Functional Near-Infrared Spectroscopy (fNIRS) imaging of Functional Connectivity and Task-Activity in the Cerebral Cortex of patients with mTBI
Bibliographic record
Abstract
Functional near-infrared spectroscopy (fNIRS) is a novel neuroimaging technology which has shown potential as a biomarker of mTBI. In this thesis, we developed novel easy-to-use software for analyzing measures of functional coherence and task-activity in the brain using fNIRS. We then applied this software to characterize fNIRS measures in healthy individuals, and to investigate whether fNIRS is sensitive to altered brain activity following mTBI. The software provides a full pipeline for preprocessing and utilizes wavelet analysis methods to estimate coherence, variability, phase, and power of fNIRS hemodynamic data. Using the software, we investigated the within-subject and between-subject variability of fNIRS coherence in healthy adults, finding poor-moderate between-subject reproducibility and high within-subject reliability, as well as task-effects of reduced interhemispheric coherence (IHC) and reduced power of low-frequency oscillations (LFOs) that were focused in the prefrontal brain regions during execution of a working memory task. In youth 30 days following mTBI, reduced IHC and IHC variability in the prefrontal cortex was observed during working memory, as well as a group effect of mTBI on power of LFO, compared to controls. In adults, fNIRS was used to study brain activity in pre, post, and two weeks following therapeutic repetitive transcranial magnetic stimulation (rTMS) as a treatment for persistent mTBI-related headache. In this case series, abnormal activation in the prefrontal cortex during working memory was observed on fNIRS in one subject prior to treatment which persisted at the post-treatment time point, but appeared to normalize by two weeks post-treatment, in comparison to controls, suggesting fNIRS as a potential method to study treatment effect in therapeutic rTMS trials in patients with mTBI. In this thesis, software was developed and published in the online repository GitHub, and utilized to characterize the variability and task effect in healthy individuals, informing future studies wanting to apply wavelet methodologies to investigate clinical populations. In subsequent observational studies, we observed alterations in fNIRS brain activity in pediatric patients with mTBI 1 month post-injury, as well as in an adult mTBI patient undergoing rTMS treatment, suggesting a potential role for fNIRS as an accessible technology to study mTBI-associated pathophysiology.
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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 | 0.001 |
| 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) | 0.001 | 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".