Anterior brain glucose hypometabolism predates dementia in progranulin mutation carriers
Bibliographic record
Abstract
OBJECTIVE: In this prospective cohort study, we investigated cerebral glucose metabolism reductions on [(18)F]-fluorodeoxyglucose (FDG)-PET in progranulin (GRN) mutation carriers prior to frontotemporal dementia (FTD) onset. METHODS: Nine mutation carriers (age 51.5 ± 13.5 years) and 11 noncarriers (age 52.7 ± 9.5 years) from 5 families with FTD due to GRN mutations underwent brain scanning with FDG-PET and MRI and clinical evaluation. Normalized FDG uptake values were calculated with reference to the pons. PET images were analyzed with regions of interest (ROI) and statistical parametric mapping (SPM) approaches. RESULTS: Compared with noncarriers, GRN mutation carriers had a lowered anterior-to-posterior (AP) ratio of FDG uptake (0.86 ± 0.09 vs 0.92 ± 0.05) and less left-right asymmetry, consistent with an overall pattern of right anterior cerebral hypometabolism. This pattern was observed regardless of whether they were deemed clinically symptomatic no dementia or asymptomatic. Individual ROIs with lowered FDG uptake included right anterior cingulate, insula, and gyrus rectus. SPM analysis supported and extended these findings, demonstrating abnormalities in the right and left medial frontal regions, right insular cortex, right precentral and middle frontal gyri, and right cerebellum. Right AP ratio was correlated with cognitive and clinical scores (modified Mini-Mental State Examination r = 0.74; Functional Rating Scale r = -0.73) but not age and years to estimated onset in mutation carriers. CONCLUSION: The frontotemporal lobar degenerative process associated with GRN mutations appears to begin many years prior to the average age at FTD onset (late 50s-early 60s). Right medial and ventral frontal cortex and insula may be affected in this process but the specific regional patterns associated with specific clinical variants remain to be elucidated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".