Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics

Abstract

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.

Comorbid Chronic Pain and Depression: Shared Risk Factors and Differential Antidepressant Effectiveness

“…Along with animal model and human pharmacogenetic studies (53-56, 116,117,[121][122][123][124][125], there is also independent converging evidence for the critical role of subcortical brain regions in mediating pain and mood (126). The application of rigorous statistical genetics methodologies to large-scale neuroimaging data, for example, has already produced several major discoveries, such as advancing our understanding of causal pathways (subcortical), brain networks and medication response markers in mood disorders (127)(128)(129)(130)(131)(132)(133). Our study suggests pharmacoepidemiological approaches in psychiatry and pain medicine research will be increasingly valuable as a costeffective, first-line strategy to enhance the design, feasibility, and clinical utility of randomized controlled trials (134), particularly as they routinely exclude patients with specific comorbidities and thus are not representative of the inherent individual variation across the population (104,107,135).…”

Kynurenines Increase MRS Metabolites in Basal Ganglia and Decrease Resting State Connectivity in Frontostriatal Reward Circuitry in Depression

“…For instance, water (during dehydration) or rest (during inflammation and infection) may be preferred over more powerful, reinforcing hedonic stimuli such as sucrose water 68 . Impairments of DMPFC might identify a distinct subgroup of depressed patients who may respond to targeted stimulation of this region [69][70][71] . DMPFC and its downstream connections have been proposed as promising stimulation targets using transcranial magnetic stimulation (TMS) for anhedonic depression 72 .…”

Emotion processing and regulation in major depressive disorder: A 7T resting‐state fMRI study

“…Recent lesion‐ or TMS‐network mapping (Padmanabhan et al, 2019; Siddiqi et al, 2020) may also help to reveal the interactions of EP‐ and ER‐related areas with subsequent regulatory behavior. Finally, major depression is a disorder of heterogeneity (Buch & Liston, 2020). Although low sample size did not allow us, but prospective studies can take relevant clinical factors (i.e., episode onset, number, duration, etc.)…”