Parul Verma

This study uses a biophysical model to investigate how neural mechanisms change during BCI training, finding training-induced excitatory-inhibitory reconfiguration in sensorimotor areas.

This paper introduces a biophysical, connectivity-based generative model for rs-fMRI that integrates spectral and structural information to quantify structure-function relationships.

This study shows how local brain manipulations affect global brain dynamics using simulations and experimental data, revealing specific network changes and localization of effects.

This paper develops a Bayesian procedure for inferring spectral graph model parameters using simulation-based inference, showing comparable performance to annealing algorithms while enabling uncertainty quantification.

The paper analyzes the stability and dynamics of a spectral graph model of brain oscillations, showing it can capture alpha band oscillations without mesoscopic oscillations and estimate time-varying parameters from MEG data.

The paper examines associations between neuronal subpopulation parameters and tau/A{beta} in AD patients using MEG and PET data.

This paper introduces an improved spectral graph theory-based model that captures MEG frequency spectra and spatial patterns of alpha and beta bands from resting healthy subjects.

The paper develops a computational model of tau-modified axonal transport to show how directional biases in tau spread arise from transport feedback and conformation-specific dysregulation.

This paper uses bifurcation theory to analyze how paclitaxel induces hyperexcitability in DRG neurons, identifying Nav1.8 and delayed rectifier potassium channels as critical factors.

This paper uses bifurcation theory to model pain sensation threshold variability in neurons and shows how parameters influence this threshold.