Analyzing the Brain's Dynamic Response to Targeted Stimulation using Generative Modeling

TL;DR

This paper explores how generative models combined with targeted brain stimulation can reveal new mechanisms of brain dynamics, distinct from spontaneous activity, by integrating experimental perturbations with quantitative modeling.

Contribution

It introduces a framework for understanding stimulus-evoked brain dynamics through generative models and dynamical systems theory, highlighting the potential to uncover novel neural mechanisms.

Findings

Targeted stimulation can induce and analyze new brain states.

Generative models help differentiate mechanisms of spontaneous and evoked dynamics.

Integration of experiments and modeling uncovers mechanisms hard to detect otherwise.

Abstract

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: (i) 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 (ii) 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.