Evidence map›Paper›PMID 30508681›Full record

ArticleNeuroImage2019

Sex differences in network controllability as a predictor of executive function in youth.

Eli J Cornblath, Evelyn Tang, Graham L Baum, Tyler M Moore, Azeez Adebimpe, David R Roalf, Ruben C Gur, Raquel E Gur, Fabio Pasqualetti, Theodore D Satterthwaite and 1 more

Open access · hybridAbstract read
In one paragraph

Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed
0.9field-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

44 citing papers in PubMed, 8 citations in OpenAlex.

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  2. Mindful attention promotes control of brain network dynamics for self-regulation and discontinues the past from the present.Proceedings of the National Academy of Sciences of the United States of America · 2023
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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

11 authors at 2 institutions in 1 country.

Eli J CornblathDepartment of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Evelyn TangDepartment of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Graham L BaumDepartment of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Tyler M MooreDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Azeez AdebimpeDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
David R RoalfDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Ruben C GurDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Raquel E GurDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Fabio PasqualettiDepartment of Mechanical Engineering, University of California, Riverside, CA, 92521, USA.
Theodore D SatterthwaiteDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Danielle S BassettDepartment of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA. Electronic address: dsb@seas.upenn.edu.
University of Pennsylvania · USUniversity of California, Riverside · US

Funding

Longitudinal Mapping of Network Development Underlying Executive Dysfunction in AdolescenceR01MH113550 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI Danielle Smith Bassett, Theodore Satterthwaite · 2018 to 2026
$6.9M
Linguistic and NonLinguistic Functions of Frontal CortexR01DC009209 · NIDCD · UNIVERSITY OF PENNSYLVANIA · PI THOMPSONSCHILL, SHARON L · 2008 to 2018
$5.2M
Network Control and Functional Context: Mechanisms for TMS ResponseRF1MH116920 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH, OATHES, DESMOND · 2018 to 2018
$2.8M
Longitudinal multi-modal neuroimaging of irritability in youthR01MH107703 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI SATTERTHWAITE, THEODORE · 2015 to 2018
$2.6M
Virtual Resection to Treat EpilepsyR01NS099348 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH, LITT, BRIAN · 2016 to 2019
$2.3M
Brain Network Mechanisms of Instructed LearningR01MH109520 · NIMH · RUTGERS THE STATE UNIV OF NJ NEWARK · PI COLE, MICHAEL WILLIAM · 2016 to 2020
$2.0M
Biomedical Image Computing and Informatics ClusterS10OD023495 · OD · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2017 to 2017
$1.9M
Multimodal brain maturation indices modulating psychopathology and neurocognitionR01MH107235 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI GUR, RUBEN C. · 2015 to 2017
$1.5M
Neurocognitive variability in schizophrenia and youth at-risk for psychosisK01MH102609 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI ROALF, DAVID R · 2014 to 2017
$685k
Evolution of the Linked Architecture of Network Control and Executive Function in AdolescenceR21MH106799 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH, SATTERTHWAITE, THEODORE · 2016 to 2017
$515k
CRCNS: US-France Modeling & Predicting BCI Learning from Dynamic NetworksR01HD086888 · NICHD · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH · 2015 to 2018
$506k
NICHD NIH HHS R01 HD086888NIDCD NIH HHS R01 DC009209NIH HHS S10 OD023495NIMH NIH HHS K01 MH102609NIMH NIH HHS R01 MH107235NIMH NIH HHS R01 MH107703NIMH NIH HHS R01 MH109520NIMH NIH HHS R01 MH113550NIMH NIH HHS R21 MH106799NIMH NIH HHS RF1 MH116920NINDS NIH HHS R01 NS099348
6 · The paper itself

Abstract

Executive function is a quintessential human capacity that emerges late in development and displays different developmental trends in males and females. Sex differences in executive function in youth have been linked to vulnerability to psychopathology as well as to behaviors that impinge on health, wellbeing, and longevity. Yet, the neurobiological basis of these differences is not well understood, in part due to the spatiotemporal complexity inherent in patterns of brain network maturation supporting executive function. Here we test the hypothesis that sex differences in impulsivity in youth stem from sex differences in the controllability of structural brain networks as they rewire over development. Combining methods from network neuroscience and network control theory, we characterize the network control properties of structural brain networks estimated from diffusion imaging data acquired in males and females in a sample of 879 youth aged 8-22 years. We summarize the control properties of these networks by estimating average and modal controllability, two statistics that probe the ease with which brain areas can drive the network towards easy versus difficult-to-reach states. We find that females have higher modal controllability in frontal, parietal, and subcortical regions while males have higher average controllability in frontal and subcortical regions. Furthermore, controllability profiles in males are negatively related to the false positive rate on a continuous performance task, a common measure of impulsivity. Finally, we find associations between average controllability and individual differences in activation during an n-back working memory task. Taken together, our findings support the notion that sex differences in the controllability of structural brain networks can partially explain sex differences in executive function. Controllability of structural brain networks also predicts features of task-relevant activation, suggesting the potential for controllability to represent context-specific constraints on network state more generally.

Indexed as

Models, NeurologicalSex CharacteristicsAdolescentBrainChildExecutive FunctionFemaleHumansImpulsive BehaviorMaleNeural PathwaysYoung AdultDiffusion tensor imagingExecutive functionfMRI BOLDNetwork controllabilityNeurodevelopmentSex differencesWorking memory

Identifiers

PMID30508681
PMCPMC6401302
OpenAlexW2783449962

What OpenQuestion holds

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LicenceTDM
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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.