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Record W4382933015 · doi:10.1212/wnl.0000000000207516

Expressive Prosody in Patients With Focal Anterior Temporal Neurodegeneration

2023· article· en· W4382933015 on OpenAlexfundno aff
Amandine Géraudie, Peter Pressman, Jérémie Pariente, Carly Millanski, Eleanor R. Palser, Buddhika Ratnasiri, Giovanni Battistella, Maria Luisa Mandelli, Zachary Miller, Bruce L. Miller, Virginia E. Sturm, Katherine P. Rankin, Maria Luisa Gorno‐Tempini, Maxime Montembeault

Bibliographic record

VenueNeurology · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeNational Institute on Deafness and Other Communication DisordersNational Institute on AgingMcGill University
KeywordsFrontotemporal dementiaAudiologyProsodySocioemotional selectivity theoryPsychologyPrimary progressive aphasiaFrontotemporal lobar degenerationSemantic dementiaNeuropsychologyAphasiaInsulaDementiaMedicineCognitive psychologyNeurosciencePathologyCognitionLinguistics

Abstract

fetched live from OpenAlex

Introduction: Progressive focal anterior temporal lobe (ATL) neurodegeneration has been historically called semantic dementia. More recently, semantic variant primary progressive aphasia (svPPA) and semantic behavioral variant frontotemporal dementia (sbvFTD) have been linked with predominant left or right ATL neurodegeneration, respectively. Nonetheless, clinical tools for accurate diagnosis of sbvFTD are still lacking. Expressive prosody refers to the modulation of pitch, loudness, tempo, and quality of voice used to convey emotional and linguistic information and has been linked to bilateral but right-predominant frontotemporal functioning. Changes in expressive prosody can be detected with semi-automated methods and could represent a useful diagnostic marker of socio-emotional functioning in sbvFTD. Methods: Participants underwent a comprehensive neuropsychological and language evaluation and a 3T MRI at UCSF. Each participant provided a verbal description of the picnic scene from the Western Aphasia Battery. The fundamental frequency (f0) range, an acoustic measure of pitch variability, was extracted for each participant. We compared the f0 range between groups and investigated associations with an informant-rated measure of empathy, a facial emotion labelling task, and gray matter volumes using voxel-based morphometry. Results: Twenty-eight patients with svPPA, 18 with sbvFTD, and 18 healthy controls (HC) were included. f0 range was significantly different across groups: sbvFTD patients showed reduced f0 in comparison to both patients with svPPA (mean difference of -1.4±2.4 semitones; 95% CI [-2.4, -0.4]; p < .005), and HC (mean difference of -1.9±3.0 semitones; 95% CI [-3.0, -0.7]; p < .001). f0 range was significantly positively correlated with informant-rated empathy (r = .355; p ≤ .05), but not facial emotion labelling. Finally, the f0 range was significantly correlated with gray matter volume in the right superior temporal gyrus, encompassing anterior and posterior portions (p < .05 FWE cluster corrected). Conclusion: Expressive prosody may be a useful clinical marker of sbvFTD. Reduced empathy is a core symptom in sbvFTD; the present results extend this to prosody, a core component of social interaction, at the intersection of speech and emotion. They also inform the long-standing debate on the lateralization of expressive prosody in the brain, highlighting the critical role of the right superior temporal lobe.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.014
GPT teacher head0.247
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2023
Admission routes1
Has abstractyes

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