• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

阿尔茨海默病谱系中低频波动(ALFF)及分数ALFF振幅的渐进性紊乱

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

作者信息

Yang Liu, Yan Yan, Wang Yonghao, Hu Xiaochen, Lu Jie, Chan Piu, Yan Tianyi, Han Ying

机构信息

Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.

School of Life Science, Beijing Institute of Technology, Beijing, China.

出版信息

Front Neurosci. 2018 Dec 20;12:975. doi: 10.3389/fnins.2018.00975. eCollection 2018.

DOI:10.3389/fnins.2018.00975
PMID:30618593
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6306691/
Abstract

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. 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. 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. 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. ClinicalTrials.gov, identifier NCT02353884 and NCT02225964.

摘要

阿尔茨海默病(AD)是一种常见的神经退行性疾病,在症状明显出现之前,大脑会经历数十年的变化。主观认知下降(SCD)表现为自我诉说认知功能持续下降,尤其是记忆力,但在标准神经心理学测试中表现正常。存在AD病理的SCD是阿尔茨海默病连续体的过渡阶段2,早于前驱阶段轻度认知障碍(MCI),而MCI似乎是研究AD的最佳靶点。在本研究中,我们旨在检测随着疾病负担加重,大脑内在活动的转变模式。在本研究中,我们总共招募了44名SCD患者、55名遗忘型MCI(aMCI)患者、47名AD痴呆(d-AD)患者和57名正常对照(NC)。利用机器学习分类,通过使用低频振幅(ALFF)、分数低频振幅(fALFF)以及将ALFF与fALFF特征融合来检测组间的识别准确率。然后,我们测量了三个频段(经典频段:0.01 - 0.1Hz;慢波5频段:0.01 - 0.027Hz;慢波4频段:0.027 - 0.073Hz)的低频波动振幅(ALFF)和分数低频振幅(fALFF)水平,并将患者与NC的变化进行比较。在机器学习验证中,将ALFF和fALFF特征融合时,从NC中识别SCD、aMCI、d-AD的准确率(分别为76.44%、81.94%和91.83%)高于仅使用ALFF或fALFF特征。在这四组中,几个脑区在这些频段内的ALFF/fALFF存在显著差异:脑区呈现值下降趋势的包括扣带回中部右侧(AAL)、双侧小脑下叶、双侧楔前叶以及扣带回前部右侧(AAL);随着疾病进展,在海马体左侧(AAL)、额中眶回右侧(AAL)、额上回右侧(AAL)和中央旁小叶右侧(AAL)检测到值呈上升趋势。这些特征的归一化ALFF/fALFF值与神经心理学测试分数显著相关。本研究揭示了随着疾病进展大脑内在活动的逐渐紊乱:SCD中正常的客观表现可能依赖于代偿;随着疾病进展,认知功能在aMCI中逐渐受损并失代偿,在d-AD中更严重。我们的结果表明,ALFF和fALFF可能有助于检测AD连续体中的潜在病理机制。ClinicalTrials.gov标识符:NCT02353884和NCT02225964。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/020f/6306691/4292a8d2e13e/fnins-12-00975-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/020f/6306691/a8ff5a532503/fnins-12-00975-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/020f/6306691/5e9a2822f4f8/fnins-12-00975-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/020f/6306691/37956db4ddf9/fnins-12-00975-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/020f/6306691/53b655abe888/fnins-12-00975-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/020f/6306691/173193e92671/fnins-12-00975-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/020f/6306691/fdcb9641449b/fnins-12-00975-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/020f/6306691/4292a8d2e13e/fnins-12-00975-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/020f/6306691/a8ff5a532503/fnins-12-00975-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/020f/6306691/5e9a2822f4f8/fnins-12-00975-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/020f/6306691/37956db4ddf9/fnins-12-00975-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/020f/6306691/53b655abe888/fnins-12-00975-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/020f/6306691/173193e92671/fnins-12-00975-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/020f/6306691/fdcb9641449b/fnins-12-00975-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/020f/6306691/4292a8d2e13e/fnins-12-00975-g007.jpg

相似文献

1
Gradual Disturbances of the Amplitude of Low-Frequency Fluctuations (ALFF) and Fractional ALFF in Alzheimer Spectrum.阿尔茨海默病谱系中低频波动(ALFF)及分数ALFF振幅的渐进性紊乱
Front Neurosci. 2018 Dec 20;12:975. doi: 10.3389/fnins.2018.00975. eCollection 2018.
2
Altered Patterns of Amplitude of Low-Frequency Fluctuations and Fractional Amplitude of Low-Frequency Fluctuations Between Amnestic and Vascular Mild Cognitive Impairment: An ALE-Based Comparative Meta-Analysis.遗忘型与血管型轻度认知障碍之间低频波动幅度和低频波动分数幅度的改变模式:基于激活可能性估计的比较性荟萃分析
Front Aging Neurosci. 2021 Aug 31;13:711023. doi: 10.3389/fnagi.2021.711023. eCollection 2021.
3
Frequency-Dependent Changes in the Amplitude of Low-Frequency Fluctuations in Mild Cognitive Impairment with Mild Depression.轻度认知障碍伴轻度抑郁患者低频振幅的频率依赖性变化。
J Alzheimers Dis. 2017;58(4):1175-1187. doi: 10.3233/JAD-161282.
4
Frequency-dependent changes in fractional amplitude of low-frequency oscillations in Alzheimer's disease: a resting-state fMRI study.阿尔茨海默病低频振幅分数变化的频率依赖性:一项静息态 fMRI 研究。
Brain Imaging Behav. 2020 Dec;14(6):2187-2201. doi: 10.1007/s11682-019-00169-6.
5
Altered Frequency-Dependent Brain Activation and White Matter Integrity Associated With Cognition in Characterizing Preclinical Alzheimer's Disease Stages.在临床前阿尔茨海默病阶段特征描述中,与认知相关的频率依赖性脑激活和白质完整性改变
Front Hum Neurosci. 2021 Feb 16;15:625232. doi: 10.3389/fnhum.2021.625232. eCollection 2021.
6
Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: a resting-state fMRI study.遗忘型轻度认知障碍低频振幅的频率依赖性变化:静息态 fMRI 研究。
Neuroimage. 2011 Mar 1;55(1):287-95. doi: 10.1016/j.neuroimage.2010.11.059. Epub 2010 Nov 28.
7
Spontaneous brain activity in patients with central retinal artery occlusion: a resting-state functional MRI study using machine learning.基于机器学习的静息态功能磁共振成像在视网膜中央动脉阻塞患者自发性脑活动研究中的应用。
Neuroreport. 2024 Aug 7;35(12):790-799. doi: 10.1097/WNR.0000000000002068. Epub 2024 Jun 21.
8
Amplitude of low-frequency fluctuations in multiple-frequency bands in patients with intracranial tuberculosis: a prospective cross-sectional study.颅内结核患者多频段低频波动幅度:一项前瞻性横断面研究。
Quant Imaging Med Surg. 2022 Aug;12(8):4120-4134. doi: 10.21037/qims-22-17.
9
Frequency-specific alternations in the amplitude of low-frequency fluctuations in chronic tinnitus.慢性耳鸣低频波动幅度的频率特异性改变
Front Neural Circuits. 2015 Oct 29;9:67. doi: 10.3389/fncir.2015.00067. eCollection 2015.
10
An Effective Brain Imaging Biomarker for AD and aMCI: ALFF in Slow-5 Frequency Band.一种用于阿尔茨海默病(AD)和轻度认知障碍(aMCI)的有效脑成像生物标志物:慢5频段的局部低频波动(ALFF)
Curr Alzheimer Res. 2021;18(1):45-55. doi: 10.2174/1567205018666210324130502.

引用本文的文献

1
Resting-State fMRI Reveals the Neural Correlates of Acupuncture in the Treatment of Vascular Cognitive Impairment.静息态功能磁共振成像揭示针刺治疗血管性认知障碍的神经关联。
Clin Interv Aging. 2025 Aug 8;20:1191-1204. doi: 10.2147/CIA.S529416. eCollection 2025.
2
Uncovering abnormal gray and white matter connectivity patterns in Alzheimer's disease spectrum: a dynamic graph theory analysis for early detection.揭示阿尔茨海默病谱系中异常的灰质和白质连接模式:用于早期检测的动态图论分析
Front Aging Neurosci. 2025 Jul 22;17:1589018. doi: 10.3389/fnagi.2025.1589018. eCollection 2025.
3
Brain functional alterations in early stage of coal workers' pneumoconiosis with alcoholism: insights from a resting-state fMRI investigation.

本文引用的文献

1
Amnestic mild cognitive impairment individuals with dissimilar pathologic origins show common regional vulnerability in the default mode network.具有不同病理起源的遗忘型轻度认知障碍个体在默认模式网络中表现出共同的区域易损性。
Alzheimers Dement (Amst). 2018 Sep 20;10:717-725. doi: 10.1016/j.dadm.2018.08.004. eCollection 2018.
2
Aberrant memory system connectivity and working memory performance in subjective cognitive decline.主观认知下降中的记忆系统连接异常和工作记忆表现。
Neuroimage. 2019 Jan 15;185:556-564. doi: 10.1016/j.neuroimage.2018.10.015. Epub 2018 Oct 9.
3
Not all, but specific types of cognitive complaints predict decline to MCI.
合并酒精中毒的煤工尘肺早期脑功能改变:静息态功能磁共振成像研究的见解
Front Neurosci. 2025 Jun 16;19:1610657. doi: 10.3389/fnins.2025.1610657. eCollection 2025.
4
Investigating Resting-State Brain Activity in Adolescents with High-Functioning Autism Spectrum Disorder: Linking Fractional Amplitude of Low-Frequency Fluctuation to Clinical Symptoms.研究高功能自闭症谱系障碍青少年的静息态脑活动:低频波动分数振幅与临床症状的关联
J Autism Dev Disord. 2025 Jun 20. doi: 10.1007/s10803-025-06867-z.
5
Prediction Model and Nomogram for Amyloid Positivity Using Clinical and MRI Features in Individuals With Subjective Cognitive Decline.使用主观认知衰退个体的临床和MRI特征预测淀粉样蛋白阳性的模型和列线图
Hum Brain Mapp. 2025 Jun 1;46(8):e70238. doi: 10.1002/hbm.70238.
6
Relationship Between Resting-State Functional Magnetic Resonance Imaging and Different Time in Target Glucose Range in Elderly Patients with Type 2 Diabetes Mellitus.2型糖尿病老年患者静息态功能磁共振成像与目标血糖范围内不同时间的关系
Diabetes Metab Syndr Obes. 2025 Apr 16;18:1151-1164. doi: 10.2147/DMSO.S510628. eCollection 2025.
7
Fractional amplitude of low-frequency fluctuations during music-evoked autobiographical memories in neurotypical older adults.典型老年人大脑中音乐诱发的自传体记忆期间低频波动的分数振幅
Front Neurosci. 2025 Jan 23;18:1479150. doi: 10.3389/fnins.2024.1479150. eCollection 2024.
8
The amplitude of low frequency fluctuation and spontaneous brain activity alterations in age-related macular degeneration.年龄相关性黄斑变性中低频波动幅度及自发脑活动改变
Front Med (Lausanne). 2025 Jan 22;11:1507971. doi: 10.3389/fmed.2024.1507971. eCollection 2024.
9
The relationship between fractional amplitude of low-frequency fluctuations (fALFF) and the severity of neglect in patients with unilateral spatial neglect (USN) after stroke: A functional near-infrared spectroscopy study.中风后单侧空间忽视(USN)患者低频振幅分数(fALFF)与忽视严重程度之间的关系:一项功能近红外光谱研究。
IBRO Neurosci Rep. 2024 Dec 12;18:31-38. doi: 10.1016/j.ibneur.2024.12.005. eCollection 2025 Jun.
10
Using interpretable deep learning radiomics model to diagnose and predict progression of early AD disease spectrum: a preliminary [F]FDG PET study.使用可解释的深度学习放射组学模型诊断和预测早期阿尔茨海默病疾病谱的进展:一项初步的[F]FDG PET研究。
Eur Radiol. 2025 May;35(5):2620-2633. doi: 10.1007/s00330-024-11158-9. Epub 2024 Oct 31.
并非所有认知方面的主诉,而是特定类型的认知主诉预示着会发展为轻度认知障碍。
Neurology. 2018 Jul 24;91(4):153-154. doi: 10.1212/WNL.0000000000005872. Epub 2018 Jun 29.
4
NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease.NIA-AA 研究框架:迈向阿尔茨海默病的生物学定义。
Alzheimers Dement. 2018 Apr;14(4):535-562. doi: 10.1016/j.jalz.2018.02.018.
5
A more randomly organized grey matter network is associated with deteriorating language and global cognition in individuals with subjective cognitive decline.一个更随机组织的灰质网络与主观认知下降个体的语言和整体认知能力下降有关。
Hum Brain Mapp. 2018 Aug;39(8):3143-3151. doi: 10.1002/hbm.24065. Epub 2018 Mar 30.
6
Regularized continuous-time Markov Model via elastic net.通过弹性网络的正则化连续时间马尔可夫模型
Biometrics. 2018 Sep;74(3):1045-1054. doi: 10.1111/biom.12868. Epub 2018 Mar 13.
7
Group-Level Progressive Alterations in Brain Connectivity Patterns Revealed by Diffusion-Tensor Brain Networks across Severity Stages in Alzheimer's Disease.通过扩散张量脑网络揭示的阿尔茨海默病严重程度阶段脑连接模式的组水平渐进性改变
Front Aging Neurosci. 2017 Jul 7;9:215. doi: 10.3389/fnagi.2017.00215. eCollection 2017.
8
Stop Alzheimer's before it starts.在阿尔茨海默病发作之前阻止它。
Nature. 2017 Jul 12;547(7662):153-155. doi: 10.1038/547153a.
9
Intrinsic cerebral activity at resting state in adults with major depressive disorder: A meta-analysis.成年重度抑郁症患者静息状态下的脑内固有活动:一项荟萃分析。
Prog Neuropsychopharmacol Biol Psychiatry. 2017 Apr 3;75:157-164. doi: 10.1016/j.pnpbp.2017.02.001. Epub 2017 Feb 4.
10
Identify the Atrophy of Alzheimer's Disease, Mild Cognitive Impairment and Normal Aging Using Morphometric MRI Analysis.使用形态计量磁共振成像分析识别阿尔茨海默病、轻度认知障碍和正常衰老中的萎缩
Front Aging Neurosci. 2016 Oct 18;8:243. doi: 10.3389/fnagi.2016.00243. eCollection 2016.