Hippocampal subfield-specific imaging in depression: the translational power of ultra-high field MRI

Major depressive disorder is a highly prevalent psychiatric condition characterized by diverse symptom profiles and variable treatment responses. The hippocampus has long been implicated in its pathophysiology, yet most human neuroimaging studies have treated it as a unitary structure, overlooking the distinct cytoarchitecture and connectivity of its subfields. Preclinical research demonstrates that subfields such as cornu ammonis 1 (CA1) and the dentate gyrus (DG) are differentially involved in stress susceptibility and antidepressant response. However, limited spatial resolution of conventional human neuroimaging has constrained in vivo characterization of these subfields and testing of translational hypotheses in humans. While advances in high resolution 3 tesla (T) imaging have improved subfield delineation, ultra-high field (UHF) MRI (7 T and above) extends these capabilities by combining whole-brain coverage with greater sensitivity to structural, functional and microstructural contrasts. This review synthesizes emerging applications of UHF MRI in depression, highlighting its advantages over conventional imaging approaches and early evidence for subfield-specific alterations. While most studies to date have focused on volumetric analyses, preliminary literature suggests structural and microstructural abnormalities in CA1 and DG, paralleling preclinical findings and implicating these subfields in network-level dysfunction and plasticity-related mechanisms. Future research should move beyond volumetric analyses to adopt more diverse MRI protocols capable of probing how subfield-specific architecture contributes to whole-brain networks and relates to clinical heterogeneity. UHF MRI offers a powerful platform to test mechanistic hypotheses derived from animal models, identify subfield-specific biomarkers, and ultimately guide personalized interventions targeting hippocampal circuits most relevant to individual symptom profiles.