Purpose: The purpose of this study was to examine dysfluency characteristics of individuals with Parkinson Disease (PD) relative to linguistic features of grammatical class and position within word. Few studies have reported dysfluency characteristics of PD relative to these characteristics. Those that do report on these characteristics include one case study and a study of six individuals with PD. No previous research is known to have examined dysfluency related to grammatical class and position within words for a large sample of individuals with PD.
Method: Dysfluencies from 32 individuals with PD were analyzed according to position within a word and grammatical class.
Results: Participants produced significantly more dysfluencies in the initial position of words compared to medial or final positions, and a significantly higher percent dysfluency for content words versus function words.
Conclusion: Effects of linguistic features of grammatical class and position within a word on dysfluencies are present within a population with PD and are similar to the linguistic features associated with developmental stuttering. Clinical implications of the effect of linguistic features on speech dysfluencies in PD are discussed.
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http://dx.doi.org/10.1016/j.jfludis.2021.105845 | DOI Listing |
J Child Lang
January 2025
ELTE-HUN-REN NAP Comparative Ethology research group, Research Centre for Natural Sciences, Institute of Cognitive Neuroscience and Psychology, Budapest, Hungary.
By comparing infant-directed speech to spouse- and dog-directed talk, we aimed to investigate how pitch and utterance length are modulated by speakers considering the speech context and the partner's expected needs and capabilities. We found that mean pitch was modulated in line with the partner's attentional needs, while pitch range and utterance length were modulated according to the partner's expected linguistic competence. In a situation with a nursery rhyme, speakers used the highest pitch and widest pitch range with all partners suggesting that infant-directed context greatly influences these acoustic features.
View Article and Find Full Text PDFJ Speech Lang Hear Res
January 2025
Department of Psychology, University of Western Ontario, London, Canada.
Purpose: Recent advances in artificial intelligence provide opportunities to capture and represent complex features of human language in a more automated manner, offering potential means of improving the efficiency of language assessment. This review article presents computerized approaches for the analysis of narrative language and identification of language disorders in children.
Method: We first describe the current barriers to clinicians' use of language sample analysis, narrative language sampling approaches, and the data processing stages that precede analysis.
Brain Struct Funct
January 2025
CHRIST (Deemed to be University), Bangalore, Karnataka, India.
In this investigation, we delve into the neural underpinnings of auditory processing of Sanskrit verse comprehension, an area not previously explored by neuroscientific research. Our study examines a diverse group of 44 bilingual individuals, including both proficient and non-proficient Sanskrit speakers, to uncover the intricate neural patterns involved in processing verses of this ancient language. Employing an integrated neuroimaging approach that combines functional connectivity-multivariate pattern analysis (fc-MVPA), voxel-based univariate analysis, seed-based connectivity analysis, and the use of sparse fMRI techniques to minimize the interference of scanner noise, we highlight the brain's adaptability and ability to integrate multiple types of information.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Cognitive Neuroscience Center, University of San Andrés, Victoria, Buenos Aires, Argentina.
Background: Beyond dementia syndromes, cognitive symptoms are highly prevalent in Parkinson's disease (PD), often manifesting as mild cognitive impairment (MCI). Yet, their detection and characterization remain suboptimal because standard approaches rely on subjective impressions derived from lengthy, univariate tests. Here we introduce a novel approach to detect cognitive symptom severity and identify MCI in PD using fully automated word property analyses on brief verbal fluency tasks.
View Article and Find Full Text PDFBackground: Mild Cognitive Impairment (MCI) is the prodromal stage of dementia, including Alzheimer's Disease (AD). Early identification and accurate assessment of MCI are critical for clinical trial enrichment as well as the early intervention of AD. Digital makers offered a unique opportunity for ecologically valid and affordable early detection approaches.
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