Evidence map›Paper›PMID 41679104›Full record

ArticleNeuroImage. Clinical2026

Bridging mental health, cognition and the brain in mild traumatic brain injury: A multilayer network analysis of the TRACK-TBI study.

Juan F Domínguez D, Mervyn Singh, Lyndon Firman-Sadler, Jade Guarnera, Ivan L Simpson-Kent, Phoebe Imms, Andrei Irimia, Karen Caeyenberghs, TRACK-TBI Investigators

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Article in NeuroImage. Clinical, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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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

9 authors.

Juan F Domínguez DCognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, VIC, Australia. Electronic address: juan.dominguezduque@deakin.edu.au.
Mervyn SinghChild and Adolescent Imaging Research (CAIR) Program, University of Calgary, Calgary, Alberta T2N 1N4, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta T2N 1N4, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta T2N 1N4, Canada; Department of Radiology, Cumming School of Medicine, Calgary, Alberta T2N 1N4, Canada.
Lyndon Firman-SadlerCognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, VIC, Australia.
Jade GuarneraCognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, VIC, Australia.
Ivan L Simpson-KentInstitute of Psychology, Developmental and Educational Psychology Unit, Leiden University, Leiden, the Netherlands.
Phoebe ImmsSchool of Biomedical Sciences, the University of Queensland, Brisbane, Queensland; QIMR Berghofer Medical Research Institute, Herston, QLD, Australia.
Andrei IrimiaEthel Percy Andrus Gerontology Centre, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA; Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA; Department of Quantitative & Computational Biology, Dana and David Dornsife College of Arts & Sciences, University of Southern California, Los Angeles, CA, USA.
Karen CaeyenberghsCognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, VIC, Australia.
TRACK-TBI Investigators

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPeople with mild traumatic brain injury (mTBI) suffer from several mental health symptoms (e.g., anxiety, depressive symptoms) and cognitive deficits (e.g., attentional deficits, slowed processing speed). However, symptoms in TBI are largely investigated in isolation, using univariate approaches, ignoring interactions between symptoms and the underlying large-scale brain networks. We constructed the first multilayer network in mTBI to examine relationships between networks of cognition, mental health and structural brain measures and to identify key variables bridging relationships across these networks.

methodsChronic phase cross-sectional data (6-month follow-up) from 457 mTBI participants was extracted from the TRACK-TBI Longitudinal study. We selected four variables from self-report mental health questionnaires (affective layer), eight cognitive test scores from the NIH toolbox (cognitive layer), and gray matter volumes from eight brain regions of the central executive and salience networks from anatomical MRI scans (brain layer). We used a multilayer network approach to examine the relationships (edges) between all variables (nodes) across layers. We then used the bridge strength centrality metric to identify nodes that 'bridge' the affective, cognitive, and brain layers.

resultsIn this sample of mTBI participants, across all affective and cognitive layer nodes, only impairments in insomnia were noted. Multilayer network analysis revealed insomnia severity, immediate verbal memory, somatisation and processing speed nodes exceeded an a priori 80th percentile threshold on the bridge strength scores and may therefore be regarded as key nodes potentially bridging relationships across affective, cognitive and brain layers.

conclusionsThe bridging nodes identified in our multilayer network analyses may suggest targets for future studies to develop more customized, efficient, and efficacious treatments to alleviate mental health symptoms and cognitive deficits in mTBI.

Indexed as

BrainBrain ConcussionCognitionMental HealthNerve NetAdultCross-Sectional StudiesFemaleHumansLongitudinal StudiesMagnetic Resonance ImagingMaleMiddle AgedNeuropsychological TestsBrain structureCognitionMental-healthMultilayer Network AnalysisTraumatic Brain Injury

Identifiers

PMID41679104
PMCPMC12914815

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LicenceCC BY
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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.