Deep Semantic Architecture with discriminative feature visualization for neuroimage analysis

TL;DR

This paper introduces a deep convolutional neural network with a novel visualization method to identify and interpret discriminative neural activity patterns in EEG data, revealing exercise-related brain differences.

Contribution

It presents a new deep learning architecture and ccCAM method for neuroimaging analysis, enabling visualization of neural activity propagation and discrimination of subtle EEG patterns.

Findings

Deep network accurately classifies exercise vs. control EEG signals.

ccCAM identifies specific frequency band features related to exercise effects.

Visualization reveals differences in neural activity propagation across cortex.

Abstract

Neuroimaging data analysis often involves \emph{a-priori} selection of data features to study the underlying neural activity. Since this could lead to sub-optimal feature selection and thereby prevent the detection of subtle patterns in neural activity, data-driven methods have recently gained popularity for optimizing neuroimaging data analysis pipelines and thereby, improving our understanding of neural mechanisms. In this context, we developed a deep convolutional architecture that can identify discriminating patterns in neuroimaging data and applied it to electroencephalography (EEG) recordings collected from 25 subjects performing a hand motor task before and after a rest period or a bout of exercise. The deep network was trained to classify subjects into exercise and control groups based on differences in their EEG signals. Subsequently, we developed a novel method termed the cue-combination for Class Activation Map (ccCAM), which enabled us to identify discriminating spatio-temporal features within definite frequency bands (23--33 Hz) and assess the effects of exercise on the brain. Additionally, the proposed architecture allowed the visualization of the differences in the propagation of underlying neural activity across the cortex between the two groups, for the first time in our knowledge. Our results demonstrate the feasibility of using deep network architectures for neuroimaging analysis in different contexts such as, for the identification of robust brain biomarkers to better characterize and potentially treat neurological disorders.