By Diagnostics World News Staff
August 13, 2026 | Researchers have uncovered evidence that the brain coordinates information across multiple timescales without requiring those processes to be synchronized moment to moment, a finding that could eventually inform new approaches to neurological and psychiatric diagnostics.
The study, led by researchers at the Beckman Institute for Advanced Science and Technology at the University of Illinois Urbana-Champaign and collaborators, combined functional magnetic resonance imaging (fMRI) with source-localized electroencephalography (EEG) to examine resting-state brain activity. The findings were published in Proceedings of the National Academy of Sciences (DOI: 10.1073/pnas.2535464123).
While fMRI provides detailed spatial information, its blood-oxygen-based signal changes relatively slowly. EEG, meanwhile, captures electrical activity on much faster timescales but traditionally offers limited spatial resolution. The researchers used concurrent measurements to examine how brain connectivity patterns behave across these different temporal scales.
The team analyzed data from 26 healthy participants, dividing the EEG signal into five frequency bands and combining those measurements with fMRI to examine six timescales. Rather than simply averaging activity over a scan, the researchers tracked how connectivity patterns emerged and transitioned over time.
The results revealed that the same spatial connectivity patterns appeared across different timescales, suggesting an overarching spatial organization. However, the specific patterns did not necessarily occur simultaneously across timescales. Instead, the researchers found that the transitions between patterns were preserved: if one connectivity state tended to precede another in one timescale, a similar sequence appeared in others.
The researchers compare the phenomenon to an orchestra in which different sections play at different tempos while following a shared musical language. The findings were independently validated using data from 24 additional participants with concurrent EEG-fMRI and a separate EEG-only dataset involving 443 individuals.
For diagnostics, the approach could eventually provide a way to identify individual differences in “multi-timescale profiles” that may be more sensitive to cognitive function or disease risk than measurements based on a single timescale. The researchers also suggest the framework could potentially support biofeedback-based interventions for conditions including anxiety, depression, and PTSD, although more research is needed to establish clinical utility.