MAPs: Methods And Primers for Computational Psychiatry and Neuroeconomics

Quantitative methods in psychiatry have largely focused on static or short-duration analyses of brain activity and functional connectivity. However, the analysis of long-duration spatiotemporal dynamics is critical for understanding brain activity at rest, and in response to various stimuli, tasks, and treatments. In this 4-part series, we will introduce a technique called Geometric Scattering Trajectory Homology (GSTH), which employs graph signal processing and dimensionality reduction to generate easily interpretable low-dimensional trajectories of brain activity from neuroimaging data, e.g. EEG, fNIRS, and fMRI.

GSTH was originally developed to quantify the Ca2+ signaling dynamics of stem cells in the mouse epidermis. We later adapted it for applications in neuroscience. The technique is generally applicable to any biophysical system that exhibits complex spatiotemporal dynamics. Using techniques from topological data analysis and geometry, GSTH produces quantitative readouts that capture the overall shape of the dynamic trajectories, thus enabling the identification of ‘neural motifs’ (patterns of neural activity) associated with different stimuli, tasks and neurological disorders.

June 20, 4:00 pm - 6:00 pm, Dhananjay Bhaskar, PhD, Yale School of MedicineBrian Zaboski, PhD, Yale School of Medicine

In the third session, we will introduce the Geometry Scattering Trajectory Homology (GSTH) approach and discuss some applications for clinical research.