Predictive neuroimaging biomarkers of major depressive disorder treatment response: An umbrella review

Psychiatry Clin Neurosci. 2026 Apr 5. doi: 10.1111/pcn.70053. Online ahead of print.

ABSTRACT

Major depressive disorder (MDD) is a heterogeneous condition with varied responses to pharmacological, psychotherapeutic, and neuromodulation interventions. Identifying neuroimaging biomarkers predictive of treatment response could facilitate personalized treatment selection. This umbrella review synthesized findings from systematic reviews and meta-analyses evaluating neuroimaging biomarkers predictive of treatment response in MDD. A comprehensive search was conducted across PubMed, Scopus, Web of Science, and Embase. Fourteen systematic reviews and meta-analyses, encompassing 17,855 individuals with MDD, were included. Imaging modalities assessed included structural MRI, functional MRI (resting-state and task-based), diffusion tensor imaging, PET, MEG, and fNIRS. Methodological quality was evaluated using the Measurement Tool to Assess Systematic Reviews (AMSTAR 2 tool). The most consistently predictive biomarkers were increased volume and activity in the anterior cingulate cortex (ACC) and hippocampus and altered functional connectivity in the default mode network (DMN) and fronto-limbic circuits. Predictive patterns varied by treatment modality: for example, larger hippocampal volume predicted pharmacotherapy response, while smaller hippocampal volume was associated with better outcomes in ECT. Machine learning models integrating multimodal data achieved high predictive accuracy (AUC >0.85), though most lacked external validation. Evidence quality was low to very low among the included studies due to methodological heterogeneity. Neuroimaging biomarkers, particularly involving ACC, hippocampus, and large-scale functional networks, hold promise for guiding treatment selection in MDD. Integration of multimodal imaging and computational approaches may enhance predictive accuracy. However, standardization and prospective validation in clinical settings are needed for translation into practice.

PMID:41935973 | DOI:10.1111/pcn.70053