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Neuropsychological Testing and Machine Learning Distinguish Alzheimer’s Disease from Other Causes for Cognitive Impairment
With promising results in recent treatment trials for Alzheimer’s disease (AD), it becomes increasingly important to distinguish AD at early stages from other causes for cognitive impairment. However, existing diagnostic methods are either invasive (lumbar punctures, PET) or inaccurate Magnetic Reso...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2017
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5403832/ https://www.ncbi.nlm.nih.gov/pubmed/28487650 http://dx.doi.org/10.3389/fnagi.2017.00114 |
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author | Gurevich, Pavel Stuke, Hannes Kastrup, Andreas Stuke, Heiner Hildebrandt, Helmut |
author_facet | Gurevich, Pavel Stuke, Hannes Kastrup, Andreas Stuke, Heiner Hildebrandt, Helmut |
author_sort | Gurevich, Pavel |
collection | PubMed |
description | With promising results in recent treatment trials for Alzheimer’s disease (AD), it becomes increasingly important to distinguish AD at early stages from other causes for cognitive impairment. However, existing diagnostic methods are either invasive (lumbar punctures, PET) or inaccurate Magnetic Resonance Imaging (MRI). This study investigates the potential of neuropsychological testing (NPT) to specifically identify those patients with possible AD among a sample of 158 patients with Mild Cognitive Impairment (MCI) or dementia for various causes. Patients were divided into an early stage and a late stage group according to their Mini Mental State Examination (MMSE) score and labeled as AD or non-AD patients based on a post-mortem validated threshold of the ratio between total tau and beta amyloid in the cerebrospinal fluid (CSF; Total tau/Aβ(1–42) ratio, TB ratio). All patients completed the established Consortium to Establish a Registry for Alzheimer’s Disease—Neuropsychological Assessment Battery (CERAD-NAB) test battery and two additional newly-developed neuropsychological tests (recollection and verbal comprehension) that aimed at carving out specific Alzheimer-typical deficits. Based on these test results, an underlying AD (pathologically increased TB ratio) was predicted with a machine learning algorithm. To this end, the algorithm was trained in each case on all patients except the one to predict (leave-one-out validation). In the total group, 82% of the patients could be correctly identified as AD or non-AD. In the early group with small general cognitive impairment, classification accuracy was increased to 89%. NPT thus seems to be capable of discriminating between AD patients and patients with cognitive impairment due to other neurodegenerative or vascular causes with a high accuracy, and may be used for screening in clinical routine and drug studies, especially in the early course of this disease. |
format | Online Article Text |
id | pubmed-5403832 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-54038322017-05-09 Neuropsychological Testing and Machine Learning Distinguish Alzheimer’s Disease from Other Causes for Cognitive Impairment Gurevich, Pavel Stuke, Hannes Kastrup, Andreas Stuke, Heiner Hildebrandt, Helmut Front Aging Neurosci Neuroscience With promising results in recent treatment trials for Alzheimer’s disease (AD), it becomes increasingly important to distinguish AD at early stages from other causes for cognitive impairment. However, existing diagnostic methods are either invasive (lumbar punctures, PET) or inaccurate Magnetic Resonance Imaging (MRI). This study investigates the potential of neuropsychological testing (NPT) to specifically identify those patients with possible AD among a sample of 158 patients with Mild Cognitive Impairment (MCI) or dementia for various causes. Patients were divided into an early stage and a late stage group according to their Mini Mental State Examination (MMSE) score and labeled as AD or non-AD patients based on a post-mortem validated threshold of the ratio between total tau and beta amyloid in the cerebrospinal fluid (CSF; Total tau/Aβ(1–42) ratio, TB ratio). All patients completed the established Consortium to Establish a Registry for Alzheimer’s Disease—Neuropsychological Assessment Battery (CERAD-NAB) test battery and two additional newly-developed neuropsychological tests (recollection and verbal comprehension) that aimed at carving out specific Alzheimer-typical deficits. Based on these test results, an underlying AD (pathologically increased TB ratio) was predicted with a machine learning algorithm. To this end, the algorithm was trained in each case on all patients except the one to predict (leave-one-out validation). In the total group, 82% of the patients could be correctly identified as AD or non-AD. In the early group with small general cognitive impairment, classification accuracy was increased to 89%. NPT thus seems to be capable of discriminating between AD patients and patients with cognitive impairment due to other neurodegenerative or vascular causes with a high accuracy, and may be used for screening in clinical routine and drug studies, especially in the early course of this disease. Frontiers Media S.A. 2017-04-25 /pmc/articles/PMC5403832/ /pubmed/28487650 http://dx.doi.org/10.3389/fnagi.2017.00114 Text en Copyright © 2017 Gurevich, Stuke, Kastrup, Stuke and Hildebrandt. 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 Gurevich, Pavel Stuke, Hannes Kastrup, Andreas Stuke, Heiner Hildebrandt, Helmut Neuropsychological Testing and Machine Learning Distinguish Alzheimer’s Disease from Other Causes for Cognitive Impairment |
title | Neuropsychological Testing and Machine Learning Distinguish Alzheimer’s Disease from Other Causes for Cognitive Impairment |
title_full | Neuropsychological Testing and Machine Learning Distinguish Alzheimer’s Disease from Other Causes for Cognitive Impairment |
title_fullStr | Neuropsychological Testing and Machine Learning Distinguish Alzheimer’s Disease from Other Causes for Cognitive Impairment |
title_full_unstemmed | Neuropsychological Testing and Machine Learning Distinguish Alzheimer’s Disease from Other Causes for Cognitive Impairment |
title_short | Neuropsychological Testing and Machine Learning Distinguish Alzheimer’s Disease from Other Causes for Cognitive Impairment |
title_sort | neuropsychological testing and machine learning distinguish alzheimer’s disease from other causes for cognitive impairment |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5403832/ https://www.ncbi.nlm.nih.gov/pubmed/28487650 http://dx.doi.org/10.3389/fnagi.2017.00114 |
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