Event date:
Apr 19 2023 2:00 pm

Artificial Intelligence for Making Useful Inferences from Neuroscience Data

Speaker(s)
Dr. Muhammad Furqan Afzal, Center for Advanced Circuit Therapeutics (C-ACT), Rajan Lab, Mount Sinai West, Icahn School of Medicine at Mount Sinai
Venue
Dean’s Smart Lab, 4th floor SBASSE Building
Abstract
Artificial Intelligence (AI) and Machine Learning (ML) have had a significant impact on the field of neuroscience, enabling researchers to gain insights from complex datasets that would otherwise be challenging to obtain. In this talk, I will introduce innovative AI approaches for analyzing high dimensional brain activity datasets, specifically focusing on their applications in healthcare datasets related to various neurological and psychiatric conditions. Neurostimulation of specific brain regions, such as the subthalamic nucleus, subcallosal cingulate, and medial temporal lobe, is now being used to treat refractory conditions like Parkinson's disease, depression, and epilepsy, respectively. By recording neural activity from these regions and applying AI tools, we can identify neural biomarkers associated with different symptoms, such as tremors in Parkinson's disease. These biomarkers, discovered through AI, can then guide therapy adjustments for patients. I will also discuss the development of neuroscience-inspired AI architectures and frameworks from a theoretical perspective and their implications in this field.

Dr. Muhammad Furqan Afzal recently completed his PhD in Computational Neuroscience at the Icahn School of Medicine at Mount Sinai in the USA, where he focused on the intersection of neuroscience and AI, and developed AI tools for extracting insights from brain activity data. He holds a Master of Science degree in Computational Neuroscience from the University of Cincinnati, and obtained his Bachelor of Science degree in Electrical Engineering from LUMS SSE in 2014. Dr. Afzal has also worked as a Research Scientist at Stanford University, where he utilized AI and signal processing techniques to identify neural biomarkers associated with symptoms in Parkinson's disease. He has also gained experience in neurotech companies and startups, contributing to the development of AI-based tools for brain-computer interfaces (BCI). His research interests encompass various areas including neuroscience, artificial intelligence and its applications in healthcare.