Setareh Rahimi

This study evaluates whether graph-based gene essentiality prediction relies on biological network structure or generic graph topology, finding that biological connectivity is critical for performance.

The paper introduces mvTL-MDPC, a multivariate and multidimensional functional connectivity method for EEG/MEG data, showing it improves accuracy over bivariate methods in identifying direct brain connections.

This paper introduces TL-MDPC, a novel functional connectivity metric for EEG/MEG data that captures multidimensional pattern transformations, showing improved sensitivity in detecting semantic task-related connectivity.

This paper introduces nTL-MDPC, a nonlinear functional connectivity method for EEG/MEG, and shows it detects nonlinear relationships better than its linear counterpart in simulations but with subtle differences in real data.

This study examines how semantic task demands modulate spatiotemporal brain dynamics and connectivity in semantic networks using EEG/MEG data, revealing distinct patterns in activation and functional connectivity across different brain regions.