TY - JOUR
T1 - An unbiased data-driven age-related structural brain parcellation for the identification of intrinsic brain volume changes over the adult lifespan
AU - Bagarinao, Epifanio
AU - Watanabe, Hirohisa
AU - Maesawa, Satoshi
AU - Mori, Daisuke
AU - Hara, Kazuhiro
AU - Kawabata, Kazuya
AU - Yoneyama, Noritaka
AU - Ohdake, Reiko
AU - Imai, Kazunori
AU - Masuda, Michihito
AU - Yokoi, Takamasa
AU - Ogura, Aya
AU - Wakabayashi, Toshihiko
AU - Kuzuya, Masafumi
AU - Ozaki, Norio
AU - Hoshiyama, Minoru
AU - Isoda, Haruo
AU - Naganawa, Shinji
AU - Sobue, Gen
N1 - Publisher Copyright:
© 2017
PY - 2018/4/1
Y1 - 2018/4/1
N2 - This study aims to elucidate age-related intrinsic brain volume changes over the adult lifespan using an unbiased data-driven structural brain parcellation. Anatomical brain images from a cohort of 293 healthy volunteers ranging in age from 21 to 86 years were analyzed using independent component analysis (ICA). ICA-based parcellation identified 192 component images, of which 174 (90.6%) showed a significant negative correlation with age and with some components being more vulnerable to aging effects than others. Seven components demonstrated a convex slope with aging; 3 components had an inverted U-shaped trajectory, and 4 had a U-shaped trajectory. Linear combination of 86 components provided reliable prediction of chronological age with a mean absolute prediction error of approximately 7.2 years. Structural co-variation analysis showed strong interhemispheric, short-distance positive correlations and long-distance, inter-lobar negative correlations. Estimated network measures either exhibited a U- or an inverted U-shaped relationship with age, with the vertex occurring at approximately 45–50 years. Overall, these findings could contribute to our knowledge about healthy brain aging and could help provide a framework to distinguish the normal aging processes from that associated with age-related neurodegenerative diseases.
AB - This study aims to elucidate age-related intrinsic brain volume changes over the adult lifespan using an unbiased data-driven structural brain parcellation. Anatomical brain images from a cohort of 293 healthy volunteers ranging in age from 21 to 86 years were analyzed using independent component analysis (ICA). ICA-based parcellation identified 192 component images, of which 174 (90.6%) showed a significant negative correlation with age and with some components being more vulnerable to aging effects than others. Seven components demonstrated a convex slope with aging; 3 components had an inverted U-shaped trajectory, and 4 had a U-shaped trajectory. Linear combination of 86 components provided reliable prediction of chronological age with a mean absolute prediction error of approximately 7.2 years. Structural co-variation analysis showed strong interhemispheric, short-distance positive correlations and long-distance, inter-lobar negative correlations. Estimated network measures either exhibited a U- or an inverted U-shaped relationship with age, with the vertex occurring at approximately 45–50 years. Overall, these findings could contribute to our knowledge about healthy brain aging and could help provide a framework to distinguish the normal aging processes from that associated with age-related neurodegenerative diseases.
KW - Brain parcellation
KW - Brain-age prediction
KW - Healthy aging
KW - Independent component analysis
KW - Structural co-variation analysis
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UR - http://www.scopus.com/inward/citedby.url?scp=85038111705&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2017.12.014
DO - 10.1016/j.neuroimage.2017.12.014
M3 - Article
C2 - 29225065
AN - SCOPUS:85038111705
SN - 1053-8119
VL - 169
SP - 134
EP - 144
JO - NeuroImage
JF - NeuroImage
ER -