Investigator, Asst Prof Ext Fd Athinoula A. Martinos Center for Biomedical Imaging, Mass General Research Institute

Assistant Professor of Radiology Harvard Medical School

Master of Engineering École supérieure d'électricité, CentraleSupélec 2006

autism spectrum disorder; data analysis; digital health; eeg; functional brain connectivity; magnetoencephalography; meg; mne; neurodegenerative diseases; neurodevelopmental disorders; neuroimaging; neurology I am an Assistant Professor at the Department of Radiology, Massachusetts General Hospital (MGH), and Harvard Medical School. I have over 8 years of research and clinical experience working with different neuroimaging modalities, with an emphasis on MEG. For the past 9 years, I have studied the brain using magnetoencephalography (MEG) for clinical and experimental research. I have not only published several seminal findings on brain function in patients with Autism Spectral Disorder (ASD); my work has pushed forward the development of novel techniques for analyzing neurophysiological signals.

I have trained with world-leading MEG scientists, including Sylvian Baillet, Matti Hämäläinen, and David Cohen. I have been developing software for basic and clinical MEG/EEG research and, since 2006, have been part of development teams of widely used MEG/EEG processing packages (MNE/Brainstorm). The tools that I developed are routinely used in MEG/EEG research and have led to several high impact publications.
During my postdoc under Dr. Tal Kenet, I have developed advanced signal processing methods for understanding the neural underpinnings of ASD. In our recent papers, Khan et al., BRAIN, 2015, and Khan et al., PNAS, 2013 sheds new light on functional connectivity in ASD. The neurophysiological metrics presented in these papers can be used to blindly identify individuals with ASD with high accuracy and correlate with the severity of ASD. For these projects, I have been awarded Nancy Lurie Marks Fellowship by Harvard Medical School to investigate local functional connectivity in autism.

My other important research contribution is that I am routinely involved in training MEG users. My integrated MEG processing pipeline is now widely used in MGH. Since 2010, I am part of the teaching faculty for the Annual Multi-modal Neuroimaging course organized by MGH/HST Martinos Center for Biomedical Imaging. Participants from all over the world get trained in different neuroimaging modalities. I have been invited to participate as a jury member at the annual Office for Research Career Development (ORCD), Research Fellows Poster Celebration in 2014 and 2015. Besides that, I also contribute to CME courses organized by Harvard Catalyst. I also regularly mentor several scientists at undergraduate, graduate, and postdoctoral levels in MEG acquisition, analysis, and data interpretation.

Before joining MGH, I completed my Ph.D. (summa cum laude) in Applied Mathematics at Ecole Polytechnique, France, under the supervision of Drs Sylvain Baillet and Habib Ammari. My Ph.D. research binds together two of the most important aspects of MEG research: spatial and temporal dynamics. I have also received a Masters in Signal Processing from SUPELEC, France in 2006, and a Bachelor of Engineering in Electronics from NED University, Pakistan, in 2001 (summa cum laude). I believe my efforts are bridging the detrimental gap between the field of MEG methods development and the various neuroscience fields that use MEG. My research is not only helping us understand the neurophysiological mechanisms, but is also geared towards the identification of an objective diagnostic biomarker for ASD.