Using Topological Data Analysis to characterize fluctuations in brain activity patterns
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- Опубликовано: 22 авг 2024
- Speaker: Prof. Manish Sagger
Tashia and John Morgridge Endowed Faculty Scholar in Pediatric Translational Medicine, Stanford Maternal & Child Health Research Institute
Assistant Professor, Department of Psychiatry & Behavioral Sciences
Principal Investigator, Brain Dynamics Lab
Stanford University School of Medicine
Abstract: Understanding the neurobiological underpinnings of psychiatric disorders has long been a challenge. This talk addresses this issue by exploring how noninvasive neuroimaging, despite its inherent limitations, can be leveraged to anchor psychiatric disorders into neurobiology. Two main challenges in this endeavor are identified: (a) the inherent noise in noninvasive neuroimaging devices and (b) the limited utilization of biophysical models. To tackle the first challenge, we propose the application of Topological Data Analysis (TDA), specifically Mapper, as a novel approach. I present some promising results on how Mapper can capture evoked transitions during tasks, intrinsic transitions during resting states, changes in the landscape or shape associated with psychiatric disorders, and various pharmacological interventions and neuromodulation techniques. I will highlight a few methodological advances for Mapper that could enhance its applicability in noninvasive neuroimaging studies. Finally, the talk concludes by posing open questions to understand the neurobiological basis of psychiatric disorders better and pave the way for innovative therapeutic strategies.
Demo talk title: A short tutorial on Topological Data Analysis based Mapper approach
Abstract: In this tutorial, I will introduce and provide a high-level overview of Topological Data Analysis, mainly the Mapper approach. The hands-on portion of this tutorial will include a brief introduction to the DyNeuSR package from my lab (more information here - braindynamicsl.... DyNeuSR is a Python visualization library for topological representations of neuroimaging data. Developed with neuroimaging data analysis in mind, DyNeuSR connects existing implementations of Mapper (e.g. KeplerMapper) with network analysis tools (e.g. NetworkX) and other neuroimaging data visualization libraries (e.g. Nilearn) and provides a high-level interface for interacting with and manipulating shape graph representations of neuroimaging data and relating these representations to neurophysiology.
About the presenter: Manish Saggar is an assistant professor in the Psychiatry & Behavioral Sciences department at Stanford University and currently directs the Brain Dynamics Lab. His lab aims to develop computational methods for anchoring psychiatric diagnosis into biological features (e.g., neural circuits and spatiotemporal neurodynamics). Manish received his Ph.D. in Computer Science from the University of Texas at Austin and later received postdoctoral training in Psychiatry from Stanford University.