Synaptic Loss in Frontotemporal Dementia Revealed by [ <scp> <sup>11</sup> C </scp> ] <scp>UCB‐J Positron Emission Tomography</scp>
Bibliographic record
Abstract
Objective Synaptic loss is an early feature of neurodegenerative disease models, and is severe in post mortem clinical studies, including frontotemporal dementia. Positron emission tomography (PET) with radiotracers that bind to synaptic vesicle glycoprotein 2A enables quantification of synaptic density in vivo. This study used [ 11 C]UCB‐J PET in participants with behavioral variant frontotemporal dementia (bvFTD), testing the hypothesis that synaptic loss is severe and related to clinical severity. Methods Eleven participants with clinically probable bvFTD and 25 age‐ and sex‐matched healthy controls were included. Participants underwent dynamic [ 11 C]UCB‐J PET, structural magnetic resonance imaging, and a neuropsychological battery, including the revised Addenbrooke Cognitive Examination, and INECO frontal screening. General linear models compared [ 11 C]UCB‐J binding potential maps and gray matter volume between groups, and assessed associations between synaptic density and clinical severity in patients. Analyses were also performed using partial volume corrected [ 11 C]UCB‐J binding potential from regions of interest (ROIs). Results Patients with bvFTD showed severe synaptic loss compared to controls. [ 11 C]UCB‐J binding was reduced bilaterally in medial and dorsolateral frontal regions, inferior frontal gyri, anterior and posterior cingulate gyrus, insular cortex, and medial temporal lobe. Synaptic loss in the frontal and cingulate regions correlated significantly with cognitive impairments. Synaptic loss was more severe than atrophy. Results from ROI‐based analyses mirrored the voxelwise results. Interpretation In accordance with preclinical models, and human postmortem evidence, there is widespread frontotemporal loss of synapses in symptomatic bvFTD, in proportion to severity. [ 11 C]UCB‐J PET could support translational studies and experimental medicine strategies for new disease‐modifying treatments for neurodegeneration. ANN NEUROL 2023;93:142–154
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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 | 0.000 | 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".