Obtaining leaner deep neural networks for decoding brain functional connectome in a single shot

[摘要] Neuroscientific knowledge points to the presence of redundancy in the correlations of the brain's functional activity. These redundancies can be removed to mitigate the problem of overfitting when deep neural network (DNN) models are used to classify neuroimaging datasets. We propose an algorithm that removes insignificant nodes of DNNs in a layerwise manner and then adds a subset of correlated features in a single shot. When performing experiments with functional MRI datasets for classifying patients from healthy controls, we were able to obtain simpler and more generalizable DNNs. The obtained DNNs maintained a similar performance as the full network with only around 2% of the initial trainable parameters. Further, we used the trained network to identify salient brain regions and connections from functional connectome for multiple brain disorders. The identified biomarkers were found to closely correspond to previously known disease biomarkers. The proposed methods have cross-modal applications in obtaining leaner DNNs that seem to fit neuroimaging data better. (C) 2021 Elsevier B.V. All rights reserved.

[发布日期] 2021-09-17

[关键词] Alzheimer's disease;Attention deficit hyperactivity disorder;Brain decoding;Deep neural networks;Feature selection;Major depressive disorder;Mild cognitive impairment