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MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression

Objective: Late-life depression (LLD) is associated with development of different types of dementia. Identification of LLD patients, who will develop cognitive decline, i.e., the early stage of dementia would help to implement interventions earlier. The purpose of this study was to assess whether st...

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Autores principales: Lebedeva, Aleksandra K., Westman, Eric, Borza, Tom, Beyer, Mona K., Engedal, Knut, Aarsland, Dag, Selbaek, Geir, Haberg, Asta K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5288688/
https://www.ncbi.nlm.nih.gov/pubmed/28210220
http://dx.doi.org/10.3389/fnagi.2017.00013
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author Lebedeva, Aleksandra K.
Westman, Eric
Borza, Tom
Beyer, Mona K.
Engedal, Knut
Aarsland, Dag
Selbaek, Geir
Haberg, Asta K.
author_facet Lebedeva, Aleksandra K.
Westman, Eric
Borza, Tom
Beyer, Mona K.
Engedal, Knut
Aarsland, Dag
Selbaek, Geir
Haberg, Asta K.
author_sort Lebedeva, Aleksandra K.
collection PubMed
description Objective: Late-life depression (LLD) is associated with development of different types of dementia. Identification of LLD patients, who will develop cognitive decline, i.e., the early stage of dementia would help to implement interventions earlier. The purpose of this study was to assess whether structural brain magnetic resonance imaging (MRI) in LLD patients can predict mild cognitive impairment (MCI) or dementia 1 year prior to the diagnosis. Methods: LLD patients underwent brain MRI at baseline and repeated clinical assessment after 1-year. Structural brain measurements were obtained using Freesurfer software (v. 5.1) from the T1W brain MRI images. MRI-based Random Forest classifier was used to discriminate between LLD who developed MCI or dementia after 1-year follow-up and cognitively stable LLD. Additionally, a previously established Random Forest model trained on 185 patients with Alzheimer’s disease (AD) vs. 225 cognitively normal elderly from the Alzheimer’s disease Neuroimaging Initiative was tested on the LLD data set (ADNI model). Results: MCI and dementia diagnoses were predicted in LLD patients with 76%/68%/84% accuracy/sensitivity/specificity. Adding the baseline Mini-Mental State Examination (MMSE) scores to the models improved accuracy/sensitivity/specificity to 81%/75%/86%. The best model predicted MCI status alone using MRI and baseline MMSE scores with accuracy/sensitivity/specificity of 89%/85%/90%. The most important region for all the models was right ventral diencephalon, including hypothalamus. Its volume correlated negatively with the number of depressive episodes. ADNI model trained on AD vs. Controls using SV could predict MCI-DEM patients with 67% accuracy. Conclusion: LDD patients developing MCI and dementia can be discriminated from LLD patients remaining cognitively stable with good accuracy based on baseline structural MRI alone. Baseline MMSE score improves prediction accuracy. Ventral diencephalon, including the hypothalamus might play an important role in preservation of cognitive functions in LLD.
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spelling pubmed-52886882017-02-16 MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression Lebedeva, Aleksandra K. Westman, Eric Borza, Tom Beyer, Mona K. Engedal, Knut Aarsland, Dag Selbaek, Geir Haberg, Asta K. Front Aging Neurosci Neuroscience Objective: Late-life depression (LLD) is associated with development of different types of dementia. Identification of LLD patients, who will develop cognitive decline, i.e., the early stage of dementia would help to implement interventions earlier. The purpose of this study was to assess whether structural brain magnetic resonance imaging (MRI) in LLD patients can predict mild cognitive impairment (MCI) or dementia 1 year prior to the diagnosis. Methods: LLD patients underwent brain MRI at baseline and repeated clinical assessment after 1-year. Structural brain measurements were obtained using Freesurfer software (v. 5.1) from the T1W brain MRI images. MRI-based Random Forest classifier was used to discriminate between LLD who developed MCI or dementia after 1-year follow-up and cognitively stable LLD. Additionally, a previously established Random Forest model trained on 185 patients with Alzheimer’s disease (AD) vs. 225 cognitively normal elderly from the Alzheimer’s disease Neuroimaging Initiative was tested on the LLD data set (ADNI model). Results: MCI and dementia diagnoses were predicted in LLD patients with 76%/68%/84% accuracy/sensitivity/specificity. Adding the baseline Mini-Mental State Examination (MMSE) scores to the models improved accuracy/sensitivity/specificity to 81%/75%/86%. The best model predicted MCI status alone using MRI and baseline MMSE scores with accuracy/sensitivity/specificity of 89%/85%/90%. The most important region for all the models was right ventral diencephalon, including hypothalamus. Its volume correlated negatively with the number of depressive episodes. ADNI model trained on AD vs. Controls using SV could predict MCI-DEM patients with 67% accuracy. Conclusion: LDD patients developing MCI and dementia can be discriminated from LLD patients remaining cognitively stable with good accuracy based on baseline structural MRI alone. Baseline MMSE score improves prediction accuracy. Ventral diencephalon, including the hypothalamus might play an important role in preservation of cognitive functions in LLD. Frontiers Media S.A. 2017-02-02 /pmc/articles/PMC5288688/ /pubmed/28210220 http://dx.doi.org/10.3389/fnagi.2017.00013 Text en Copyright © 2017 Lebedeva, Westman, Borza, Beyer, Engedal, Aarsland, Selbaek and Haberg. http://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) or licensor 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
Lebedeva, Aleksandra K.
Westman, Eric
Borza, Tom
Beyer, Mona K.
Engedal, Knut
Aarsland, Dag
Selbaek, Geir
Haberg, Asta K.
MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression
title MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression
title_full MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression
title_fullStr MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression
title_full_unstemmed MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression
title_short MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression
title_sort mri-based classification models in prediction of mild cognitive impairment and dementia in late-life depression
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5288688/
https://www.ncbi.nlm.nih.gov/pubmed/28210220
http://dx.doi.org/10.3389/fnagi.2017.00013
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