Bridging mental health, cognition and the brain in mild traumatic brain injury: A multilayer network analysis of the TRACK-TBI study
Bibliographic record
Abstract
ABSTRACT Background Mild traumatic brain injury (mTBI) patients 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 the interactions between symptoms and the underlying large-scale brain networks. We constructed the first multilayer network in mTBI to examine the relationships between networks of cognition, mental health and structural brain measures and to identify key variables bridging relationships across these networks. Methods Chronic phase cross-sectional data (6-month follow-up) from 457 mTBI patients 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 grey 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. Results Insomnia severity, immediate verbal memory, somatisation and processing speed nodes exceeded an a priori 80th percentile threshold on the bridge strength scores and can therefore be regarded as key nodes bridging relationships across affective, cognitive and brain layers. Conclusions The bridging nodes identified in our multilayer network analyses suggest targets for future studies to develop more customized, efficient, and efficacious treatments to alleviate mental health symptoms and cognitive deficits in mTBI patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".