Summary: Researchers mapped the brain connectivity of 960 individuals to uncover how fast and slow neural processes unite to support complex behavior. They found that intrinsic neural timescales—each region’s characteristic window for processing information—are directly shaped by white-matter pathways that distribute signals across the brain. Individuals with a closer match between their wiring and regional timescale demands showed more efficient transitions between behavior-linked brain states.
These fast–slow integration patterns were also tied to genetic and molecular features and were conserved in mouse data, grounding the findings in fundamental neurobiology. The results reveal a mechanistic link between brain architecture, information-processing speed, and cognitive capacity.
Key Facts
- Intrinsic Timescales: Each brain region processes information over its own characteristic window, from fast sensory updates to slow integrative signals.
- Connectivity Integration: White-matter pathways link these regions, allowing fast and slow processes to converge into coherent behavior.
- Individual Differences: People whose wiring better supports cross-timescale communication show stronger cognitive performance.
Source: Rutgers University
The human brain is constantly processing information that unfolds at different speeds – from split-second reactions to sudden environmental changes to slower, more reflective processes such as understanding context or meaning.
A new study from Rutgers Health, published in Nature Communications, sheds light on how the brain integrates these fast and slow signals across its complex web of white matter connectivity pathways to support cognition and behavior.
Different regions of the brain are specialized for processing information over specific time windows, a property known as intrinsic neural timescales, or INTs for short.
“To affect our environment through action, our brains must combine information processed over different timescales,” said Linden Parkes, assistant professor of Psychiatry at Rutgers Health and the senior author of the study.
“The brain achieves this by leveraging its white matter connectivity to share information across regions, and this integration is crucial for human behavior.”
To investigate how this integration works, Parkes and his team analyzed multimodal brain imaging data from 960 individuals. They built detailed maps of each person’s brain connectivity, known as connectomes, and applied mathematical models that describe how complex systems change over time to understand how information flows through these networks.
“Our work probes the mechanisms underlying this process in humans by directly modeling regions’ INTs from their connectivity,” said Parkes, a core member of the Rutgers Brain Health Institute and the Center for Advanced Human Brain Imaging Research.
“This draws a direct link between how brain regions process information locally and how that processing is shared across the brain to produce behavior.”
Rutgers researchers found that the distribution of neural timescales across the cortex plays a crucial role in how efficiently the brain switches between large-scale activity patterns related to behavior. Importantly, this organization varies across individuals.
“We found that differences in how the brain processes information at different speeds help explain why people vary in their cognitive abilities,” Parkes said.
The researchers also discovered that these patterns are linked to genetic, molecular and cellular features of brain regions, grounding the findings in fundamental neurobiology. Similar relationships were observed in the mouse brain, suggesting that the mechanisms are conserved across species.
“Our work highlights a fundamental link between the brain’s white-matter connectivity and its local computational properties,” Parkes said. “People whose brain wiring is better matched to the way different regions handle fast and slow information tend to show higher cognitive capacity.”
Building on these findings, the team is now extending the work to study neuropsychiatric conditions, including schizophrenia, bipolar disorder and depression, examining how disruptions in brain connectivity may alter information processing.
The study was conducted in collaboration with Avram Holmes, an associate professor of psychiatry and a core member of the Rutgers Brain Health Institute and the Center for Advanced Human Brain Imaging Research, along with postdoctoral researchers Ahmad Beyh and Amber Howell, as well as Jason Z. Kim from Cornell University.
Key Questions Answered:
A: Different regions operate on intrinsic neural timescales, handling fast or slow information depending on their specialization.
A: Individuals whose white-matter wiring aligns well with these fast–slow processing demands show more efficient brain-state switching and higher cognitive performance.
A: Disruptions in connectivity or timescale organization may alter information flow, offering a target for understanding conditions such as schizophrenia, bipolar disorder, and depression.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- Journal paper reviewed in full.
- Additional context added by our staff.
About this neuroscience and cognition research news
Author: Tongyue Zhang
Source: Rutgers University
Contact: Tongyue Zhang – Rutgers University
Image: The image is credited to Neuroscience News
Original Research: Open access.
“Inferring intrinsic neural timescales using optimal control theory” by Linden Parkes et al. Nature Communications
Abstract
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.
