Reconstruction Of Resting State FMRI Using Lstm Variational Auto-Encoder On Subcortical Surface To Detect Epilepsy

Functional MRI offers unique insights for the characterization and presurgical evaluation of people with epilepsy (PWE). In this paper, we develop a graph-based variational auto-encoder (gVAEs) to 1) learn the patterns of resting state functional MRI (rsfMRI) within the brain's subcortical structures in healthy subjects and 2) reconstruct it in PWE to identify findings unique to patients with epilepsy. The gVAE was enriched with Sequential Long Short Term Memory (LSTM) and perceptual loss to learn temporal rsfMRI features and smooth the reconstructed signals. Using a cross-validation approach on healthy controls, our best model yielded an average spatial correlation of 0.791 and an average temporal correlation of 0.793. When applied to PWE, the average and spatial correlation decreased to 0.752 and 0.750 respectively. Our findings pave the path to the development of a whole brain data-driven tool that may be valuable for the characterization of abnormalities within the epileptic brain. This may advance our understanding as to how these abnormalities are related to the location of seizure onset and can inform the care of patients with epilepsy. The code is available at: [GitHub]