Longitudinal Neuroimaging Analysis Using Non-Negative Matrix Factorization

Abstract

Longitudinal analysis of neuroimaging data is becoming an important research area. In the last few years analysis of longitudinal data become a crucial point to better understand pathological mechanisms of complex brain diseases such as multiple sclerosis (MS) where white matter (WM) fiber bundles are variably altered by inflammatory events. In this work, we propose a new fully automated method to detect significant longitudinal changes in diffusivity metrics along WM fiber-bundles. This method consists of two steps: i) preprocessing of longitudinal diffusion acquisitions and WM fiber-bundles extraction, ii) application of a new hierarchical non negative matrix factorization (hNMF) algorithm to detect "pathological" changes. This method was applied first, on simulated longitudinal variations, and second, on MS patients longitudinal data. High level of precision, recall and F-Measure were obtained for the detection of small longitudinal changes along the WM fiber-bundles.