Evidence map›Paper›PMID 30035011›Full record

ArticleNeuroImage. Clinical2018

The cerebellum and learning of non-motor associations in individuals at clinical-high risk for psychosis.

Jessica A Bernard, Joseph M Orr, Derek J Dean, Vijay A Mittal

Open access · goldAbstract read
In one paragraph

Article in NeuroImage. Clinical, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.8field-weighted citation impact, top 16% 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

7 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Motor precision deficits in clinical high risk for psychosis.European archives of psychiatry and clinical neuroscience · 2024
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Prediction, Psychosis, and the Cerebellum.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2019
    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

4 authors at 3 institutions in 1 country.

Jessica A BernardDepartment of Psychological and Brain Sciences, Texas A&M University, United States; Texas A&M Institute for Neuroscience, Texas A&M University, United States. Electronic address: Jessica.bernard@tamu.edu.
Joseph M OrrDepartment of Psychological and Brain Sciences, Texas A&M University, United States; Texas A&M Institute for Neuroscience, Texas A&M University, United States.
Derek J DeanDepartment of Psychology and Neuroscience, University of Colorado Boulder, United States; Center for Neuroscience, University of Colorado Boulder, United States.
Vijay A MittalDepartment of Psychology, Northwestern University, United States; Department of Psychiatry, Northwestern University, United States; Institute of Policy Research, Northwestern University, United States; Department of Medical Social Sciences, Northwestern University, United States; Institute for Innovations in Developmental Sciences, Northwestern University, United States.
Texas A&M University · USNorthwestern University · USUniversity of Colorado Boulder · US

Funding

Frontal-Subcortical Development, Movement Abnormalities, and Risk for PsychosisR01MH094650 · NIMH · UNIVERSITY OF COLORADO · PI MITTAL, VIJAY A · 2011 to 2015
$2.3M
Exercise and markers of medial temporal health in youth at-risk for psychosisR33MH103231 · NIMH · NORTHWESTERN UNIVERSITY · PI BRYAN, ANGELA, MITTAL, VIJAY A · 2016 to 2018
$689k
Neural Habituation in Ultra High-Risk YouthR21MH110374 · NIMH · NORTHWESTERN UNIVERSITY · PI MITTAL, VIJAY A · 2017 to 2018
$445k
NIMH NIH HHS R01 MH094650NIMH NIH HHS R21 MH110374NIMH NIH HHS R33 MH103231
6 · The paper itself

Abstract

The cerebello-thalamo-cortical circuit (CTCC) has been implicated in schizophrenia. However, this work has been limited to structural and functional networks, or behavior, and to date, has not been evaluated in clinical high-risk (CHR) youth, a group at elevated risk for psychosis. Here, we used an innovative learning paradigm known to activate the CTCC (while limiting potential motor confounds) to evaluate CHR and healthy control individuals during functional magnetic resonance imaging (fMRI). 20 CHR and 21 healthy control individuals performed a second-order rule learning task while undergoing fMRI. This was preceded and followed by the paradigm under dual-task conditions. In addition, all participants underwent structured clinical interviews to confirm a prodromal syndrome and assess symptom severity. The rate of learning did not differ between groups. However, the CHR group consistently performed more poorly under dual-task conditions, and exhibited a higher dual-task cost after learning. Further, learning rate in the CHR group was significantly associated with symptom severity. Both groups showed activation in regions of the CTCC. During early learning, the CHR group exhibited greater engagement of regions of the default mode network, suggesting that they were less able to engage the appropriate task positive networks. During late learning, there were qualitative differences wherein controls showed more prefrontal cortical activation. Higher order cognitive rule learning is related to symptom severity in CHR individuals. fMRI revealed that CHR individuals may not reliably disengage the default mode network, and during late learning high-risk youth may not engage the prefrontal cortex as extensively as controls.

Indexed as

AdolescentAdultBrain MappingCerebellumFemaleHumansLearningMagnetic Resonance ImagingMaleNeural PathwaysPrefrontal CortexProdromal SymptomsPsychotic DisordersRiskSchizophreniaYoung AdultCerebellumClinical high-riskDisorganized symptomsfMRIPsychosisSecond-order rule learning

Identifiers

PMID30035011
PMCPMC6051312
OpenAlexW2794102868

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.