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Generalizable structure–function covariation predictive of antidepressant response revealed by target-oriented multimodal fusion

Nature Mental Health volume 4, pages 85–101 (2026)Cite this article

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Abstract

Major depressive disorder (MDD) is a prevalent condition that profoundly impairs quality of life across diverse populations. Despite widespread use, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates. Progress in developing effective therapies is hampered by the insufficiently understood heterogeneity of MDD and its elusive underlying mechanisms. Here, to address these challenges, we develop a novel machine learning framework that identifies structure–function covariation through target-oriented fusion of structural and functional connectivity, which robustly predicts individual-level antidepressant response (sertraline, R2 = 0.31; placebo, R2 = 0.22). Validation in an independent escitalopram-medicated MDD cohort confirms the biomarker’s generalizability (P = 0.01) and suggests an overlap of psychopharmacological signatures across selective serotonin reuptake inhibitors. Our models highlight the right precuneus as a common key region for both sertraline and placebo responses, with the right middle frontal gyrus and left fusiform gyrus specific to sertraline and the left inferior and middle frontal gyri to placebo. We also find that structural connectivity is more predictive of sertraline response, while functional connectivity better predicts placebo response. The framework further decomposes the overall predictive patterns into three constitutive network constellations (default-mode regulatory, affective and sensory processing), which exhibit distinct generalizable structure–function covariation and treatment-specific association with personality traits and behavioral/cognitive profiles. These findings provide unique insights to the structure–function covariation in patients with MDD, its association to the heterogeneity in antidepressant response and the dissection of the intricate MDD neuropsychopharmacology, paving the way for precision medicine and development of more targeted antidepressant therapeutics. Clinicaltrials.gov registration: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT01407094.

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Fig. 1: Characterization of structure–function covariation predicting antidepressant and placebo responses.
Fig. 2: Multimodal neuroimaging biomarkers for sertraline-induced antidepressant response.
Fig. 3: Multimodal neuroimaging biomarkers for placebo-induced antidepressant response.
Fig. 4: Generalization analysis of multimodal biomarkers for sertraline and placebo response in the CAN-BIND-1 cohort.
Fig. 5: Network constellations in multimodal biomarkers for sertraline and placebo response.

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Data availability

The EMBARC cohort is publicly available through the National Institute of Mental Health Data Archive (NDA) (https://nda.nih.gov/edit_collection.html?id=2199). The CAN-BIND-1 cohort is available under a data use agreement with Brain-CODE, based at the Ontario Brain Institute (https://www.braincode.ca/content/canadian-biomarker-integration-network-depression-can-bind-0).

Code availability

All analyses were conducted in MATLAB (version R2022b), and the code is available via our GitHub repository at https://github.com/Xiaoyu-Tong/TargetOrientedMultimodalFusion--AntidepressantResponse.

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Acknowledgements

This work was supported by NIH grant R01MH129694 to Y.Z. and philanthropic funding and grants from the One Mind-Baszucki Brain Research Fund, the SEAL Future Foundation and the Brain and Behavior Research Foundation to G.A.F. The funders had no role in the design and conduct of the study, and the collection, management, analysis and interpretation of the data, nor were they involved in the decision to submit the paper for publication. We would also like to acknowledge the individuals and organizations that have made data available for this research, including CAN-BIND, the Ontario Brain Institute, the Brain-CODE platform and the government of Ontario.

Author information

Authors and Affiliations

  1. Department of Bioengineering, Lehigh University, Bethlehem, PA, USA

    Xiaoyu Tong & Kanhao Zhao

  2. Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA

    Gregory A. Fonzo & Charles B. Nemeroff

  3. Center for Neuroscience Research, Children’s National Hospital, Washington, DC, USA

    Hua Xie

  4. George Washington University School of Medicine, Washington, DC, USA

    Hua Xie

  5. Department of Psychology, Lehigh University, Bethlehem, PA, USA

    Nancy B. Carlisle

  6. Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA

    Corey J. Keller, Amit Etkin & Yu Zhang

  7. Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA

    Corey J. Keller & Yu Zhang

  8. Sierra-Pacific Mental Illness Research, Education and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA

    Corey J. Keller

  9. Center for Brain Imaging and Stimulation, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Desmond J. Oathes

  10. Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Desmond J. Oathes & Yvette Sheline

  11. Penn Brain Science, Translation, Innovation, and Modulation Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Desmond J. Oathes

  12. Departments of Neurology, Neurosurgery, Bioengineering and Neuroscience, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Desmond J. Oathes

  13. The University of Texas Southwestern Medical Center, Department of Psychiatry, Center for Depression Research and Clinical Care, Dallas, TX, USA

    Madhukar Trivedi

  14. Alto Neuroscience Inc., Mountain View, CA, USA

    Amit Etkin

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  1. Xiaoyu Tong
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  2. Kanhao Zhao
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  4. Hua Xie
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  12. Yu Zhang
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Contributions

X.T. conceptualized and designed the work, wrote the code, analyzed and interpreted the data and drafted and revised the paper. K.Z. preprocessed and interpreted the data and revised the paper. G.A.F., H.X. and N.B.C. interpreted the data, refined the design of the work and revised the paper. C.J.K., D.J.O., Y.S., C.B.N., M.T. and A.E. interpreted the data and revised the paper. Y.Z. conceptualized and designed the work, oversaw the analysis and interpretation of the data and revised the paper.

Corresponding author

Correspondence to Yu Zhang.

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G.A.F. received monetary compensation for consulting work for SynapseBio AI and owns equity in Alto Neuroscience. C.J.K. reports equity from Alto Neuroscience. C.B.N. is supported by the National Institutes of Health, the National Institute of Mental Health, the Texas Child Mental Health Consortium and the National Institute of Alcohol Abuse and Alcoholism. C.B.N. is a consultant for ANeuroTech, Abbott Laboratories, Engrail Therapeutics, Clexio Biosciences Ltd., Sero (previously Galen Mental Health LLC), Goodcap Pharmaceuticals, Sage Therapeutics, Senseye Inc., Precisement Health, Autobahn Therapeutics Inc., EMA Wellness, Denovo Biopharma LLC, Alvogen, Acadia Pharmaceuticals, Inc., Reunion Neuroscience, Kivira Health, Inc., Wave Neuroscience, Patient Square Capital LP, Invisalert Solutions Inc. and Neurocrine Biosciences, LLC. C.B.N. owns the following patents: method and devices for transdermal delivery of lithium (US patent no. 6,375,990B1), method of assessing antidepressant drug therapy via transport inhibition of monoamine neurotransmitters by ex vivo assay (US patent no. 7,148,027B2) and compounds, compositions, methods of synthesis and methods of treatment (CRF receptor binding ligand) (US patent no. 8,551, 996 B2). C.B.N. owns stock in Corcept Therapeutics Company, EMA Wellness, Precisement Health, Relmada Therapeutics Inc., Signant Health, Galen Mental Health LLC, Kivira Health, Inc., Denovo Biopharma LLC and Senseye Inc. A.E. reports salary and equity from Alto Neuroscience and equity in Mindstrong Health. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Data driven dimensionality and dissimilarity of dimension compositions of the latent space.

As the degree of sparsity increases, the latent space dimensions derived from both the L0-regularized (a) and L1-regularized (b) framework first exhibit a shrinkage of degree of freedom (decrease in number of dimensions), demonstrating the data driven dimensionality property that the framework can judge on its own whether a feature is adequately informative to be included in the sparse latent space. However, while the latent dimensions derived with L0-regularization show decent dissimilarity with each other (as quantified by their pairwise cosine similarity), the dimensions derived with L1-regularization gradually converge. After the dimensionality reaches a relative stable number, the L0-regularized framework further enhance the degree of orthogonality across dimensions, while the L1-regularized counterpart further aggregates information from dimensions and emphasizes the dominant dimension. Together, the differences in the properties of latent space demonstrate the unique advantage of L0-regularized framework over its L1-regularized counterpart — the L0-regularization imposes penalty on the occurrence of features in dimensions, especially the features with contribution to multiple dimensions, therefore yielding distinct dimensions with decent dissimilarity with each other. The sparsity parameters (\({\lambda }_{{fusion}}\) and \({\lambda }_{{pred}}\)) are harmoniously adjusted and kept same for L0- and L1-regularization. The colormap of latent space shows the weights of informative connectivity features with respect to each of the latent dimensions. The blankness in the rightmost part of latent space is the eliminated dimensions that are automatically set to zero vectors by the framework due to their inadequate informativeness. The latent space of one cross-validation fold is shown for each regularization paradigm as the representative example.

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Tong, X., Zhao, K., Fonzo, G.A. et al. Generalizable structure–function covariation predictive of antidepressant response revealed by target-oriented multimodal fusion. Nat. Mental Health 4, 85–101 (2026). https://doi.org/10.1038/s44220-025-00541-0

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