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Home
Inferring Intrinsic Neural Timescales Using Optimal Control Theory.

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Inferring Intrinsic Neural Timescales Using Optimal Control Theory.

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Inferring intrinsic neural timescales using optimal control theory.

Jason Z Kim¹, Richard F Betzel^2,3, Ahmad Beyh⁴

¹Department of Physics, Cornell University, Ithaca, NY, USA.

Nature Communications

|November 26, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a network control theory framework to estimate intrinsic neural timescales (INTs), revealing how brain connectivity and neurobiology influence whole-brain dynamics for better state control.

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Area of Science:

Neuroscience
Computational Neuroscience
Network Science

Background:

Whole-brain activity evolves through complex interactions within and between regions.
These interactions are governed by neurobiology and structural connectivity.
Understanding this temporal evolution is key to deciphering brain function.

Purpose of the Study:

To present a novel framework for studying brain activity dynamics.
To estimate intrinsic neural timescales (INTs) using network control theory (NCT).
To link brain connectivity, neurobiology, and cognitive measures through estimated INTs.

Main Methods:

Utilized network control theory (NCT) to model brain network dynamics.
Estimated intrinsic neural timescales (INTs) from the network model.

Validated model-based INTs against empirical functional neuroimaging data.

Correlated INTs with gene expression, cell-type densities, and cognitive measures.

Tested findings across multiple datasets and species.

Main Results:

The NCT framework successfully estimates intrinsic neural timescales (INTs).
Model-based INTs show significant correlations with empirical INTs, neurobiology, and cognition.
The framework improves the alignment between brain connectivity and state-space traversal.
Demonstrated consistent results across diverse datasets and species.
Showcased efficient brain state control using fewer regions based on model-based INTs.

Conclusions:

The proposed framework offers a biophysically realistic model of brain structure-function interplay.
Model-based INTs provide a powerful tool for understanding brain dynamics and control.
This approach enhances our ability to capture the relationship between intrinsic brain dynamics and overall function.