stages, and is with a decreasing trend with respect to disease severity. Both frontal-occipital and parietal-occipital connectivity patterns in the metabolic network using 18F-FDG PET are the key feature for differentiating disease groups in AD.
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Morbelli S, Bauckneht M, Arnaldi D, Picco A, Pardini M, Brugnolo A, Buschiazzo A, Pagani M, Girtler N, Nieri A, Chincarini A, De Carli F, Sambuceti G, Nobili F. 18F-FDG PET diagnostic and prognostic patterns do not overlap in Alzheimer's disease (AD) patients at the mild cognitive impairment (MCI) stage. Eur J Nucl Med Mol Imaging 2017; 44:2073-2083. [PMID: 28785843 DOI: 10.1007/s00259-017-3790-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2017] [Accepted: 07/23/2017] [Indexed: 11/24/2022]
Abstract
PURPOSE We aimed to identify the cortical regions where hypometabolism can predict the speed of conversion to dementia in mild cognitive impairment due to Alzheimer's disease (MCI-AD). METHODS We selected from the clinical database of our tertiary center memory clinic, eighty-two consecutive MCI-AD that underwent 18F-fluorodeoxyglucose (FDG) PET at baseline during the first diagnostic work-up and were followed up at least until their clinical conversion to AD dementia. The whole group of MCI-AD was compared in SPM8 with a group of age-matched healthy controls (CTR) to verify the presence of AD diagnostic-pattern; then the correlation between conversion time and brain metabolism was assessed to identify the prognostic-pattern. Significance threshold was set at p < 0.05 False-Discovery-Rate (FDR) corrected at peak and at cluster level. Each MCI-AD was then compared with CTR by means of a SPM single-subject analysis and grouped according to presence of AD diagnostic-pattern and prognostic-pattern. Kaplan-Meier-analysis was used to evaluate if diagnostic- and/or prognostic-patterns can predict speed of conversion to dementia. RESULTS Diagnostic-pattern corresponded to typical posterior hypometabolism (BA 7, 18, 19, 30, 31 and 40) and did not correlate with time to conversion, which was instead correlated with metabolic levels in right middle and inferior temporal gyri as well as in the fusiform gyrus (prognostic-pattern, BA 20, 21 and 38). At Kaplan-Meier analysis, patients with hypometabolism in the prognostic pattern converted to AD-dementia significantly earlier than patients not showing significant hypometabolism in the right middle and inferior temporal cortex (9 versus 19 months; Log rank p < 0.02, Breslow test: p < 0.003, Tarone-Ware test: p < 0.007). CONCLUSION The present findings support the role of FDG PET as a robust progression biomarker even in a naturalist population of MCI-AD. However, not the AD-typical diagnostic-pattern in posterior regions but the middle and inferior temporal metabolism captures speed of conversion to dementia in MCI-AD since baseline. The highlighted prognostic pattern is a further, independent source of heterogeneity in MCI-AD and affects a primary-endpoint on interventional clinical trials (time of conversion to dementia).
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Affiliation(s)
- Silvia Morbelli
- Nuclear Medicine Unit, IRCCS AOU San Martino, IST and Department of Health Sciences, University of Genoa, Largo R. Benzi 10, 16132, Genoa, Italy.
| | - Matteo Bauckneht
- Nuclear Medicine Unit, IRCCS AOU San Martino, IST and Department of Health Sciences, University of Genoa, Largo R. Benzi 10, 16132, Genoa, Italy
| | - Dario Arnaldi
- Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Agnese Picco
- Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Matteo Pardini
- Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Andrea Brugnolo
- Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Ambra Buschiazzo
- Nuclear Medicine Unit, IRCCS AOU San Martino, IST and Department of Health Sciences, University of Genoa, Largo R. Benzi 10, 16132, Genoa, Italy
| | - Marco Pagani
- Institute of Cognitive Sciences and Technologies, CNR, Rome, Italy
- Department of Nuclear Medicine, Karolinska Hospital, Stockholm, Sweden
| | - Nicola Girtler
- Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
| | - Alberto Nieri
- Nuclear Medicine Unit, IRCCS AOU San Martino, IST and Department of Health Sciences, University of Genoa, Largo R. Benzi 10, 16132, Genoa, Italy
| | - Andrea Chincarini
- Istituto Nazionale di Fisica Nucleare, Sezione di Genova, Genoa, Italy
| | - Fabrizio De Carli
- Institute of Bioimaging and Molecular Physiology, National Research Council, Genoa, Italy
| | - Gianmario Sambuceti
- Nuclear Medicine Unit, IRCCS AOU San Martino, IST and Department of Health Sciences, University of Genoa, Largo R. Benzi 10, 16132, Genoa, Italy
| | - Flavio Nobili
- Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy
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Qiu X, Zhang Y, Feng H, Jiang D. Positron Emission Tomography Reveals Abnormal Topological Organization in Functional Brain Network in Diabetic Patients. Front Neurosci 2016; 10:235. [PMID: 27303259 PMCID: PMC4882320 DOI: 10.3389/fnins.2016.00235] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Accepted: 05/12/2016] [Indexed: 12/12/2022] Open
Abstract
Recent studies have demonstrated alterations in the topological organization of structural brain networks in diabetes mellitus (DM). However, the DM-related changes in the topological properties in functional brain networks are unexplored so far. We therefore used fluoro-D-glucose positron emission tomography (FDG-PET) data to construct functional brain networks of 73 DM patients and 91 sex- and age-matched normal controls (NCs), followed by a graph theoretical analysis. We found that both DM patients and NCs had a small-world topology in functional brain network. In comparison to the NC group, the DM group was found to have significantly lower small-world index, lower normalized clustering coefficients and higher normalized characteristic path length. Moreover, for diabetic patients, the nodal centrality was significantly reduced in the right rectus, the right cuneus, the left middle occipital gyrus, and the left postcentral gyrus, and it was significantly increased in the orbitofrontal region of the left middle frontal gyrus, the left olfactory region, and the right paracentral lobule. Our results demonstrated that the diabetic brain was associated with disrupted topological organization in the functional PET network, thus providing functional evidence for the abnormalities of brain networks in DM.
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Affiliation(s)
- Xiangzhe Qiu
- Department of Nuclear Medicine, The First Affiliated Hospital of Dalian Medical University Dalian, China
| | - Yanjun Zhang
- Department of Nuclear Medicine, The First Affiliated Hospital of Dalian Medical University Dalian, China
| | - Hongbo Feng
- Department of Nuclear Medicine, The First Affiliated Hospital of Dalian Medical University Dalian, China
| | - Donglang Jiang
- Department of Nuclear Medicine, The First Affiliated Hospital of Dalian Medical University Dalian, China
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Aiello M, Cavaliere C, Salvatore M. Hybrid PET/MR Imaging and Brain Connectivity. Front Neurosci 2016; 10:64. [PMID: 26973446 PMCID: PMC4771762 DOI: 10.3389/fnins.2016.00064] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Accepted: 02/10/2016] [Indexed: 12/13/2022] Open
Abstract
In recent years, brain connectivity is gaining ever-increasing interest from the interdisciplinary research community. The study of brain connectivity is characterized by a multifaceted approach providing both structural and functional evidence of the relationship between cerebral regions at different scales. Although magnetic resonance (MR) is the most established imaging modality for investigating connectivity in vivo, the recent advent of hybrid positron emission tomography (PET)/MR scanners paved the way for more comprehensive investigation of brain organization and physiology. Due to the high sensitivity and biochemical specificity of radiotracers, combining MR with PET imaging may enrich our ability to investigate connectivity by introducing the concept of metabolic connectivity and cometomics and promoting new insights on the physiological and molecular bases underlying high-level neural organization. This review aims to describe and summarize the main methods of analysis of brain connectivity employed in MR imaging and nuclear medicine. Moreover, it will discuss practical aspects and state-of-the-art techniques for exploiting hybrid PET/MR imaging to investigate the relationship of physiological processes and brain connectivity.
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Affiliation(s)
- Marco Aiello
- IRCCS SDN, Istituto Ricerca Diagnostica Nucleare Naples, Italy
| | - Carlo Cavaliere
- IRCCS SDN, Istituto Ricerca Diagnostica Nucleare Naples, Italy
| | - Marco Salvatore
- IRCCS SDN, Istituto Ricerca Diagnostica Nucleare Naples, Italy
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Bauckneht M, Picco A, Nobili F, Morbelli S. Amyloid positron emission tomography and cognitive reserve. World J Radiol 2015; 7:475-483. [PMID: 26753062 PMCID: PMC4697121 DOI: 10.4329/wjr.v7.i12.475] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Revised: 09/01/2015] [Accepted: 10/19/2015] [Indexed: 02/06/2023] Open
Abstract
Alzheimer’s disease (AD) is characterized by a non-linear progressive course and several aspects influence the relationship between cerebral amount of AD pathology and the clinical expression of the disease. Brain cognitive reserve (CR) refers to the hypothesized capacity of an adult brain to cope with brain damage in order to minimize symptomatology. CR phenomenon contributed to explain the disjunction between the degree of neurodegeneration and the clinical phenotype of AD. The possibility to track brain amyloidosis (Aβ) in vivo has huge relevance for AD diagnosis and new therapeutic approaches. The clinical repercussions of positron emission tomography (PET)-assessed Aβ load are certainly mediated by CR thus potentially hampering the prognostic meaning of amyloid PET in selected groups of patients. Similarly, amyloid PET and cerebrospinal fluid amyloidosis biomarkers have recently provided new evidence for CR. The present review discusses the concept of CR in the framework of available neuroimaging studies and specifically deals with the reciprocal influences between amyloid PET and CR in AD patients and with the potential consequent interventional strategies for AD.
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Carbonell F, Charil A, Zijdenbos AP, Evans AC, Bedell BJ. Hierarchical multivariate covariance analysis of metabolic connectivity. J Cereb Blood Flow Metab 2014; 34:1936-43. [PMID: 25294129 PMCID: PMC4269748 DOI: 10.1038/jcbfm.2014.165] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2014] [Revised: 08/23/2014] [Accepted: 09/04/2014] [Indexed: 01/28/2023]
Abstract
Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).
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Affiliation(s)
| | | | | | - Alan C Evans
- 1] Biospective Inc., Montreal, QC, Canada [2] Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Barry J Bedell
- 1] Biospective Inc., Montreal, QC, Canada [2] Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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β-Amyloid is associated with aberrant metabolic connectivity in subjects with mild cognitive impairment. J Cereb Blood Flow Metab 2014; 34:1169-79. [PMID: 24736891 PMCID: PMC4083380 DOI: 10.1038/jcbfm.2014.66] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2013] [Revised: 02/27/2014] [Accepted: 03/25/2014] [Indexed: 01/09/2023]
Abstract
Positron emission tomography (PET) studies using [18F]2-fluoro-2-deoxyglucose (FDG) have identified a well-defined pattern of glucose hypometabolism in Alzheimer's disease (AD). The assessment of the metabolic relationship among brain regions has the potential to provide unique information regarding the disease process. Previous studies of metabolic correlation patterns have demonstrated alterations in AD subjects relative to age-matched, healthy control subjects. The objective of this study was to examine the associations between β-amyloid, apolipoprotein E ɛ4 (APOE ɛ4) genotype, and metabolic correlations patterns in subjects diagnosed with mild cognitive impairment (MCI). Mild cognitive impairment subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study were categorized into β-amyloid-low and β-amyloid-high groups, based on quantitative analysis of [18F]florbetapir PET scans, and APOE ɛ4 non-carriers and carriers based on genotyping. We generated voxel-wise metabolic correlation strength maps across the entire cerebral cortex for each group, and, subsequently, performed a seed-based analysis. We found that the APOE ɛ4 genotype was closely related to regional glucose hypometabolism, while elevated, fibrillar β-amyloid burden was associated with specific derangements of the metabolic correlation patterns.
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