Sayan is currently pursuing a Ph.D. in the Department of Medical Biophysics at the University of Toronto. He received his Bachelor of Engineering degree in Electrical Engineering from the Jadavpur University, India. His research interests include image processing, computer vision, computational neuroscience and artificial intelligence. His technical strengths are Python, MATLAB, C/C++, Java, JavaScript, HTML, CSS, XML and Julia. His hobbies include painting/sketching, playing soccer, cricket and volleyball.
Effective Brain Connectivity for fMRI using Dynamic Causal Modelling. Experimental manipulations directly perturb neural activity, which is manifested in the fMRI response. In order to determine neuronal activity from experimental fMRI data, several biophysical generative graphical models like Dynamic Causal Model (DCM) and its variants have been proposed, which have great potential in computational psychiatry. These models can be employed to infer pathophysiological mechanisms from non‐invasively obtained measurements, which can guide differential prognosis and treatment prediction in individual patients. The physiologically-informed DCM (pDCM), which is developed in the lab of Dr. Uludag, is the state-of-the-art model. It is inspired by experimental observations about the physiological underpinnings of the fMRI signal to study effective connectivity in the brain. Along the same lines, I am using existing approaches such as pDCM and modifying them to study the effective connectivity in the brain on a bigger scale and dynamically.