Avniel Singh Ghuman, PhD, joined the Department of Neurological Surgery in September of 2011.
Dr. Ghuman received his undergraduate education in math and physics at The Johns Hopkins University. He 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 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.
Specialized Areas of Interest
Professional Organization Membership
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
Morett LM, O’Hearn K, Luna B, Ghuman AS. Altered Gesture and Speech Production in Autism Spectrum Disorders Detract from In-Person Communication Quality. Journal of Autism and Developmental Disorders 46(3):998-1012, 2016.
Alhourani A, McDowell MM, Randazzo M, Wozny T, Kondylis E, Lipski W, Beck S, Karp JF, Ghuman AS, Richardson RM. Network Effects of Deep Brain Stimulation. Journal of Neurophysiology 114(4):2105-2117, 2015.
Ghuman AS, Brunet NM, Li Y, Konecky RO, Pyles JA, Walls SA, Destefino V, Wang W, Richardson, R.M. (2014). Dynamic encoding of face information in the human fusiform gyrus. Nature Communications 5:5672, 2014.
Hwang K, Ghuman AS, Manoach DS, Jones S, Luna B. Cortical Neurodynamics of Inhibitory Control. Journal of Neuroscience 34(29):9551-9561, 2013.
Ghuman AS, McDaniel JR, Martin A. A Wavelet-Based Method for Measuring the Oscillatory Dynamics of Resting-State Functional Connectivity in MEG. NeuroImage 56(1):69-77, 2011.
Kverega K, Ghuman AS, Kassam KS, Aminoff EM, Hämäläinen MS, Chaumon M, Bar M. Neural Synchronization in the Contextual Association Network. Proceedings of the National Academy of Science 108(8):3389-3394, 2011.
Ghuman AS, McDaniel JR, Martin A. Face Adaptation Without A Face. Current Biology 20(1):32-36, 2010.
Ghuman AS, Bar M, Dobbins I, Schnyer D. The Effects of Priming on Frontal-Temporal Communication. Proceedings of the National Academy of Science 105(24):8405-8409, 2008.
A complete list of Dr. Ghuman's publications can be reviewed through the National Library of Medicine's publication database.
Over the past year, the Dr. Ghuman’s lab has made a number of new and ongoing discoveries. Using intracranial recordings in epilepsy patients, the lab has found a novel, dynamic model for how information is represented in the brain, providing a potential solution for an important enduring neuroscientific debate. In addition, the lab has made substantial progress in understanding how faces are processed in the real world, recording neural activity while patients go about their day and have conversations with their friends and family. Summaries of these projects follow.
An enduring neuroscientific debate concerns the extent to which neural representation is restricted to networks of patches specialized for particular domains of perceptual input, or distributed outside of these patches to broad areas of cortex as well. A critical level for this debate is the localization of the neural representation of the identity of individual images, such as individual-level face or written word recognition. To address this debate, intracranial recordings from 489 electrodes throughout ventral temporal cortex across 17 human subjects were used to assess the spatiotemporal dynamics of individual word and face processing within and outside cortical patches strongly selective for these categories of visual information. Individual faces and words were first represented primarily only in strongly selective patches and then represented in both strongly and weakly selective areas approximately 200 milliseconds later. Both strongly and weakly selective areas contributed non-redundant information to the representation of individual images. These results can reconcile previous results endorsing disparate poles of the domain specificity debate by highlighting the temporally segregated contributions of different functionally defined cortical areas to individual level representations. Taken together, this work supports a dynamic model of neural representation characterized by successive domain-specific and distributed processing stages.
Using machine learning and AI to analyze multiple hours of neural recordings while epilepsy patients have natural, real world conversations with their friends and family, Dr. Ghuman’s lab has been able to decode who subjects are looking at, reconstruct the faces of the people they are looking at, and even predict who they will look at next. Efforts are ongoing to understand what aspects of brain activity code for these different kinds of information about real world social interactions. These initial successes suggest that the lab will be successful in developing novel paradigms and computational frameworks to address a profound question that has not been properly studied – what is the neural basis of real world social interactions?
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