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Etkin, A.

Publications and source records attributed to Etkin, A..

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Genetic factors influencing a neurobiological substrate for psychiatric disorders

A retrospective meta-analysis of magnetic resonance imaging voxel-based morphometry studies proposed that reduced gray matter volumes in the dorsal anterior cingulate and the left and right anterior insular cortex - areas that constitute hub nodes of the salience network - represent a common substrate for major psychiatric disorders. Here, we investigated the hypothesis that the common substrate serves as an intermediate phenotype to detect genetic risk variants relevant for psychiatric disease. To this end, after a data reduction step, we conducted genome-wide association studies of a combined common substrate measure in four population-based cohorts (n=2,271), followed by meta-analysis and replication in a fifth cohort (n=865). After correction for covariates, the heritability of the common substrate was estimated at 0.50 (standard error 0.18). The top single-nucleotide polymorphism (SNP) rs17076061 was associated with the common substrate at genome-wide significance and replicated, explaining 1.2% of the common substrate variance. This SNP mapped to a locus on chromosome 5q35.2 harboring genes involved in neuronal development and regeneration. In follow-up analyses, rs17076061 was not robustly associated with psychiatric disease, and no overlap was found between the broader genetic architecture of the common substrate and genetic risk for major depressive disorder, bipolar disorder, or schizophrenia. In conclusion, our study identified that common genetic variation indeed influences the common substrate, but that these variants do not directly translate to increased disease risk. Future studies should investigate gene-by-environment interactions and employ functional imaging to understand how salience network structure translates to psychiatric disorder risk.

genetics

A computational knowledge engine for human neuroscience

Functional neuroimaging has been a mainstay of human neuroscience for the past 25 years. The goal for this research has largely been to understand how activity across brain structures relates to mental constructs and computations. However, interpretation of fMRI data has often occurred within knowledge frameworks crafted by experts, which have the potential to reify historical trends and amplify the subjective biases that limit the replicability of findings. 1 In other words, we lack a comprehensive data-driven ontology for structure-function mapping in the human brain, through which we can also test the explanatory value of current dominant conceptual frameworks. Ontologies in other fields are popular tools for automated data synthesis,2, 3 yet relatively few attempts have been made to engineer ontologies in a data-driven manner.4 Here, we employ a computational approach to derive a data-driven ontology for neurobiological domains that synthesizes the texts and data of nearly 20,000 human neuroimaging articles. The data-driven ontology includes 6 domains, each defined by a circuit of brain structures and its associated mental functions. Several of these domains are omitted from the leading framework in neuroscience, while others uncover novel combinations of mental functions related to common brain circuitry. Crucially, the structure-function links in each domain better replicate across articles in held-out data than those mapped from the dominant frameworks in neuroscience and psychiatry. We further show that the data-driven ontology partitions the literature into modular subfields, for which the domains serve as generalizable archetypes of the structure-function patterns observed in single articles. The approach to computational ontology we present here is the most comprehensive functional characterization of human brain circuits quantifiable with fMRI. Moreover, our methods can be extended to synthesize other scientific literatures, yielding ontologies that are built up from the data of the field.

neuroscience