Towards closed-loop precision psychiatry: Integrating MRI biomarkers for individualized care of major depressive disorder

Abstract

Magnetic resonance imaging (MRI) biomarkers have shown considerable potential in elucidating the neurobiological underpinnings of major depressive disorder (MDD). However, clinical translation of these biomarkers remains limited due to reliance on group-level analyses, which fail to capture the individual variability inherent in MDD. Precision psychiatry, which advocates for individualized approaches, offers a framework that could enhance the clinical utility of MRI biomarkers across multiple domains, including diagnostic classification, treatment response prediction, and individualized interventions. Despite this potential, current research applying MRI biomarkers to MDD within the framework of precision psychiatry remains fragmented, lacking an integrated clinical system that seamlessly combines these components. This review introduces the concept of a closed-loop clinical system, emphasizing the integration of diagnostic classification, treatment response prediction, and individualized interventions into a unified approach at the individual patient level. We summarize recent advances in these three clinical domains, highlight existing fragmentation, and discuss the challenges of achieving a cohesive system. Finally, we propose that the integration of MRI biomarkers into a closed-loop clinical system, as envisioned by precision psychiatry, holds great promise for the individualized management of MDD, improving clinical outcomes from diagnosis through recovery.

Keywords

magnetic resonance imaging / major depressive disorder / precision psychiatry / biomarkers / individual variability

Author contributions

Qing-Lin Gao (Conceptualization, Visualization, Writing - original draft), Xiao Chen (Conceptualization, Writing - review & editing), Francisco Xavier Castellanos (Conceptualization, Writing - review & editing), Bin Lu (Conceptualization, Funding acquisition, Writing - review & editing), and Chao-Gan Yan (Conceptualization, Funding acquisition, Project administration, Supervision, Writing - review & editing)

Conflict of interest

All authors declare no conflict of interest.

Acknowledgments

This work was supported by Beijing Nova Program of Science and Technology (grant number: 20230484465), Beijing Natural Science Foundation (grant number: J230040), the National Natural Science Foundation of China (grant number: 82122035) and the Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences (grant number: E3CX1315).

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