Brain age prediction based on resting-state functional connectivity patterns using convolutional neural networks

@article{Li2018BrainAP,
  title={Brain age prediction based on resting-state functional connectivity patterns using convolutional neural networks},
  author={Hongming Li and Theodore Daniel Satterthwaite and Yong Fan},
  journal={2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)},
  year={2018},
  pages={101-104}
}
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… 
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