Georgia State UniversityPhysics & Astronomy
Dhamala Lab brain-network logoDhamala LabNeuroPhysics · Systems Neuroscience

M Dhamala

Profile

Biography

Brief Biography

Mukesh Dhamala earned his PhD in Physics from the University of Kansas and trained as a postdoctoral fellow at Georgia Tech, Emory University, and the Center for Complex Systems & Brain Sciences at Florida Atlantic University before joining Georgia State University, where he now leads the NeuroPhysics & Systems Neuroscience Laboratory. His work bridges physics, neuroscience, and computation to study how dynamic brain networks support cognition and how their disruption drives neurological disorders.

Professional Preparation

Research Profile

Personal Statement

My research program seeks to understand the neural mechanisms of cognition, decision-making, brain plasticity, and brain dysfunction through multimodal neuroimaging, electrophysiology, computational neuroscience, and dynamical-systems approaches. Our laboratory develops and applies advanced methods to characterize large-scale brain-network dynamics across spatial and temporal scales using EEG, fMRI, diffusion MRI, intracranial recordings, and computational modeling. A central goal is to identify mechanistic and clinically meaningful markers of cognitive enhancement and maladaptive neural dynamics in neurological and psychiatric disorders.

Over the past two decades, my work has focused on directed functional connectivity, neural oscillations, multimodal brain imaging, and computational modeling. Our laboratory pioneered nonparametric Granger-causality methods for estimating directed information flow and has applied these tools to perceptual decision-making, cognitive control, epilepsy, depression, neurodegeneration, and experience-dependent plasticity associated with action video game play.

Selected Contributions

Scientific Contributions

Directed Brain Connectivity and Information Flow

Developed and advanced nonparametric Fourier- and wavelet-based Granger-causality methods that reduce reliance on restrictive autoregressive assumptions. Related work in spectral factorization, time-varying connectivity, and current-source-density analysis provides a methodological foundation for studying dynamic neural communication across multiple scales.

Decision-Making, Cognitive Control, and Brain Plasticity

Identified oscillatory and network mechanisms supporting sensory evidence accumulation, salience detection, attention, visuomotor integration, and adaptive decisions. Multimodal studies of action video game experience have shown faster perceptual decisions, enhanced visuomotor performance, and reorganization of attention, salience, frontoparietal, visual, and sensorimotor networks.

Biomarkers of Neurological and Psychiatric Disorders

Applied EEG, intracranial electrophysiology, fMRI, structural imaging, and computational modeling to characterize abnormal oscillations and network interactions in epilepsy, depression, and neurodegenerative disease. This work includes clinically relevant markers of seizure onset and propagation, altered frontostriatal and salience-network dynamics, and sex-dependent patterns of gray-matter atrophy in Alzheimer’s disease.