Understanding the brain in health and in illness using large-scale data analyses

Abstract

Since the inception of population-based neuroimaging studies there has been great debate regarding the appropriate sample size that can be used in order to glean a robust reproducible finding related in states of brain function or dysfunction due to a neuropsychiatric disorder.  However, promising new data sharing initiatives have been initiated in order to remedy problems related to the lack of reproducibility across studies.  In theory, different groups parsing the same data set should settle on the same results.  Surprisingly, this has not been the case due to methodological shortcomings.  In this talk I will present new methods for large-scale neuroimaging studies.  First I will discuss the use of these measures to better understand the connectivity of the cerebellum with the cortex.  Then I demonstrate how these techniques can be used to generate converging and diverging neuroanatomical and functional phenotypes across neuropsychiatric disorders.

Biography

Mallar Chakravarty is a Computational Neuroscientist in the Cerebral Imaging Centre at Douglas Mental Health University Institute. He is also an Assistant Professor in the Department of Psychiatry and an Associate Member of the Department of Biomedical Engineering at McGill University. 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) and the Hospital Sick Children in Toronto, Canada. Between fellowships, Dr. Chakravarty worked at the Allen Institute for Brain Science (Seatte, WA, USA). He is interested in the anatomy of the brain. His group focuses on how anatomy changes through development, aging, and in illness and how the dynamics of brain anatomy are influenced by genetics and environment.