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Individualized fMRI connectivity defines signatures of antidepressant and placebo responses in major depression

Molecular Psychiatry (2023)Cite this article

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Abstract

Though sertraline is commonly prescribed in patients with major depressive disorder (MDD), its superiority over placebo is only marginal. This is in part due to the neurobiological heterogeneity of the individuals. Characterizing individual-unique functional architecture of the brain may help better dissect the heterogeneity, thereby defining treatment-predictive signatures to guide personalized medication. In this study, we investigate whether individualized brain functional connectivity (FC) can define more predictable signatures of antidepressant and placebo treatment in MDD. The data used in the present work were collected by the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Patients (N = 296) were randomly assigned to antidepressant sertraline or placebo double-blind treatment for 8 weeks. The whole-brain FC networks were constructed from pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI). Then, FC was individualized by removing the common components extracted from the raw baseline FC to train regression-based connectivity predictive models. With individualized FC features, the established prediction models successfully identified signatures that explained 22% variance for the sertraline group and 31% variance for the placebo group in predicting HAMD17 change. Compared with the raw FC-based models, the individualized FC-defined signatures significantly improved the prediction performance, as confirmed by cross-validation. For sertraline treatment, predictive FC metrics were predominantly located in the left middle temporal cortex and right insula. For placebo, predictive FC metrics were primarily located in the bilateral cingulate cortex and left superior temporal cortex. Our findings demonstrated that through the removal of common FC components, individualization of FC metrics enhanced the prediction performance compared to raw FC. Associated with previous MDD clinical studies, our identified predictive biomarkers provided new insights into the neuropathology of antidepressant and placebo treatment.

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Fig. 1: Illustration of the proposed analytical framework.
Fig. 2: Prediction of the outcome specific to sertraline arm in 10 × 10 cross validations.
Fig. 3: Prediction of the outcome specific to placebo arm in 10 × 10 cross validations.
Fig. 4: The visualization of the critical changed connectivity patterns after FCs individualization.

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Funding

This work was supported in part by NIH grant no. R01MH129694, and Lehigh University FIG, CORE, and Accelerator grants. Portions of this research were conducted on Lehigh University’s Research Computing infrastructure partially supported by NSF Award 2019035. AE was supported by NIH grant nos. DP1MH116506 and R44MH123373. GAF was supported by NIH grant nos. K23MH114023 and R01MH125886 and grants from the Brain and Behavior Research Foundation and One Mind – Baszucki Brain Research Fund.

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Authors and Affiliations

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

    Kanhao Zhao, Xiaoyu Tong & Yu Zhang

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

    Hua Xie

  3. 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

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

    Nancy Carlisle & Matthieu Chidharom

  5. Alto Neuroscience, Inc, Los Altos, CA, USA

    Amit Etkin

  6. Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA

    Yu Zhang

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  1. Kanhao Zhao
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Contributions

KZ contributed to the method development, data analyses and interpretation, and writing the manuscript. HX and GAF contributed to data interpretation and review and editing of the manuscript. XT, NC, MC, and AE contributed to review and editing of the manuscript. YZ provided supervision of the method development and contributed to data interpretation and writing the manuscript.

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Correspondence to Yu Zhang.

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AE reports salary and equity from Alto Neuroscience. AE additionally holds equity in Akili Interactive and Mindstrong Health. GAF holds equity in Alto Neuroscience and reports salary from SynapseBio. The remaining authors declare no competing interests.

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Zhao, K., Xie, H., Fonzo, G.A. et al. Individualized fMRI connectivity defines signatures of antidepressant and placebo responses in major depression. Mol Psychiatry (2023). https://doi.org/10.1038/s41380-023-01958-8

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