Challenges and Data Collection Series: Brain Hacking Rare Vascular Disease

Vascular cognitive decline can manifest in a diverse array of symptoms observable in brain imaging data sets. This work will present the results of a hackathon aimed at applying several machine-learning methods to identify and track biomarkers within a new consortium of subjects exhibiting Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL), an inheritable cerebrovascular disease which affects arterial walls and can manifest in white matter lesions detectable with magnetic resonance imaging (MRI). The presenters first performed group difference analysis of several derivatives from structural, functional, and diffusion MRI gathered from the CADASIL consortium. In their analysis, they found significant group differences between CADASIL and control groups in White Matter Hyperintensity (WMH) lesion load, dynamic and static FNC states, and peak mean diffusivity metrics.