Evidence map›Paper›PMID 32127647›Full record

ArticleMolecular psychiatry2021

Functional connectome-wide associations of schizophrenia polygenic risk.

Hengyi Cao, Hang Zhou, Tyrone D Cannon

Open access · greenAbstract read
In one paragraph

Article in Molecular psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 1 pooled it
6.1field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

50 citing papers in PubMed, 1 synthesis or guideline pooled it, 84 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Hengyi CaoDepartment of Psychology, Yale University, New Haven, CT, USA. hengyi.cao@yale.edu.
Hang ZhouDepartment of Psychiatry, Yale University, New Haven, CT, USA.ORCID http://orcid.org/0000-0002-7694-6391
Tyrone D CannonDepartment of Psychology, Yale University, New Haven, CT, USA.
Yale University · US

Funding

1/9 Predictors and Mechanisms of Conversion to PsychosisU01MH081902 · NIMH · YALE UNIVERSITY · PI CANNON, TYRONE D · 2008 to 2018
$12.4M
NIMH NIH HHS U01 MH081902
6 · The paper itself

Abstract

Schizophrenia is a highly heritable mental disorder characterized by functional dysconnectivity across the brain. However, the relationships between polygenic risk factors and connectome-wide neural mechanisms are unclear. Here, combining genetic and multiparadigm fMRI data of 623 healthy Caucasian adults drawn from the Human Connectome Project, we found that higher schizophrenia polygenic risk scores were significantly correlated with lower functional connectivity in a large-scale brain network primarily encompassing the visual system, default-mode system, and frontoparietal system. Such correlation was robustly observed across multiple fMRI paradigms, suggesting a brain-state-independent neural phenotype underlying individual genetic liability to schizophrenia. Moreover, using an independent clinical dataset acquired from the Consortium for Neuropsychiatric Phenomics, we further demonstrated that the connectivity of the identified network was reduced in patients with schizophrenia and significantly correlated with general cognitive ability. These findings provide the first evidence for connectome-wide associations of schizophrenia polygenic risk at the systems level and suggest that disrupted integration of sensori-cognitive information may be a hallmark of genetic effects on the brain that contributes to the pathogenesis of schizophrenia.

Indexed as

ConnectomePsychotic DisordersSchizophreniaBrainHumansMagnetic Resonance ImagingNerve Net

Identifiers

PMID32127647
PMCPMC9557214
OpenAlexW3009085602

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.