Cargando…
Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment
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...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10512723/ https://www.ncbi.nlm.nih.gov/pubmed/37746151 http://dx.doi.org/10.3389/fnins.2023.1221401 |
_version_ | 1785108424428617728 |
---|---|
author | García-Gutiérrez, Fernando Marquié, Marta Muñoz, Nathalia Alegret, Montserrat Cano, Amanda de Rojas, Itziar García-González, Pablo Olivé, Clàudia Puerta, Raquel Orellana, Adelina Montrreal, Laura Pytel, Vanesa Ricciardi, Mario Zaldua, Carla Gabirondo, Peru Hinzen, Wolfram Lleonart, Núria García-Sánchez, Ainhoa Tárraga, Lluís Ruiz, Agustín Boada, Mercè Valero, Sergi |
author_facet | García-Gutiérrez, Fernando Marquié, Marta Muñoz, Nathalia Alegret, Montserrat Cano, Amanda de Rojas, Itziar García-González, Pablo Olivé, Clàudia Puerta, Raquel Orellana, Adelina Montrreal, Laura Pytel, Vanesa Ricciardi, Mario Zaldua, Carla Gabirondo, Peru Hinzen, Wolfram Lleonart, Núria García-Sánchez, Ainhoa Tárraga, Lluís Ruiz, Agustín Boada, Mercè Valero, Sergi |
author_sort | García-Gutiérrez, Fernando |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10512723 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105127232023-09-22 Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment García-Gutiérrez, Fernando Marquié, Marta Muñoz, Nathalia Alegret, Montserrat Cano, Amanda de Rojas, Itziar García-González, Pablo Olivé, Clàudia Puerta, Raquel Orellana, Adelina Montrreal, Laura Pytel, Vanesa Ricciardi, Mario Zaldua, Carla Gabirondo, Peru Hinzen, Wolfram Lleonart, Núria García-Sánchez, Ainhoa Tárraga, Lluís Ruiz, Agustín Boada, Mercè Valero, Sergi Front Neurosci Neuroscience 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. Frontiers Media S.A. 2023-09-07 /pmc/articles/PMC10512723/ /pubmed/37746151 http://dx.doi.org/10.3389/fnins.2023.1221401 Text en 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. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience García-Gutiérrez, Fernando Marquié, Marta Muñoz, Nathalia Alegret, Montserrat Cano, Amanda de Rojas, Itziar García-González, Pablo Olivé, Clàudia Puerta, Raquel Orellana, Adelina Montrreal, Laura Pytel, Vanesa Ricciardi, Mario Zaldua, Carla Gabirondo, Peru Hinzen, Wolfram Lleonart, Núria García-Sánchez, Ainhoa Tárraga, Lluís Ruiz, Agustín Boada, Mercè Valero, Sergi Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment |
title | Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment |
title_full | Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment |
title_fullStr | Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment |
title_full_unstemmed | Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment |
title_short | Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment |
title_sort | harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10512723/ https://www.ncbi.nlm.nih.gov/pubmed/37746151 http://dx.doi.org/10.3389/fnins.2023.1221401 |
work_keys_str_mv | AT garciagutierrezfernando harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT marquiemarta harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT munoznathalia harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT alegretmontserrat harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT canoamanda harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT derojasitziar harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT garciagonzalezpablo harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT oliveclaudia harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT puertaraquel harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT orellanaadelina harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT montrreallaura harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT pytelvanesa harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT ricciardimario harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT zalduacarla harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT gabirondoperu harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT hinzenwolfram harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT lleonartnuria harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT garciasanchezainhoa harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT tarragalluis harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT ruizagustin harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT boadamerce harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment AT valerosergi harnessingacousticspeechparameterstodecipheramyloidstatusinindividualswithmildcognitiveimpairment |