  In this paper, we propose a reservoir computing-based and directed graph
analysis pipeline. The goal of this pipeline is to define an efficient brain
representation for connectivity in stroke data derived from magnetic resonance
imaging. Ultimately, this representation is used within a directed graph
convolutional architecture and investigated with explainable artificial
intelligence (AI) tools.
  Stroke is one of the leading causes of mortality and morbidity worldwide, and
it demands precise diagnostic tools for timely intervention and improved
patient outcomes. Neuroimaging data, with their rich structural and functional
information, provide a fertile ground for biomarker discovery. However, the
complexity and variability of information flow in the brain requires advanced
analysis, especially if we consider the case of disrupted networks as those
given by the brain connectome of stroke patients. To address the needs given by
this complex scenario we proposed an end-to-end pipeline. This pipeline begins
with reservoir computing causality, to define effective connectivity of the
brain. This allows directed graph network representations which have not been
fully investigated so far by graph convolutional network classifiers. Indeed,
the pipeline subsequently incorporates a classification module to categorize
the effective connectivity (directed graphs) of brain networks of patients
versus matched healthy control. The classification led to an area under the
curve of 0.69 with the given heterogeneous dataset. Thanks to explainable
tools, an interpretation of disrupted networks across the brain networks was
possible. This elucidates the effective connectivity biomarker's contribution
to stroke classification, fostering insights into disease mechanisms and
treatment responses.
