It's about time: Linking dynamical systems with human neuroimaging to understand the brain

Yohan J John; Kayle S Sawyer; Karthik Srinivasan; Eli J Müller; Brandon R Munn; James M Shine

doi:10.1162/netn_a_00230

It's about time: Linking dynamical systems with human neuroimaging to understand the brain

Netw Neurosci. 2022 Oct 1;6(4):960-979. doi: 10.1162/netn_a_00230. eCollection 2022.

Authors

Yohan J John¹, Kayle S Sawyer^{2

3

4

5}, Karthik Srinivasan⁶, Eli J Müller⁷, Brandon R Munn⁷, James M Shine⁷

Affiliations

¹ Neural Systems Laboratory, Department of Health Sciences, Boston University, Boston, MA, USA.
² Departments of Anatomy and Neurobiology, Boston University, Boston University, Boston, MA, USA.
³ Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
⁴ Boston VA Healthcare System, Boston, MA, USA.
⁵ Sawyer Scientific, LLC, Boston, MA, USA.
⁶ McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
⁷ Brain and Mind Center, University of Sydney, Sydney, NSW, Australia.

Abstract

Most human neuroscience research to date has focused on statistical approaches that describe stationary patterns of localized neural activity or blood flow. While these patterns are often interpreted in light of dynamic, information-processing concepts, the static, local, and inferential nature of the statistical approach makes it challenging to directly link neuroimaging results to plausible underlying neural mechanisms. Here, we argue that dynamical systems theory provides the crucial mechanistic framework for characterizing both the brain's time-varying quality and its partial stability in the face of perturbations, and hence, that this perspective can have a profound impact on the interpretation of human neuroimaging results and their relationship with behavior. After briefly reviewing some key terminology, we identify three key ways in which neuroimaging analyses can embrace a dynamical systems perspective: by shifting from a local to a more global perspective, by focusing on dynamics instead of static snapshots of neural activity, and by embracing modeling approaches that map neural dynamics using "forward" models. Through this approach, we envisage ample opportunities for neuroimaging researchers to enrich their understanding of the dynamic neural mechanisms that support a wide array of brain functions, both in health and in the setting of psychopathology.

Keywords: Attractor landscapes; Bifurcations; Dynamics; Neuroscience; fMRI.