Aperiodic neural timescales in prefrontal cortex dilate with increased task abstraction

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Neuroscience

Background:

• The brain integrates information across multiple timescales, from rapid sensory processing to long-term narrative comprehension.
• Cortical hierarchy exhibits a gradient of neural timescales, with dynamic sensory cortices feeding into more stable association cortices.
• Distinguishing between oscillatory and aperiodic neural activity is challenging due to mutual confounding influences, complicating the study of neural timescales in cognition.

Purpose of the Study:

• To investigate how neural timescales change dynamically with cognitive demands, specifically during a task manipulating task abstraction.
• To disentangle the contributions of aperiodic neural activity from oscillatory power when measuring neural timescales.
• To test the hypothesis that neural timescales increase with task demands, particularly under high task abstraction conditions.

Main Methods:

• Analysis of electroencephalography (EEG) data from participants performing a cognitive control task.
• Development and application of methods to separate aperiodic neural timescales from oscillatory power.
• Estimation of neural timescales using the autocorrelation function, focusing on prefrontal cortical regions.

Main Results:

• Task abstraction significantly dilated the aperiodic neural timescale, indicating a slower processing of information.
• This dilation of neural timescales was observed specifically over prefrontal cortical regions.
• The findings demonstrate that neural timescales are not fixed but dynamically adjust to meet the requirements of cognitive tasks.

Conclusions:

• Neural timescales are a flexible property of the cerebral cortex that can be modulated by cognitive control.
• Increased task abstraction leads to an expansion of neural timescales, likely supporting the integration of contextual information.
• This research provides a refined method for analyzing neural timescales, separating aperiodic from oscillatory components, and offers insights into the neural basis of cognitive flexibility.