The Benson Complex Figure Test detects deficits in visuoconstruction and visual memory in symptomatic familial frontotemporal dementia: A GENFI study
Bibliographic record
Abstract
OBJECTIVE: Sensitive cognitive markers are still needed for frontotemporal dementia (FTD). The Benson Complex Figure Test (BCFT) is an interesting candidate test, as it assesses visuospatial, visual memory, and executive abilities, allowing the detection of multiple mechanisms of cognitive impairment. To investigate differences in BCFT Copy, Recall and Recognition in presymptomatic and symptomatic FTD mutation carriers, and to explore its cognitive and neuroimaging correlates. METHOD: tests. We investigated associations with neuropsychological test scores and grey matter volume using partial correlations and multiple regression models respectively. RESULTS: No significant differences were found between groups at CDR® NACC-FTLD 0-0.5. Symptomatic GRN and C9orf72 mutation carriers had lower Copy scores at CDR® NACC-FTLD ≥2. All three groups had lower Recall scores at CDR® NACC-FTLD ≥2, with MAPT mutation carriers starting at CDR® NACC-FTLD ≥1. All three groups had lower Recognition scores at CDR® NACC FTLD ≥2. Performance correlated with tests for visuoconstruction, memory, and executive function. Copy scores correlated with frontal-subcortical grey matter atrophy, while Recall scores correlated with temporal lobe atrophy. CONCLUSIONS: In the symptomatic stage, the BCFT identifies differential mechanisms of cognitive impairment depending on the genetic mutation, corroborated by gene-specific cognitive and neuroimaging correlates. Our findings suggest that impaired performance on the BCFT occurs relatively late in the genetic FTD disease process. Therefore its potential as cognitive biomarker for upcoming clinical trials in presymptomatic to early-stage FTD is most likely limited.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".