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Spontaneous brain regional dynamics contribute to generalizable brain–behaviour associations

Nature Human Behaviour volume 10, pages 384–402 (2026) Cite this article

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Abstract

Spontaneous brain activity is fundamental to understanding the neural basis of inter-individual differences, making its characterization central to brain-wide association studies. While inter-regional coupling patterns have been extensively studied, intra-regional dynamics remain largely unexplored. Here, analysing data from four neuroimaging cohorts (ages 8–82 years; N = 30,148), we extracted ~5,000 time-series features from resting-state haemodynamic signals across 271 brain regions, offering a comprehensive characterization of intra-regional dynamics. We identified a reliable subset that serves as an individual-specific ‘barcode’, capturing multifaceted dynamic dimensions that stably reflect inter-individual variation across datasets. These barcodes linked nonlinear autocorrelations in unimodal regions to substance use traits and random walk dynamics in higher-order networks to general cognitive abilities. Importantly, these brain–behaviour associations generalized across life stages and populations, with substance use showing age-specific variation and cognition exhibiting consistent patterns across age groups. This work advances large-scale, generalizable brain-wide association studies by highlighting the potential of intra-regional dynamics.

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Fig. 1: Research framework.
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Fig. 2: Dynamic properties and individual specificity of RSRD features.
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Fig. 3: Characteristics of brain–behaviour associations.
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Fig. 4: Generalization of brain–behaviour associations with HCP-D and UK Biobank cohorts.
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Fig. 5: Representative spatiotemporal dynamic patterns for each brain–behaviour association.
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Data availability

This study used four publicly available human neuroimaging datasets. The Human Connectome Project Young Adult dataset is accessible at https://db.humanconnectome.org/ to users who agree to the open-access data use terms. Data from the Midnight Scan Club are available via the OpenNeuro repository at https://openneuro.org/datasets/ds000224/versions/1.0.4. The Lifespan Human Connectome Project Development dataset is available through the National Institute of Mental Health Data Archive (https://nda.nih.gov/ccf) and was accessed under approval obtained via Beijing Normal University (application no. 1811129). The UK Biobank resource (https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=100) is available to approved researchers through a formal application process; data in this study were accessed under application no. 85139, with A.L as the principal investigator and B.L. as lead collaborator.

Code availability

Code for analyses is available via GitHub at https://github.com/linktianx/RSRD_BWAS and via Zenodo at https://doi.org/10.5281/zenodo.17115208 (ref. 130).

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Acknowledgements

This research was conducted using resources from the Human Connectome Project, WU-Minn Consortium (principal investigators: D. Van Essen and K. Ugurbil), Lifespan Human Connectome Project Development (principal investigators: E. Yacoub and D. Van Essen), Midnight Scan Club dataset (principal investigators: E. M. Gordon and N. U. F. Dosenbach) and the UK Biobank Resource (chief executive: R. Collins). We thank the hctsa team for developing and maintaining the hctsa toolbox. We also thank H. Yan, L. Fan, Y. Chen and Y. Liu for their valuable comments on the paper. This work was supported by STI2030-Major Projects (grant no. 2022ZD0211900 to A.L.), the Natural Science Foundation of China (grant nos. 82425024 and 82372049 to B.L. and 82171543 to A.L.) and the Beijing Nova Program (grant no. 20230484425 to B.L. and 20250484761 to A.L.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

Author information

Author notes

  1. These authors contributed equally: Xiaohan Tian, Yingjie Peng.

Authors and Affiliations

  1. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China

    Xiaohan Tian, Meng Wang, Yuqing Sun, Jing Lou, Junxing Xian, Yini He, Xi-Nian Zuo, Zhanjun Zhang & Bing Liu

  2. State Key Laboratory of Cognitive Science and Mental Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

    Yingjie Peng, Yini He, Tian Gao, Shangzheng Huang, Kaixin Li & Ang Li

  3. Key Laboratory of Genetic Evolution and Animal Models, National Research Facility for Phenotypic and Genetic Analysis of Model Animals (Primate Facility), National Resource Center for Non-Human Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China

    Shu Liu

  4. Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Golia Shafiei

  5. Lifespan Brain Institute of Perelman School of Medicine and Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA

    Golia Shafiei

  6. Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Ke Hu, Qi Wang & Chaoyue Ding

  7. Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China

    Tian Gao

  8. College of Science, China Agricultural University, Beijing, China

    Qi Wang

  9. IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

    Xi-Nian Zuo, Zhanjun Zhang & Bing Liu

  10. Chinese Institute for Brain Research, Beijing, China

    Bing Liu

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Contributions

B.L. and A.L. led the project. B.L., A.L. and X.T. developed the study concept and design. Y.P. assisted with computational implementation. X.T. and Y.P. conducted the analyses, supported by M.W., Y.S., J.L., J.X., Y.H., Q.W. (affiliation 6) and S.H. Data preprocessing was completed by X.T., Y.P., K.H., C.D., T.G. and K.L. S.L., G.S., Q.W. (affiliation 8) and Z.Z. provided feedback on the analyses. X.T. created the figures, and B.L., A.L. and X.T. drafted the paper. B.L., A.L., X.-N.Z., S.L. and G.S. made substantial contributions to revision and editing.

Corresponding authors

Correspondence to Ang Li or Bing Liu.

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Extended data

Extended Data Fig. 1 Four major dynamic dimensions in refined RSRD profiles.

(a) Schematic of the four dynamic dimensions (distribution, nonlinearity, nonstationarity, and stochasticity) captured by the 44 features constituting the refined RSRD profiles. hctsa toolbox keywords are shown below each dimension to summarize feature properties (see Supplementary Table 3 for details). (b) Dendrogram illustrates the similarity among the 44 investigated features based on the group-level average RSRD matrix. The feature colors correspond to the dynamic dimensions shown in (a). The dendrogram was constructed using hierarchical average linkage clustering, with feature similarity measured by Spearman correlation.

Extended Data Fig. 2 Consistency between dense and refined RSRD profiles in capturing the spatial organization of regional brain dynamics.

(a–b) Inter-regional similarity matrices computed from the dense RSRD profiles (4,945 features; a) and the refined RSRD profiles (44 high-ICC features; b), where each entry denotes the Pearson correlation between RSRD feature vectors of a brain region pair. (c) Scatter plot (as in Fig. 2b) comparing the upper triangular elements of the two similarity matrices, showing a strong partial Spearman correlation (r = 0.866, p < 0.001) between the dense and refined representations after controlling for inter-regional spatial distance.

Extended Data Fig. 3 Individual identification performance of RSRD and RSFC features.

(a) Edge-wise RSFC ICC matrix in the HCP-YA cohort. (b) Top 1% of edges ranked by RSFC ICC (ICC ≥ 0.68). (c) Top 44 brain regions ranked by region-wise RSFC ICC. Surface map shows the spatial distribution of high-reliability regions. Histogram shows the full distribution of region-wise ICCs; the red dashed line marks the ICC range for the top 44 regions ( ≥ 0.46). (d) Top 366 edges ranked by RSFC ICC, visualized using a circos plot. A histogram shows the distribution of edge-wise ICCs, with the 1% threshold (0.68) indicated using the red dashed line. Functional network abbreviations: Vis (Visual), SomMot (Somatomotor), DorsAttn (Dorsal Attention), SalVentAttn (Salience/Ventral Attention), Limbic (Limbic), Cont (Control), Default (Default Mode). (e) Individual identification accuracy in an independent HCP-YA sample (n = 898) using five feature sets across varying sample sizes. Bars represent mean accuracy, and error bars indicate the standard error across 100 identification tasks, each based on randomly selecting (N) participants from the 898 individuals independent of feature selection. Black dots display accuracy values from individual identification tasks. The dashed line denotes the 95% accuracy reference threshold. Both edge-wise refined RSFC and region-level refined RSRD features exceeded 95% accuracy, suggesting that distinct feature types can achieve high identification performance through complementary strategies.

Extended Data Fig. 4 Cross-cohort evaluation of edge-wise and region-wise RSFC reliability.

(a) Edge-wise ICC matrices for RSFC features computed in three cohorts. Functional network abbreviations: Vis (Visual), SomMot (Somatomotor), DorsAttn (Dorsal Attention), SalVentAttn (Salience/Ventral Attention), Limbic (Limbic), Cont (Control), Default (Default Mode). (b) Pairwise scatter plots comparing edge-wise ICCs for RSFC features across cohort pairs. Each dot represents an edge, with ICCs from two cohorts plotted on the x- and y-axes. Pairwise correlations were assessed using Spearman’s rank correlation. (c) Pairwise scatter plots comparing region-wise RSFC ICCs across cohort pairs. Each dot represents a brain region, with ICCs from two cohorts plotted on the x- and y-axes; dot color indicates the functional network or anatomical structure. Pairwise correlations were assessed using Spearman’s rank correlation. Correlation coefficients and significance levels are shown in the bottom-right corner of each scatter plot.

Extended Data Fig. 5 Post hoc analysis of sex effects on brain-behavior associations.

To assess the influence of sex, we conducted partial correlation analyses controlling for sex effects on brain dynamic patterns in each CCA mode. (a–b) Spatial patterns of brain dynamics after regressing out sex effects for the Substance Use Mode (a) and the Cognition Mode (b). For the behavioral relevance, we examined Spearman correlations between CCA-derived behavioral scores and the input behavioral phenotypes, separately for males (c and d) and females (e and f). Bars are color-coded by behavioral domain and ranked by the strength of the Spearman’s rank correlation with the CCA behavioral score.

Extended Data Fig. 6 Calculation of latent scores for CCA modes in independent cohorts.

Latent scores were computed for two CCA modes, mode 1 (Substance Use Mode) and mode 2 (Cognition Mode), using CCA pipeline trained on the HCP-YA cohort, which included normalization, PCA-based dimensionality reduction, and estimation of the CCA projection matrix. The fitted pipeline was then applied to external cohorts (HCP-D and UK Biobank) to generate latent scores. Purple text denotes models and parameters estimated from the HCP-YA training cohort, whereas teal arrows and shaded areas depict their transfer to independent populations.

Extended Data Fig. 7 Demographics and behavioral data distributions in the UK Biobank cohort.

Data from 28,596 UK Biobank participants was included in the cross-cohort generalization analyses. (a) Covariates controlled for in all correlation analyses. (b) Distributions of eight behavioral phenotypes and the composite measures of Substance Use and Externalizing Problems derived using PCA. PCA loadings and explained variance are shown in Supplementary Fig. 7a and 7b. (c) Distributions of the five cognitive phenotypes used for generalizing the Cognition Mode, with the number of valid observations indicated in each histogram.

Extended Data Table. 8 Generalization of CCA modes across UK Biobank age groups 45–60 and 60–82.

(a) Age and sex distributions for UK Biobank participants aged 45–60 and 60–82 included in the CCA generalization analyses. (b–c) Generalization of the Substance Use Mode in each age group: (b) primary validation using the Substance Use PC1; (c) complementary validation using the Externalizing Problems PC1. (d–e) Generalization of the Cognition Mode in each age group. Scatter plots in (d) illustrate representative results for a fluid intelligence phenotype, with sample sizes, confounder-adjusted correlation coefficients, and significance levels reported in the bottom right corner of each panel. Bar plots in (e) show generalization results for additional cognitive phenotypes. Statistical significance was evaluated using Bonferroni correction for family-wise error rate (FWE) control, with corrected P-values shown next to each bar. All analyses presented in this figure were conducted using the same procedures and covariate adjustments described in the main text (Methods). Detailed statistical results are provided in Supplementary Table 6.

Extended Data Fig. 9 Cross-cohort spatial pattern consistency for each CCA mode.

Cross-cohort spatial pattern consistency was assessed using Spearman correlations between representative spatiotemporal dynamic patterns for (a) Substance Use Mode and (b) Cognition Mode. Statistical significance for each cohort pair was evaluated with a spatial autocorrelation-preserving null model (Methods). In each scatter plot, each dot represents the correlation coefficient between a brain region’s RSRD feature and the behavioral phenotype across the two cohorts, color-coded by functional network or anatomical structure. Correlation coefficients and significance values for each cohort pair are shown in the bottom-right corner of the scatter plots. The histograms below each scatter plot show null distributions generated from 10,000 surrogate maps, either randomly shuffled (red) or spatial autocorrelation-preserving (blue), based on the x-axis spatial maps. The dashed black line indicates the empirically observed correlation.

Supplementary information

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Supplementary Methods, Figs. 1–9 and Tables 4–6.

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Tian, X., Peng, Y., Liu, S. et al. Spontaneous brain regional dynamics contribute to generalizable brain–behaviour associations. Nat Hum Behav 10, 384–402 (2026). https://doi.org/10.1038/s41562-025-02332-0

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