Alzheimer’s Disease-Related Metabolic Pattern in Diverse Forms of Neurodegenerative Diseases
Dementia is broadly characterized by cognitive and psychological dysfunction that significantly impairs daily functioning. Dementia has many causes including Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), and frontotemporal lobar degeneration (FTLD). Detection and differential diagnosis...
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2021
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oai:doaj.org-article:5711df1740d7470facf7dee62a6332e12021-11-25T17:20:51ZAlzheimer’s Disease-Related Metabolic Pattern in Diverse Forms of Neurodegenerative Diseases10.3390/diagnostics111120232075-4418https://doaj.org/article/5711df1740d7470facf7dee62a6332e12021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4418/11/11/2023https://doaj.org/toc/2075-4418Dementia is broadly characterized by cognitive and psychological dysfunction that significantly impairs daily functioning. Dementia has many causes including Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), and frontotemporal lobar degeneration (FTLD). Detection and differential diagnosis in the early stages of dementia remains challenging. Fueled by AD Neuroimaging Initiatives (ADNI) (Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. As such, the investigators within ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.), a number of neuroimaging biomarkers for AD have been proposed, yet it remains to be seen whether these markers are also sensitive to other types of dementia. We assessed AD-related metabolic patterns in 27 patients with diverse forms of dementia (five had probable/possible AD while others had atypical cases) and 20 non-demented individuals. All participants had positron emission tomography (PET) scans on file. We used a pre-trained machine learning-based AD designation (MAD) framework to investigate the AD-related metabolic pattern among the participants under study. The MAD algorithm showed a sensitivity of 0.67 and specificity of 0.90 for distinguishing dementia patients from non-dementia participants. A total of 18/27 dementia patients and 2/20 non-dementia patients were identified as having AD-like patterns of metabolism. These results highlight that many underlying causes of dementia have similar hypometabolic pattern as AD and this similarity is an interesting avenue for future research.Angus LauIman BeheshtiMandana ModirroustaTiffany A. KolesarAndrew L. GoertzenJi Hyun KoMDPI AGarticleFDG-PETmachine learningsupport vector machinemetabolic classificationneurodegenerative diseasedementiaMedicine (General)R5-920ENDiagnostics, Vol 11, Iss 2023, p 2023 (2021) |
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FDG-PET machine learning support vector machine metabolic classification neurodegenerative disease dementia Medicine (General) R5-920 |
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FDG-PET machine learning support vector machine metabolic classification neurodegenerative disease dementia Medicine (General) R5-920 Angus Lau Iman Beheshti Mandana Modirrousta Tiffany A. Kolesar Andrew L. Goertzen Ji Hyun Ko Alzheimer’s Disease-Related Metabolic Pattern in Diverse Forms of Neurodegenerative Diseases |
description |
Dementia is broadly characterized by cognitive and psychological dysfunction that significantly impairs daily functioning. Dementia has many causes including Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), and frontotemporal lobar degeneration (FTLD). Detection and differential diagnosis in the early stages of dementia remains challenging. Fueled by AD Neuroimaging Initiatives (ADNI) (Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. As such, the investigators within ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.), a number of neuroimaging biomarkers for AD have been proposed, yet it remains to be seen whether these markers are also sensitive to other types of dementia. We assessed AD-related metabolic patterns in 27 patients with diverse forms of dementia (five had probable/possible AD while others had atypical cases) and 20 non-demented individuals. All participants had positron emission tomography (PET) scans on file. We used a pre-trained machine learning-based AD designation (MAD) framework to investigate the AD-related metabolic pattern among the participants under study. The MAD algorithm showed a sensitivity of 0.67 and specificity of 0.90 for distinguishing dementia patients from non-dementia participants. A total of 18/27 dementia patients and 2/20 non-dementia patients were identified as having AD-like patterns of metabolism. These results highlight that many underlying causes of dementia have similar hypometabolic pattern as AD and this similarity is an interesting avenue for future research. |
format |
article |
author |
Angus Lau Iman Beheshti Mandana Modirrousta Tiffany A. Kolesar Andrew L. Goertzen Ji Hyun Ko |
author_facet |
Angus Lau Iman Beheshti Mandana Modirrousta Tiffany A. Kolesar Andrew L. Goertzen Ji Hyun Ko |
author_sort |
Angus Lau |
title |
Alzheimer’s Disease-Related Metabolic Pattern in Diverse Forms of Neurodegenerative Diseases |
title_short |
Alzheimer’s Disease-Related Metabolic Pattern in Diverse Forms of Neurodegenerative Diseases |
title_full |
Alzheimer’s Disease-Related Metabolic Pattern in Diverse Forms of Neurodegenerative Diseases |
title_fullStr |
Alzheimer’s Disease-Related Metabolic Pattern in Diverse Forms of Neurodegenerative Diseases |
title_full_unstemmed |
Alzheimer’s Disease-Related Metabolic Pattern in Diverse Forms of Neurodegenerative Diseases |
title_sort |
alzheimer’s disease-related metabolic pattern in diverse forms of neurodegenerative diseases |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doaj.org/article/5711df1740d7470facf7dee62a6332e1 |
work_keys_str_mv |
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