Multimodal longitudinal imaging of brain and cervical cord as an ALS disease biomarker using microstructure statistics and morphometry

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PROJECT SUMMARY Recent developments in Magnetic Resonance Imaging (MRI), biophysical modeling, and computing have improved the sensitivity of imaging metrics to detect disease-related changes in the central nervous system in neurological disorders. This improved sensitivity has paved the way for utilizing these metrics as potential biomarkers of disease, in particular, to measure disease progression over short durations. We hypothesize that the multimodal analysis of MRI biomarkers (microstructure and morphology) from the brain and spine will improve sensitivity to detect disease-related changes over durations as short as 3 to 6 months. Our hypothesis is based on our prior work detecting longitudinal changes in brain microstructure over 6 months in an ALS cohort with modest change in functional measures over that time, and that a multimodal analysis combining brain and spine MRI measures can improve disease diagnosis accuracy. In this project, we will establish the scalability, sensitivity over shorter durations, and overall clinical trial readiness of these metrics through a two-site study. We also propose to improve the sensitivity of imaging metrics by combining multiple complementary measures from the brain and spine in a longitudinal multimodal statistical framework. Additionally, we will demonstrate how these imaging metrics correlate with fluid biomarkers and functional progression measures. We will acquire structural (T1 and T2) and diffusion MRI data from 40 participants with ALS and 10 control participants at two sites: the University of Minnesota (host institution) and the University of Florida. We will scan the brain and cervical spine of participants at baseline and 3 follow-up visits (3, 6 and 12 months). We will complete a neurological examination, ALSFRS-R, and UMN score at enrollment and obtain longitudinal ALSFRS-R and plasma neurofilament light (NfL) measurements. We will extract microstructural and morphological information from MRI data using dedicated computational methods and modeling. We will also apply novel statistical tools to combine those complementary imaging metrics into a multimodal analysis. Finally, we will analyze correlations between NfL, change in ALSFRS-R, and multimodal MRI metrics. Upon completion of our project, we anticipate that the enhanced sensitivity of our proposed longitudinal MRI biomarkers will have an impact on ALS treatment by providing novel surrogate markers as potential outcome measures for clinical trials. The expected increased effect size will also reduce the cohort size needed to conduct trials, thereby increasing their feasibility. Beyond the scope of clinical trials, our multimodal MRI biomarkers will serve as an objective measure of upper motor neuron degeneration at the single patient level. Our MRI measures will also be cross validated with fluid biomarkers and will contribute to efforts to stratify ALS patients into clinically homogeneous cohorts. 1