Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

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

Molecular Psychiatry volume 28, pages 2490–2499 (2023)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

References

  1. Gartlehner G, Wagner G, Matyas N, Titscher V, Greimel J, Lux L, et al. Pharmacological and non-pharmacological treatments for major depressive disorder: review of systematic reviews. BMJ Open. 2017;7:e014912.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Hillhouse TM, Porter JH. A brief history of the development of antidepressant drugs: from monoamines to glutamate. Exp Clin Psychopharmacol. 2015;23:1–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Khan A, Brown WA. Antidepressants versus placebo in major depression: an overview. World Psychiatry. 2015;14:294–300.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Cipriani A, Furukawa TA, Salanti G, Chaimani A, Atkinson LZ, Ogawa Y, et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet. 2018;391:1357–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Stone MB, Yaseen ZS, Miller BJ, Richardville K, Kalaria SN, Kirsch I. Response to acute monotherapy for major depressive disorder in randomized, placebo controlled trials submitted to the US Food and Drug Administration: individual participant data analysis. BMJ. 2022;378:e067606.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Zhang Y, Wu W, Toll RT, Naparstek S, Maron-Katz A, Watts M, et al. Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography. Nat Biomed Eng. 2021;5:309–23.

    Article  PubMed  Google Scholar 

  7. Wu W, Zhang Y, Jiang J, Lucas MV, Fonzo GA, Rolle CE, et al. An electroencephalographic signature predicts antidepressant response in major depression. Nat Biotechnol. 2020;38:439–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Fonzo G, Etkin A, Zhang Y, Wu W, Crystal C, Chin-Fatt C, et al. Brain regulation of emotional conflict predicts antidepressant treatment response for depression. Nat Hum Behav. 2019;13:1319–31.

    Article  Google Scholar 

  9. Chen ZS, Galatzer-Levy IR, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. Patterns. 2022;3:100602.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Lee MH, Smyser CD, Shimony JS. Resting-state fMRI: a review of methods and clinical applications. Am J Neuroradiol. 2013;34:1866–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp. 2020;41:3468–535.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Friston KJ, Frith CD, Liddle PF, Frackowiak RS. Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab. 1993;13:5–14.

    Article  CAS  PubMed  Google Scholar 

  13. van den Heuvel MP, Hulshoff Pol HE. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol. 2010;20:519–34.

    Article  PubMed  Google Scholar 

  14. Zhao K, Duka B, Xie H, Oathes DJ, Calhoun V, Zhang Y. A dynamic graph neural network framework reveals new insights into connectome dysfunctions in ADHD. NeuroImage. 2022;246:118774.

    Article  PubMed  Google Scholar 

  15. Tong X, Xie H, Carlisle N, Fonzo GA, Oathes DJ, Jiang J, et al. Transdiagnostic connectome signatures from resting-state fMRI predict individual-level intellectual capacity. Transl Psychiatry. 2022;12:1–11.

    Article  Google Scholar 

  16. Gao S, Calhoun VD, Sui J. Machine learning in major depression: From classification to treatment outcome prediction. CNS Neurosci Ther. 2018;24:1037–52.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA. Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity. JAMA Psychiatry. 2015;72:603–11.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Yan CG, Chen X, Li L, Castellanos FX, Bai TJ, Bo QJ, et al. Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc Natl Acad Sci USA. 2019;116:9078–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Connolly CG, Wu J, Ho TC, Hoeft F, Wolkowitz O, Eisendrath S, et al. Resting-state functional connectivity of subgenual anterior cingulate cortex in depressed adolescents. Biol Psychiatry. 2013;74:898–907.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Dunlop K, Talishinsky A, Liston C. Intrinsic brain network biomarkers of antidepressant response: a review. Curr Psychiatry Rep. 2019;21:87.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Fan S, Nemati S, Akiki TJ, Roscoe J, Averill CL, Fouda S, et al. Pretreatment brain connectome fingerprint predicts treatment response in major depressive disorder. Chronic Stress. 2020;4:2470547020984726.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Goldstein-Piekarski AN, Staveland BR, Ball TM, Yesavage J, Korgaonkar MS, Williams LM. Intrinsic functional connectivity predicts remission on antidepressants: a randomized controlled trial to identify clinically applicable imaging biomarkers. Transl Psychiatry. 2018;8:57.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Rolle CE, Fonzo GA, Wu W, Toll R, Jha MK, Cooper C, et al. Cortical connectivity moderators of antidepressant vs placebo treatment response in major depressive disorder: secondary analysis of a randomized clinical trial. JAMA Psychiatry. 2020;77:397–408.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Whitton AE, Webb CA, Dillon DG, Kayser J, Rutherford A, Goer F, et al. Pretreatment rostral anterior cingulate cortex connectivity with salience network predicts depression recovery: findings from the EMBARC randomized clinical trial. Biol Psychiatry. 2019;85:872–80.

    Article  PubMed  Google Scholar 

  25. Fu Y, Long Z, Luo Q, Xu Z, Xiang Y, Du W, et al. Functional and structural connectivity between the left dorsolateral prefrontal cortex and insula could predict the antidepressant effects of repetitive transcranial magnetic stimulation. Front Neurosci. 2021;15:645936.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Goldman-Rakic PS. Topography of cognition: parallel distributed networks in primate association cortex. Annu Rev Neurosci. 1988;11:137–56.

    Article  CAS  PubMed  Google Scholar 

  27. Wang D, Buckner RL, Fox MD, Holt DJ, Holmes AJ, Stoecklein S, et al. Parcellating cortical functional networks in individuals. Nat Neurosci. 2015;18:1853–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Mueller S, Wang D, Fox MD, Yeo BT, Sepulcre J, Sabuncu MR, et al. Individual variability in functional connectivity architecture of the human brain. Neuron. 2013;77:586–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Gordon EM, Laumann TO, Gilmore AW, Newbold DJ, Greene DJ, Berg JJ, et al. Precision functional mapping of individual human brains. Neuron. 2017;95:791–807.e797.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Brennan BP, Wang D, Li M, Perriello C, Ren J, Elias JA, et al. Use of an individual-level approach to identify cortical connectivity biomarkers in obsessive-compulsive disorder. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4:27–38.

    PubMed  Google Scholar 

  31. Wang D, Li M, Wang M, Schoeppe F, Ren J, Chen H, et al. Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illness. Mol Psychiatry. 2018;25:2119–29.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Cui Z, Li H, Xia CH, Larsen B, Adebimpe A, Baum GL, et al. Individual variation in functional topography of association networks in youth. Neuron. 2020;106:340–53.e348.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kashyap R, Kong R, Bhattacharjee S, Li J, Zhou J, Thomas Yeo BT. Individual-specific fMRI-Subspaces improve functional connectivity prediction of behavior. Neuroimage. 2019;189:804–12.

    Article  PubMed  Google Scholar 

  34. Trivedi MH, McGrath PJ, Fava M, Parsey RV, Kurian BT, Phillips ML, et al. Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): Rationale and design. J Psychiatr Res. 2016;78:11–23.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Zhou G, Cichocki A, Zhang Y, Mandic DP. Group component analysis for multiblock data: Common and individual feature extraction. IEEE Trans Neural Netw Learn Syst. 2015;27:2426–39.

    Article  PubMed  Google Scholar 

  36. Hamilton M. The Hamilton rating scale for depression. Assessment of depression. Springer; Berlin, Heidelberg; 1986. p. 143–52.

  37. Chin Fatt CR, Jha MK, Cooper CM, Fonzo G, South C, Grannemann B, et al. Effect of intrinsic patterns of functional brain connectivity in moderating antidepressant treatment response in major depression. Am J Psychiatry. 2020;177:143–54.

    Article  PubMed  Google Scholar 

  38. Esteban O, Markiewicz CJ, Blair RW, Moodie CA, Isik AI, Erramuzpe A, et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods. 2019;16:111–6.

    Article  CAS  PubMed  Google Scholar 

  39. Schaefer A, Kong R, Gordon EM, Laumann TO, Zuo XN, Holmes AJ, et al. Local-global parcellation of the human cerebral cortex from Intrinsic functional connectivity MRI. Cereb Cortex. 2018;28:3095–114.

    Article  PubMed  Google Scholar 

  40. Sagonas C, Ververas E, Panagakis Y, Zafeiriou S. Recovering joint and individual components in facial data. IEEE Trans Pattern Anal Mach Intell. 2018;40:2668–81.

    Article  PubMed  Google Scholar 

  41. Lorenzo H, Saracco J, Thiébaut R. Supervised learning for multi-block incomplete data. 2019. https://arxiv.org/abs/1901.04380.

  42. Shen X, Finn ES, Scheinost D, Rosenberg MD, Chun MM, Papademetris X, et al. Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nat Protoc. 2017;12:506–18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Yu M, Linn KA, Cook PA, Phillips ML, McInnis M, Fava M, et al. Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. Hum Brain Mapp. 2018;39:4213–27.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106:1125–65.

    Article  PubMed  Google Scholar 

  45. Mayberg HS, Starkstein SE, Sadzot B, Preziosi T, Andrezejewski PL, Dannals RF, et al. Selective hypometabolism in the inferior frontal lobe in depressed patients with Parkinson’s disease. Ann Neurol. 1990;28:57–64.

    Article  CAS  PubMed  Google Scholar 

  46. Singh A, Black SE, Herrmann N, Leibovitch FS, Ebert PL, Lawrence J, et al. Functional and neuroanatomic correlations in poststroke depression: the Sunnybrook. Stroke Study Stroke. 2000;31:637–44.

    Article  CAS  PubMed  Google Scholar 

  47. Rolls ET, Cheng W, Du J, Wei D, Qiu J, Dai D, et al. Functional connectivity of the right inferior frontal gyrus and orbitofrontal cortex in depression. Soc Cogn Affect Neurosci. 2020;15:75–86.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Mayberg HS, Silva JA, Brannan SK, Tekell JL, Mahurin RK, McGinnis S, et al. The functional neuroanatomy of the placebo effect. Am J Psychiatry. 2002;159:728–37.

    Article  PubMed  Google Scholar 

  49. Pecina M, Bohnert AS, Sikora M, Avery ET, Langenecker SA, Mickey BJ, et al. Association between placebo-activated neural systems and antidepressant responses: neurochemistry of placebo effects in major depression. JAMA Psychiatry. 2015;72:1087–94.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Carlson PJ, Diazgranados N, Nugent AC, Ibrahim L, Luckenbaugh DA, Brutsche N, et al. Neural correlates of rapid antidepressant response to ketamine in treatment-resistant unipolar depression: a preliminary positron emission tomography study. Biol Psychiatry. 2013;73:1213–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Yuan H, Zhu X, Tang W, Cai Y, Shi S, Luo Q. Connectivity between the anterior insula and dorsolateral prefrontal cortex links early symptom improvement to treatment response. J Affect Disord. 2020;260:490–7.

    Article  PubMed  Google Scholar 

  52. Tomioka H, Yamagata B, Kawasaki S, Pu S, Iwanami A, Hirano J, et al. A longitudinal functional neuroimaging study in medication-naive depression after antidepressant treatment. PLoS ONE. 2015;10:e0120828.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Yuen GS, Gunning-Dixon FM, Hoptman MJ, AbdelMalak B, McGovern AR, Seirup JK, et al. The salience network in the apathy of late-life depression. Int J Geriatr Psychiatry. 2014;29:1116–24.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Arce E, Simmons AN, Lovero KL, Stein MB, Paulus MP. Escitalopram effects on insula and amygdala BOLD activation during emotional processing. Psychopharmacology. 2008;196:661–72.

    Article  CAS  PubMed  Google Scholar 

  55. Ma Y. Neuropsychological mechanism underlying antidepressant effect: a systematic meta-analysis. Mol Psychiatry. 2015;20:311–9.

    Article  CAS  PubMed  Google Scholar 

  56. Parra MA, Pattan V, Wong D, Beaglehole A, Lonie J, Wan HI, et al. Medial temporal lobe function during emotional memory in early Alzheimer’s disease, mild cognitive impairment and healthy ageing: an fMRI study. BMC psychiatry. 2013;13:1–12.

    Article  Google Scholar 

  57. Dolcos F, LaBar KS, Cabeza R. Interaction between the amygdala and the medial temporal lobe memory system predicts better memory for emotional events. Neuron. 2004;42:855–63.

    Article  CAS  PubMed  Google Scholar 

  58. Uddin LQ, Nomi JS, Hebert-Seropian B, Ghaziri J, Boucher O. Structure and function of the human insula. J Clin Neurophysiol. 2017;34:300–6.

    Article  PubMed  PubMed Central  Google Scholar 

  59. McNamara RK, Rider T, Jandacek R, Tso P. Abnormal fatty acid pattern in the superior temporal gyrus distinguishes bipolar disorder from major depression and schizophrenia and resembles multiple sclerosis. Psychiatry Res. 2014;215:560–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Yoshimura S, Okamoto Y, Onoda K, Matsunaga M, Okada G, Kunisato Y, et al. Cognitive behavioral therapy for depression changes medial prefrontal and ventral anterior cingulate cortex activity associated with self-referential processing. Soc Cogn Affect Neurosci. 2014;9:487–93.

    Article  PubMed  Google Scholar 

  61. Allison T, Puce A, McCarthy G. Social perception from visual cues: role of the STS region. Trends Cogn Sci. 2000;4:267–78.

    Article  CAS  PubMed  Google Scholar 

  62. Zilcha-Mano S, Wang Z, Peterson BS, Wall MM, Chen Y, Wager TD, et al. Neural mechanisms of expectancy-based placebo effects in antidepressant clinical trials. J Psychiatr Res. 2019;116:19–25.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Vogt BA. Midcingulate cortex: Structure, connections, homologies, functions and diseases. J Chem Neuroanat. 2016;74:28–46.

    Article  CAS  PubMed  Google Scholar 

  64. Vallance AK. A systematic review comparing the functional neuroanatomy of patients with depression who respond to placebo to those who recover spontaneously: is there a biological basis for the placebo effect in depression? J Affect Disord. 2007;98:177–85.

    Article  PubMed  Google Scholar 

  65. Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am Psychiatr Assoc. 2010;167:748–51.

    Article  Google Scholar 

  66. Xia CH, Ma Z, Ciric R, Gu S, Betzel RF, Kaczkurkin AN, et al. Linked dimensions of psychopathology and connectivity in functional brain networks. Nat Commun. 2018;9:3003.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Kebets V, Holmes AJ, Orban C, Tang S, Li J, Sun N, et al. Somatosensory-motor dysconnectivity spans multiple transdiagnostic dimensions of psychopathology. Biol Psychiatry. 2019;86:779–91.

    Article  PubMed  Google Scholar 

  68. Marek S, Tervo-Clemmens B, Calabro FJ, Montez DF, Kay BP, Hatoum AS, et al. Reproducible brain-wide association studies require thousands of individuals. Nature. 2022;603:654–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

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.

Author information

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

Authors
  1. Kanhao Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hua Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gregory A. Fonzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoyu Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nancy Carlisle
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Matthieu Chidharom
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Amit Etkin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Corresponding author

Correspondence to Yu Zhang.

Ethics declarations

Competing interests

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, K., Xie, H., Fonzo, G.A. et al. Individualized fMRI connectivity defines signatures of antidepressant and placebo responses in major depression. Mol Psychiatry 28, 2490–2499 (2023). https://doi.org/10.1038/s41380-023-01958-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-023-01958-8

This article is cited by

Search

Advanced search

Quick links