TY - JOUR
T1 - Morphometric network differences in ageing versus Alzheimer’s disease dementia
AU - Alzheimer's Disease Neuroimaging Initiative
AU - PREVENT-AD Research Group
AU - Binette, Alexa Pichet
AU - Gonneaud, Julie
AU - Vogel, Jacob W.
AU - La Joie, Renaud
AU - Rosa-Neto, Pedro
AU - Louis Collins, D.
AU - Poirier, Judes
AU - Breitner, John C.S.
AU - Villeneuve, Sylvia
AU - Vachon-Presseau, Etienne
AU - Tremblay-Mercier, Jennifer
AU - Madjar, Cécile
N1 - Publisher Copyright:
© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - Age being the main risk factor for Alzheimer’s disease, it is particularly challenging to disentangle structural changes related to normal brain ageing from those specific to Alzheimer’s disease. Most studies aiming to make this distinction focused on older adults only and on a priori anatomical regions. Drawing on a large, multi-cohort dataset ranging from young adults (n = 468; age range 18–35 years), to older adults with intact cognition (n = 431; age range 55–90 years) and with Alzheimer’s disease (n = 50 with late mild cognitive impairment and 71 with Alzheimer’s dementia, age range 56–88 years), we investigated grey matter organization and volume differences in ageing and Alzheimer’s disease. Using independent component analysis on all participants’ structural MRI, we first derived morphometric networks and extracted grey matter volume in each network. We also derived a measure of whole-brain grey matter pattern organization by correlating grey matter volume in all networks across all participants from the same cohort. We used logistic regressions and receiver operating characteristic analyses to evaluate how well grey matter volume in each network and whole-brain pattern could discriminate between ageing and Alzheimer’s disease. Because increased heterogeneity is often reported as one of the main features characterizing brain ageing, we also evaluated interindividual heterogeneity within morphometric networks and across the whole-brain organization in ageing and Alzheimer’s disease. Finally, to investigate the clinical validity of the different grey matter features, we evaluated whether grey matter volume or whole-brain pattern was related to clinical progression in cognitively normal older adults. Ageing and Alzheimer’s disease contributed additive effects on grey matter volume in nearly all networks, except frontal lobe networks, where differences in grey matter were more specific to ageing. While no networks specifically discriminated Alzheimer’s disease from ageing, heterogeneity in grey matter volumes across morphometric networks and in the whole-brain grey matter pattern characterized individuals with cognitive impairments. Preservation of the whole-brain grey matter pattern was also related to lower risk of developing cognitive impairment, more so than grey matter volume. These results suggest both ageing and Alzheimer’s disease involve widespread atrophy, but that the clinical expression of Alzheimer’s disease is uniquely associated with disruption of morphometric organization.
AB - Age being the main risk factor for Alzheimer’s disease, it is particularly challenging to disentangle structural changes related to normal brain ageing from those specific to Alzheimer’s disease. Most studies aiming to make this distinction focused on older adults only and on a priori anatomical regions. Drawing on a large, multi-cohort dataset ranging from young adults (n = 468; age range 18–35 years), to older adults with intact cognition (n = 431; age range 55–90 years) and with Alzheimer’s disease (n = 50 with late mild cognitive impairment and 71 with Alzheimer’s dementia, age range 56–88 years), we investigated grey matter organization and volume differences in ageing and Alzheimer’s disease. Using independent component analysis on all participants’ structural MRI, we first derived morphometric networks and extracted grey matter volume in each network. We also derived a measure of whole-brain grey matter pattern organization by correlating grey matter volume in all networks across all participants from the same cohort. We used logistic regressions and receiver operating characteristic analyses to evaluate how well grey matter volume in each network and whole-brain pattern could discriminate between ageing and Alzheimer’s disease. Because increased heterogeneity is often reported as one of the main features characterizing brain ageing, we also evaluated interindividual heterogeneity within morphometric networks and across the whole-brain organization in ageing and Alzheimer’s disease. Finally, to investigate the clinical validity of the different grey matter features, we evaluated whether grey matter volume or whole-brain pattern was related to clinical progression in cognitively normal older adults. Ageing and Alzheimer’s disease contributed additive effects on grey matter volume in nearly all networks, except frontal lobe networks, where differences in grey matter were more specific to ageing. While no networks specifically discriminated Alzheimer’s disease from ageing, heterogeneity in grey matter volumes across morphometric networks and in the whole-brain grey matter pattern characterized individuals with cognitive impairments. Preservation of the whole-brain grey matter pattern was also related to lower risk of developing cognitive impairment, more so than grey matter volume. These results suggest both ageing and Alzheimer’s disease involve widespread atrophy, but that the clinical expression of Alzheimer’s disease is uniquely associated with disruption of morphometric organization.
KW - Grey matter
KW - Lifespan
KW - MRI
KW - Neurodegeneration
KW - Structural covariance
UR - http://www.scopus.com/inward/record.url?scp=85079209798&partnerID=8YFLogxK
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U2 - 10.1093/brain/awz414
DO - 10.1093/brain/awz414
M3 - Article
C2 - 32040564
AN - SCOPUS:85079209798
SN - 0006-8950
VL - 143
SP - 635
EP - 649
JO - Brain
JF - Brain
IS - 2
ER -