Depression is a heterogeneous neuropsychiatric disorder with variable clinical presentation and response to treatment. This variability has motivated interest in neuroimaging biomarkers capable of disease characterization and therapeutic prediction. Positron emission tomography (PET) enables in vivo assessment of cerebral glucose utilization, neurochemical targets, inflammatory markers, and cerebral blood flow. This narrative review synthesizes PET studies conducted predominantly in adults with major depressive disorder diagnosed using DSM-based criteria, with bipolar disorder included only when imaging was performed during a depressive episode. Studies were identified through a structured, non-systematic literature search of major databases. Depression is consistently associated with regionally specific PET alterations within cortico-limbic and cortico-striatal circuits; studies most frequently report reduced glucose-derived PET measures in prefrontal and anterior cingulate regions at baseline, with treatment responders showing relative increases or redistribution of these measures following interventions. Neurochemical PET studies demonstrate altered receptor, transporter, or enzyme-related binding in serotonergic, dopaminergic, and noradrenergic systems, while neuroinflammatory and perfusion studies reveal regionally increased PET signals in subsets of patients. Overall, PET findings indicate convergent, region-specific and neurochemical alterations associated with depressive episodes and treatment response. Interpretation is constrained by methodological and clinical heterogeneity, underscoring the need for harmonized, longitudinal PET studies.

Although many methods are available for the analysis of microscopy images, there is still a need for user-friendly tools that enable automated yet flexible image analysis and reduce the workload of researchers. In this work, we present LNSCA (Lymph Node Spatial Cell Analyzer), a user-friendly ImageJ/Fiji plugin that measures the spatial distribution of cells within lymph nodes on fluorescence micrographs. The plugin is open-source, implemented in Jython, and provides a semi-automated batch image analysis pipeline with easily adjustable automatic cell segmentation. The pipeline consists of four main steps: manual delimitation of analysis regions (capsule(s), follicle(s), exclusion zone(s)), adaptable cell segmentation, manual verification and correction of the segmentation if needed, and spatial analysis. The output comprises CSV files containing the distances from each cell to the capsule(s), follicle(s), and lymph node center, as well as annotated images with graphical representations of these distances for straightforward visual inspection. By standardizing and partially automating spatial cell analysis in lymph nodes relative to their microanatomical structures, LNSCA reduces workload and analysis time while remaining easy to use and adapt, making it suitable for both beginners and experienced researchers in image analysis applied to immunology.