BRAIN AGE PREDICTION BASED ON RESTING-STATE FUNCTIONAL CONNECTIVITY PATTERNS USING CONVOLUTIONAL NEURAL NETWORKS.

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TitleBRAIN AGE PREDICTION BASED ON RESTING-STATE FUNCTIONAL CONNECTIVITY PATTERNS USING CONVOLUTIONAL NEURAL NETWORKS.
Publication TypeJournal Article
Year of Publication2018
AuthorsLi, H, Satterthwaite, TD, Fan, Y
JournalProc IEEE Int Symp Biomed Imaging
Volume2018
Pagination101-104
Date Published2018 Apr
ISSN1945-7928
Abstract

Brain age prediction based on neuroimaging data could help characterize both the typical brain development and neuropsychiatric disorders. Pattern recognition models built upon functional connectivity (FC) measures derived from resting state fMRI (rsfMRI) data have been successfully used to predict the brain age. However, most existing studies focus on coarse-grained FC measures between brain regions or intrinsic connectivity networks (ICNs), which may sacrifice fine-grained FC information of the rsfMRI data. Whole brain voxel-wise FC measures could provide fine-grained FC information of the brain and may improve the prediction performance. In this study, we develop a deep learning method to use convolutional neural networks (CNNs) to learn informative features from the fine-grained whole brain FC measures for the brain age prediction. Experimental results on a large dataset of resting-state fMRI demonstrate that the deep learning model with fine-grained FC measures could better predict the brain age.

DOI10.1109/ISBI.2018.8363532
Alternate JournalProc IEEE Int Symp Biomed Imaging
PubMed ID30079125
PubMed Central IDPMC6074039
Grant ListR01 EB022573 / EB / NIBIB NIH HHS / United States
U54 DA039002 / DA / NIDA NIH HHS / United States
P50 DK114786 / DK / NIDDK NIH HHS / United States
R01 DA039215 / DA / NIDA NIH HHS / United States
R01 MH107703 / MH / NIMH NIH HHS / United States
R21 CA223358 / CA / NCI NIH HHS / United States