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Energy inefficiency underpinning brain state dysregulation in individuals with major depressive disorder

Nature Mental Health (2026)Cite this article

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Abstract

Disruptions in brain state dynamics are a hallmark of major depressive disorder (MDD), yet their underlying mechanisms remain unclear. Here, building on network control theory, this case–control study reveals that energy inefficiency, characterized by elevated energy costs and reduced control stability, drives decreased state stability and increased state-switching frequency in MDD. Key brain regions, including the left dorsolateral prefrontal cortex and insula, exhibited impaired energy regulation capacity (a metric validated against cerebral metabolism). Moreover, these region-specific energy patterns were correlated with depressive symptom severity. Neurotransmitter and gene expression association analyses linked these energy deficits to intrinsic biological factors, notably the serotonin 5-HT2A receptor and astrocytes. These findings shed light on the energetic mechanism underlying brain state dysregulation in MDD and its associated biological underpinnings, highlighting brain energy dynamics as a potential biomarker by which to explore therapeutic targets and advance precise interventions for restoring healthy brain dynamics in depression.

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Fig. 1: Schematic of methods and the study design.
Fig. 2: Altered temporal brain dynamics in MDD.
Fig. 3: Energy inefficiency in MDD.
Fig. 4: Impaired rERC in MDD and its link to clinical PHQ-2 scores.
Fig. 5: Gene expression profiles related to energy regulation deficits in MDD.

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Data availability

The large-scale neuroimaging and phenotypic data supporting the findings of this study are available via the UK Biobank at https://www.ukbiobank.ac.uk/ via their standard data access procedure. Access is subject to restrictions, and researchers can apply for access to the UK Biobank data via the Access Management System (AMS) at https://www.ukbiobank.ac.uk/use-our-data/apply-for-access/. This research was conducted using the UK Biobank resource under application number 88660. All other datasets utilized in this study, including normative metabolic maps, gene expression profiles and atlases, are publicly available. The normative map of cerebral oxygen metabolism can be obtained from the neuromaps toolbox via GitHub at https://github.com/netneurolab/neuromaps. The normative map of cerebral glucose metabolism is available via GitHub at https://github.com/NeuroenergeticsLab/control_costs/blob/master/data/annotations/tum/cmrglc. Human gene expression data from the AHBA are available at https://human.brain-map.org/static/download. A list of the cell-type-specific genes is available via GitHub at https://github.com/jms290/PolySyn_MSNs/blob/master/Data/AHBA/celltypes_PSP.csv. PET receptor and transporter maps are available via GitHub at https://github.com/netneurolab/hansen_receptors. The Brainnetome atlas can be downloaded at https://atlas.brainnetome.org/download.html. Source data are provided with this paper.

Code availability

The analyses in this study were performed using open-source libraries and pipelines implemented in Python (v3.9.21), MATLAB (R2022b) and R (v4.4.2). All specific toolboxes and code repositories adapted for this study are publicly available and listed below. The processing pipeline for structural connectome construction and functional time series extraction was adapted from publicly available code via GitHub at https://github.com/sina-mansour/UKB-connectomics, incorporating Freesurfer (v7.2.0), FMRIB Software Library (v6.0.4) and MRtrix3 (v3.0.3). Brain states were extracted via GitHub at https://github.com/NeuroenergeticsLab/control_costs, and their temporal characteristics were computed using code available via GitHub at https://github.com/singlesp/energy_landscape. Control energy calculations utilized the nctpy toolbox (v1.0.1; https://github.com/LindenParkesLab/nctpy). Meta-analytic functional decoding was performed using NiMARE (v0.5.2; https://github.com/neurostuff/NiMARE). The PLSC analysis was implemented using a modified version of myPLS available via GitHub at https://github.com/danizoeller/myPLS to include spin-based permutation tests. The PLSR for gene analysis utilized code available via GitHub at https://github.com/KirstieJane/NSPN_WhitakerVertes_PNAS2016. Spatial permutation testing (spin tests) was performed using code available via GitHub at https://github.com/frantisekvasa/rotate_parcellation and the ‘permutation_testing function‘ from the ENIGMA Toolbox. Gene enrichment analysis was conducted via Metascape (https://metascape.org), and the cell type deconvolution code is available via GitHub at https://github.com/netneurolab/hansen_genescognition. Degree-preserving null models were generated using the Brain Connectivity Toolbox (https://sites.google.com/site/bctnet), and code for geometry-preserving null networks is available at https://www.brainnetworkslab.com/coderesources. Brain maps were fetched using Neuromaps (v0.0.5; https://github.com/netneurolab/neuromaps) and the ENIGMA Toolbox (v2.0.1; https://github.com/MICA-MNI/ENIGMA). Custom code developed specifically for this study is available via GitHub at https://github.com/YidaoWen/MDD-BrainState-Energy.

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Acknowledgements

This work was supported by the STI2030-Major Projects (grant no. 2021ZD0200200 to T.J.), National Natural Science Foundation of China (grant nos. 62327805 to T.J., 12301642 to H.X. and 62403465 to W.S.) and China Postdoctoral Science Foundation (grant nos. GZC20232999 and 2024M753502 to W.S.). The research was conducted using the UK Biobank resources, with approved project number 88660. We sincerely appreciate the participants for their contributions and the UK Biobank team for their dedication to data collection, processing and dissemination. We are grateful to our colleagues at the Brainnetome Center for their support and insightful discussions, as well as R. E. Perozzi and E. F. Perozzi for their valuable assistance in reviewing and refining the English and content of this article.

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Author notes

  1. These authors contributed equally: Qianhui Liu, Hui Xiong.

Authors and Affiliations

  1. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

    Qianhui Liu & Tianzi Jiang

  2. Beijing Key Laboratory of Brainnetome and Brain-Computer Interface, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Qianhui Liu, Hui Xiong, Weiyang Shi, Shiqi Di, Xinle Cheng, Nianyi Liu, Na Luo & Tianzi Jiang

  3. Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Qianhui Liu, Hui Xiong, Weiyang Shi, Shiqi Di, Xinle Cheng, Nianyi Liu, Na Luo & Tianzi Jiang

  4. School of Systems Science and Statistics, Beijing Wuzi University, Beijing, China

    Hui Xiong

  5. School of Psychology, Shanghai Jiao Tong University, Shanghai, China

    Yu Zhang

  6. International Institute of Brain-Machine Intelligence, Harbin Institute of Technology, Shenzhen, China

    Tianzi Jiang

  7. School of Biomedical Engineering, Harbin Institute of Technology, Shenzhen, China

    Tianzi Jiang

  8. Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou, China

    Tianzi Jiang

Authors
  1. Qianhui Liu
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Contributions

Q.L. and H.X. conceptualized the study. Q.L., H.X. and W.S. developed the methodology. Q.L., W.S., S.D. and X.C. curated the data. Q.L., H.X., W.S., S.D., X.C., N. Liu, N. Luo and Y.Z. conducted the investigation. Q.L. and W.S. contributed to the visualization. T.J. supervised the study. Q.L. and H.X. drafted the original paper. Q.L., H.X., W.S. and T.J. reviewed and edited the paper.

Corresponding author

Correspondence to Tianzi Jiang.

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Nature Mental Health thanks J. Paul Hamilton, Diego Pizzagalli and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Methods, Results, Figs. 1–15, Tables 1–10 and References.

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Liu, Q., Xiong, H., Shi, W. et al. Energy inefficiency underpinning brain state dysregulation in individuals with major depressive disorder. Nat. Mental Health (2026). https://doi.org/10.1038/s44220-025-00583-4

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