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Functional Magnetic Resonance Imaging (fMRI) captures the temporal dynamicsof neural activity as a function of spatial location in the brain. Thus, fMRIscans are represented as 4-Dimensional (3-space + 1-time) tensors. And it iswidely believed that the spatio-temporal patterns in fMRI manifests asbehaviour and clinical symptoms. Because of the high dimensionality ($\sim$ 1Million) of fMRI, and the added constraints of limited cardinality of datasets, extracting such patterns are challenging. A standard approach to overcomethese hurdles is to reduce the dimensionality of the data by either summarizingactivation over time or space at the expense of possible loss of usefulinformation. Here, we introduce an end-to-end algorithm capable of extractingspatiotemporal features from the full 4-D data using 3-D CNNs and 3-DConvolutional LSTMs. We evaluate our proposed model on the publicly availableABIDE dataset to demonstrate the capability of our model to classify AutismSpectrum Disorder (ASD) from resting-state fMRI data. Our results show that theproposed model achieves state of the art results on single sites with F1-scoresof 0.78 and 0.7 on NYU and UM sites, respectively.

A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study