A DEEP LEARNING FRAMEWORK TO LOCALIZE THE EPILEPTOGENIC ZONE FROM DYNAMIC FUNCTIONAL CONNECTIVITY USING A COMBINED GRAPH CONVOLUTIONAL AND TRANSFORMER NETWORK.

Localizing the epileptogenic zone (EZ) is a critical step in the treatment of medically refractory epilepsy. Resting-state fMRI (rs-fMRI) offers a new window into this task by capturing dynamically evolving co-activation patterns, also known as connectivity, in the brain. In this work, we present the first automated framework that uses dynamic functional connectivity from rs-fMRI to localize the EZ across a heterogeneous epilepsy cohort. Our framework uses a graph convolutional network for feature extraction, followed by a transformer network, whose attention mechanism learns which time points of the rs-fMRI scan are important for EZ localization. We train our framework on augmented data derived from the Human Connectome Project and evaluate it on a clinical epilepsy dataset. Our results demonstrate the clear advantages of our convolutional + transformer combination and data augmentation procedure over ablated and comparison models.