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Cui L, Zhang Z, Huang YL, Xie F, Guan YH, Lo CYZ, Guo YH, Jiang JH, Guo QH. Brain amyloid-β deposition associated functional connectivity changes of ultra-large structural scale in mild cognitive impairment. Brain Imaging Behav 2023; 17:494-506. [PMID: 37188840 DOI: 10.1007/s11682-023-00780-8] [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] [Accepted: 04/19/2023] [Indexed: 05/17/2023]
Abstract
In preclinical Alzheimer's disease, neuro-functional changes due to amyloid-β (Aβ) deposition are not synchronized in different brain lobes and subcortical nuclei. This study aimed to explore the correlation between brain Aβ burden, connectivity changes in an ultra-large structural scale, and cognitive function in mild cognitive impairment. Participants with mild cognitive impairment were recruited and underwent florbetapir (F18-AV45) PET, resting-state functional MRI, and multidomain neuropsychological tests. AV-45 standardized uptake value ratio (SUVR) and functional connectivity of all participants were calculated. Of the total 144 participants, 72 were put in the low Aβ burden group and 72 in the high Aβ burden group. In the low Aβ burden group, all connectivities between lobes and nuclei had no correlation with SUVR. In the high Aβ burden group, SUVR showed negative correlations with the Subcortical-Occipital connectivity (r=-0.36, P = 0.02) and Subcortical-Parietal connectivity (r=-0.26, P = 0.026). Meanwhile, in the high Aβ burden group, SUVR showed positive correlations with the Temporal-Prefrontal connectivity (r = 0.27, P = 0.023), Temporal-Occipital connectivity (r = 0.24, P = 0.038), and Temporal-Parietal connectivity (r = 0.32, P = 0.006). Subcortical to Occipital and Parietal connectivities had positive correlations with general cognition, language, memory, and executive function. Temporal to Prefrontal, Occipital, and Parietal connectivities had negative correlations with memory function, executive function, and visuospatial function, and a positive correlation with language function. In conclusion, Individuals with mild cognitive impairment with high Aβ burden have Aβ-related bidirectional functional connectivity changes between lobes and subcortical nuclei that are associated with cognitive decline in multiple domains. These connectivity changes reflect neurological impairment and failed compensation.
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Affiliation(s)
- Liang Cui
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China
| | - Zhen Zhang
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China
| | - Yan-Lu Huang
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China
| | - Fang Xie
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200040, China
| | - Yi-Hui Guan
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200040, China
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Yi-Han Guo
- Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Jie-Hui Jiang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China.
| | - Qi-Hao Guo
- Department of Gerontology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, China.
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Li X, Salami A, Persson J. Hub architecture of the human structural connectome: Links to aging and processing speed. Neuroimage 2023; 278:120270. [PMID: 37423273 DOI: 10.1016/j.neuroimage.2023.120270] [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: 04/08/2023] [Revised: 06/30/2023] [Accepted: 07/06/2023] [Indexed: 07/11/2023] Open
Abstract
The human structural brain network, or connectome, has a rich-club organization with a small number of brain regions showing high network connectivity, called hubs. Hubs are centrally located in the network, energy costly, and critical for human cognition. Aging has been associated with changes in brain structure, function, and cognitive decline, such as processing speed. At a molecular level, the aging process is a progressive accumulation of oxidative damage, which leads to subsequent energy depletion in the neuron and causes cell death. However, it is still unclear how age affects hub connections in the human connectome. The current study aims to address this research gap by constructing structural connectome using fiber bundle capacity (FBC). FBC is derived from Constrained Spherical Deconvolution (CSD) modeling of white-matter fiber bundles, which represents the capacity of a fiber bundle to transfer information. Compared to the raw number of streamlines, FBC is less bias for quantifying connection strength within biological pathways. We found that hubs exhibit longer-distance connections and higher metabolic rates compared to peripheral brain regions, suggesting that hubs are biologically costly. Although the landscape of structural hubs was relatively age-invariant, there were wide-spread age effects on FBC in the connectome. Critically, these age effects were larger in connections within hub compared to peripheral brain connections. These findings were supported by both a cross-sectional sample with wide age-range (N = 137) and a longitudinal sample across 5 years (N = 83). Moreover, our results demonstrated that associations between FBC and processing speed were more concentrated in hub connections than chance level, and FBC in hub connections mediated the age-effects on processing speed. Overall, our findings indicate that structural connections of hubs, which demonstrate greater energy demands, are particular vulnerable to aging. The vulnerability may contribute to age-related impairments in processing speed among older adults.
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Affiliation(s)
- Xin Li
- Aging Research Center, Karolinska Institute and Stockholm University, Stockholm 171 65, Sweden.
| | - Alireza Salami
- Aging Research Center, Karolinska Institute and Stockholm University, Stockholm 171 65, Sweden; Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå 901 87, Sweden; Wallenberg Centre for Molecular Medicine, Umeå University, Umeå 901 87, Sweden; Department of Integrative Medical Biology, Umeå University, Umeå 901 87, Sweden
| | - Jonas Persson
- Aging Research Center, Karolinska Institute and Stockholm University, Stockholm 171 65, Sweden; Center for Lifespan Developmental Research (LEADER), School of Behavioral, Social and Legal Sciences, Örebro University, Örebro 701 82, Sweden
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Veitch DP, Weiner MW, Aisen PS, Beckett LA, DeCarli C, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Okonkwo O, Perrin RJ, Petersen RC, Rivera‐Mindt M, Saykin AJ, Shaw LM, Toga AW, Tosun D, Trojanowski JQ. Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease. Alzheimers Dement 2022; 18:824-857. [PMID: 34581485 PMCID: PMC9158456 DOI: 10.1002/alz.12422] [Citation(s) in RCA: 55] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 06/08/2021] [Accepted: 06/09/2021] [Indexed: 02/06/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020. METHODS We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion. RESULTS Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity. DISCUSSION ADNI has had a profound impact in improving clinical trials for AD.
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Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of MedicineUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of PsychiatryUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Laurel A. Beckett
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | - Charles DeCarli
- Department of Neurology and Center for NeuroscienceUniversity of California DavisDavisCaliforniaUSA
| | - Robert C. Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Broad Institute, Ariadne Labsand Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | | | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and Informatics, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - John Q. Trojanowski
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Zhukovsky P, Savulich G, Morgan S, Dalley JW, Williams GB, Ersche KD. Morphometric similarity deviations in stimulant use disorder point towards abnormal brain ageing. Brain Commun 2022; 4:fcac079. [PMID: 35694145 PMCID: PMC9178962 DOI: 10.1093/braincomms/fcac079] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 12/17/2021] [Accepted: 03/27/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Chronic drug use negatively impacts ageing, resulting in diminished health and quality of life. However, little is known about biomarkers of abnormal ageing in stimulant drug users. Using morphometric similarity network mapping, a novel approach to structural connectomics, we first mapped cross-sectional morphometric similarity trajectories of ageing in the publicly available Rockland Sample (20-80 years of age, n = 665). We then compared morphometric similarity and neuropsychological function between non-treatment-seeking, actively using patients with stimulant use disorder (n = 183, mean age 35.6 years) and healthy control participants (n = 148, mean age 36.0 years). Significantly altered mean regional morphometric similarity was found in 43 cortical regions including the inferior and orbital frontal gyri, pre/postcentral gyri and anterior temporal, superior parietal and occipital areas. Deviations from normative morphometric similarity trajectories in patients with stimulant use disorder suggested abnormal brain ageing. Furthermore, deficits in paired associates learning were consistent with neuropathology associated with both ageing and stimulant use disorder. Morphometric similarity mapping provides a promising biomarker for ageing in health and disease and may complement existing neuropsychological markers of age-related cognitive decline. Neuropathological ageing mechanisms in stimulant use disorder warrant further investigation to develop more age-appropriate treatments for older people addicted to stimulant drugs.
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Affiliation(s)
- Peter Zhukovsky
- Department of Psychology, University of Cambridge, UK
- Department of Psychiatry, University of Cambridge, School of Clinical Medicine, UK
| | - George Savulich
- Department of Psychiatry, University of Cambridge, School of Clinical Medicine, UK
| | - Sarah Morgan
- Department of Psychiatry, University of Cambridge, School of Clinical Medicine, UK
- Department of Computer Science and Technology, University of Cambridge, UK
- The Alan Turing Institute, London, UK
| | | | - Guy B. Williams
- Department of Clinical Neurosciences, University of Cambridge, UK
- Wolfson Brain Imaging Centre, Cambridge Biomedical Campus, Cambridge UK
| | - Karen D. Ersche
- Department of Psychiatry, University of Cambridge, School of Clinical Medicine, UK
- Department of Systems Neuroscience, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
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Common genetic variation is associated with longitudinal decline and network features in behavioral variant frontotemporal degeneration. Neurobiol Aging 2021; 108:16-23. [PMID: 34474300 PMCID: PMC8616801 DOI: 10.1016/j.neurobiolaging.2021.07.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 07/25/2021] [Accepted: 07/28/2021] [Indexed: 01/28/2023]
Abstract
The T allele in rs1768208 located in or near the myelin oligodendrocyte basic protein gene (MOBP) is a risk factor for frontotemporal degeneration pathology. We evaluated the hypothesis that the presence of a T allele in rs1768208 will be associated with rate of cognitive decline in behavioral variant frontotemporal degeneration (bvFTD) related to compromised frontal networks. We studied 81 individuals clinically diagnosed with bvFTD who were genotyped for rs1768208 and coded using a dominant model reflecting the presence (i.e., MOBP +) or absence (MOBP -) of the T risk allele. Linear mixed-effects models assessed the association of genotype on neuropsychological performance over time. Regression analyses examined differences in network structure by MOBP genotype. We found a genotype by time interaction for declining cognitive performance, whereby MOBP + individuals demonstrated faster rates of decline in executive function. The presence of a MOBP risk allele was associated with degradation of white matter network features in the frontal lobe. These findings suggest that individual genetic variation may contribute to heterogeneity in clinical progression.
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Zou Y, Ma H, Liu B, Li D, Liu D, Wang X, Wang S, Fan W, Han P. Disrupted Topological Organization in White Matter Networks in Unilateral Sudden Sensorineural Hearing Loss. Front Neurosci 2021; 15:666651. [PMID: 34321993 PMCID: PMC8312563 DOI: 10.3389/fnins.2021.666651] [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: 02/10/2021] [Accepted: 05/10/2021] [Indexed: 12/12/2022] Open
Abstract
Sudden sensorineural hearing loss (SSNHL) is a sudden-onset hearing impairment that rapidly develops within 72 h and is mostly unilateral. Only a few patients can be identified with a defined cause by routine clinical examinations. Recently, some studies have shown that unilateral SSNHL is associated with alterations in the central nervous system. However, little is known about the topological organization of white matter (WM) networks in unilateral SSNHL patients in the acute phase. In this study, 145 patients with SSNHL and 91 age-, gender-, and education-matched healthy controls were evaluated using diffusion tensor imaging (DTI) and graph theoretical approaches. The topological properties of WM networks, including global and nodal parameters, were investigated. At the global level, SSNHL patients displayed decreased clustering coefficient, local efficiency, global efficiency, normalized clustering coefficient, normalized characteristic path length, and small-worldness and increased characteristic path length (p < 0.05) compared with healthy controls. At the nodal level, altered nodal centralities in brain regions involved the auditory network, visual network, attention network, default mode network (DMN), sensorimotor network, and subcortical network (p < 0.05, Bonferroni corrected). These findings indicate a shift of the WM network topology in SSNHL patients toward randomization, which is characterized by decreased global network integration and segregation and is reflected by decreased global connectivity and altered nodal centralities. This study could help us understand the potential pathophysiology of unilateral SSNHL.
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Affiliation(s)
- Yan Zou
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Key Laboratory of Molecular Imaging, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hui Ma
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Key Laboratory of Molecular Imaging, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bo Liu
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dan Li
- Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Dingxi Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Key Laboratory of Molecular Imaging, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | | | - Siqi Wang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Key Laboratory of Molecular Imaging, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenliang Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Key Laboratory of Molecular Imaging, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ping Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Hubei Key Laboratory of Molecular Imaging, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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7
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Kwak S, Kim H, Kim H, Youm Y, Chey J. Distributed functional connectivity predicts neuropsychological test performance among older adults. Hum Brain Mapp 2021; 42:3305-3325. [PMID: 33960591 PMCID: PMC8193511 DOI: 10.1002/hbm.25436] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 01/30/2023] Open
Abstract
Neuropsychological test is an essential tool in assessing cognitive and functional changes associated with late-life neurocognitive disorders. Despite the utility of the neuropsychological test, the brain-wide neural basis of the test performance remains unclear. Using the predictive modeling approach, we aimed to identify the optimal combination of functional connectivities that predicts neuropsychological test scores of novel individuals. Resting-state functional connectivity and neuropsychological tests included in the OASIS-3 dataset (n = 428) were used to train the predictive models, and the identified models were iteratively applied to the holdout internal test set (n = 216) and external test set (KSHAP, n = 151). We found that the connectivity-based predicted score tracked the actual behavioral test scores (r = 0.08-0.44). The predictive models utilizing most of the connectivity features showed better accuracy than those composed of focal connectivity features, suggesting that its neural basis is largely distributed across multiple brain systems. The discriminant and clinical validity of the predictive models were further assessed. Our results suggest that late-life neuropsychological test performance can be formally characterized with distributed connectome-based predictive models, and further translational evidence is needed when developing theoretically valid and clinically incremental predictive models.
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Affiliation(s)
- Seyul Kwak
- Department of PsychologySeoul National UniversitySeoulRepublic of Korea
| | - Hairin Kim
- Department of PsychologySeoul National UniversitySeoulRepublic of Korea
| | - Hoyoung Kim
- Department of PsychologyChonbuk National UniversityJeonjuRepublic of Korea
| | - Yoosik Youm
- Department of SociologyYonsei UniversitySeoulRepublic of Korea
| | - Jeanyung Chey
- Department of PsychologySeoul National UniversitySeoulRepublic of Korea
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8
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Fischer FU, Wolf D, Tüscher O, Fellgiebel A. Structural Network Efficiency Predicts Resilience to Cognitive Decline in Elderly at Risk for Alzheimer's Disease. Front Aging Neurosci 2021; 13:637002. [PMID: 33692682 PMCID: PMC7937862 DOI: 10.3389/fnagi.2021.637002] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 01/25/2021] [Indexed: 01/10/2023] Open
Abstract
Introduction: Functional imaging studies have demonstrated the recruitment of additional neural resources as a possible mechanism to compensate for age and Alzheimer's disease (AD)-related cerebral pathology, the efficacy of which is potentially modulated by underlying structural network connectivity. Additionally, structural network efficiency (SNE) is associated with intelligence across the lifespan, which is a known factor for resilience to cognitive decline. We hypothesized that SNE may be a surrogate of the physiological basis of resilience to cognitive decline in elderly persons without dementia and with age- and AD-related cerebral pathology.Methods: We included 85 cognitively normal elderly subjects or mild cognitive impairment (MCI) patients submitted to baseline diffusion imaging, liquor specimens, amyloid-PET and longitudinal cognitive assessments. SNE was calculated from baseline MRI scans using fiber tractography and graph theory. Mixed linear effects models were estimated to investigate the association of higher resilience to cognitive decline with higher SNE and the modulation of this association by increased cerebral amyloid, liquor tau or WMHV. Results: For the majority of cognitive outcome measures, higher SNE was associated with higher resilience to cognitive decline (p-values: 0.011-0.039). Additionally, subjects with higher SNE showed more resilience to cognitive decline at higher cerebral amyloid burden (p-values: <0.001-0.036) and lower tau levels (p-values: 0.002-0.015).Conclusion: These results suggest that SNE to some extent may quantify the physiological basis of resilience to cognitive decline most effective at the earliest stages of AD, namely at increased amyloid burden and before increased tauopathy.
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Affiliation(s)
- Florian U. Fischer
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Johannes Gutenberg University Mainz, Mainz, Germany
- Center for Mental Health in Old Age, Landeskrankenhaus (AöR), Mainz, Germany
| | - Dominik Wolf
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Johannes Gutenberg University Mainz, Mainz, Germany
- Center for Mental Health in Old Age, Landeskrankenhaus (AöR), Mainz, Germany
| | - Oliver Tüscher
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Johannes Gutenberg University Mainz, Mainz, Germany
- Leibniz Institute for Resilience Research (LIR), Mainz, Germany
| | - Andreas Fellgiebel
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Johannes Gutenberg University Mainz, Mainz, Germany
- Center for Mental Health in Old Age, Landeskrankenhaus (AöR), Mainz, Germany
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Srivishagan S, Perera AAI, Hojjat A, Ratnarajah N. Brain Network Measures for Groups of Nodes: Application to Normal Aging and Alzheimer's Disease. Brain Connect 2020; 10:316-327. [PMID: 32458697 DOI: 10.1089/brain.2020.0747] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: The nodal brain network measures (e.g., centrality measures) are defined for a single node and the global network measures (e.g., global efficiency) are defined for the whole brain in the literature. But a meaningful group of nodes will be benefited from a formulation that applies to a group of nodes rather than a single node or the whole brain. The question such as "which brain lobe is more structurally central in the older-adult brain?" could be answered to some extent by the application of a centrality measure that applied to the group of nodes from each lobe. In the brain asymmetric studies, path-based global measures were applied to the left and right hemispherical networks separately, considering only intrahemispheric edges. However, for a valid comparison, such global measures should include the interhemispheric edges as well. This problem can be solved by considering both hemispherical nodes as two groups in one network. Methods: Novel definitions for group nodes network measures are presented in this study, to solve a number of such group-context problems in the brain networks analysis. We apply the group measures to the structural connectomes of older adults and Alzheimer's disease (AD) subjects based on the brain lobes and hemispherical groups to demonstrate the effectiveness of the proposed measures. Results: The temporal and parietal lobes are the most central lobes in older adults and AD, but the strength of these lobes has been heavily affected in AD. However, the rewiring of the AD brain preserves the paths for communication between other regions through these lobes. Leftward efficiency revealed in older adults and the asymmetry disappeared in the rewired AD. Conclusion: We prove that the concepts of group network measures have the potential to solve a number of such group-context problems in the brain networks analysis and the group network measures change the way of analyzing brain networks.
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Affiliation(s)
- Subaramya Srivishagan
- Faculty of Applied Science, Vavuniya Campus of the University of Jaffna, Vavuniya, Sri Lanka
| | | | - Ali Hojjat
- School of Computer Science, University of Kent, Canterbury, United Kingdom
| | - Nagulan Ratnarajah
- Faculty of Applied Science, Vavuniya Campus of the University of Jaffna, Vavuniya, Sri Lanka
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