Superficial White Matter Classification using Diffusion MRI Tractography with Spiking Neural Networks

Motivation: The investigation of superficial white matter (SWM) poses challenges due to its small size, variability, delicate structure, high curvature, and fiber crossings in diffusion MRI tractography. Goal(s): Our goal is to develop an innovative methodology for classifying SWM streamline clusters using diffusion MRI tractography, leveraging brain-inspired learning-based techniques. Approach: A dual-phase method with Spiking Neural Networks (SNNs) and leaky integrate and fire (LIF) neurons is developed for the classification of 199 SWM clusters. Results: Experiments were conducted using two open-source datasets, and our method achieves accurate SWM classification results with an accuracy of 93.73%. Impact: The findings of this study on SWM classification hold great potential for facilitating analyses of SWM within neuroscientific research, contributing to understanding the complexities and alterations in SWM associated with various health conditions and neurological disorders.