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...
Autores principales: | , , , , , , , , , , , , |
---|---|
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 |