TY - JOUR
T1 - Heterogeneity of structural and functional imaging patterns of advanced brain aging revealed via machine learning methods
AU - Eavani, Harini
AU - Habes, Mohamad
AU - Satterthwaite, Theodore D.
AU - An, Yang
AU - Hsieh, Meng Kang
AU - Honnorat, Nicolas
AU - Erus, Guray
AU - Doshi, Jimit
AU - Ferrucci, Luigi
AU - Beason-Held, Lori L.
AU - Resnick, Susan M.
AU - Davatzikos, Christos
N1 - Funding Information:
This study was supported in part by NIH grant AG014971, the Intramural Research Program, National Institute on Aging, NIH, and NIA contract HHSN2712013000284P to the University of Pennsylvania. TDS was supported by NIMH R01MH10770. This work has been supported in part by NIH (grant no. 1RF1AG054409).
PY - 2018/11
Y1 - 2018/11
N2 - Disentangling the heterogeneity of brain aging in cognitively normal older adults is challenging, as multiple co-occurring pathologic processes result in diverse functional and structural changes. Capitalizing on machine learning methods applied to magnetic resonance imaging data from 400 participants aged 50 to 96 years in the Baltimore Longitudinal Study of Aging, we constructed normative cross-sectional brain aging trajectories of structural and functional changes. Deviations from typical trajectories identified individuals with resilient brain aging and multiple subtypes of advanced brain aging. We identified 5 distinct phenotypes of advanced brain aging. One group included individuals with relatively extensive structural and functional loss and high white matter hyperintensity burden. Another subgroup showed focal hippocampal atrophy and lower posterior-cingulate functional coherence, low white matter hyperintensity burden, and higher medial-temporal connectivity, potentially reflecting high brain tissue reserve counterbalancing brain loss that is consistent with early stages of Alzheimer's disease. Other subgroups displayed distinct patterns. These results indicate that brain changes should not be measured seeking a single signature of brain aging but rather via methods capturing heterogeneity and subtypes of brain aging. Our findings inform future studies aiming to better understand the neurobiological underpinnings of brain aging imaging patterns.
AB - Disentangling the heterogeneity of brain aging in cognitively normal older adults is challenging, as multiple co-occurring pathologic processes result in diverse functional and structural changes. Capitalizing on machine learning methods applied to magnetic resonance imaging data from 400 participants aged 50 to 96 years in the Baltimore Longitudinal Study of Aging, we constructed normative cross-sectional brain aging trajectories of structural and functional changes. Deviations from typical trajectories identified individuals with resilient brain aging and multiple subtypes of advanced brain aging. We identified 5 distinct phenotypes of advanced brain aging. One group included individuals with relatively extensive structural and functional loss and high white matter hyperintensity burden. Another subgroup showed focal hippocampal atrophy and lower posterior-cingulate functional coherence, low white matter hyperintensity burden, and higher medial-temporal connectivity, potentially reflecting high brain tissue reserve counterbalancing brain loss that is consistent with early stages of Alzheimer's disease. Other subgroups displayed distinct patterns. These results indicate that brain changes should not be measured seeking a single signature of brain aging but rather via methods capturing heterogeneity and subtypes of brain aging. Our findings inform future studies aiming to better understand the neurobiological underpinnings of brain aging imaging patterns.
KW - Functional connectivity
KW - Functional connectivity
KW - Heterogeneity brain aging
KW - Resting-state fMRI
KW - Structural MRI
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U2 - 10.1016/j.neurobiolaging.2018.06.013
DO - 10.1016/j.neurobiolaging.2018.06.013
M3 - Article
C2 - 30077821
AN - SCOPUS:85050873292
VL - 71
SP - 41
EP - 50
JO - Neurobiology of Aging
JF - Neurobiology of Aging
SN - 0197-4580
ER -