The network-based underpinnings of persisting symptoms after concussion: a multimodal neuroimaging meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Persisting symptoms after concussion (PSaC) represent a complex and poorly understood neuropsychiatric phenomenon with limited treatment options. Neural network dysfunction has been associated with PSaC, and neuromodulation, particularly repetitive transcranial magnetic stimulation, may be a promising intervention. However, neuroimaging findings have been inconsistent, limiting understanding of underlying network dysfunction. We aimed to identify a core neural network associated with PSaC and explore whether this network could yield candidate cortical targets for neuromodulation at the individual level. We hypothesized that differences in network disruption would be evident between individuals with high versus low symptom burden in PSaC. Here we show that a convergent multi-analytic approach combining symptom-activation maps generated from existing fMRI datasets, systematic review of resting-state fMRI studies of PSaC, and network-based meta-analysis of coordinates derived from these studies co-localize to the salience network in high symptom burden PSaC. Using Human Connectome Project data, we mapped this network to cortical regions that could serve as individualized targets for neuromodulation. This aligns with current clinical models of PSaC and may present a new direction for network-based therapy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it