IC‐P‐011: Differentiating Alzheimer's disease from FTD: Do PIB, FDG and MRI tell the same story?
Bibliographic record
Abstract
Alzheimer's disease (AD) has a typical course characterizedby prominent memory impairment while Frontotemporal dementia (FTD)is characterized by early and prominentbehavioural/ personality/ executive function loss, and/or language problems.Many AD cases overlap clinically with (FTD). We wondered whether multi-modal imaging with magneticresonance imaging (MRI), Flouro-deoxyglucose PET (FDG) and C-11 amyloidPET imaging with Pittsburgh B Compound (PIB) might help elucidate these atypical cases in a coherent fashion.We hypothesized that these imagingmodalities would be convergent, and usually all point in the direction of AD vs. FTD/PPA. Eighty-four subjects were studied, including 24 normal elderlycontrols and 20 typical AD subjects. For the 40 remaining “atypical” subjects, clinical likelihood of AD vs non-AD diagnosis was rated as low, medium, or high. 1.5 tesla MRI imaging with thin 1 mm cuts wascarried out. Distribution of cortical atrophy and Grey matter VBM were noted. FDG PET scans were assessed as typical AD [ie., bitemporo-parietal decreased glucose uptake] or not. PIB PET amyloid imaging was considered positive if there was SUVR >1.5 for association cortex regions, corresponding to medium or large PIB uptakeon visual inspection. In AD subjects, all 3 imaging modalities were suggestive of AD positive in 80% of cases. Three AD subjects were “PIB negative”, and they were young or showed slower progression. Two of 24 normal elderly subjects were “PIB Positive”. The six most convincing cases of FTD (behavioural variant or nonfluent Progressive Aphasia) all were PIB Negative,with frontotemporal MRI atrophy and VBM changes and frontal FDG PET in 5/6. Of the 34 other “atypical” cases, PIB was positive in over 2/3 suggesting Alzheimer's as underlying etiology. However, convergence between MRI, PIB, and FDG PET was found in less than 1/3. Among those clinically labeled as FTD, those with convincing clinical presentations do not demonstrate amyloid positivity, and multi-modal imaging confirms FTD. In a typical dementias and uncertain FTD/PPA cases, use of PIB increased diagnostic certainty . The different imaging modalities [MRI, FDG PET, PIB PET] did not always converge. An emerging issue will be how to use these multiple modalities together in an efficient manner.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".