A Method of Feature Selection via Deep Convolution Neural Networks For Encoding Nonlinear Functional Network Connectivity and Its Application To The Classification of Mental Disorders

In functional magnetic resonance imaging (fMRI) studies, it is common to evaluate the brain's functional network connectivity (FNC) which captures the temporal coupling between hemodynamic signals. FNC has been linked to various psychological phenomena. However, current FNCs mainly represent linear statistical relationships, which may not capture the fully complexity of the interactions among brain intrinsic connectivity networks (ICNs). Therefore, it is crucial to explore approaches that can better account for possible intricate nonlinear interactions involved in cognitive operations and the changes observed in psychiatric conditions such as schizophrenia. This exploration can lead to a better understanding of brain function and provide crucial insights into the underlying mechanisms of various psychological and psychiatric conditions. In this paper, we present an innovative approach which utilizes a deep convolutional neural network (DCNN) to extract nonlinear heatmaps from functional network connectivity matrices. By analyzing the heatmaps, multi-level nonlinear interactions can be derived from the corresponding input FNC data. Our results show these networks represent a significant improvement over previous approaches and offer a robust framework for understanding the complex inter-actions between brain regions. By incorporating two stages in the training process, our method ensures optimal efficiency and effectiveness. In the initial stage, a deep convolutional neural network is trained to create heatmaps from various convolution layers of the network. In the next stage, by utilizing a t-test-based feature selection method, we can effectively analyze each heatmap from different convolution layers. This approach ensures that we are able to extract linear and nonlinear functional connectivity from the heatmaps that play an important role in distinguishing different groups. We used a large dataset consisting of both schizophrenia patients and healthy controls, which were divided into separate training and validation sets to evaluate this approach. Our system shows the potential to accurately distinguish differences between the schizophrenia and healthy control groups with high accuracy.