PI Profile: Dr. Mallar Chakravarty

PI Profile: Dr. Mallar Chakravarty

Dr. Mallar Chakravarty is a Computational Neuroscientist whose laboratory is based at the Cerebral Imaging Centre, at the Douglas Mental Health University Institute.  Dr. Chakravarty received his Bachelor's Degree in Electrical Engineering from the University of Waterloo and his PhD in Biomedical Engineering from McGill University.  He went on to do postdoctoral fellowships in Aarhus, Denmark and jointly at the Rotman Research Institute and at the Mouse Imaging Centre (MICe) at the Hospital for Sick Children in Toronto, Canada.  Between fellowships, Dr. Chakravarty worked in the Informatics group at Allen Institute for Brain Science (Seattle, WA, USA).  He leads a the Computational Brain Anatomy (CoBrA) Lab, a multi-disciplinary team (consisting of neuroscientists, engineers, computer scientists, and physicists) devoted to improving our understanding of brain anatomy and how it is impacted by development and maturation, ageing, genetics, and environment.

In order to perform their research the CoBrA Lab uses two very important strategies.  The first is the use of sophisticated magnetic resonance imaging (MRI) techniques and analysis algorithms that identify specific regions of the brain, their morphology, their connectivity with one another, and their morphological variation in the context of different neuropsychiatric disorders such as Alzheimer's disease and schizophrenia.  This requires the development of novel multivariate and machine learning analyses capable of integrating diverse neuroimaging, genetics, and behavioural information to better understand group differences, the impact of risk factors on brain phenotypes, and to predict the clinical trajectories of patients and "at-risk" individuals.  The second strategy is to perform these analyses in a fully translation manner.  To this end, the CoBrA lab performs detailed investigations of specific risk factors or genes using animal models (and high-resolution MRI) and in clinical populations using primary data collection and retrospective evaluation of large publicly available databases.

For more information about the CoBrA lab, please visit: cobralab.ca