Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment

Frontiers in Neuroscience

Neuroscience

Peru Gabirondo¹  Carla Zaldua¹  Laura Montrreal²  Núria Lleonart²  Vanesa Pytel²  Raquel Puerta²  Clàudia Olivé²  Ainhoa García-Sánchez²  Mario Ricciardi²  Nathalia Muñoz²  Fernando García-Gutiérrez²  Pablo García-González²  Adelina Orellana³  Montserrat Alegret³  Sergi Valero³  Lluís Tárraga³  Agustín Ruiz³  Amanda Cano³  Marta Marquié³  Mercè Boada³  Itziar de Rojas³  Wolfram Hinzen⁴ 

[1] Accexible Impacto s.l., Urduliz, Bizkaia, Spain;Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain;Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain;Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain;Department of Translation and Language Sciences, Universitat Pompeu Fabra, Barcelona, Spain;Institut Català de Recerca i Estudis Avançats (ICREA), Barcelona, Spain;

关键词: Alzheimer's disease;    mild cognitive impairment;    early diagnosis;    cerebrospinal fluid;    biomarkers;    machine learning;    speech acoustics;    automated pattern recognition;   

DOI  :  10.3389/fnins.2023.1221401

 received in 2023-05-12, accepted in 2023-08-08,  发布年份 2023

来源: Frontiers

【 摘 要 】

Alzheimer's disease (AD) is a neurodegenerative condition characterized by a gradual decline in cognitive functions. Currently, there are no effective treatments for AD, underscoring the importance of identifying individuals in the preclinical stages of mild cognitive impairment (MCI) to enable early interventions. Among the neuropathological events associated with the onset of the disease is the accumulation of amyloid protein in the brain, which correlates with decreased levels of Aβ42 peptide in the cerebrospinal fluid (CSF). Consequently, the development of non-invasive, low-cost, and easy-to-administer proxies for detecting Aβ42 positivity in CSF becomes particularly valuable. A promising approach to achieve this is spontaneous speech analysis, which combined with machine learning (ML) techniques, has proven highly useful in AD. In this study, we examined the relationship between amyloid status in CSF and acoustic features derived from the description of the Cookie Theft picture in MCI patients from a memory clinic. The cohort consisted of fifty-two patients with MCI (mean age 73 years, 65% female, and 57% positive amyloid status). Eighty-eight acoustic parameters were extracted from voice recordings using the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS), and several ML models were used to classify the amyloid status. Furthermore, interpretability techniques were employed to examine the influence of input variables on the determination of amyloid-positive status. The best model, based on acoustic variables, achieved an accuracy of 75% with an area under the curve (AUC) of 0.79 in the prediction of amyloid status evaluated by bootstrapping and Leave-One-Out Cross Validation (LOOCV), outperforming conventional neuropsychological tests (AUC = 0.66). Our results showed that the automated analysis of voice recordings derived from spontaneous speech tests offers valuable insights into AD biomarkers during the preclinical stages. These findings introduce novel possibilities for the use of digital biomarkers to identify subjects at high risk of developing AD.

【 授权许可】

Unknown   
Copyright © 2023 García-Gutiérrez, Marquié, Muñoz, Alegret, Cano, de Rojas, García-González, Olivé, Puerta, Orellana, Montrreal, Pytel, Ricciardi, Zaldua, Gabirondo, Hinzen, Lleonart, García-Sánchez, Tárraga, Ruiz, Boada and Valero.