Investigating the Roles of Anterior Cingulate in Behavioral Variant Frontotemporal Dementia: A PET/MRI Study
Bibliographic record
Abstract
BACKGROUND: The anterior cingulate cortex (ACC) seems to play an important role in behavioral deficits and executive dysfunctions in patients with behavioral variant frontotemporal dementia (bvFTD), while its specific and independent contribution requires clarification. OBJECTIVE: To identify whether ACC abnormalities in gray matter (GM) volume and standardized uptake value ratio (SUVR) images are associated with disease severity of bvFTD, by analyzing hybrid T1 and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET). METHODS: We enrolled 21 bvFTD patients and 21 healthy controls in the study. Each subject underwent a hybrid PET/MRI study and a standardized neuropsychologic assessment battery. GM volume and SUVR are voxel-wise calculated and compared. Then we estimate the mean value inside ACC for further partial Pearson's correlation to explore the association between GM volume/SUVR of the ACC and severity of behavioral deficit as well as executive dysfunction. RESULTS: ACC was shown to be involved in both atrophy and hypometabolism patterns. The partial Pearson's correlation analysis showed that the SUVR of the ACC was strongly correlated with frontal behavior inventory total score (left r = -0.85, right r = -0.85, p < 0.0001), disinhibition subscale score (left r = -0.72, p = 0.002; right = -0.75, p < 0.0001), and apathy subscale score (left = -0.87, right = -0.85, p < 0.0001). CONCLUSION: These findings demonstrated decreased ACC activity contributes to behavioral disturbances of both apathetic and disinhibition syndromes of bvFTD, which can be sensitively detected using 18F-FDG PET.
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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 | 0.000 |
| 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".