Poster - 06: Fractal Analysis of the brain blood oxygenation level dependent (BOLD) signal of mild traumatic brain injury (mTBI) patients
Bibliographic record
Abstract
Conventional imaging techniques are unable to detect abnormalities in the brain of Mild traumatic brain injury (mTBI) patients that have shown delayed response on neuropsychological evaluation. Our goal was to explore a novel analysis approach involving measurement of the temporal fractal nature of the resting state blood oxygen level depending (rsBOLD) signal. Fifteen subjects (13.4±2.3 y/o) and 56 age-matched (13.5±2.34 y/o) healthy controls were scanned using a GE-MR750-3T MRI and 32-channel RF-coil. Axial FSPGR-3D images were used to prescribe the rsBOLD. Motion correction was performed and the anatomical and functional images were aligned and spatially warped to a standard space. Fractal analysis, performed over the gray matter, was assessed by calculating the Hurst exponent according to Eke's procedure . Voxel-based fractal dimension (FD) was calculated for every subject in the control group to generate the mean and standard deviation maps for the Z-score analysis. We generated Z-score maps for each mTBI patient and the regions where |Z|>2 were analyzed. We found that the most affected regions were the amygdala, the vermis, the caudate head, the hippocampus, and the hypothalamus, which have been previously reported as dysfunctional after mTBI. This preliminary study suggests that fractal analysis of the rsBOLD signal could possibly provide further information in mTBI. It is well known that the brain is best modeled as a complex system and therefore a measure of complexity using FD could provide an additional method to approach this global problem.
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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) | 0.003 | 0.001 |
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".