Faster Cortical Thinning and Surface Area Loss in Presymptomatic and Symptomatic <i>C9orf72</i> Repeat Expansion Adult Carriers
Bibliographic record
Abstract
OBJECTIVE: C9orf72 expansion is the most common genetic cause of frontotemporal dementia (FTD). We examined aging trajectories of cortical thickness (CTh) and surface area in C9orf72 expansion adult carriers compared to healthy controls to characterize preclinical cerebral changes leading to symptoms. METHODS: Data were obtained from the Genetic Frontotemporal Dementia Initiative. T1-weighted magnetic resonance imaging scans were processed with CIVET 2.1 to extract vertex-wide CTh and cortical surface area (CSA). Symptomatic and presymptomatic subjects were compared to age-matched controls using mixed-effects models, controlling for demographic variables. Aging trajectories were compared between carriers and noncarriers by testing the "age by genetic status" interaction. False discovery rate corrections were applied to all vertex-wide analyses. RESULTS: The sample included 640 scans from 386 subjects, including 54 symptomatic C9orf72 carriers (72.2% behavioral variant FTD), 83 asymptomatic carriers, and 249 controls (age range = 18-86 years). Symptomatic carriers showed fairly symmetric reduction in CTh/CSA in most of the frontal lobes, in addition to large temporoparietal areas. Presymptomatic subjects had reduced CTh/CSA in more restricted areas of the medial frontoparietal lobes, in addition to scattered lateral frontal, parietal, and temporal areas. These differences were explained by faster cortical thinning linearly throughout adulthood in a similar anatomical distribution, with differences emerging in the early 30s. CSA reduction was also faster in mutation carriers predominantly in the ventrofrontal regions. INTERPRETATION: C9orf72 mutation carriers have faster cortical thinning and surface loss throughout adulthood in regions that show atrophy in symptomatic subjects. This suggests that the pathogenic effects of the mutation lead to structural cerebral changes decades prior to symptoms. ANN NEUROL 2020 ANN NEUROL 2020;88:113-122.
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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) | 0.003 | 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".