Statistical Parametric Mapping and Cluster Counting Analysis of [ <sup>18</sup> F] FDG-PET Imaging in Traumatic Brain Injury
Bibliographic record
Abstract
In this study we investigated regional cerebral glucose metabolism abnormalities of [(18)F] fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging in traumatic brain injury (TBI). PET images of 81 TBI patients and 68 normal controls were acquired and a word list learning task was administered during the uptake period. The TBI group included 35 patients with positive structural imaging (CT or MRI) findings soon after injury, 40 patients with negative findings, and 6 cases without structural imaging. Statistical parametric mapping (SPM) analysis was applied with several levels of spatial smoothing. Cluster counting analysis was performed for each subject to identify abnormal clusters with contiguous voxel values that deviated by two standard deviations or more from the mean of the normal controls, and to count the number of clusters in 10 size categories. SPM maps demonstrated that the 81 patients had significantly lower FDG uptake than normal controls, widely across the cortex (including bilateral frontal and temporal regions), and in the thalamus. Cluster counting results indicated that TBI patients had a higher proportion of larger clusters than controls. These large low-FDG-uptake clusters of the TBI patients were closer to the brain edge than those of controls. These results suggest that deficits of cerebral metabolism in TBI are spread over multiple brain areas, that they are closer to the cortical surface than clusters in controls, and that group spatial patterns of abnormal cerebral metabolism may be similar in TBI patients with cognitive deficits with and without obvious acute abnormalities identified on structural imaging.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".