Evidence map›Paper›PMID 39199467›Full record

ArticleBrain sciences2024

An Exploratory Study of Large-Scale Brain Networks during Gambling Using SEEG.

Christopher Taylor, Macauley Smith Breault, Daniel Dorman, Patrick Greene, Pierre Sacré, Aaron Sampson, Ernst Niebur, Veit Stuphorn, Jorge González-Martínez, Sridevi Sarma

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Brain sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

10 authors.

Christopher TaylorDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Macauley Smith BreaultThe Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.ORCID 0000-0003-1327-5372
Daniel DormanDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0001-7006-4593
Patrick GreeneDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Pierre SacréDepartment of Electrical Engineering and Computer Science, University of Liège, 4000 Liège, Belgium.ORCID 0000-0002-7228-1628
Aaron SampsonSolomon Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21218, USA.
Ernst NieburSolomon Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21218, USA.
Veit StuphornSolomon Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21218, USA.
Jorge González-MartínezSchool of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.
Sridevi SarmaDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

Funding

Neural mechanisms of risk-attitudeR01DA049147 · NIDA · JOHNS HOPKINS UNIVERSITY · PI STUPHORN, VEIT · 2019 to 2023
$2.5M
CRCNS: MOVE!-MOdeling of fast Movement for Enhancement via neuroprostheticsR01NS110423 · NINDS · JOHNS HOPKINS UNIVERSITY · PI SARMA, SRIDEVI V. · 2018 to 2022
$1.7M
NIDA NIH HHS R01 DA049147NIH HHS 1R01NS110423-18S1NINDS NIH HHS R01 NS110423
6 · The paper itself

Abstract

Decision-making is a cognitive process involving working memory, executive function, and attention. However, the connectivity of large-scale brain networks during decision-making is not well understood. This is because gaining access to large-scale brain networks in humans is still a novel process. Here, we used SEEG (stereoelectroencephalography) to record neural activity from the default mode network (DMN), dorsal attention network (DAN), and frontoparietal network (FN) in ten humans while they performed a gambling task in the form of the card game, "War". By observing these networks during a decision-making period, we related the activity of and connectivity between these networks. In particular, we found that gamma band activity was directly related to a participant's ability to bet logically, deciding what betting amount would result in the highest monetary gain or lowest monetary loss throughout a session of the game. We also found connectivity between the DAN and the relation to a participant's performance. Specifically, participants with higher connectivity between and within these networks had higher earnings. Our preliminary findings suggest that connectivity and activity between these networks are essential during decision-making.

Indexed as

decision-makingdefault mode networkdorsal attention networkfrontoparietal networkfunctional connectivitygamblingintracranial EEG

Identifiers

PMID39199467
PMCPMC11352602

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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.