Evidence map›Paper›PMID 38605171›Full record

ArticleMolecular psychiatry2024

Robust and replicable functional brain signatures of 22q11.2 deletion syndrome and associated psychosis: a deep neural network-based multi-cohort study.

Kaustubh Supekar, Carlo de Los Angeles, Srikanth Ryali, Leila Kushan, Charlie Schleifer, Gabriela Repetto, Nicolas A Crossley, Tony Simon, Carrie E Bearden, Vinod Menon

Abstract read
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In one paragraph

Article in Molecular psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 32% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
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

10 authors at 6 institutions in 3 countries.

Kaustubh SupekarDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA. ksupekar@stanford.edu.ORCID 0000-0003-0255-4093
Carlo de Los AngelesDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Srikanth RyaliDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0001-9773-0381
Leila KushanDepartment of Psychiatry and Behavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0000-0002-2650-7147
Charlie SchleiferDepartment of Psychology, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0000-0001-8411-0968
Gabriela RepettoCenter for Genetics and Genomics, Facultad de Medicina, Clinica Alemana Universidad del Desarrollo, Santiago, Chile.
Nicolas A CrossleyDepartment of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile.
Tony SimonDepartment of Psychiatry and Behavioral Sciences, University of California, Davis, Sacramento, CA, USA.
Carrie E BeardenDepartment of Psychiatry and Behavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0000-0002-8516-923X
Vinod MenonDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA. menon@stanford.edu.ORCID 0000-0003-1622-9857
University of California, Los Angeles · USNeurosciences Institute · USStanford University · USPontificia Universidad Católica de Chile · CLUniversidad del Desarrollo · CLUniversity of California, Davis · US

Funding

Integrative computational models of latent behavioral and neural constructs in children: a longitudinal developmental big-data approachR01MH121069 · NIMH · STANFORD UNIVERSITY · PI MENON, VINOD · 2019 to 2023
$3.9M
3/9 Dissecting the effects of genomic variants on neurobehavioral dimensions in CNVs enriched for neuropsychiatric disordersU01MH119736 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BEARDEN, CARRIE E · 2019 to 2024
$1.2M
Identification of Brain Circuit Markers for Psychosis in Alzheimer's Disease by Leveraging Big Data and Machine LearningR21AG072114 · NIA · STANFORD UNIVERSITY · PI SUPEKAR, KAUSTUBH SATYENDRA · 2021 to 2022
$433k
NIA NIH HHS R21 AG072114U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) R01MH121069U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) U01MH119736U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) R21AG072114
6 · The paper itself

Abstract

A major genetic risk factor for psychosis is 22q11.2 deletion (22q11.2DS). However, robust and replicable functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis remain elusive due to small sample sizes and a focus on small single-site cohorts. Here, we identify functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis, and their links with idiopathic early psychosis, using one of the largest multi-cohort data to date. We obtained multi-cohort clinical phenotypic and task-free fMRI data from 856 participants (101 22q11.2DS, 120 idiopathic early psychosis, 101 idiopathic autism, 123 idiopathic ADHD, and 411 healthy controls) in a case-control design. A novel spatiotemporal deep neural network (stDNN)-based analysis was applied to the multi-cohort data to identify functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis. Next, stDNN was used to test the hypothesis that the functional brain signatures of 22q11.2DS-associated psychosis overlap with idiopathic early psychosis but not with autism and ADHD. stDNN-derived brain signatures distinguished 22q11.2DS from controls, and 22q11.2DS-associated psychosis with very high accuracies (86-94%) in the primary cohort and two fully independent cohorts without additional training. Robust distinguishing features of 22q11.2DS-associated psychosis emerged in the anterior insula node of the salience network and the striatum node of the dopaminergic reward pathway. These features also distinguished individuals with idiopathic early psychosis from controls, but not idiopathic autism or ADHD. Our results reveal that individuals with 22q11.2DS exhibit a highly distinct functional brain organization compared to controls. Additionally, the brain signatures of 22q11.2DS-associated psychosis overlap with those of idiopathic early psychosis in the salience network and dopaminergic reward pathway, providing substantial empirical support for the theoretical aberrant salience-based model of psychosis. Collectively, our findings, replicated across multiple independent cohorts, advance the understanding of 22q11.2DS and associated psychosis, underscoring the value of 22q11.2DS as a genetic model for probing the neurobiological underpinnings of psychosis and its progression.

Indexed as

BrainDiGeorge SyndromeMagnetic Resonance ImagingPsychotic DisordersAdolescentAdultAutistic DisorderBrain MappingCase-Control StudiesCohort StudiesFemaleHumansMaleNeural Networks, ComputerYoung Adult

Identifiers

PMID38605171
OpenAlexW4394762601

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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.