3D convolutional neural network for feature extraction and classification of fMRI volumes

Recently, deep learning (DL) techniques have been gaining interest in the neuroimaging community. In this study, we present 3D convolutional neural network (3D-CNN) as an end-to-end model to label a target task among four sensorimotor tasks for each functional magnetic resonance imaging (fMRI) volume. To the best of our knowledge, this is the first study that employs a single blood-oxygenation-level-dependent (BOLD) fMRI volume as the input of the 3D-CNN for task classification. We hypothesized that 3D-CNN has the capability to extract potentially shift-invariant features in local brain areas while preserving the overall spatial layout of the whole brain fMRI volume. We designed a 3D-CNN model by extending the LeNet-5 CNN for 2D image classification to 3D volume classification. The designed 3D-CNN model was thoroughly evaluated using BOLD fMRI volumes acquired from four sensorimotor tasks in terms of the classification performance and feature representations for each of the four sensorimotor tasks.