Simple 1-D Convolutional Networks for Resting-State fMRI Based Classification in Autism
This work addresses the challenge of processing high-dimensional neuroimaging data for psychiatric disorder diagnosis, though it is incremental as it matches rather than surpasses existing performance.
The authors tackled the problem of classifying Autism spectrum disorders using high-dimensional resting-state fMRI data by proposing a simple transformation that captures temporal dynamics while subsampling spatial extent, resulting in a fast-to-train 1-D convolutional network that performs at par with state-of-the-art methods.
Deep learning methods are increasingly being used with neuroimaging data like structural and function magnetic resonance imaging (MRI) to predict the diagnosis of neuropsychiatric and neurological disorders. For psychiatric disorders in particular, it is believed that one of the most promising modality is the resting-state functional MRI (rsfMRI), which captures the intrinsic connectivity between regions in the brain. Because rsfMRI data points are inherently high-dimensional (~1M), it is impossible to process the entire input in its raw form. In this paper, we propose a very simple transformation of the rsfMRI images that captures all of the temporal dynamics of the signal but sub-samples its spatial extent. As a result, we use a very simple 1-D convolutional network which is fast to train, requires minimal preprocessing and performs at par with the state-of-the-art on the classification of Autism spectrum disorders.