Multistage classification identifies altered cortical phase- and amplitude-coupling in Multiple Sclerosis

Distinguishing groups of subjects or experimental conditions in a high-dimensional feature space is a common goal in modern neuroimaging studies. Successful classification depends on the selection of relevant features as not every neuronal signal component or parameter is informative about the research question at hand. Here, we developed a novel unsupervised multistage analysis approach that combines dimensionality reduction, boot-strap aggregating and multivariate classification to select relevant neuronal features. We tested the approach by identifying changes of brain-wide electrophysiological coupling in Multiple Sclerosis. Multiple Sclerosis is a de-myelinating disease of the central nervous system that can result in cognitive decline and physical disability. How-ever, related changes in large-scale brain interactions remain poorly understood and corresponding non-invasive biomarkers are sparse. We thus compared brain-wide phase-and amplitude-coupling of frequency specific neu-ronal activity in relapsing-remitting Multiple Sclerosis patients ( n = 17) and healthy controls ( n = 17) using magnetoencephalography. Changes in this dataset included both, increased and decreased phase-and amplitude -coupling in wide-spread, bilateral neuronal networks across a broad range of frequencies. These changes allowed to successfully classify patients and controls with an accuracy of 84%. Furthermore, classification confidence predicted behavioral scores of disease severity. In sum, our results unravel systematic changes of large-scale phase-and amplitude coupling in Multiple Sclerosis. Furthermore, our results establish a new analysis approach to efficiently contrast high-dimensional neuroimaging data between experimental groups or conditions.

Automated summary

The study developed a novel unsupervised multistage analysis approach and successfully compared changes in brain-wide electrophysiological coupling between Multiple Sclerosis patients and healthy controls, achieving an accuracy of 84% in classifying patients and controls.