Resting State Functional Connectivity is Directly Related to Clinical Presentation of Mild Traumatic Brain Injury
Bibliographic record
Abstract
Objective To determine the utility of fractional amplitude of low frequency fluctuations (fALFF) during resting state fMRI (rs-fMRI) as an advanced neuroimaging biomarker for Mild Traumatic Brain Injury (mTBI). Background mTBI is defined by a constellation of functional rather than structural deficits. As a measure of functional connectivity, fALFF has been implicated in long-term outcomes post-mTBI. It is unclear however, how longitudinal changes in fALFF may relate to the clinical presentation of mTBI. Design/Methods 111 patients and 32 controls (15–50 years old) were enrolled acutely after mTBI and followed with up to 4 standardized serial assessments. Patients were enrolled at either Encounter 1 (E1), within 72 hours, or Encounter 2 (E2), 5–10 days post-injury, and returned for Encounter 3 (E3) at 15–29 days and Encounter 4 (E4) at 83–97 days. Each encounter included a clinical exam, neuropsychological assessment, as well as rs-fMRI imaging. fALFF was analyzed independently in 14 functional networks and, in grey and white matter as a function of symptom severity. Symptom severity scores (SSS) ranged from 0–132 as defined by the SCAT2 symptom evaluation. Results In mTBI patients, fALFF scores across 5 functional brain networks (language, sensorimotor, visuospatial, higher-order visual, and posterior salience) differed between mTBI patients with low versus high SSS (SSS <5 and >30, respectively). Overall, greater SSS were indexed by reduced connectivity (p < 0.03, Bonferroni corrected). Further analysis also identified corresponding network pairs which were most predictive of increased SSS. White matter fALFF was not correlated with symptom severity, however, decreased grey matter fALFF was significantly correlated with greater SSS (r = −0.25, p = 0.002). Conclusions Grey matter fALFF was correlated with mTBI symptom burden suggesting that patterns of neural connectivity relate directly to the clinical presentation of mTBI. Furthermore, differences in functional network connectivity as a function of SSS may reflect which networks are implicated in recovery of mTBI.
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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 | 0.000 | 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".