Evidence map›Paper›PMID 37732277›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Differences in White Matter Structural Networks in Family Risk of Major Depressive Disorder and Suicidality: A Connectome Analysis.

Nora Clancy Kelsall, Yun Wang, Marc J Gameroff, Jiook Cha, Jonathan Posner, Ardesheer Talati, Myrna M Weissman, Milenna Tamara van Dijk

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Nora Clancy Kelsall
Yun Wang
Marc J Gameroff
Jiook Cha
Jonathan Posner
Ardesheer Talati
Myrna M Weissman
Milenna Tamara van Dijk

Funding

Hippocampal and Genetic Mechanisms Underlying Development of Depression in Children at High Family RiskK99MH129611 · NIMH · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · PI VAN DIJK, MILENNA TAMARA · 2022 to 2023
$255k
NIMH NIH HHS K99 MH129611
6 · The paper itself

Abstract

Background: Depression and suicide are leading global causes of disability and death and are highly familial. Family and individual history of depression are associated with neurobiological differences including decreased white matter connectivity; however, this has only been shown for individual regions. We use graph theory models to account for the network structure of the brain with high levels of specialization and integration and examine whether they differ by family history of depression or of suicidality within a three-generation longitudinal family study with well-characterized clinical histories. Methods: Clinician interviews across three generations were used to classify family risk of depression and suicidality. Then, we created weighted network models using 108 cortical and subcortical regions of interest for 96 individuals using diffusion tensor imaging derived fiber tracts. Global and local summary measures (clustering coefficient, characteristic path length, and global and local efficiencies) and network-based statistics were utilized for group comparison of family history of depression and, separately, of suicidality, adjusted for personal psychopathology. Results: Clustering coefficient (connectivity between neighboring regions) was lower in individuals at high family risk of depression and was associated with concurrent clinical symptoms. Network-based statistics showed hypoconnected subnetworks in individuals with high family risk of depression and of suicidality, after controlling for personal psychopathology. These subnetworks highlighted cortical-subcortical connections including between the superior frontal cortex, thalamus, precuneus, and putamen. Conclusions: Family history of depression and of suicidality are associated with hypoconnectivity between subcortical and cortical regions, suggesting brain-wide impaired information processing, even in those personally unaffected.

Identifiers

PMID37732277
PMCPMC10508803

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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.