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Multimodal population study reveals the neurobiological underpinnings of chronotype

Abstract

The rapid shifts in society have altered human behavioural patterns, with increased evening activities, increased screen time and changed sleep schedules. As an explicit manifestation of circadian rhythms, chronotype is closely intertwined with physical and mental health. Night owls often exhibit unhealthier lifestyle habits, are more susceptible to mood disorders and have poorer physical fitness compared with early risers. Although individual differences in chronotype yield varying consequences, their neurobiological underpinnings remain elusive. Here we conducted a pattern-learning analysis with three brain-imaging modalities (grey matter volume, white-matter integrity and functional connectivity) and capitalized on 976 phenotypes in 27,030 UK Biobank participants. The resulting multilevel analysis reveals convergence on the basal ganglia, limbic system, hippocampus and cerebellum. The pattern derived from modelling actigraphy wearables data of daily movement further highlighted these key brain features. Overall, our population-level study comprehensively investigates chronotype, emphasizing its close connections with habit formation, reward processing and emotional regulation.

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Fig. 1: Manhattan plot of phenome-wide associations of chronotypes early birds versus night owls.
Fig. 2: Chronotypes are especially tied to the basal ganglia, limbic system and cerebellum vermis.
Fig. 3: Functional connectivity analysis reveals widespread chronotype-associated brain network.
Fig. 4: Sex differences in chronotype revealed significant variations in limbic system structures and functional couplings related to basal ganglia and attention networks.
Fig. 5: Mapping brain features to hourly movement data from wrist wearables device.

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Data availability

The UK Biobank data are available to other investigators online (https://www.ukbiobank.ac.uk/). The Harvard–Oxford atlas, Probabilistic cerebellar atlas, and Johns Hopkins University atlas are accessible online (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases). Source data are provided with this paper.

Code availability

The processing scripts used in this work were written in Python (v.3.8.8) and utilized the following packages: sklearn (1.1.3), numpy (1.23.4), pandas (1.5.1), matplotlib (3.6.2), seaborn (0.12.2), mne (1.4.0) and mne-connectivity (0.5.0). These scripts are publicly accessible in GitHub at https://github.com/dblabs-mcgill-mila/Chronotype_Neurobiological_Basis (ref. 152).

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Acknowledgements

D.B. was supported by the Brain Canada Foundation through the Canada Brain Research Fund, with the financial support of Health Canada, the National Institutes of Health (NIH R01 AG068563A, NIH R01 DA053301-01A1 and NIH R01 MH129858-01A1), the Canadian Institute of Health Research (CIHR 438531 and CIHR 470425), the Healthy Brains Healthy Lives initiative (Canada First Research Excellence fund), Google (Research Award, Teaching Award), and by the CIFAR Artificial Intelligence Chairs programme (Canada Institute for Advanced Research). L.Z. was funded by the China Scholarship Council (CSC: 202106070134). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Contributions

D.B. and L.Z. conceived and executed the project, and wrote the paper. K.S., J.C., K.-F.S. and R.I.M.D. contributed to the analysis and interpretation of the data, as well as revision of the paper. D.B. led data analytics.

Corresponding author

Correspondence to Danilo Bzdok.

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D.B. is a shareholder and advisory board member of MindState Design Labs, USA. The other authors declare no competing interests.

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Nature Human Behaviour thanks Jonas Obleser, Meenakshi Sinha and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–3, analysis and Tables 1–5.

Reporting Summary

Supplementary Table 1

The data fields from the UK Biobank utilized in the current study.

Source data

Source Data Fig. 1

PheWAS statistical source data.

Source Data Fig. 2

LDA coefficients for sMRI and dMRI.

Source Data Fig. 3

LDA coefficients for resting-state fMRI.

Source Data Fig. 4

LDA coefficients for sex differences.

Source Data Fig. 5

Brain loadings, actigraphy loadings and y scores.

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Zhou, L., Saltoun, K., Carrier, J. et al. Multimodal population study reveals the neurobiological underpinnings of chronotype. Nat Hum Behav 9, 1442–1456 (2025). https://doi.org/10.1038/s41562-025-02182-w

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