Prodromal language impairment in genetic frontotemporal dementia within the GENFI cohort
Bibliographic record
Abstract
OBJECTIVE: To identify whether language impairment exists presymptomatically in genetic frontotemporal dementia (FTD), and if so, the key differences between the main genetic mutation groups. METHODS: 682 participants from the international multicentre Genetic FTD Initiative (GENFI) study were recruited: 290 asymptomatic and 82 prodromal mutation carriers (with C9orf72, GRN, and MAPT mutations) as well as 310 mutation-negative controls. Language was assessed using items from the Progressive Aphasia Severity Scale, as well as the Boston Naming Test (BNT), modified Camel and Cactus Test (mCCT) and a category fluency task. Participants also underwent a 3 T volumetric T1-weighted MRI from which regional brain volumes within the language network were derived and compared between the groups. RESULTS: 3% of asymptomatic (4% C9orf72, 4% GRN, 2% MAPT) and 48% of prodromal (46% C9orf72, 42% GRN, 64% MAPT) mutation carriers had impairment in at least one language symptom compared with 13% of controls. In prodromal mutation carriers significantly impaired word retrieval was seen in all three genetic groups whilst significantly impaired grammar/syntax and decreased fluency was seen only in C9orf72 and GRN mutation carriers, and impaired articulation only in the C9orf72 group. Prodromal MAPT mutation carriers had significant impairment on the category fluency task and the BNT whilst prodromal C9orf72 mutation carriers were impaired on the category fluency task only. Atrophy in the dominant perisylvian language regions differed between groups, with earlier, more widespread volume loss in C9orf72, and later focal atrophy in the temporal lobe in MAPT mutation carriers. CONCLUSIONS: Language deficits exist in the prodromal but not asymptomatic stages of genetic FTD across all three genetic groups. Improved understanding of the language phenotype prior to phenoconversion to fully symptomatic FTD will help develop outcome measures for future presymptomatic trials.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".