Evidence map›Paper›PMID 24918068›Full record

ArticleNeuroImage. Clinical2014

Multimodal evidence of regional midcingulate gray matter volume underlying conflict monitoring.

Muhammad A Parvaz, Thomas Maloney, Scott J Moeller, Pias Malaker, Anna B Konova, Nelly Alia-Klein, Rita Z Goldstein

Open access · goldAbstract read
In one paragraph

Article in NeuroImage. Clinical, 2014. 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
0.8field-weighted citation impact, top 29% 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, 18 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

7 authors at 1 institution in 1 country.

Muhammad A ParvazDepartment of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Thomas MaloneyDepartment of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Scott J MoellerDepartment of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Pias MalakerDepartment of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Anna B KonovaDepartment of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA ; Department of Psychology, Stony Brook University, Stony Brook, NY 11790, USA.
Nelly Alia-KleinDepartment of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Rita Z GoldsteinDepartment of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Icahn School of Medicine at Mount Sinai · US

Funding

The prefrontal cortex in salience and control in cocaine addiction: phFMRI studyR01DA023579 · NIDA · BROOKHAVEN SCIENCE ASSOC-BROOKHAVEN LAB · PI GOLDSTEIN, RITA Z · 2008 to 2012
$2.8M
Machine learning discovery of patterns of self regulation in drug addiction and IR21DA034954 · NIDA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI GOLDSTEIN, RITA Z · 2012 to 2013
$822k
BCI-based feedback system to promote cognitive control of cravingF32DA033088 · NIDA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI PARVAZ, MUHAMMAD ADEEL · 2012 to 2015
$158k
NIDA NIH HHS 1R01DA023579NIDA NIH HHS F32 DA033088NIDA NIH HHS IF32DA0330088-01NIDA NIH HHS R01 DA023579NIDA NIH HHS R21 DA034954
6 · The paper itself

Abstract

Functional neuroimaging studies have long implicated the mid-cingulate cortex (MCC) in conflict monitoring, but it is not clear whether its structural integrity (i.e., the gray matter volume) influences its conflict monitoring function. In this multimodal study, we used T1-weighted MRI scans as well as event-related potentials (ERPs) to test whether the MCC gray matter volume is associated with the electrocortical marker (i.e., No-go N200 ERP component) of conflict monitoring in healthy individuals. The specificity of such a relationship in health was determined in two ways: by (A) acquiring the same data from individuals with cocaine use disorder (CUD), known to have deficits in executive function including behavioral monitoring; and (B) acquiring the P300 ERP component that is linked with attention allocation and not specifically with conflict monitoring. Twenty-five (39.1 ± 8.4 years; 8 females) healthy individuals and 25 (42.7 ± 5.9 years; 6 females) individuals with CUD underwent a rewarded Go/No-go task during which the ERP data was collected, and they also underwent a structural MRI scan. The whole brain regression analysis showed a significant correlation between MCC structural integrity and the well-known ERP measure of conflict monitoring (N200, but not the P300) in healthy individuals, which was absent in CUD who were characterized by reduced MCC gray matter volume, N200 abnormalities as well as reduced task accuracy. In individuals with CUD instead, the N200 amplitude was associated with drug addiction symptomatology. These results show that the integrity of MCC volume is directly associated with the electrocortical correlates of conflict monitoring in healthy individuals, and such an association breaks down in psychopathologies that impact these brain processes. Taken together, this MCC-N200 association may serve as a biomarker of improved behavioral monitoring processes in diseased populations.

Indexed as

Conflict, PsychologicalAdultAttentionCocaine-Related DisordersElectroencephalographyEvoked PotentialsExecutive FunctionFemaleGray MatterGyrus CinguliHumansMagnetic Resonance ImagingMaleMiddle AgedNeuropsychological TestsOrgan SizeConflict monitoringERPMidcingulate cortexMRIN200

Identifiers

PMID24918068
PMCPMC4050316
OpenAlexW2095327830

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.