Evidence map›Paper›PMID 31327685›Full record

ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2019

Transdiagnostic Multimodal Neuroimaging in Psychosis: Structural, Resting-State, and Task Magnetic Resonance Imaging Correlates of Cognitive Control.

Dov B Lerman-Sinkoff, Sridhar Kandala, Vince D Calhoun, Deanna M Barch, Daniel T Mamah

Abstract read
In one paragraph

Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

6 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Article
  6. Article
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

5 authors.

Dov B Lerman-SinkoffDepartment of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri; Medical Scientist Training Program, Washington University in St. Louis, St. Louis, Missouri. Electronic address: lermand@wustl.edu.
Sridhar KandalaDepartment of Psychiatry, Washington University in St. Louis, St. Louis, Missouri.
Vince D CalhounMedical Image Analysis Lab, The Mind Research Network, Albuquerque, New Mexico; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico.
Deanna M BarchDepartment of Psychiatry, Washington University in St. Louis, St. Louis, Missouri; Department of Psychological and Brain Science, Washington University in St. Louis, St. Louis, Missouri; Department of Radiology, Washington University in St. Louis, St. Louis, Missouri.
Daniel T MamahDepartment of Psychiatry, Washington University in St. Louis, St. Louis, Missouri.

Funding

NATIONAL RESEARCH SERVICE AWARD-MEDICAL SCIENTISTT32GM007200 · NIGMS · WASHINGTON UNIVERSITY · PI YOKOYAMA, WAYNE M. · 1985 to 2024
$59.8M
Transcranial Direct Current Stimulation for Treatment of Auditory Verbal HalluciP20GM103472 · NIGMS · THE MIND RESEARCH NETWORK · PI CALHOUN, VINCE D · 2012 to 2017
$14.3M
Multivariate methods for identifying multitask/multimodal brain imaging biomarkersR01EB006841 · NIBIB · THE MIND RESEARCH NETWORK · PI VINCE D CALHOUN, Sergey Plis · 2007 to 2026
$9.8M
Effort-Based Decision Making and Motivated Behavior in Everyday LifeR37MH066031 · NIMH · WASHINGTON UNIVERSITY · PI Deanna Barch · 2018 to 2026
$6.5M
Informed Data-Driven Fusion of Behavior, Brain Function, and GenesR01EB005846 · NIBIB · THE MIND RESEARCH NETWORK · PI CALHOUN, VINCE D · 2005 to 2016
$5.5M
CONNECTOMICS IN PSYCHIATRIC CLASSIFICATIONR01MH104414 · NIMH · WASHINGTON UNIVERSITY · PI MAMAH, DANIEL · 2014 to 2018
$2.6M
HIGH PERFORMANCE BIOMEDICAL IMAGING COMPUTER RESOURCESS10RR022984 · NCRR · WASHINGTON UNIVERSITY · PI PRIOR, FRED WILLIAM · 2009 to 2009
$1.9M
Interdisciplinary Training in Cognitive, Computational and Systems NeuroscienceT32NS073547 · NINDS · WASHINGTON UNIVERSITY · PI BARBOUR, DENNIS L, PETERSEN, STEVEN E. · 2011 to 2015
$850k
GPU COMPUTING RESOURCE TO ENABLE INNOVATION IN IMAGING AND NETWORK BIOLOGYS10OD018091 · OD · WASHINGTON UNIVERSITY · PI PAPPU, ROHIT V, PRIOR, FRED WILLIAM · 2014 to 2014
$598k
Identifying transdiagnostic multimodal neural predictors of psychosis dimensionsF30MH109294 · NIMH · WASHINGTON UNIVERSITY · PI LERMAN-SINKOFF, DOV BERNARD · 2016 to 2019
$160k
NCRR NIH HHS S10 RR022984NIBIB NIH HHS R01 EB005846NIBIB NIH HHS R01 EB006841NIGMS NIH HHS P20 GM103472NIGMS NIH HHS T32 GM007200NIH HHS S10 OD018091NIMH NIH HHS F30 MH109294NIMH NIH HHS R01 MH104414NIMH NIH HHS R37 MH066031NINDS NIH HHS T32 NS073547
6 · The paper itself

Abstract

backgroundDisorders with psychotic features, including schizophrenia and some bipolar disorders, are associated with impairments in regulation of goal-directed behavior, termed cognitive control. Cognitive control-related neural alterations have been studied in psychosis. However, studies are typically unimodal, and relationships across modalities of brain function and structure remain unclear. Thus, we performed transdiagnostic multimodal analyses to examine cognitive control-related neural variation in psychosis.

methodsStructural, resting, and working memory task imaging for 31 control participants, 27 participants with bipolar disorder, and 23 participants with schizophrenia were collected and processed identically to the Human Connectome Project, enabling identification of relationships with prior multimodal work. Two cognitive control-related independent components (ICs) derived from the Human Connectome Project using multiset canonical correlation analysis with joint IC analysis were used to predict performance in psychosis. De novo multiset canonical correlation analysis with joint IC analysis was performed, and the results were correlated with cognitive control.

resultsA priori working memory and cortical thickness maps significantly predicted cognitive control in psychosis. De novo multiset canonical correlation analysis with joint IC analysis identified an IC correlated with cognitive control that also discriminated groups. Structural contributions included insular and cingulate regions; task contributions included precentral, posterior parietal, cingulate, and visual regions; and resting-state contributions highlighted canonical network organization. Follow-up analyses suggested that correlations with cognitive control were primarily influenced by participants with schizophrenia.

conclusionsA priori and de novo imaging replicably identified a set of interrelated patterns across modalities and the healthy-to-psychosis spectrum, suggesting robustness of these features. Relationships between imaging and cognitive control performance suggest that shared symptomatology may be key to identifying transdiagnostic relationships in psychosis.

Indexed as

NeuroimagingAdultBipolar DisorderCognitionConnectomeFemaleGyrus CinguliHumansMaleMemory, Short-TermNeuropsychological TestsPsychotic DisordersSchizophreniaBipolar disorderCognitive controlmCCA+jICAMultimodal fusionSchizophreniaTransdiagnostic psychosis

Identifiers

PMID31327685
PMCPMC6842450

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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.