Comparison of Single and Multitask Learning for Predicting Cognitive Decline Based on MRI Data

The Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) is a neuropsychological tool that has been designed to assess the severity of cognitive symptoms of dementia. Personalized prediction of the changes in ADAS-Cog scores could help in timing therapeutic interventions in...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Vandad Imani, Mithilesh Prakash, Marzieh Zare, Jussi Tohka
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Alzheimer’s disease prediction MRI ADAS heterogeneity reduction transfer learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/ee5170abe6c748e0b07b65313ef11c6b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ee5170abe6c748e0b07b65313ef11c6b
record_format	dspace
spelling	oai:doaj.org-article:ee5170abe6c748e0b07b65313ef11c6b2021-11-24T00:02:56ZComparison of Single and Multitask Learning for Predicting Cognitive Decline Based on MRI Data2169-353610.1109/ACCESS.2021.3127276https://doaj.org/article/ee5170abe6c748e0b07b65313ef11c6b2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9611243/https://doaj.org/toc/2169-3536The Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) is a neuropsychological tool that has been designed to assess the severity of cognitive symptoms of dementia. Personalized prediction of the changes in ADAS-Cog scores could help in timing therapeutic interventions in dementia and at-risk populations. In the present work, we compared single and multitask learning approaches to predict the changes in ADAS-Cog scores based on T1-weighted anatomical magnetic resonance imaging (MRI). In contrast to most machine learning-based prediction methods ADAS-Cog changes, we stratified the subjects based on their baseline diagnoses and evaluated the prediction performances in each group. Our experiments indicated a positive relationship between the predicted and observed ADAS-Cog score changes in each diagnostic group, suggesting that T1-weighted MRI has a predictive value for evaluating cognitive decline in the entire AD continuum. We further studied whether correction of the differences in the magnetic field strength of MRI would improve the ADAS-Cog score prediction. The partial least square-based domain adaptation slightly improved the prediction performance, but the improvement was marginal. In summary, this study demonstrated that ADAS-Cog change could be, to some extent, predicted based on anatomical MRI. Based on this study, the recommended method for learning the predictive models is a single-task regularized linear regression due to its simplicity and good performance. It appears important to combine the training data across all subject groups for the most effective predictive models.Vandad ImaniMithilesh PrakashMarzieh ZareJussi TohkaIEEEarticleAlzheimer’s diseasepredictionMRIADASheterogeneity reductiontransfer learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154275-154291 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Alzheimer’s disease prediction MRI ADAS heterogeneity reduction transfer learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Alzheimer’s disease prediction MRI ADAS heterogeneity reduction transfer learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Vandad Imani Mithilesh Prakash Marzieh Zare Jussi Tohka Comparison of Single and Multitask Learning for Predicting Cognitive Decline Based on MRI Data
description	The Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) is a neuropsychological tool that has been designed to assess the severity of cognitive symptoms of dementia. Personalized prediction of the changes in ADAS-Cog scores could help in timing therapeutic interventions in dementia and at-risk populations. In the present work, we compared single and multitask learning approaches to predict the changes in ADAS-Cog scores based on T1-weighted anatomical magnetic resonance imaging (MRI). In contrast to most machine learning-based prediction methods ADAS-Cog changes, we stratified the subjects based on their baseline diagnoses and evaluated the prediction performances in each group. Our experiments indicated a positive relationship between the predicted and observed ADAS-Cog score changes in each diagnostic group, suggesting that T1-weighted MRI has a predictive value for evaluating cognitive decline in the entire AD continuum. We further studied whether correction of the differences in the magnetic field strength of MRI would improve the ADAS-Cog score prediction. The partial least square-based domain adaptation slightly improved the prediction performance, but the improvement was marginal. In summary, this study demonstrated that ADAS-Cog change could be, to some extent, predicted based on anatomical MRI. Based on this study, the recommended method for learning the predictive models is a single-task regularized linear regression due to its simplicity and good performance. It appears important to combine the training data across all subject groups for the most effective predictive models.
format	article
author	Vandad Imani Mithilesh Prakash Marzieh Zare Jussi Tohka
author_facet	Vandad Imani Mithilesh Prakash Marzieh Zare Jussi Tohka
author_sort	Vandad Imani
title	Comparison of Single and Multitask Learning for Predicting Cognitive Decline Based on MRI Data
title_short	Comparison of Single and Multitask Learning for Predicting Cognitive Decline Based on MRI Data
title_full	Comparison of Single and Multitask Learning for Predicting Cognitive Decline Based on MRI Data
title_fullStr	Comparison of Single and Multitask Learning for Predicting Cognitive Decline Based on MRI Data
title_full_unstemmed	Comparison of Single and Multitask Learning for Predicting Cognitive Decline Based on MRI Data
title_sort	comparison of single and multitask learning for predicting cognitive decline based on mri data
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/ee5170abe6c748e0b07b65313ef11c6b
work_keys_str_mv	AT vandadimani comparisonofsingleandmultitasklearningforpredictingcognitivedeclinebasedonmridata AT mithileshprakash comparisonofsingleandmultitasklearningforpredictingcognitivedeclinebasedonmridata AT marziehzare comparisonofsingleandmultitasklearningforpredictingcognitivedeclinebasedonmridata AT jussitohka comparisonofsingleandmultitasklearningforpredictingcognitivedeclinebasedonmridata
_version_	1718416128456261632

Comparison of Single and Multitask Learning for Predicting Cognitive Decline Based on MRI Data

Ejemplares similares