Avniel Singh Ghuman, PhD

  • Associate Professor
  • Director, Cognitive Neurodynamics Lab

Avniel Singh Ghuman, PhD, joined the Department of Neurological Surgery in September of 2011. He received his undergraduate education in math and physics at The Johns Hopkins University and completed his doctoral education in biophysics at Harvard University. He completed his postdoctoral training at the National Institute of Mental Health prior to joining the faculty at the University of Pittsburgh.

As director of MEG (Magnetoencephalography) Research, one of Dr. Ghuman’s primary roles is to facilitate, develop, and advance clinical and basic neuroscience research using MEG. To this end, he is helping to develop new research applications for MEG in collaboration with researchers throughout the community. MEG is the most powerful functional neuroimaging technique for noninvasively recording magnetic fields generated by electrophysiological brain activity, providing millisecond temporal resolution and adequate spatial resolution of neural events.

Dr. Ghuman’s research focuses on how our brain turns what falls upon our eyes into the rich meaningful experience that we perceive in the world around us. Specifically, his lab studies the neural basis of the visual perception of objects, faces, words, and social and affective visual images. His lab examines the spatiotemporal dynamics of how neural activity reflects the stages of information processing and how information flow through brain networks responsible for visual perception.

To accomplish these research goals, Dr. Ghuman’s lab records electrophysiological brain activity from humans using both invasive (intracranial EEG; iEEG — in collaboration with Taylor Abel, MD, and Jorge Gonzalez-Martinez, MD, PhD) and non-invasive (magnetoencephalography; MEG) measures. In conjunction with these millisecond scale recordings they use multivariate machine learning methods, network analysis, and advanced signal processing techniques to assess the information processing dynamics reflected in brain activity. Additionally, his lab uses direct neural stimulation to examine how disrupting and modulating brain activity alters visual perception. This combination of modalities and analysis techniques allow Dr. Ghuman to ask fine-grained questions about neural information processing and information flow at both the scale of local brain regions and broadly distributed networks.More information on Dr. Ghuman's research can be found on the Laboratory of Cognitive Neurodynamics webpage.

Dr. Ghuman's publications can be reviewed through the National Library of Medicine's publication database.

Specialized Areas of Interest

The dynamics of brain interactions; visual cognition; magnetoencephalography (MEG), intracranial EEG (iEEG); face recognition; reading; social and affective perception.

Professional Organization Membership

Cognitive Neuroscience Society
Organization for Human Brain Mapping
Society for Neuroscience
Vision Sciences Society

Education & Training

  • BA, Math and Physics, The John Hopkins University, 1998
  • PhD, Biophysics, Harvard University, 2007

Honors & Awards

  • Young Investigator Award, NARSAD, 2012
  • Award for Innovative New Scientists, National Institute of Mental Health, 2015

Research Activities

Over the past year, Dr. Ghuman’s lab has made a number of new and ongoing discoveries. Using intracranial recordings in epilepsy patients and MEG in Parkinson’s patients, the lab has illuminated how brain networks behave during real world behavior, how deep brain stimulation modulates cortical brain networks, and described a novel model regarding how the brain processes written words.

The mechanism of action of deep brain stimulation (DBS) to the basal ganglia for Parkinson’s disease remains unclear. Studies have shown that DBS decreases pathological beta hypersynchrony between the basal ganglia and motor cortex. However, little is known about DBS’s effects on long range corticocortical synchronization. Here, Dr. Ghuman uses machine learning combined with graph theory to compare resting-state cortical connectivity between the off and on-stimulation states and to healthy controls. He found that turning DBS on increased high beta and gamma band synchrony (26 to 50 Hz) in a cortical circuit spanning the motor, occipitoparietal, middle temporal, and prefrontal cortices. The synchrony in this network was greater in DBS on relative to both DBS off and controls, with no significant difference between DBS off and controls. Turning DBS on also increased network efficiency and strength and subnetwork modularity relative to both DBS off and controls in the beta and gamma band. Thus, unlike DBS’s subcortical normalization of pathological basal ganglia activity, it introduces greater synchrony relative to healthy controls in cortical circuitry that includes both motor and non-motor systems. This increased high beta/gamma synchronization may reflect compensatory mechanisms related to DBS’s clinical benefits, as well as undesirable non-motor side effects.

During the course of a day, our brains must accomplish a wide range of tasks and demonstrate a remarkable amount of flexibility despite their anatomic stability. How do ecologically valid brain states balance the tension between these demands of flexibility and stability? To answer this question, Dr. Ghuman explored how the human functional connectome changes using continuous intracranial electroencephalography recordings in six epilepsy patients while they went about their day: eating, talking with visitors, reading, etc. over the course of a week. By tracking how the coherence between all pairs of the100-120 electrodes implanted in each patient changes over each five second time window over the course of the entire week, he was able to use unsupervised autoregressive methods to identify the prevalent dynamic patterns of connectivity. 

Two major patterns emerged. First, brain networks had a stable baseline state that the brain would consistently return to after individual subnetworks took excursions of various types throughout the day. This stable state was similar across all our subjects, consisting of elevated lower beta coherence and decreased theta and gamma coherence. His second finding was that there was a discrete set of probable ways to leave this baseline state. Different sub-networks of the brain were not activated or inactivated randomly to each other: they formed a specific set of patterns of which networks could be activated together over which frequencies. These patterns were well-preserved from day to day: if one network’s beta activation were linked to another network’s gamma inactivation in one day, the same would generally hold true in other days. Additionally, the length of the excursion (e.g. the autocorrelation of each dynamic pattern) was consistent from day-to-day. 

These patterns show that, after perturbations, the brain’s functional networks are pulled to return within a stable baseline dynamic range, which may represent an optimal homeostatic state for the functional connectome. Excursions from this state occur frequently, presumably to accomplish tasks such as sleep or heightened activity, but the excursions are always marked by a return back to homeostasis. The day to day consistency of the largest excursions from homeostasis may indicate some underlying anatomic or energy limitation that forces departures from homeostasis to follow characteristic trajectories. Taken together, these results suggest a homeostasis-like mechanism by which the functional connectome achieves stability, while allowing for neurocognitive flexibility, through characteristic perturbations and return to this homeostatic state.

Scientists have long debated the nature of the visual networks that support humans’ unique ability to read. Reading is built upon visuo-linguistic transformations that map written words to their sounds and meanings. Independent of reading, computationally parallel visuo-linguistic transformations well-suited to perform operations necessary for word recognition underpin the perception of social-communication and visual object and face naming. A key node of the reading brain, the visual word form area (VWFA), lies where circuits that underpin visuo-linguistic transformations diverge from earlier visual processing in ventral occipitotemporal cortex. Dr. Ghuman proposes a model in which literacy leverages preexisting circuits that perform visuo-linguistic transformations well-suited to those required for fluent reading.

Media Appearances

Ability to Recognize Faces Grows With Age, Study Finds
January 5, 2017
The Wall Street Journal

Epilepsy Research Leads To New Insights Into How Our Brains Read
August 16, 2016
WESA Radio Pittsburgh Tech Report

Study shows how words are represented in the brain
July 20, 2016

Decoding Reading in the Brain
July 19, 2016
Cognitive Neuroscience Society

“Reading” The Reading Mind
July 8, 2016