Amrit Kashyap

Using an Ordinary Differential Equation model to separate Rest and Task signals in fMRI

This study uses an ODE model to separate rest and task signals in fMRI data, showing task-specific dynamics are subsets of rest dynamics and improving reaction time predictions.

Lesion aware automated processing pipeline for multimodal neuroimaging stroke data and The Virtual Brain (TVB)

The paper develops and validates LeAPP, an automated processing pipeline for stroke MRI data that improves volumetric and connectome reconstructions compared to standard pipelines.

A DEEP LEARNING APPROACH TO ESTIMATING INITIAL CONDITIONS OF BRAIN NETWORK MODELS IN REFERENCE TO MEASURED FMRI DATA

This paper uses Neural ODE to estimate initial conditions for brain network models to synchronize simulations with fMRI data, showing agreement between short-term and long-term parameterizations.

Resting brain fluctuations are intrinsically coupled to visual response dynamics

The paper shows that resting brain fluctuations in mice are intrinsically linked to visual response dynamics through quasiperiodic patterns.

Quasi-periodic patterns contribute to functional connectivity in the brain

This study examines how quasi-periodic patterns (QPPs) of low-frequency neural activity contribute to functional connectivity in the brain, showing differences between resting-state and task-performing individuals.

Dynamic Properties of Simulated Brain Network Models and Empirical Resting State Data

This study compares dynamic properties of simulated brain network models with empirical resting state fMRI data to identify which features best distinguish between models and real brain activity.

Instantaneous Brain Dynamics Mapped to a Continuous State Space

The paper maps whole-brain dynamics into a continuous state space using scale transformations and identifies stimulus-dependent brain states from resting and task data.