Cerebellum-specific 18F-FDG PET analysis for the detection of subregional glucose metabolism changes in spinocerebellar ataxia
Bibliographic record
Abstract
The cerebellum (CB) consists of complex anatomical and functional subregions. To better investigate the complicated functional anatomy, a detailed subregional analysis and/or a precise spatial normalization of the fluorine-18 fluorodeoxyglucose (F-FDG) PET imaging data are essential. Here, the 28 MRIcron CB volumes of interests (VOIs) template merged into eight cerebellar subregional VOIs (bilateral anterior, superior, and inferior posterior lobes of the CB cortex, and the superior and inferior vermis) on mean F-FDG PET templates. We also developed a new spatial normalization method using a study-specific and CB-specific template (CBSST) to better localize the VOIs and to minimize interparticipant differences for the locations of whole and subregional CB VOIs, as well as to increase the accuracy of the subregional mean F-FDG uptake. Using VOIs of individual F-FDG PET images normalized to the F-FDG template, we analyzed subregional cerebellar glucose metabolism in patients with spinocerebellar ataxia, a representative disease involving the spinocerebellum, and compared them with age-matched and sex-matched healthy normal controls. We achieved significant improvement over the Montreal Neurological Institute template in spatial normalization accuracy using our CBSST approach for CB VOI location agreement increases (79 vs. 90%) and VOI uptake error decreases in many CB subregions. We also found significant decreases in the anterior/posterior ratio of F-FDG uptake in patients with spinocerebellar ataxia (0.45) compared with those in normal controls (0.73) only using our CBSST approach. Therefore, we established an accurate CB subregional VOI analysis framework, and this may be useful for understanding and differentiating many of the cerebellar ataxia diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".