BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer's Disease.

Alzheimer's disease (AD), the most common form of dementia, shares many aspects of abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based biomarker that predicts the individual progression of mild cognitive impairment (MCI) to AD on the basis of pathological brain aging...

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Main Authors: Christian Gaser, Katja Franke, Stefan Klöppel, Nikolaos Koutsouleris, Heinrich Sauer, Alzheimer's Disease Neuroimaging Initiative
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2013-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3695013?pdf=render
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author Christian Gaser
Katja Franke
Stefan Klöppel
Nikolaos Koutsouleris
Heinrich Sauer
Alzheimer's Disease Neuroimaging Initiative
author_facet Christian Gaser
Katja Franke
Stefan Klöppel
Nikolaos Koutsouleris
Heinrich Sauer
Alzheimer's Disease Neuroimaging Initiative
author_sort Christian Gaser
collection DOAJ
description Alzheimer's disease (AD), the most common form of dementia, shares many aspects of abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based biomarker that predicts the individual progression of mild cognitive impairment (MCI) to AD on the basis of pathological brain aging patterns. By employing kernel regression methods, the expression of normal brain-aging patterns forms the basis to estimate the brain age of a given new subject. If the estimated age is higher than the chronological age, a positive brain age gap estimation (BrainAGE) score indicates accelerated atrophy and is considered a risk factor for conversion to AD. Here, the BrainAGE framework was applied to predict the individual brain ages of 195 subjects with MCI at baseline, of which a total of 133 developed AD during 36 months of follow-up (corresponding to a pre-test probability of 68%). The ability of the BrainAGE framework to correctly identify MCI-converters was compared with the performance of commonly used cognitive scales, hippocampus volume, and state-of-the-art biomarkers derived from cerebrospinal fluid (CSF). With accuracy rates of up to 81%, BrainAGE outperformed all cognitive scales and CSF biomarkers in predicting conversion of MCI to AD within 3 years of follow-up. Each additional year in the BrainAGE score was associated with a 10% greater risk of developing AD (hazard rate: 1.10 [CI: 1.07-1.13]). Furthermore, the post-test probability was increased to 90% when using baseline BrainAGE scores to predict conversion to AD. The presented framework allows an accurate prediction even with multicenter data. Its fast and fully automated nature facilitates the integration into the clinical workflow. It can be exploited as a tool for screening as well as for monitoring treatment options.
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spelling doaj.art-ce872aede32e4db0b3a23850a7c17c7c2022-12-22T03:45:30ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0186e6734610.1371/journal.pone.0067346BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer's Disease.Christian GaserKatja FrankeStefan KlöppelNikolaos KoutsoulerisHeinrich SauerAlzheimer's Disease Neuroimaging InitiativeAlzheimer's disease (AD), the most common form of dementia, shares many aspects of abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based biomarker that predicts the individual progression of mild cognitive impairment (MCI) to AD on the basis of pathological brain aging patterns. By employing kernel regression methods, the expression of normal brain-aging patterns forms the basis to estimate the brain age of a given new subject. If the estimated age is higher than the chronological age, a positive brain age gap estimation (BrainAGE) score indicates accelerated atrophy and is considered a risk factor for conversion to AD. Here, the BrainAGE framework was applied to predict the individual brain ages of 195 subjects with MCI at baseline, of which a total of 133 developed AD during 36 months of follow-up (corresponding to a pre-test probability of 68%). The ability of the BrainAGE framework to correctly identify MCI-converters was compared with the performance of commonly used cognitive scales, hippocampus volume, and state-of-the-art biomarkers derived from cerebrospinal fluid (CSF). With accuracy rates of up to 81%, BrainAGE outperformed all cognitive scales and CSF biomarkers in predicting conversion of MCI to AD within 3 years of follow-up. Each additional year in the BrainAGE score was associated with a 10% greater risk of developing AD (hazard rate: 1.10 [CI: 1.07-1.13]). Furthermore, the post-test probability was increased to 90% when using baseline BrainAGE scores to predict conversion to AD. The presented framework allows an accurate prediction even with multicenter data. Its fast and fully automated nature facilitates the integration into the clinical workflow. It can be exploited as a tool for screening as well as for monitoring treatment options.http://europepmc.org/articles/PMC3695013?pdf=render
spellingShingle Christian Gaser
Katja Franke
Stefan Klöppel
Nikolaos Koutsouleris
Heinrich Sauer
Alzheimer's Disease Neuroimaging Initiative
BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer's Disease.
PLoS ONE
title BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer's Disease.
title_full BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer's Disease.
title_fullStr BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer's Disease.
title_full_unstemmed BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer's Disease.
title_short BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer's Disease.
title_sort brainage in mild cognitive impaired patients predicting the conversion to alzheimer s disease
url http://europepmc.org/articles/PMC3695013?pdf=render
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