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阿尔茨海默病的早期检测：使用返回随机游走链接预测器检测不对称性。

Early Detection of Alzheimer's Disease: Detecting Asymmetries with a Return Random Walk Link Predictor.

作者信息

Curado Manuel, Escolano Francisco, Lozano Miguel A, Hancock Edwin R

机构信息

Polytechnic School, Catholic University of Murcia, 30107 Murcia, Spain.

Department of Computer Science and AI, University of Alicante, 03690 Alicante, Spain.

出版信息

Entropy (Basel). 2020 Apr 19;22(4):465. doi: 10.3390/e22040465.

DOI:10.3390/e22040465
PMID:33286239
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7516949/
Abstract

Alzheimer's disease has been extensively studied using undirected graphs to represent the correlations of BOLD signals in different anatomical regions through functional magnetic resonance imaging (fMRI). However, there has been relatively little analysis of this kind of data using directed graphs, which potentially offer the potential to capture asymmetries in the interactions between different anatomical brain regions. The detection of these asymmetries is relevant to detect the disease in an early stage. For this reason, in this paper, we analyze data extracted from fMRI images using the algorithm to infer a directed graph from the available BOLD signals, and then seek to determine asymmetries between the left and right hemispheres of the brain using a directed version of the Return Random Walk (RRW). Experimental evaluation of this method reveals that it leads to the identification of anatomical brain regions known to be implicated in the early development of Alzheimer's disease in clinical studies.

摘要

阿尔茨海默病已通过无向图进行了广泛研究，以通过功能磁共振成像（fMRI）来表示不同解剖区域中血氧水平依赖（BOLD）信号的相关性。然而，使用有向图对这类数据的分析相对较少，而有向图可能具有捕捉不同解剖脑区之间相互作用不对称性的潜力。检测这些不对称性与疾病早期检测相关。因此，在本文中，我们使用该算法分析从fMRI图像中提取的数据，以从可用的BOLD信号推断出有向图，然后试图使用有向版本的返回随机游走（RRW）来确定大脑左右半球之间的不对称性。对该方法的实验评估表明，它能够识别临床研究中已知与阿尔茨海默病早期发展有关的解剖脑区。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/74d5/7516949/27487bc46d47/entropy-22-00465-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/74d5/7516949/27487bc46d47/entropy-22-00465-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/74d5/7516949/74d29f2fbdc3/entropy-22-00465-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/74d5/7516949/675a5b4ea3c0/entropy-22-00465-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/74d5/7516949/93c74fa4748e/entropy-22-00465-g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/74d5/7516949/85a422898156/entropy-22-00465-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/74d5/7516949/ed902c038697/entropy-22-00465-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/74d5/7516949/b6eb6abff0b2/entropy-22-00465-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/74d5/7516949/8e0c27b0178f/entropy-22-00465-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/74d5/7516949/ae39caac481c/entropy-22-00465-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/74d5/7516949/71d26b3c504b/entropy-22-00465-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/74d5/7516949/27487bc46d47/entropy-22-00465-g012.jpg

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本文引用的文献

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Entropy (Basel). 2020 Feb 20;22(2):239. doi: 10.3390/e22020239.
2
EEG Characterization of the Alzheimer's Disease Continuum by Means of Multiscale Entropies.基于多尺度熵的阿尔茨海默病连续体的脑电图特征分析
Entropy (Basel). 2019 May 28;21(6):544. doi: 10.3390/e21060544.
3
Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation.
Crosstalk between Depression and Dementia with Resting-State fMRI Studies and Its Relationship with Cognitive Functioning.
静息态功能磁共振成像研究中抑郁症与痴呆症之间的相互作用及其与认知功能的关系
Biomedicines. 2021 Jan 16;9(1):82. doi: 10.3390/biomedicines9010082.
卷积神经网络在阿尔茨海默病分类中的应用：综述与可重现性评估。
Med Image Anal. 2020 Jul;63:101694. doi: 10.1016/j.media.2020.101694. Epub 2020 May 1.
4
Differentiating Between Healthy Control Participants and Those with Mild Cognitive Impairment Using Volumetric MRI Data.利用容积 MRI 数据区分健康对照参与者和轻度认知障碍患者。
J Int Neuropsychol Soc. 2019 Sep;25(8):800-810. doi: 10.1017/S135561771900047X. Epub 2019 May 27.
5
Predicting progression from mild cognitive impairment to Alzheimer's disease on an individual subject basis by applying the CARE index across different independent cohorts.通过在不同独立队列中应用CARE指数，在个体层面预测从轻度认知障碍进展为阿尔茨海默病的情况。
Aging (Albany NY). 2019 Apr 30;11(8):2185-2201. doi: 10.18632/aging.101883.
6
Predicting conversion from mild cognitive impairment to Alzheimer's disease using brain H-MRS and volumetric changes: A two- year retrospective follow-up study.使用脑 H-MRS 和容积变化预测轻度认知障碍向阿尔茨海默病的转化：一项为期两年的回顾性随访研究。
Neuroimage Clin. 2019;23:101843. doi: 10.1016/j.nicl.2019.101843. Epub 2019 Apr 30.
7
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J Neurosci Methods. 2019 Jul 1;322:88-95. doi: 10.1016/j.jneumeth.2019.04.013. Epub 2019 May 2.
8
Predicting Early Mild Cognitive Impairment With Free Recall: The Primacy of Primacy.通过自由回忆预测早期轻度认知障碍：首因效应的首要地位。
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9
Across the blood-brain barrier: Neurotherapeutic screening and characterization of naringenin as a novel CRMP-2 inhibitor in the treatment of Alzheimer's disease using bioinformatics and computational tools.穿越血脑屏障：使用生物信息学和计算工具筛选和表征柚皮素作为治疗阿尔茨海默病的新型 CRMP-2 抑制剂的神经治疗
Comput Biol Med. 2018 Jul 1;98:168-177. doi: 10.1016/j.compbiomed.2018.05.012. Epub 2018 May 16.
10
Complex network analysis of resting-state fMRI of the brain.大脑静息态功能磁共振成像的复杂网络分析
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:3598-3601. doi: 10.1109/EMBC.2016.7591506.