Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics.

ABSTRACT: There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there are few direct comparisons of relative utility. Here, we compared the performance of three DNN architectures and a classical machine learning algorithm (kernel regression) in predicting individual phenotypes from whole-brain resting-state functional connectivity (RSFC) patterns. One of the DNNs was a generic fully-connected feedforward neural network, while the other two DNNs were recently published approaches specifically designed to exploit the structure of connectome data. By using a combined sample of almost 10,000 participants from the Hu

SUBMITTER: He T 

PROVIDER: S-EPMC6984975 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature