A Biomarker for Alzheimer’s Disease Based on Patterns of Regional Brain Atrophy
Introduction: It has been shown that Alzheimer’s disease (AD) is accompanied by marked structural brain changes that can be detected several years before clinical diagnosis via structural magnetic resonance (MR) imaging. In this study, we developed a structural MR-based biomarker for in vivo detecti...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-01-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fpsyt.2019.00953/full |
| _version_ | 1828515332092854272 |
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| author | Stefan Frenzel Katharina Wittfeld Katharina Wittfeld Mohamad Habes Johanna Klinger-König Robin Bülow Henry Völzke Hans Jörgen Grabe Hans Jörgen Grabe |
| author_facet | Stefan Frenzel Katharina Wittfeld Katharina Wittfeld Mohamad Habes Johanna Klinger-König Robin Bülow Henry Völzke Hans Jörgen Grabe Hans Jörgen Grabe |
| author_sort | Stefan Frenzel |
| collection | DOAJ |
| description | Introduction: It has been shown that Alzheimer’s disease (AD) is accompanied by marked structural brain changes that can be detected several years before clinical diagnosis via structural magnetic resonance (MR) imaging. In this study, we developed a structural MR-based biomarker for in vivo detection of AD using a supervised machine learning approach. Based on an individual’s pattern of brain atrophy a continuous AD score is assigned which measures the similarity with brain atrophy patterns seen in clinical cases of AD.Methods: The underlying statistical model was trained with MR scans of patients and healthy controls from the Alzheimer’s Disease Neuroimaging Initiative (ADNI-1 screening). Validation was performed within ADNI-1 and in an independent patient sample from the Open Access Series of Imaging Studies (OASIS-1). In addition, our analyses included data from a large general population sample of the Study of Health in Pomerania (SHIP-Trend).Results: Based on the proposed AD score we were able to differentiate patients from healthy controls in ADNI-1 and OASIS-1 with an accuracy of 89% (AUC = 95%) and 87% (AUC = 93%), respectively. Moreover, we found the AD score to be significantly associated with cognitive functioning as assessed by the Mini-Mental State Examination in the OASIS-1 sample after correcting for diagnosis, age, sex, age·sex, and total intracranial volume (Cohen’s f2 = 0.13). Additional analyses showed that the prediction accuracy of AD status based on both the AD score and the MMSE score is significantly higher than when using just one of them. In SHIP-Trend we found the AD score to be weakly but significantly associated with a test of verbal memory consisting of an immediate and a delayed word list recall (again after correcting for age, sex, age·sex, and total intracranial volume, Cohen’s f2 = 0.009). This association was mainly driven by the immediate recall performance.Discussion: In summary, our proposed biomarker well differentiated between patients and healthy controls in an independent test sample. It was associated with measures of cognitive functioning both in a patient sample and a general population sample. Our approach might be useful for defining robust MR-based biomarkers for other neurodegenerative diseases, too. |
| first_indexed | 2024-12-11T18:12:54Z |
| format | Article |
| id | doaj.art-af850ede664340f6a6b1922ef3f013a9 |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-12-11T18:12:54Z |
| publishDate | 2020-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj.art-af850ede664340f6a6b1922ef3f013a92022-12-22T00:55:32ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402020-01-011010.3389/fpsyt.2019.00953506346A Biomarker for Alzheimer’s Disease Based on Patterns of Regional Brain AtrophyStefan FrenzelKatharina WittfeldKatharina WittfeldMohamad HabesJohanna Klinger-KönigRobin BülowHenry VölzkeHans Jörgen GrabeHans Jörgen GrabeIntroduction: It has been shown that Alzheimer’s disease (AD) is accompanied by marked structural brain changes that can be detected several years before clinical diagnosis via structural magnetic resonance (MR) imaging. In this study, we developed a structural MR-based biomarker for in vivo detection of AD using a supervised machine learning approach. Based on an individual’s pattern of brain atrophy a continuous AD score is assigned which measures the similarity with brain atrophy patterns seen in clinical cases of AD.Methods: The underlying statistical model was trained with MR scans of patients and healthy controls from the Alzheimer’s Disease Neuroimaging Initiative (ADNI-1 screening). Validation was performed within ADNI-1 and in an independent patient sample from the Open Access Series of Imaging Studies (OASIS-1). In addition, our analyses included data from a large general population sample of the Study of Health in Pomerania (SHIP-Trend).Results: Based on the proposed AD score we were able to differentiate patients from healthy controls in ADNI-1 and OASIS-1 with an accuracy of 89% (AUC = 95%) and 87% (AUC = 93%), respectively. Moreover, we found the AD score to be significantly associated with cognitive functioning as assessed by the Mini-Mental State Examination in the OASIS-1 sample after correcting for diagnosis, age, sex, age·sex, and total intracranial volume (Cohen’s f2 = 0.13). Additional analyses showed that the prediction accuracy of AD status based on both the AD score and the MMSE score is significantly higher than when using just one of them. In SHIP-Trend we found the AD score to be weakly but significantly associated with a test of verbal memory consisting of an immediate and a delayed word list recall (again after correcting for age, sex, age·sex, and total intracranial volume, Cohen’s f2 = 0.009). This association was mainly driven by the immediate recall performance.Discussion: In summary, our proposed biomarker well differentiated between patients and healthy controls in an independent test sample. It was associated with measures of cognitive functioning both in a patient sample and a general population sample. Our approach might be useful for defining robust MR-based biomarkers for other neurodegenerative diseases, too.https://www.frontiersin.org/article/10.3389/fpsyt.2019.00953/fullAlzheimer's diseasemachine learningdementiamagnetic resonance imagingFreeSurfer |
| spellingShingle | Stefan Frenzel Katharina Wittfeld Katharina Wittfeld Mohamad Habes Johanna Klinger-König Robin Bülow Henry Völzke Hans Jörgen Grabe Hans Jörgen Grabe A Biomarker for Alzheimer’s Disease Based on Patterns of Regional Brain Atrophy Frontiers in Psychiatry Alzheimer's disease machine learning dementia magnetic resonance imaging FreeSurfer |
| title | A Biomarker for Alzheimer’s Disease Based on Patterns of Regional Brain Atrophy |
| title_full | A Biomarker for Alzheimer’s Disease Based on Patterns of Regional Brain Atrophy |
| title_fullStr | A Biomarker for Alzheimer’s Disease Based on Patterns of Regional Brain Atrophy |
| title_full_unstemmed | A Biomarker for Alzheimer’s Disease Based on Patterns of Regional Brain Atrophy |
| title_short | A Biomarker for Alzheimer’s Disease Based on Patterns of Regional Brain Atrophy |
| title_sort | biomarker for alzheimer s disease based on patterns of regional brain atrophy |
| topic | Alzheimer's disease machine learning dementia magnetic resonance imaging FreeSurfer |
| url | https://www.frontiersin.org/article/10.3389/fpsyt.2019.00953/full |
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