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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.

SUBMITTER: Buch AM

PROVIDER: S-EPMC7688954 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:A commonly held assumption in cognitive neuroscience is that, because measures of human brain function are closer to underlying biology than distal indices of behavior/cognition, they hold more promise for uncovering genetic pathways. Supporting this view is an influential fMRI-based study of sentence reading/listening by Pinel et al. (2012), who reported that common DNA variants in specific candidate genes were associated with altered neural activation in language-related regions of healthy individuals that carried them. In particular, different single-nucleotide polymorphisms (SNPs) of FOXP2 correlated with variation in task-based activation in left inferior frontal and precentral gyri, whereas a SNP at the KIAA0319/TTRAP/THEM2 locus was associated with variable functional asymmetry of the superior temporal sulcus. Here, we directly test each claim using a closely matched neuroimaging genetics approach in independent cohorts comprising 427 participants, four times larger than the original study of 94 participants. Despite demonstrating power to detect associations with substantially smaller effect sizes than those of the original report, we do not replicate any of the reported associations. Moreover, formal Bayesian analyses reveal substantial to strong evidence in support of the null hypothesis (no effect). We highlight key aspects of the original investigation, common to functional neuroimaging genetics studies, which could have yielded elevated false-positive rates. Genetic accounts of individual differences in cognitive functional neuroimaging are likely to be as complex as behavioral/cognitive tests, involving many common genetic variants, each of tiny effect. Reliable identification of true biological signals requires large sample sizes, power calculations, and validation in independent cohorts with equivalent paradigms.SIGNIFICANCE STATEMENT A pervasive idea in neuroscience is that neuroimaging-based measures of brain function, being closer to underlying neurobiology, are more amenable for uncovering links to genetics. This is a core assumption of prominent studies that associate common DNA variants with altered activations in task-based fMRI, despite using samples (10-100 people) that lack power for detecting the tiny effect sizes typical of genetically complex traits. Here, we test central findings from one of the most influential prior studies. Using matching paradigms and substantially larger samples, coupled to power calculations and formal Bayesian statistics, our data strongly refute the original findings. We demonstrate that neuroimaging genetics with task-based fMRI should be subject to the same rigorous standards as studies of other complex traits.

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Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics.

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