Najme Soleimani

This paper introduces a chronnectogram-ICA framework to track continuously evolving and overlapping brain states in fMRI data, revealing new insights into schizophrenia's connectivity dynamics.

This study identifies a dynamic directional coordination axis in the cortical hierarchy that is selectively reorganized in schizophrenia, linked to symptoms and cognition.

This paper introduces spatio-dynamical decomposition for fMRI BSS, showing it better recovers sources using both spatial and temporal criteria than methods that use only one.

This paper introduces DyCoM, a framework that unifies dynamic connectivity analysis in neuroimaging by decomposing estimators into fundamental operations, revealing distinct biological interpretations from different computational choices.

The paper develops a timescale-aligned framework to measure amplitude balance in brain networks and finds greater imbalance and slower recovery in schizophrenia, linked to symptoms and cognition.

This paper introduces a structural co-modulation measure to quantify individualized interactions between brain components using source-based morphometry, finding reduced co-modulation in schizophrenia patients.

This paper introduces dFNG, a method to estimate dynamic functional network connectivity gradients from ICA, and applies it to schizophrenia data to reveal distinct connectivity patterns between patient and healthy groups.

This study introduces a method to identify dynamic brain states from resting-state fMRI data, revealing distinct convergence and divergence patterns in four psychiatric disorders.

This study identifies distinct functional connectivity patterns in mental disorders using dFIPs derived from rs-fMRI data, revealing disorder-specific and shared neural signatures.