Insular Connectivity Is Associated With Self-Appraisal of Cognitive Function After a Concussion
Bibliographic record
Abstract
symptom assessments and neurocognitive test (NCT) batteries. Practice guidelines recommend integrating both symptoms and NCTs into clinical decision-making, but correlations between these measures are often poor. This suggests that many patients experience difficulties in the self-appraisal of cognitive issues. It is presently unclear what neural mechanisms give rise to appraisal mismatch after a concussion. One promising target is the insula, which regulates aspects of cognition, particularly interoception and self-monitoring. The present study tested the hypothesis that appraisal mismatch is due to altered functional connectivity of the insula to frontal and midline structures, with hypo-connectivity leading to under-reporting of cognitive issues and hyper-connectivity leading to over-reporting. Data were collected from 59 acutely concussed individuals and 136 normative controls, including symptom assessments, NCTs and magnetic resonance imaging (MRI) data. Analysis of resting-state functional MRI supported the hypothesis, identifying insular networks that were associated with appraisal mismatch in concussed athletes that included frontal, sensorimotor, and cingulate connections. Subsequent analysis of diffusion tensor imaging also determined that symptom over-reporting was associated with reduced fractional anisotropy and increased mean diffusivity of posterior white matter. These findings provide new insights into the mechanisms of cognitive appraisal mismatch after a concussion. They are of particular interest given the central role of symptom assessments in the diagnosis and clinical management of concussion.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".