Education & Training
- Ph.D. Harvard University
- Postdoctoral Fellowship National Institute of Mental Health
Research Interest Summary
Computational neuroscience, Cognitive neuroscience of reading, object recognition, face perception, and social and affective perception
We use computational techniques including machine learning, signal processing, network analysis, and deep learning to understand how the brain turns the light that falls onto our eyes into the rich, meaningful experience that we perceive in the world around us. We use signals recorded directly from the human brain through electrodes implanted in epilepsy patients (intracranial electroencephalography) and non-invasive magnetoencephalography to probe the neurodynamics that underpin high level perception, including word reading, object recognition, face perception, and social and affective perception.
Boring M.J., Silson E.H., Ward M.J., Richardson R.M., Fiez J.A., Baker C.I., & Ghuman A.S. (2021). Multiple adjoining word- and face-selective regions in ventral temporal cortex exhibit distinct dynamics. Journal of Neuroscience. Jun 7;41(29):6314-6327.
Li Y., Ward M.J., Richardson R.M., G'Sell M., & Ghuman A.S. (2020). Endogenous activity modulates stimulus and circuit-specific neural tuning and predicts perceptual behavior. Nature Communications. Aug 11;11(1):4014.
Li Y., Richardson R.M., & Ghuman A.S. (2020). Posterior and Mid-Fusiform Contribute to Distinct Stages of Facial Expression Processing. Cerebral Cortex. 29(7):3209-3219.
Ghuman A.S., & Martin A. (2019). Dynamic Neural Representations: An Inferential Challenge for fMRI. Trends in Cognitive Neuroscience. 23(7), 534-536.
Boring M.J., Jessen Z.F., Wozny T.A., Ward M.J., Whiteman A.C., Richardson R.M., & Ghuman A.S. (2019). Quantitatively validating the efficacy of artifact suppression techniques to study the cortical consequences of deep brain stimulation with magnetoencephalography, Neuroimage. 199, 366-374.
Ghuman A.S., & Fiez J.A. (2018). Parcellating the Structure and Function of the Reading Circuit. Proceedings of the National Academy of Sciences. 115(42), 10542-10544.
Li Y., Richardson R.M., & Ghuman A.S. (2017). Multi-Connection Pattern Analysis: Decoding the representational content of neural communication. NeuroImage, 162, 32-44.
Huppert T.J., Barker J.W., Schmidt B., Walls S.A., Ghuman A.S. (2017). Comparison of group-level, source localized activity for simultaneous functional near-infrared spectroscopy-magnetoencephalography and simultaneous fnirs-fmri during parametric median nerve stimulation. Neurophotonics. 4(1), 015001.
Hirshorn E.A., Li Y., Ward M.J., Richardson R.M., Fiez J.A., Ghuman A.S. (2016). Decoding and disrupting left mid-fusiform gyrus activity during word reading. Proceedings of the National Academy of Sciences, 113(29), 8162-8167.
Morett L.M., O'Hearn K., Luna B., & Ghuman A.S. (2016). 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.
Ghuman A.S., Brunet N.M., Li, Y., Konecky R.O., Pyles J.A., Walls S.A., Destefino V., Wang W., & Richardson R.M. (2014). Dynamic encoding of face information in the human fusiform gyrus. Nature Communications, 5, 5672.
Ghuman A.S., McDaniel J.R., & Martin A. (2010). Face adaptation without a face. Current Biology, 20, 32-36.
Ghuman A.S., Bar M., Dobbins I.G., & Schnyer D.M. (2008). The effects of priming on frontal-temporal communication. Proceedings of the National Academy of Sciences, 105(24), 8405-8409.