Uncovering the physical and network principles of brain structure and function in health and disease.
NeuroPhysics program
The brain is a living, adaptive network. Its nodes and connections coordinate activity to support perception, decision-making, creativity, movement, and memory. When these dynamic interactions are disrupted, neurological and psychiatric disorders can emerge.
This is why our work matters. Neurological conditions are now the leading cause of illness and disability worldwide, affecting more than one-third of the global population. Dementia affects about 57 million people, with Alzheimer’s disease contributing to most cases. Epilepsy affects tens of millions of people and remains difficult to treat because seizures arise from abnormal brain-network activity that can be challenging to localize. Stroke, depression, anxiety, epilepsy, Alzheimer’s disease, and other brain disorders demonstrate that clinical symptoms often arise not from a single isolated brain region, but from disrupted communication across distributed brain networks.
The NeuroPhysics program at Georgia State University, led by Dr. Mukesh Dhamala, seeks to uncover the fundamental rules that govern these brain node and network dynamics. We combine physics, neuroscience, brain imaging, electrophysiology, computational modeling, and data-driven analysis to understand how brain networks function, adapt, and break down. This fundamental knowledge is essential for clinical translation: it can turn brain signals into biomarkers, models into predictions, and scientific discoveries into better tools for diagnosis, monitoring, treatment, and patient care.
The laboratory includes a 64-channel EEG system for recording human brain electrical activity. The group collaborates across departments at Georgia State and with institutions in Atlanta and worldwide, and uses the research-dedicated 3-Tesla MRI scanner at the Georgia State–Georgia Tech Center for Advanced Brain Imaging.
Research projects span human decision-making, creativity, epilepsy, stroke, brain-data analysis methods, and mathematical modeling of brain and neuronal processes. These activities are closely integrated with interdisciplinary student training.
The group has contributed to unified principles of brain-network oscillations in perceptual decision-making, high-frequency neural activity in epileptic seizures, and the effects of time-delayed coupling on synchronization in nonlinear systems.
Research portfolio
We study how action video game experience modulates attention, evidence accumulation, visuomotor decision-making, oscillatory dynamics, and functional and structural connectivity.
We develop noninvasive and invasive electrophysiology approaches for identifying seizure-generating networks in drug-resistant epilepsy using Granger causality, spectral decomposition, and network-ranking methods.
We analyze structural and functional neuroimaging markers of disease progression, brain-behavior relationships, and altered network dynamics across neurological and clinical populations.
We investigate the neural dynamics of perceptual choice, multisensory integration, motor planning, improvisation, and creative behavior using EEG, fMRI, and computational modeling.
We develop and apply methods for directed connectivity, spectral factorization, time-frequency analysis, source localization, network inference, and multimodal integration of EEG, MRI, and behavioral data.
We model excitatory-inhibitory neural populations, time-delayed coupling, synchronization, oscillations, and activity-flow dynamics across distributed brain systems.
Training and education
NeuroPhysics research and the Functional Neuroimaging course—PHYS 4710/6710 and NEUR 4330/6330—train undergraduate and graduate students from Georgia State University, Georgia Tech, Emory University, and other Atlanta-area programs. More than 100 students were trained through the course between 2010 and 2018, and the program continues to support cross-disciplinary education in physics, neuroscience, engineering, mathematics, and data science.
Research software
The Dhamala Lab develops and applies quantitative tools for characterizing neural oscillations, directed information flow, and large-scale brain-network dynamics across EEG, intracranial electrophysiology, and neuroimaging data.
Fourier- and wavelet-based nonparametric Granger-causality methods for estimating frequency-resolved directional interactions in multivariate neural time series.
Methods for decomposing cross-spectral density matrices and estimating network interactions without relying exclusively on restrictive autoregressive models.
Workflows for time-varying connectivity, current-source-density analysis, neural oscillations, and multimodal EEG, fMRI, dMRI, and intracranial-data integration.
Documentation, validated implementations, example datasets, and download links will be added as individual toolboxes are prepared for public release.
