Dipanjan Roy

This study examines age-related changes in global neural communication patterns using MEG data across the lifespan, finding shifts in coherence and metastability that reflect underlying coordination dynamics.

The paper links individual variability in perception to neural dynamics by analyzing EEG data and proposing a biophysical model of brain network interactions.

The paper demonstrates that each individual's brain has a converging optimal working point in The Virtual Brain model that predicts fMRI and EEG features, with inter-individual differences driven by structural connectivity.

This study investigates the functional organization of visual streams using fMRI, finding input-based dissociation and context-dependent plasticity rather than fixed ventral/dorsal segregation.

This study quantifies ASD severity by analyzing dynamic functional connectivity variability and modular organization in resting-state fMRI data across different age groups.

The paper introduces a temporal Multiple Kernel Learning model to predict resting-state functional connectivity by characterizing fMRI connectivity dynamics and shows improved performance over existing methods.

This study uses EEG and computational modeling to explain inter-individual and inter-trial variability in cross-modal perception by analyzing brain connectivity patterns and neuronal interactions.

This study investigates functional connectivity differences in ASD versus TD using graph-theoretic analysis across two datasets, finding age-related and disease-modulated changes in network properties.

This paper proposes a multiple diffusion kernel model to estimate brain functional connectivity from structural data across subjects, showing it outperforms single kernel approaches.