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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.

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Fig. 1: Approaches to parsing heterogeneity in depression.
Fig. 2: Transdiagnostic psychopathology brain connectivity-behavior dimensions.
Fig. 3: Brain connectivity-behavior dimensions of depression define novel depression subtypes that predict treatment response to TMS.
Fig. 4: Integrating neuroimaging and genetic data to uncover intermediate endophenotypes and novel depression subgroups.
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.

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Both authors contributed to the literature review and writing of this paper. A.B. created Figs. 1 and 4, and adapted the other figures from the references cited in the figure legends. Both authors read and approved the final paper.

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Correspondence to Conor Liston.

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Buch, A.M., Liston, C. Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics. Neuropsychopharmacol. 46, 156–175 (2021). https://doi.org/10.1038/s41386-020-00789-3

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