Prejaas K. B. Tewarie

This study uses neural field models to assess thalamocortical function in disorders of consciousness, finding reduced corticothalamic integrity in UWS compared to MCS.

The paper uses biophysical simulations to study recovery of neocortical activity in disorders of consciousness, finding that excitatory corticothalamic synaptic strength loss correlates with cerebral glucose uptake and potential for recovery.

This study uses multilayer network analysis of MEG data to show that musicians have distinct modular brain organization in specific frequency bands compared to non-musicians.

This study examines non-duality in advanced meditators using EEG-based intrinsic neural timescales and psychological scales, finding that advanced meditators show reduced distinction between internal and external attention.

This study examines the time-to-onset and temporal dynamics of EEG changes during breath-watching meditation across three groups, finding significant differences in power changes between groups.

This study shows LSD reduces directed information hierarchy in the brain by balancing signal sending and receiving, as measured by MEG data.

The paper constructs invariant features for EEG/MEG signals using Grassmann manifolds to improve the analysis of functional connectivity in brain activity.

This paper develops a multivariate signal processing pipeline to build high-order brain networks from fMRI data and identifies high-order hubs associated with distinct brain systems.

The paper uses EEG data to estimate biophysical model parameters in comatose cardiac arrest survivors, finding distinct patterns in good vs poor recovery groups.

The paper examines functional connectivity and network dynamics in disorders of consciousness, finding reduced metastability and network repertoire in unresponsive wakefulness syndrome compared to minimally conscious state.

The paper simulates individual functional connectivity using individual structural connectivity data and neural mass modeling, finding that individual simulations better match empirical data than group-averaged simulations.

This study compares neural mass models and average network signals in low-synchrony systems, finding that neural mass models work well for large, synchronized networks but fail when connections are sparse.