Description of connected speech across different elicitation tasks in the logopenic variant of primary progressive aphasia
Bibliographic record
Abstract
BACKGROUND: Despite its importance, in-depth analysis of connected speech is often neglected in the diagnosis of primary progressive aphasia (PPA) - especially for the logopenic variant (lvPPA) for which unreliable differential diagnosis has been documented. Only a few studies have been conducted on this topic in lvPPA. AIMS: The aim of this study was to describe and compare lexico-semantic and morphosyntactic features of connected speech in participants with lvPPA, in comparison with healthy controls, using three different elicitation tasks (i.e., picture description, story narration and semi-structured interviews). In addition to a number of discourse features, we were particularly interested in the presence or absence of syntactic deficits in this PPA variant in line with recent findings. METHODS & PROCEDURES: A prospective group study was conducted to compare lvPPA participants (n = 13) to age- and education-matched healthy controls (n = 13). For each individual, connected speech was obtained using three tasks: (1) The Cookie Theft picture description; (2) Cinderella Story; (3) Topic-directed interview. Production on each task was recorded, transcribed and analysed according to the Quantitative Production Analysis (QPA) protocol, a tool developed by Berndt et al. (2000) for the analysis of sentence production in aphasia. Differences between lvPPA and healthy controls and among elicitation tasks were analysed using repeated measures multilevel mixed-effects regression, separately for each outcome. OUTCOMES & RESULTS: Four measures were significantly different between lvPPA participants and healthy controls across all elicitation tasks. Specifically, lvPPA participants produced a reduced proportion of open-class words, a higher proportion of verbs, a higher proportion of pronouns and fewer well-formed sentences. For these measures, the difference between lvPPA and healthy controls was consistent among elicitation tasks, except for the proportion of well-formed sentences, where the difference between the two groups was significantly greater in the story narration task than in the other tasks. CONCLUSIONS & IMPLICATIONS: Across elicitation tasks that used the same analysis protocol (i.e., QPA), a similar pattern of deficits in connected speech emerged in lvPPA patients. Importantly, the findings replicate previous studies, which used different elicitation tasks and analysis protocols. Especially in relation to the documented syntactic deficits, these findings provide implications for differential diagnosis in PPA. WHAT THIS PAPER ADDS: What is already known on the subject Connected speech analysis can provide an important contribution to the language assessment for the logopenic variant of primary progressive aphasia (lvPPA). However, only a few studies have been conducted with this population. What this paper adds to existing knowledge This study highlights differences between patients with lvPPA and healthy controls regarding the proportion of open-class words, nouns, verbs and well-formed sentences. What are the potential or actual clinical implications of this work? Importantly, our results highlight syntactic deficits in the same group of individuals with lvPPA, using the same analysis protocol and across various elicitation tasks, which has implications for differential diagnosis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".