Andrea Insabato

The paper introduces a new classification framework for time series data using covariance patterns in recurrent neuronal networks, showing it can effectively capture second-order statistical features.

This paper introduces MOU-EC, a whole-brain effective connectivity model for fMRI data to study brain cognition in health and disease, showing its application in extracting task-specific biomarkers and interpreting connectivity changes.

The paper compares functional connectivity measures for brain state classification across cognitive tasks and finds that different measures yield distinct and uncorrelated informative links.

This paper extracts subject- and behavior-specific signatures from fMRI data using effective connectivity, showing they are topologically orthogonal and useful for individualized applications.