Detect probably Alzheimer's disease dementia across subsections &amp; language domains (He et al., 2023)
Creators: name: He, Rui
name: Chapin, Kayla
name: Al-Tamimi, Jalal
name: Bel, Núria
name: Marquié, Marta
name: Rosende-Roca, Maitee
name: Pytel, Vanesa
name: Tartari, Juan Pablo
name: Alegret, Montse
name: Sanabria, Angela
name: Ruiz, Agustín
name: Boada, Mercè
name: Valero, Sergi
name: Hinzen, Wolfram
Publisher: name: ASHA journals
Year: 2023
Subjects: classificationCode: 320905
subject: Neurology and neuromuscular diseases
classificationCode: 520405
subject: Psycholinguistics (incl. speech production and comprehension)

description: <b>Background:</b> Decline in language has emerged as a new potential biomarker for the early detection of Alzheimer’s disease (AD). It remains unclear how sensitive language measures are across different tasks, language domains, and languages, and to what extent changes can be reliably detected in early stages such as subjective cognitive decline (SCD) and mild cognitive impairment (MCI).<b>Method:</b> Using a scene construction task for speech elicitation in a new Spanish/Catalan speaking cohort (<i>N</i> = 119), we automatically extracted features across seven domains, three acoustic (spectral, cepstral, and voice quality), one prosodic, and three from text (morpholexical, semantic, and syntactic). They were forwarded to a random forest classifier to evaluate the discriminability of participants with probable AD dementia, amnestic and nonamnestic MCI, SCD, and cognitively healthy controls. Repeated-measures analyses of variance and paired-samples Wilcoxon signed-ranks test were used to assess whether and how performance differs significantly across groups and linguistic domains.<b>Results: </b>The performance scores of the machine learning classifier were generally satisfactorily high, with the highest scores over .9. Model performance was significantly different for linguistic domains (<i>p</i> &lt; .001), and speech versus text (<i>p</i> = .043), with speech features outperforming textual features, and voice quality performing best. High diagnostic classification accuracies were seen even within both cognitively healthy (controls vs. SCD) and MCI (amnestic and nonamnestic) groups.<b>Conclusion:</b> Speech-based machine learning is powerful in detecting cognitive decline and probable AD dementia across a range of different feature domains, though important differences exist between these domains as well.<b>Supplemental Material S1.</b> (A) The recruitment procedure, diagnostic criteria, neuropsychological battery, impact of the epidemic, and criteria for inclusion and exclusion. (B) Speech transcription instruction. (C) Feature list and definitions. (D) Detailed group-wise classifier performance from the random forest. (F) Post-hoc analysis of the RMANOVA tests. (F) Results and statistical comparison from Gradient Boosting.He, R., Chapin, K., Al-Tamimi, J., Bel, N., Marquié, M., Rosende-Roca, M., Pytel, V., Tartari, J. P., Alegret, M., Sanabria, A., Ruiz, A., Boada, M., Valero, S., &amp; Hinzen, W. (2023). Automated classification of cognitive decline and probable Alzheimer’s dementia across multiple speech and language domains. <i>American Journal of Speech-Language Pathology, 32</i>(5), 2075–2086. https://doi.org/10.1044/2023_AJSLP-22-00403