Inferring intrinsic neural timescales using optimal control theory

The temporal evolution of whole-brain activity is contingent upon complex interactions within and between brain regions that are mediated by neurobiology and connectivity, respectively. Here, we provide a framework for studying these relationships that uses network control theory (NCT) to estimate regions’ intrinsic neural timescales (INTs). Our approach broadens the range of dynamics supported by the connectome and improves the alignment between the brain’s connectivity and its traversal through state-space. We find that our model-based INTs correlate with INTs measured empirically from functional neuroimaging data, neurobiological measures of gene expression and cell-type densities, as well as measures of cognition. We demonstrate consistent results across multiple datasets and species. Finally, we show that our model-based INTs enable the efficient control of brain states from fewer brain regions. Our results provide a flexible quantitative framework that more accurately captures the interplay between brain structure, function, and intrinsic dynamics with greater biophysical realism.

Project Leads

Jason Z. Kim

Faculty Leads

Linden Parkes

Collaborators

Richard F. Betzel, Ahmad Beyh, Amber Howell, Amy Kuceyeski, Bart Larsen, Caio Seguin, Xi-Han Zhang, Avram Holmes

Project Start Date

August 2023

Current Project Status

Published in Nature Communications (2025) as Inferring intrinsic neural timescales using optimal control theory

Datasets

Human Connectome Project Young Adult (HCP-YA), Microstructure-Informed Connectomics (MICA-MICs), Allen Mouse Brain Connectivity Atlas, Allen Human Brain Atlas

Github Repository

https://github.com/LindenParkesLab/nct_xr

Path to Data on Filesystem

n/a

Publication DOI

https://doi.org/10.1038/s41467-025-66542-w

Conference Presentations

• Oral presentation at the The Organization for Human Brain Mapping Annual Meeting, June 2025. Inferring intrinsic neural timescales using optimal control theory.