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Statistical Estimation of Physiological Brain Age as a Descriptor of Senescence Rate during Adulthood

Institution:
Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Publisher:
Springer
Publication Date:
Dec-2015
Journal:
Brain Imaging Behav
Volume Number:
9
Issue Number:
4
Pages:
678-89
Citation:
Brain Imaging Behav. 2015 Dec;9(4):678-89.
PubMed ID:
25376330
PMCID:
PMC4424195
Keywords:
Physiological brain age, Connectome, Cortical thickness, Multivariate regression, Healthy aging, Mild cognitive impairment
Appears in Collections:
NA-MIC
Sponsors:
P41 EB015922/EB/NIBIB NIH HHS/United States
R41 NS081792/NS/NINDS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Irimia A., Torgerson C.M., Goh S-Y.M., Van Horn J.D. Statistical Estimation of Physiological Brain Age as a Descriptor of Senescence Rate during Adulthood. Brain Imaging Behav. 2015 Dec;9(4):678-89. PMID: 25376330. PMCID: PMC4424195.
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Mapping aging-related brain structure and connectivity changes can be helpful for assessing physiological brain age (PBA), which is distinct from chronological age (CA) because genetic and environmental factors affect individuals differently. This study proposes an approach whereby structural and connectomic information can be combined to estimate PBA as an early biomarker of brain aging. In a cohort of 136 healthy adults, magnetic resonance and diffusion tensor imaging are respectively used to measure cortical thickness over the entire cortical mantle as well as connectivity properties (mean connectivity density and mean fractional anisotropy) for white matter connections. Using multivariate regression, these measurements are then employed to (1) illustrate how CA can be predicated-and thereby also how PBA can be estimated-and to conclude that (2) healthy aging is associated with significant connectome changes during adulthood. Our study illustrates a connectomically-informed statistical approach to PBA estimation, with potential applicability to the clinical identification of patients who exhibit accelerated brain aging, and who are consequently at higher risk for developing mild cognitive impairment or dementia.

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