Evidence map›Paper›PMID 39738735›Full record

ArticleScientific reports2024

The brain selectively allocates energy to functional brain networks under cognitive control.

Majid Saberi, Jenny R Rieck, Shamim Golafshan, Cheryl L Grady, Bratislav Misic, Benjamin T Dunkley, Ali Khatibi

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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  10. Altered Functional Network Energy Across Multiscale Brain Networks in Preterm vs. Full-Term Subjects: Insights from the Adolescent Brain Cognitive Development (ABCD) Study.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025
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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.

Majid SaberiNeurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada. majidsa@umich.edu.
Jenny R RieckRotman Research Institute, Baycrest Health Sciences, Toronto, M6A 2E1, Canada.
Shamim GolafshanDepartment of Psychology, Florida International University, Miami, FL, USA.
Cheryl L GradyRotman Research Institute, Baycrest Health Sciences, Toronto, M6A 2E1, Canada.
Bratislav MisicDepartment of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada.
Benjamin T DunkleyNeurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada.
Ali KhatibiCentre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, UK. m.khatibitabatabaei@bham.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Network energy has been conceptualized based on structural balance theory in the physics of complex networks. We utilized this framework to assess the energy of functional brain networks under cognitive control and to understand how energy is allocated across canonical functional networks during various cognitive control tasks. We extracted network energy from functional connectivity patterns of subjects who underwent fMRI scans during cognitive tasks involving working memory, inhibitory control, and cognitive flexibility, in addition to task-free scans. We found that the energy of the whole-brain network increases when exposed to cognitive control tasks compared to the task-free resting state, which serves as a reference point. The brain selectively allocates this elevated energy to canonical functional networks; sensory networks receive more energy to support flexibility for processing sensory stimuli, while cognitive networks relevant to the task, functioning efficiently, require less energy. Furthermore, employing network energy, as a global network measure, improves the performance of predictive modeling, particularly in classifying cognitive control tasks and predicting chronological age. Our results highlight the robustness of this framework and the utility of network energy in understanding brain and cognitive mechanisms, including its promising potential as a biomarker for mental conditions and neurological disorders.

Indexed as

BrainCognitionMagnetic Resonance ImagingNerve NetAdultBrain MappingFemaleHumansMaleMemory, Short-TermYoung AdultBrain biomarkerCanonical functional networksCognitive controlExecutive functionsFunctional connectivityNetwork energyPredictive modelingStructural balance theory

Identifiers

PMID39738735
PMCPMC11686059

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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.