Researchers at Rutgers Health have identified how the brain integrates fast and slow processing through white matter connections, influencing cognitive abilities. Published in Nature Communications, the study analyzed data from nearly 1,000 people to map these neural timescales. Variations in this system may explain differences in thinking efficiency and hold promise for mental health research.
The human brain juggles information arriving at vastly different speeds, from immediate environmental cues to deliberate reflections on context and intent. A new investigation from Rutgers Health, detailed in Nature Communications, reveals how it achieves this balance via intrinsic neural timescales—unique processing windows for each brain region—and the white matter networks that link them.
Led by Linden Parkes, an assistant professor of psychiatry at Rutgers Health, the team examined brain imaging from 960 individuals to construct detailed connectomes. They employed mathematical models to trace information flow across these networks. "To affect our environment through action, our brains must combine information processed over different timescales," Parkes explained. "The brain achieves this by leveraging its white matter connectivity to share information across regions, and this integration is crucial for human behavior."
The findings show that the arrangement of these timescales across the cerebral cortex determines how smoothly the brain transitions between activity patterns linked to behavior. Not everyone has the same setup: "We found that differences in how the brain processes information at different speeds help explain why people vary in their cognitive abilities," Parkes noted. Those with better-aligned wiring for fast and slow signals tend to exhibit higher cognitive capacity.
These patterns also tie into genetic, molecular, and cellular brain features, with parallels observed in mice, indicating evolutionary conservation. "Our work highlights a fundamental link between the brain's white matter connectivity and its local computational properties," Parkes added.
Looking ahead, the researchers plan to apply this framework to disorders like schizophrenia, bipolar disorder, and depression to explore disruptions in temporal processing. Collaborators included Avram Holmes, Ahmad Beyh, Amber Howell, and Jason Z. Kim from Cornell University. The study appeared in Nature Communications (2025; 16(1)), with DOI: 10.1038/s41467-025-66542-w.
