Individualized Phenotyping of Functional ALS Pathology in Sensorimotor Cortex

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease characterized by the loss of motor neurons in primary motor cortex (MI), leading to muscle weakness, atrophy, and death within a median of three years. Even though ALS is characterized by different disease subtypes affecting different body parts, individiualized phenotyping of functional ALS pathology has so far not been achieved. We recorded 7 Tesla functional MRI (7T-fMRI) data while ALS patients and matched controls moved affected and non-affected body parts in the MR scanner. We applied robust Shared Response Modeling (rSRM) for capturing ALS-specific shared responses for group classification, and Partial Least Squares (PLS) regression for relating the latent variables to clinical subtypes and the degree of disease progression. We show that both functional connectivity and functional activation in MI are a predictor for disease onset site. However, disease severity could best be predicted by functional connectivity rather than pure activation changes. Critically, we show that functionally disease-defining information in MI is not strongest in the area that is behaviorally first-affected, deviating from the behavioral phenotype of the patients. When computing the model’s weight distribution of the King stage classification and projecting them back into voxel space, the highest mean weights are present in the foot and tongue/face regions that seem to drive disease progress. Our data highlight the importance of 7T-fMRI task-based functional connectivity measures for classifying ALS-patients, and provide evidence that a single 7T-MRI scan can be used for identifying a disease signature of each individual ALS patient.