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Gradual Disturbances of the Amplitude of Low-Frequency Fluctuations (ALFF) and Fractional ALFF in Alzheimer Spectrum

Background: Alzheimer’s disease (AD) is a common neurodegenerative disease in which the brain undergoes alterations for decades before symptoms become obvious. Subjective cognitive decline (SCD) have self-complain of persistent decline in cognitive function especially in memory but perform normally...

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Autores principales: Yang, Liu, Yan, Yan, Wang, Yonghao, Hu, Xiaochen, Lu, Jie, Chan, Piu, Yan, Tianyi, Han, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6306691/
https://www.ncbi.nlm.nih.gov/pubmed/30618593
http://dx.doi.org/10.3389/fnins.2018.00975
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author Yang, Liu
Yan, Yan
Wang, Yonghao
Hu, Xiaochen
Lu, Jie
Chan, Piu
Yan, Tianyi
Han, Ying
author_facet Yang, Liu
Yan, Yan
Wang, Yonghao
Hu, Xiaochen
Lu, Jie
Chan, Piu
Yan, Tianyi
Han, Ying
author_sort Yang, Liu
collection PubMed
description Background: Alzheimer’s disease (AD) is a common neurodegenerative disease in which the brain undergoes alterations for decades before symptoms become obvious. Subjective cognitive decline (SCD) have self-complain of persistent decline in cognitive function especially in memory but perform normally on standard neuropsychological tests. SCD with the presence of AD pathology is the transitional stage 2 of Alzheimer’s continuum, earlier than the prodromal stage, mild cognitive impairment (MCI), which seems to be the best target to research AD. In this study, we aimed to detect the transformational patterns of the intrinsic brain activity as the disease burden got heavy. Method: In this study, we enrolled 44 SCD, 55 amnestic MCI (aMCI), 47 AD dementia (d-AD) patients and 57 normal controls (NC) in total. A machine learning classification was utilized to detect identification accuracies between groups by using ALFF, fALFF, and fusing ALFF with fALFF features. Then, we measured the amplitude of the low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) levels in three frequency bands (classic: 0.01–0.1 Hz; slow-5: 0.01–0.027 Hz; and slow-4: 0.027–0.073 Hz) and compared alterations in patients with NC. Results: In the machine learning verification, the identification accuracy of SCD, aMCI, d-AD from NC was higher when fused ALFF and fALFF features (76.44, 81.94, and 91.83%, respectively) than only using ALFF or fALFF features. Several brain regions showed significant differences in ALFF/fALFF within these bands among four groups: brain regions presented decreasing trend of values, including the Cingulum_Mid_R (aal), bilateral inferior cerebellum lobe, bilateral precuneus, and the Cingulum_Ant_R (aal); increasing trend of values were detected in the Hippocampus_L (aal), Frontal_Mid_Orb_R (aal), Frontal_Sup_R (aal) and Paracentral_Lobule_R (aal) as disease progressed. The normalized ALFF/fALFF values of these features were significantly correlated with the neuropsychological test scores. Conclusion: This study revealed gradual disturbances in intrinsic brain activity as the disease progressed: the normal objective performance in SCD may be dependent on compensation; as disease advanced, the cognitive function gradually impaired and decompensated in aMCI, severer in d-AD. Our results indicated that the ALFF and fALFF may help detect the underlying pathological mechanism in AD continuum. Clinical Trial Registration: ClinicalTrials.gov, identifier NCT02353884 and NCT02225964.
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spelling pubmed-63066912019-01-07 Gradual Disturbances of the Amplitude of Low-Frequency Fluctuations (ALFF) and Fractional ALFF in Alzheimer Spectrum Yang, Liu Yan, Yan Wang, Yonghao Hu, Xiaochen Lu, Jie Chan, Piu Yan, Tianyi Han, Ying Front Neurosci Neuroscience Background: Alzheimer’s disease (AD) is a common neurodegenerative disease in which the brain undergoes alterations for decades before symptoms become obvious. Subjective cognitive decline (SCD) have self-complain of persistent decline in cognitive function especially in memory but perform normally on standard neuropsychological tests. SCD with the presence of AD pathology is the transitional stage 2 of Alzheimer’s continuum, earlier than the prodromal stage, mild cognitive impairment (MCI), which seems to be the best target to research AD. In this study, we aimed to detect the transformational patterns of the intrinsic brain activity as the disease burden got heavy. Method: In this study, we enrolled 44 SCD, 55 amnestic MCI (aMCI), 47 AD dementia (d-AD) patients and 57 normal controls (NC) in total. A machine learning classification was utilized to detect identification accuracies between groups by using ALFF, fALFF, and fusing ALFF with fALFF features. Then, we measured the amplitude of the low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) levels in three frequency bands (classic: 0.01–0.1 Hz; slow-5: 0.01–0.027 Hz; and slow-4: 0.027–0.073 Hz) and compared alterations in patients with NC. Results: In the machine learning verification, the identification accuracy of SCD, aMCI, d-AD from NC was higher when fused ALFF and fALFF features (76.44, 81.94, and 91.83%, respectively) than only using ALFF or fALFF features. Several brain regions showed significant differences in ALFF/fALFF within these bands among four groups: brain regions presented decreasing trend of values, including the Cingulum_Mid_R (aal), bilateral inferior cerebellum lobe, bilateral precuneus, and the Cingulum_Ant_R (aal); increasing trend of values were detected in the Hippocampus_L (aal), Frontal_Mid_Orb_R (aal), Frontal_Sup_R (aal) and Paracentral_Lobule_R (aal) as disease progressed. The normalized ALFF/fALFF values of these features were significantly correlated with the neuropsychological test scores. Conclusion: This study revealed gradual disturbances in intrinsic brain activity as the disease progressed: the normal objective performance in SCD may be dependent on compensation; as disease advanced, the cognitive function gradually impaired and decompensated in aMCI, severer in d-AD. Our results indicated that the ALFF and fALFF may help detect the underlying pathological mechanism in AD continuum. Clinical Trial Registration: ClinicalTrials.gov, identifier NCT02353884 and NCT02225964. Frontiers Media S.A. 2018-12-20 /pmc/articles/PMC6306691/ /pubmed/30618593 http://dx.doi.org/10.3389/fnins.2018.00975 Text en Copyright © 2018 Yang, Yan, Wang, Hu, Lu, Chan, Yan and Han. 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) and the copyright owner(s) 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
Yang, Liu
Yan, Yan
Wang, Yonghao
Hu, Xiaochen
Lu, Jie
Chan, Piu
Yan, Tianyi
Han, Ying
Gradual Disturbances of the Amplitude of Low-Frequency Fluctuations (ALFF) and Fractional ALFF in Alzheimer Spectrum
title Gradual Disturbances of the Amplitude of Low-Frequency Fluctuations (ALFF) and Fractional ALFF in Alzheimer Spectrum
title_full Gradual Disturbances of the Amplitude of Low-Frequency Fluctuations (ALFF) and Fractional ALFF in Alzheimer Spectrum
title_fullStr Gradual Disturbances of the Amplitude of Low-Frequency Fluctuations (ALFF) and Fractional ALFF in Alzheimer Spectrum
title_full_unstemmed Gradual Disturbances of the Amplitude of Low-Frequency Fluctuations (ALFF) and Fractional ALFF in Alzheimer Spectrum
title_short Gradual Disturbances of the Amplitude of Low-Frequency Fluctuations (ALFF) and Fractional ALFF in Alzheimer Spectrum
title_sort gradual disturbances of the amplitude of low-frequency fluctuations (alff) and fractional alff in alzheimer spectrum
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6306691/
https://www.ncbi.nlm.nih.gov/pubmed/30618593
http://dx.doi.org/10.3389/fnins.2018.00975
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