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

Brain Age Prediction Based on Resting-State Functional Connectivity Patterns Using Convolutional Neural Networks