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Structural analysis of Alzheimer's disease brain networks based on fractal features
Papers and Reports | 更新时间：2024-11-04
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Structural analysis of Alzheimer's disease brain networks based on fractal features
- Chinese Journal of Intelligent Science and Technology Vol. 6, Issue 3, Pages: 329-337(2024)
- 作者机构：
大连交通大学软件学院，辽宁 大连 116028
- 作者简介：
- 基金信息：Liaoning Provincial Department of Education Basic Scientific Research Project(JYTMS20230011)
DOI：10.11959/j.issn.2096-6652.202412
CLC： TP399Received：01 February 2024，
Revised：2024-05-13，
Published：15 September 2024
- 稿件说明：
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孙思翔,李东艳.基于分形特征的阿尔茨海默病脑网络结构分析[J].智能科学与技术学报,2024,06(03):329-337.
SUN Sixiang,LI Dongyan.Structural analysis of Alzheimer's disease brain networks based on fractal features[J].Chinese Journal of Intelligent Science and Technology,2024,06(03):329-337.
孙思翔,李东艳.基于分形特征的阿尔茨海默病脑网络结构分析[J].智能科学与技术学报,2024,06(03):329-337. DOI： 10.11959/j.issn.2096-6652.202412.
SUN Sixiang,LI Dongyan.Structural analysis of Alzheimer's disease brain networks based on fractal features[J].Chinese Journal of Intelligent Science and Technology,2024,06(03):329-337. DOI： 10.11959/j.issn.2096-6652.202412.
Structural analysis of Alzheimer's disease brain networks based on fractal features
摘要
阿尔茨海默病患者的功能脑网络在小世界及聚类系数等拓扑属性上与认知正常者存在差异。为进一步揭示阿尔茨海默病患者脑网络的结构特征及其与认知正常者脑网络结构的差异，引入分形特征进行对比分析。实验结果表明，阿尔茨海默病患者与认知正常者的脑网络在相同盒覆盖半径下的最小盒子数与连接比例存在显著差异。脑网络阈值为0.4时，认知正常者脑网络的连接比例值分布在区间[0
1]，而阿尔茨海默病患者脑网络的连接比例值分布在区间(0.5
1)。同时，不同阈值下，阿尔茨海默病患者脑网络的平均连接比例均高于认知正常者。上述网络结构差异可为阿尔茨海默病的诊断提供一种新参考。
Abstract
It had been demonstrated that the functional brain networks of Alzheimer’s patients exhibit topological properties distinct from those of cognitively normal individuals
including small-world and clustering coefficients. To further elucidate the structural characteristics of the brain networks of Alzheimer’s patients and their differences in brain network structure with cognitively normal individuals
fractal features were introduced for comparative analysis. Experimental results demonstrated a significant difference in the minimum number of boxes and connection ratio between brain networks of Alzheimer’s patients and cognitively normal individuals at the same box-covering radius. At the brain network threshold of 0.4
the connection ratio values of cognitively normal individuals’ brain networks were distributed in the [0
1] interval
while the connection ratio values of Alzheimer’s patients’ brain networks were distributed in the (0.5
1) interval. Furthermore
the average connection ratio of Alzheimer’s patients’ brain networks was found to be higher than that of cognitively normal individuals at different thresholds. These differences in network structure provide a new reference for the diagnosis of Alzheimer’s disease.
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DELBEUCK X, LINDEN M V D, COLLETTE F. Alzheimer's disease as a disconnection syndrome?[J]. Neuropsychol Rev, 2003, 13(2): 79-92.
FOX M D, RAICHLE M E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging[J]. Nat Rev Neurosci, 2007, 8(9): 700-711.
李姗, 李永超, 邹颖, 等. 基于多模态影像下的抑郁症大脑异常[J]. 智能科学与技术学报, 2020, 2(2): 116-125.
LI S, LI Y C, ZOU Y, et al. Brain abnormalities in depression based on multimodal imaging[J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(2): 116-125.
梁夏, 王金辉, 贺永. 人脑连接组研究: 脑结构网络和脑功能网络[J]. 科学通报, 2010, 55(16): 1565-1583.
LIANG X, WANG J H, HE Y. Human connectome: structural and functional brain networks[J]. Chinese Science Bulletin, 2010, 55(16): 1565-1583.
李伟凯, 高欣, 纪同俭, 等. 用于轻度认知障碍诊断的群体相似约束功能脑网络建模方法[J]. 智能科学与技术学报, 2019, 1(2): 145-153.
LI W K, GAO X, JI T J, et al. Method of functional brain network modeling with group similarity constraint for mild cognitive impairment classification[J]. Chinese Journal of Intelligent Science and Technology, 2019, 1(2): 145-153.
黄嘉爽, 接标, 丁卫平, 等. 脑网络分析方法及其应用[J]. 数据采集与处理, 2021, 36(4): 648-663.
HUANG J S, JIE B, DING W P, et al. Brain network analysis: method and application[J]. Journal of Data Acquisition and Processing, 2021, 36(4): 648-663.
SALVADOR R, SUCKLING J, COLEMAN M R, et al. Neurophysiological architecture of functional magnetic resonance images of human brain[J]. Cerebral Cortex, 2005, 15(9): 1332-1342.
LAURIENTI P, HUGENSCHMIDT C, HAYASAKA S. Modularity maps reveal community structure in the resting human brain[J]. Nature Precedings, 2009, 2009(4): 1-1.
MEUNIER D, ACHARD S, MORCOM A, et al. Age-related changes in modular organization of human brain functional networks[J]. Neuroimage, 2009, 44(3): 715-723.
SUPEKAR K, MENON V, RUBIN D, et al. Network analysis of intrinsic functional brain connectivity in Alzheimer's disease[J]. PLOS Computational Biology, 2008, 4(6): e1000100.
STAM C J, DE HAAN W, DAFFERTSHOFER A, et al. Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease[J]. Brain, 2009, 132(1): 213-224.
倪冰洁, 李炜, 陈曦. 阿尔兹海默症脑网络演化建模[J]. 智能科学与技术学报, 2019, 1(4): 369-378.
NI B J, LI W, CHEN X. Brain network evolution modeling based on Alzheimer's disease[J]. Chinese Journal of Intelligent Science and Technology, 2019, 1(4): 369-378.
SONG C, HAVLIN S, MAKSE H A. Self-similarity of complex networks[J]. Nature, 2005, 433(7024): 392-395.
SONG C, GALLOS L K, HAVLIN S, et al. How to calculate the fractal dimension of a complex network: the box covering algorithm[J]. Journal of Statistical Mechanics: Theory and Experiment, 2007, 2007(3): 297-316.
GALLOS L K, SONG C, MAKSE H A. A review of fractality and self-similarity in complex networks[J]. Physica A: Statistical Mechanics and its Applications, 2007, 386(2): 686-691.
SCHNEIDER C M, KESSELRING T A, ANDRADE JR J S, et al. Box-covering algorithm for fractal dimension of complex networks[J]. Physical Review E, 2012, 86(1): 016707.
KIM J S, GOH K I, KAHNG B, et al. Fractality and self-similarity in scale-free networks[J]. New Journal of Physics, 2007, 9(6): 177.
GAO L, HU Y, DI Z. Accuracy of the ball-covering approach for fractal dimensions of complex networks and a rank-driven algorithm[J]. Physical Review E, 2008, 78(4): 046109.
ZHOU W X, JIANG Z Q, SORNETTE D. Exploring self-similarity of complex cellular networks: the edge-covering method with simulated annealing and log-periodic sampling[J]. Physica A: Statistical Mechanics and its Applications, 2007, 375(2): 741-752.
杨翠红, 张璐雯, 王子兴, 等. 分形分析在肺部疾患鉴别中的应用进展[J]. 医学综述, 2021, 27(16): 3256-3262.
YANG C H, ZHANG L W, WANG Z X, et al. Application progress of fractal analysis in the differentiation of lung diseases[J]. Medical Review, 2021, 27(16): 3256-3262.
王飞, 魏林琳. 基于复杂网络的空中交通流分形特征分析[J]. 科学技术与工程, 2023, 23(9): 3982-3990.
WANG F, WEI L L. Fractal characteristics analysis of air traffic flow based on complex network[J]. Science Technology and Engineering, 2023, 23(9): 3982-3990.
文军, 罗心雨, 钟佳轩, 等. 中国国内航空网络的分形特征研究[J]. 工业工程, 2022, 25(5): 113-119.
WEN J, LUO X Y, ZHONG J X, et al. A study of fractal features of Chinese domestic airline network[J]. Industrial Engineering Journal, 2022, 25(5): 113-119.
潘浩, 郑巍, 张紫枫, 等. 软件网络分形结构特征研究[J]. 计算机科学, 2019, 46(2): 166-170.
PAN H, ZHENG W, ZHANG Z F, et al. Study on fractal features of software networks[J]. Computer Science, 2019, 46(2): 166-170.
柴洁玮, 樊泽泽, 柳倩, 等. 基于独立成分分析的抑郁症脑网络属性分析[J]. 科学技术与工程, 2020, 20(25): 10313-10318.
CHAI J W, FAN Z Z, LIU Q, et al. Study of brain network characteristics in patients with depression based on ICA[J]. Science Technology and Engineering, 2020, 20(25): 10313-10318.
GALLOS L K, MAKSE H A, SIGMAN M. A small world of weak ties provides optimal global integration of self-similar modules in functional brain networks[J]. Proceedings of the National Academy of Sciences, 2012, 109(8): 2825-2830.
李东艳. 面向脑网络分析的分形复杂网络建模与脆弱性研究[D]. 大连: 大连理工大学, 2020.
LI D Y. Research on the modeling and vulnerability of fractal complex networks for brain network analysis[D]. Dalian: Dalian University of Technology, 2020.
李雨明, 何璇, 朱宏博, 等. 结合表型信息的阿尔兹海默症图卷积神经网络分类方法研究[J]. 中国生物医学工程学报, 2021, 40(2): 177-187.
LI Y M, HE X, ZHU H B, et al. Study on classification method of Alzheimer's disease convolutional neural network combined with phenotypic information[J]. Chinese Journal of Biomedical Engineering, 2021, 40(2): 177-187.
范炤, 姚丽丽. 基于功能磁共振成像的个体脑网络在阿尔兹海默症早期诊断中的应用[J]. 生物医学工程研究, 2021, 40(2): 126-130.
FAN Z, YAO L L. Application of functional magnetic resonance imaging based individual brain network in early diagnosis of Alzheimer's disease[J]. Journal of Biomedical Engineering Research, 2021, 40(2): 126-130.
李涛, 邱震钰, 李瑶, 等. 基于时变特性的多层脑网络拓扑属性分析及脑疾病分类[J]. 科学技术与工程, 2023, 23(19): 8114-8123.
LI T, QIU Z Y, LI Y, et al. Topology attribute analysis and brain disease classification of multilayer brain network based on time-varying characteristics[J]. Science Technology and Engineering, 2023, 23(19): 8114-8123.
黄毅, 张胜, 戴维凯, 等. 基于信息维数的加权网络分形特性分析[J]. 复杂系统与复杂性科学, 2018, 15(2): 26-33.
HUANG Y, ZHANG S, DAI W K, et al. Fractal analysis of weighted networks by a modified information dimension method[J]. Complex Systems and Complexity Science, 2018, 15(2): 26-33.
曹珍. 复杂网络分形特性的统计研究[D]. 哈尔滨: 哈尔滨工业大学, 2013.
CAO Z. Statistical research on fractal characteristic of complex networks[D]. Harbin: Harbin Institute of Technology, 2013.
WANG J, WANG X, XIA M, et al. GRETNA: a graph theoretical network analysis toolbox for imaging connectomics[J]. Frontiers in Human Neuroscience, 2015, 9: 386.
TZOURIO-MAZOYER N, LANDEAU B, PAPATHANASSIOU D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain[J]. Neuroimage, 2002, 15(1): 273-289.
WATTS D J, STROGATZ S H. Collective dynamics of 'small-world' networks[J]. Nature, 1998, 393(6684): 440-442.
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