Cargando…

Classification of Alzheimer’s Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data

Alzheimer’s disease (AD) is a progressive neurodegenerative condition that results in impaired performance in multiple cognitive domains. Preclinical changes in eye movements and language can occur with the disease, and progress alongside worsening cognition. In this article, we present the results...

Descripción completa

Detalles Bibliográficos
Autores principales: Jang, Hyeju, Soroski, Thomas, Rizzo, Matteo, Barral, Oswald, Harisinghani, Anuj, Newton-Mason, Sally, Granby, Saffrin, Stutz da Cunha Vasco, Thiago Monnerat, Lewis, Caitlin, Tutt, Pavan, Carenini, Giuseppe, Conati, Cristina, Field, Thalia S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8488259/
https://www.ncbi.nlm.nih.gov/pubmed/34616282
http://dx.doi.org/10.3389/fnhum.2021.716670
_version_ 1784578123601281024
author Jang, Hyeju
Soroski, Thomas
Rizzo, Matteo
Barral, Oswald
Harisinghani, Anuj
Newton-Mason, Sally
Granby, Saffrin
Stutz da Cunha Vasco, Thiago Monnerat
Lewis, Caitlin
Tutt, Pavan
Carenini, Giuseppe
Conati, Cristina
Field, Thalia S.
author_facet Jang, Hyeju
Soroski, Thomas
Rizzo, Matteo
Barral, Oswald
Harisinghani, Anuj
Newton-Mason, Sally
Granby, Saffrin
Stutz da Cunha Vasco, Thiago Monnerat
Lewis, Caitlin
Tutt, Pavan
Carenini, Giuseppe
Conati, Cristina
Field, Thalia S.
author_sort Jang, Hyeju
collection PubMed
description Alzheimer’s disease (AD) is a progressive neurodegenerative condition that results in impaired performance in multiple cognitive domains. Preclinical changes in eye movements and language can occur with the disease, and progress alongside worsening cognition. In this article, we present the results from a machine learning analysis of a novel multimodal dataset for AD classification. The cohort includes data from two novel tasks not previously assessed in classification models for AD (pupil fixation and description of a pleasant past experience), as well as two established tasks (picture description and paragraph reading). Our dataset includes language and eye movement data from 79 memory clinic patients with diagnoses of mild-moderate AD, mild cognitive impairment (MCI), or subjective memory complaints (SMC), and 83 older adult controls. The analysis of the individual novel tasks showed similar classification accuracy when compared to established tasks, demonstrating their discriminative ability for memory clinic patients. Fusing the multimodal data across tasks yielded the highest overall AUC of 0.83 ± 0.01, indicating that the data from novel tasks are complementary to established tasks.
format Online
Article
Text
id pubmed-8488259
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-84882592021-10-05 Classification of Alzheimer’s Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data Jang, Hyeju Soroski, Thomas Rizzo, Matteo Barral, Oswald Harisinghani, Anuj Newton-Mason, Sally Granby, Saffrin Stutz da Cunha Vasco, Thiago Monnerat Lewis, Caitlin Tutt, Pavan Carenini, Giuseppe Conati, Cristina Field, Thalia S. Front Hum Neurosci Human Neuroscience Alzheimer’s disease (AD) is a progressive neurodegenerative condition that results in impaired performance in multiple cognitive domains. Preclinical changes in eye movements and language can occur with the disease, and progress alongside worsening cognition. In this article, we present the results from a machine learning analysis of a novel multimodal dataset for AD classification. The cohort includes data from two novel tasks not previously assessed in classification models for AD (pupil fixation and description of a pleasant past experience), as well as two established tasks (picture description and paragraph reading). Our dataset includes language and eye movement data from 79 memory clinic patients with diagnoses of mild-moderate AD, mild cognitive impairment (MCI), or subjective memory complaints (SMC), and 83 older adult controls. The analysis of the individual novel tasks showed similar classification accuracy when compared to established tasks, demonstrating their discriminative ability for memory clinic patients. Fusing the multimodal data across tasks yielded the highest overall AUC of 0.83 ± 0.01, indicating that the data from novel tasks are complementary to established tasks. Frontiers Media S.A. 2021-09-20 /pmc/articles/PMC8488259/ /pubmed/34616282 http://dx.doi.org/10.3389/fnhum.2021.716670 Text en Copyright © 2021 Jang, Soroski, Rizzo, Barral, Harisinghani, Newton-Mason, Granby, Lewis, Tutt, Carenini, Conati and Field. 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 Human Neuroscience
Jang, Hyeju
Soroski, Thomas
Rizzo, Matteo
Barral, Oswald
Harisinghani, Anuj
Newton-Mason, Sally
Granby, Saffrin
Stutz da Cunha Vasco, Thiago Monnerat
Lewis, Caitlin
Tutt, Pavan
Carenini, Giuseppe
Conati, Cristina
Field, Thalia S.
Classification of Alzheimer’s Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data
title Classification of Alzheimer’s Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data
title_full Classification of Alzheimer’s Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data
title_fullStr Classification of Alzheimer’s Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data
title_full_unstemmed Classification of Alzheimer’s Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data
title_short Classification of Alzheimer’s Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data
title_sort classification of alzheimer’s disease leveraging multi-task machine learning analysis of speech and eye-movement data
topic Human Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8488259/
https://www.ncbi.nlm.nih.gov/pubmed/34616282
http://dx.doi.org/10.3389/fnhum.2021.716670
work_keys_str_mv AT janghyeju classificationofalzheimersdiseaseleveragingmultitaskmachinelearninganalysisofspeechandeyemovementdata
AT soroskithomas classificationofalzheimersdiseaseleveragingmultitaskmachinelearninganalysisofspeechandeyemovementdata
AT rizzomatteo classificationofalzheimersdiseaseleveragingmultitaskmachinelearninganalysisofspeechandeyemovementdata
AT barraloswald classificationofalzheimersdiseaseleveragingmultitaskmachinelearninganalysisofspeechandeyemovementdata
AT harisinghanianuj classificationofalzheimersdiseaseleveragingmultitaskmachinelearninganalysisofspeechandeyemovementdata
AT newtonmasonsally classificationofalzheimersdiseaseleveragingmultitaskmachinelearninganalysisofspeechandeyemovementdata
AT granbysaffrin classificationofalzheimersdiseaseleveragingmultitaskmachinelearninganalysisofspeechandeyemovementdata
AT stutzdacunhavascothiagomonnerat classificationofalzheimersdiseaseleveragingmultitaskmachinelearninganalysisofspeechandeyemovementdata
AT lewiscaitlin classificationofalzheimersdiseaseleveragingmultitaskmachinelearninganalysisofspeechandeyemovementdata
AT tuttpavan classificationofalzheimersdiseaseleveragingmultitaskmachinelearninganalysisofspeechandeyemovementdata
AT careninigiuseppe classificationofalzheimersdiseaseleveragingmultitaskmachinelearninganalysisofspeechandeyemovementdata
AT conaticristina classificationofalzheimersdiseaseleveragingmultitaskmachinelearninganalysisofspeechandeyemovementdata
AT fieldthalias classificationofalzheimersdiseaseleveragingmultitaskmachinelearninganalysisofspeechandeyemovementdata