Depression is a heterogeneous and etiologically complex psychiatric syndrome, not a unitary disease entity, encompassing a broad spectrum of psychopathology arising from distinct pathophysiological mechanisms. Motivated by a need to advance our understanding of these mechanisms and develop new treatment strategies, there is a renewed interest in investigating the neurobiological basis of heterogeneity in depression and rethinking our approach to diagnosis for research purposes. Large-scale genome-wide association studies have now identified multiple genetic risk variants implicating excitatory neurotransmission and synapse function and underscoring a highly polygenic inheritance pattern that may be another important contributor to heterogeneity in depression. Here, we review various sources of phenotypic heterogeneity and approaches to defining and studying depression subtypes, including symptom-based subtypes and biology-based approaches to decomposing the depression syndrome. We review “dimensional,” “categorical,” and “hybrid” approaches to parsing phenotypic heterogeneity in depression and defining subtypes using functional neuroimaging. Next, we review recent progress in neuroimaging genetics (correlating neuroimaging patterns of brain function with genetic data) and its potential utility for generating testable hypotheses concerning molecular and circuit-level mechanisms. We discuss how genetic variants and transcriptomic profiles may confer risk for depression by modulating brain structure and function. We conclude by highlighting several promising areas for future research into the neurobiological underpinnings of heterogeneity, including efforts to understand sexually dimorphic mechanisms, the longitudinal dynamics of depressive episodes, and strategies for developing personalized treatments and facilitating clinical decision-making. Fig. 1 APPROACHES TO PARSING HETEROGENEITY IN DEPRESSION.: Schematic illustrating two data-driven approaches to parsing heterogeneity in depression described in the text using a “top-down” symptom-based approach (right, blue) or a “bottom-up” brain-based mechanistic approach (left, green). The goals of both approaches are to advance our understanding of neurobiological mechanisms underlying depression-related symptoms and behaviors and to develop new tools for informing diagnosis and treatment decisions. Fig. 2 TRANSDIAGNOSTIC PSYCHOPATHOLOGY BRAIN CONNECTIVITY-BEHAVIOR DIMENSIONS.: a Schematic of the analytical pipeline depicting the calculation of functional connectivity matrices from the Pearson correlations between the average BOLD fMRI signals for each of 264 spherical regions of interest (ROI) and every other ROI. Sparse canonical correlation (sCCA) was used to define linear combinations of clinical symptoms across a range of psychiatric diagnoses (lower panel) that were maximally correlated with linear combinations of functional connectivity. Psychopathology domains: psychotic and subthreshold symptoms (PSY), depression (DEP), mania (MAN), suicidality (SUI), attention-deficit hyperactivity disorder (ADD), oppositional defiant disorder (ODD), conduct disorder (CON), obsessive-compulsive disorder (OCD), separation anxiety (SEP), generalized anxiety disorder (GAD), specific phobias (PHB), mental health treatment (TRT), panic disorder (PAN), post-traumatic stress disorder (PTSD). ROI communities: somatosensory/motor network (SMT), cingulo-opercular network (COP), auditory network (AUD), default mode network (DMN), visual network (VIS), fronto-parietal network (FPT), salience network (SAL), subcortical network (SBC), ventral attention network (VAT), dorsal attention network (DAT), cerebellar. b-e Four brain-behavior dimensions of psychopathology were identified that captured individual differences in clinical symptoms. Scatter plots depict brain connectivity and clinical dimension scores, which are linear combinations of functional connectivity features and psychiatric symptoms. b Dimension 1 described mood-related symptoms, e.g. feeling sad. c Dimension 2 described psychosis-related symptoms, e.g. auditory hallucinations. d Dimension 3 described fear-related symptoms, e.g. fear of traveling. e Dimension 4 described externalizing behavior and related symptoms, e.g. trouble following instructions. f-i Network module connectivity patterns associated with each brain connectivity-behavior dimension. Heatmaps depict the magnitude and direction of correlation change to each brain-behavior dimension score (positively- or negatively correlated) in the following functional networks: default mode network (DMN), visual network (VIS), fronto-parietal network (FPT), salience network (SAL), ventral attention network (VAT), dorsal attention network (DAT). Abbreviations: a.u., arbitrary units. Figure adapted with permission from ref. [88]. Fig. 3 BRAIN CONNECTIVITY-BEHAVIOR DIMENSIONS OF DEPRESSION DEFINE NOVEL DEPRESSION SUBTYPES THAT PREDICT TREATMENT RESPONSE TO TMS.: a Four rsfMRI-based subtypes of depression, identified through hierarchical clustering on latent brain-behavior dimensions (canonical connectivity-symptom components), exhibit distinct patterns of atypical functional connectivity. Heatmaps depict the z score from a Wilcoxon rank sum test for differences between the functional connectivity of depressed subjects in each subtype and of healthy controls. b The four subtypes were associated with distinct clinical symptom profiles as indexed by item-level responses to the Hamilton Depression Rating Scale. c Boxplots depicting subtype differences in depression severity. d-e Patients in Subtypes 1 and 3 were more likely to respond to rTMS targeting the dorsomedial prefrontal cortex, compared to patients in Subtypes 2 and 4. f Distinct functional connectivity patterns prior to treatment in rTMS-responders vs. nonresponders. Heatmap depicts functional connectivity features that were significantly different in responders, including connectivity between the dorsomedial prefrontal target and the left dorsolateral prefrontal cortex and left amygdala. Figure adapted with permission from Ref. [98]. ACC anterior cingulate cortex; amyg amygdala; a.u. arbitrary units; COTC cingulo-opercular task-control network; DAN dorsal attention network; DLPFC dorsolateral prefrontal cortex; DMN default-mode network; DMPFC dorsomedial prefrontal cortex; FPTC frontoparietal task-control network; GP globus pallidus; HAMD Hamilton Depression Rating Scale; HC hippocampus; lat PFC lateral prefrontal cortex; LIMB limbic; M1 primary motor cortex; NAcc nucleus accumbens; OFC orbitofrontal cortex; PCC posterior cingulate cortex; PPC posterior parietal cortex; precun precuneus; rTMS repetitive transcranial magnetic stimulation; rsfMRI resting state functional MRI; SM primary sensorimotor cortex (M1 or S1); SS1 primary somatosensory cortex; SN salience network; subC subcortical; thal, thalamus; VAN ventral attention network; vis visual cortex; VLPFC ventrolateral prefrontal cortex; vStr ventral striatum; n.s. not significant. Fig. 4 INTEGRATING NEUROIMAGING AND GENETIC DATA TO UNCOVER INTERMEDIATE ENDOPHENOTYPES AND NOVEL DEPRESSION SUBGROUPS.: Schematic of how combining neuroimaging with genetic data can be used to parse heterogeneity in depression and uncover subgroups within the depressed population. Polygenic variation may manifest in intermediate behavior-related brain circuits that can give rise to distinct depression subgroups. The polygenic effects of risk variants on circuit dysfunction, cognition, behavior, and clinical symptoms may interact with each other. Genes indicated are examples of candidate depression risk variants from Table 3 with the known variation and locus in the genome indicated and depicted on the chromosome strand in yellow. 1. Indicates examples of brain circuits known to be dysfunctional in depression. 2. Lists examples of cognitive processes and behaviors that are altered in depression, possibly as a direct or indirect consequence of genetic risk variants. 3. Lists clinical symptoms of depression that may result directly or indirectly from dysfunction in depression brain circuits. The double-sided arrows indicate the bi-directional relationships between 1, 2, and 3 that may modulate the expression of intermediate phenotypes. Fig. 5 POLYGENIC RISK SCORES FOR ANHEDONIA PREDICT PSYCHIATRIC NEUROIMAGING PHENOTYPES AND SPATIAL PATTERNS OF GENE EXPRESSION FOR SCHIZOPHRENIA RISK GENES PREDICT SCHIZOTYPY-ASSOCIATED MYELINATION.: a-c Polygenic risk scores (PRS) for anhedonia were associated with a. regional volumes of cortical and subcortical regions of interest, b tract-specific fractional anisotropy (measure of axonal integrity and myelination), and c tract-specific mean diffusivity (measures of structural integrity in the intra- and extracellular space, neuropil, and global CSF). d Partial least squares (PLS) analysis linear combinations of genes whose spatial expression patterns co-localized with schizotypy-associated myelination patterns as indexed by an MRI magnetization transfer measure. Genes with larger “PLS1 weight” values were more important predictors of the spatial distribution of SRM myelination. e Positively weighted PLS1 genes were associated with genes known to be down-regulated in schizophrenia (“Gandal Down-Reg” and “Fromer Down-Reg”), neuron cell types, and increased SRM myelination. f Negatively weighted PLS1 genes were associated with genes known to be up-regulated in schizophrenia (“Gandal Up-Reg” and “Fromer Up-Reg”), decreased SRM myelination, and astrocyte, microglia, and neuron cell types. FDR false discovery rate; PLS partial least squares; SRM schizotypy-related magnetization; Up/Down-Reg Up/Down-Regulated. Figure adapted with permission from refs. [155] (a-c) and [160] (d-f).

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