A central challenge in neuroimaging research is how to achieve comprehensive, precise, and stable measurements of complex brain activity in order to obtain functional representations that are both individually specific and broadly generalizable across populations and contexts.

Addressing this challenge, a collaboration study from the Institute of Biophysics of the Chinese Academy of Sciences, and Beijing Normal University, published on October 30, 2025, in Nature Human Behaviour, introduced a novel analytical framework that focuses on the intrinsic dynamic patterns within individual brain regions.

The study systematically evaluated the role and generalizability of regional dynamic properties in explaining individual differences and brain-behavior associations.

The researchers developed a robust and individually distinctive functional representation framework, termed Resting-State Regional Dynamics (RSRD), and demonstrated that brain-behavior association patterns based on RSRD generalize across independent cohorts spanning the human lifespan (ages 8-82).

RSRD integrates multi-dimensional time-series features-including linear, nonlinear, nonstationary, and stochastic properties-to comprehensively characterize the multi-level dynamic attributes of regional brain activity.

Using this framework, the study revealed that the cognitive mode is primarily driven by random-walk-like dynamics within higher-order networks, whereas the substance-use mode predominantly reflects nonlinear autocorrelation features emerging from the sensorimotor network and reward-related circuits.

Further analyses in independent developmental and older-adult cohorts showed that these two association modes are not only generalizable across life stages but also uncover distinct developmental patterns: the cognitive mode appears relatively stable early in life, while the substance-use mode likely emerges gradually from adolescence into early adulthood.

The findings demonstrate that the RSRD framework can serve as a reliable "functional barcode" across analytical contexts, enabling precise characterization of individual differences.

Its systematic associations with behavior further highlight the substantial potential of the temporal dimension for understanding individual variability in brain function and brain-behavior relationships.

This work opens a new methodological direction for understanding individual variability in brain function.

Figure: Framework for Brain-Behavior Association Research Based on RSRD

(Image by LI Ang's group)

Article link: https://www.nature.com/articles/s41562-025-02332-0

Contact: LI Ang

Institute of Biophysics, Chinese Academy of Sciences

Beijing 100101, China

E-mail: al@ibp.ac.cn

(Reported by Prof. LI Ang's group)