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Front. Neurosci. | doi: 10.3389/fnins.2018.00975

Gradual disturbances of the amplitude of low-frequency fluctuations (ALFF) and fractional ALFF in Alzheimer spectrum

Liu Yang1, Yan Yan2, Yonghao Wang2,  Xiaochen Hu3,  Jie Lu1, Piu Chan1,  Tianyi Yan2* and  Ying Han1*
  • 1Xuanwu Hospital, Capital Medical University, China
  • 2Beijing Institute of Technology, China
  • 3Abteilung für Psychiatrie und Psychotherapie, Universitätsklinikum köln, Germany

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.
Conclusions: 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.

Keywords: Alzheimer's disease (AD), subjective cognitive decline (SCD), amnestic mild cognitive impairment (aMCI), Dementia - Alzheimer disease, resting-state functional MRI (rsfMRI), ALFF/fALFF, Classifier (classification tool)

Received: 09 Sep 2018; Accepted: 05 Dec 2018.

Edited by:

Jing Sui, Institute of Automation (CAS), China

Reviewed by:

Rui Li, Institute of Psychology (CAS), China
Zening Fu, Mind Research Network (MRN), United States  

Copyright: © 2018 Yang, Yan, Wang, Hu, Lu, Chan, Yan and Han. 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.

* Correspondence:
Prof. Tianyi Yan, Beijing Institute of Technology, Beijing, 100081, Beijing Municipality, China, yantianyi@bit.edu.cn
Prof. Ying Han, Xuanwu Hospital, Capital Medical University, Beijing, 100053, Beijing Municipality, China, hanying@xwh.ccmu.edu.cn