Title: Reservoir Computing with Spatial Filtering and Manifold Learning for fMRI Classification
Primary area: applications to neuroscience & cognitive science
Keywords: Reservoir Computing, Common Spatial Patterns, UMAP, fMRI, Classification, Interpretability

Abstract: We introduce a parametric framework that couples discriminative spatial filtering with  reservoir computing to distinguish spatiotemporal structure in resting-state fMRI in two classes. Temporal dependencies are encoded in a reservoir, while supervised spatial filtering on reservoir states isolates condition-specific patterns; parametric Uniform Manifold Approximation and Projection (UMAP) then yields compact nonlinear embeddings fit on training data and evaluated with cross-subject validation. On 163 participants (97 healthy controls, 66 major depressive disorder), the method reaches 87\% accuracy, outperforming network-feature pipelines using LDA, SVM, kNN, and GNN. Interpretability combines spatial-pattern maps with Shapley-value attribution, providing coherent, region-level explanations that consistently implicate cortical and subcortical areas associated with major depressive disorder. The framework offers an interpretable route to modeling spatiotemporal organization in clinical and cognitive fMRI.