Analyzing the brain’s dynamic response to targeted stimulation using generative modeling

Generative models of brain activity have been instrumental in testing hypothesized mechanisms underlying brain dynamics against experimental datasets. Beyond capturing the key mechanisms underlying spontaneous brain dynamics, these models hold an exciting potential for understanding the mechanisms underlying the dynamics evoked by targeted brain stimulation techniques. This paper delves into this emerging application, using concepts from dynamical systems theory to argue that the stimulus-evoked dynamics in such experiments may be shaped by new types of mechanisms distinct from those that dominate spontaneous dynamics. We review and discuss (a) the targeted experimental techniques across spatial scales that can both perturb the brain to novel states and resolve its relaxation trajectory back to spontaneous dynamics and (b) how we can understand these dynamics in terms of mechanisms using physiological, phenomenological, and data-driven models. A tight integration of targeted stimulation experiments with generative quantitative modeling provides an important opportunity to uncover novel mechanisms of brain dynamics that are difficult to detect in spontaneous settings.

Generative models are important tools for testing hypothesized mechanisms of brain dynamics against experimental data. This review highlights an application of generative models in analyzing a form of brain activity evoked by emerging targeted stimulation techniques. We argue that analyzing targeted stimulation dynamics can uncover mechanisms that are hidden during commonly analyzed spontaneous dynamics and explore how integrating diverse targeted stimulation experiments with existing generative models offer a significant opportunity to uncover these novel mechanisms and thereby expand our mechanistic understanding of brain dynamics.