Inferring intrinsic neural timescales using optimal control theory

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.