阿尔茨海默症三网动力学因果性分析

毛笔; 李尤君; 姚楠; 李晨曦; 黄子罡

doi:10.7652/xjtuxb202208018

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阿尔茨海默症三网动力学因果性分析

—————————————————————— | 更新时间：2025-07-09

- 阿尔茨海默症三网动力学因果性分析
- Kinetic Analysis on Causality of Triple Network in AD Patients
- 西安交通大学学报 2022年56卷第8期页码：168-177
- 作者机构：
  
  1. 西安交通大学生物医学信息工程教育部重点实验室,西安,710049
  2. 西安交通大学类脑智能研究中心,西安,710049
  3. 西安理工大学理学院,西安,710048
  4. 空军军医大学军事医学心理学系,西安,710032
- 作者简介：
  
  : 2022-03-03。作者简介: 毛笔(1998—),男,硕士生
  李尤君(通信作者),男,助理教授。基金项目: 国家重点研发计划资助项目(2021ZD0201300)
- 基金信息：
  
  : 2022-03-03。作者简介: 毛笔(1998—),男,硕士生;李尤君(通信作者),男,助理教授。基金项目: 国家重点研发计划资助项目(2021ZD0201300);国家自然科学基金资助项目(11975178);王宽诚教育基金会资助项目
- DOI：10.7652/xjtuxb202208018
  中图分类号： R318
- 网络首发：2022-08-10，
  
  纸质出版：2022
- 稿件说明：
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毛笔, 李尤君, 姚楠, 等. 阿尔茨海默症三网动力学因果性分析[J]. 西安交通大学学报, 2022,56(8):168-177.

MAO Bi, LI Youjun, YAO Nan, et al. Kinetic Analysis on Causality of Triple Network in AD Patients[J]. 2022, 56(8): 168-177.
毛笔, 李尤君, 姚楠, 等. 阿尔茨海默症三网动力学因果性分析[J]. 西安交通大学学报, 2022,56(8):168-177. DOI： 10.7652/xjtuxb202208018.

MAO Bi, LI Youjun, YAO Nan, et al. Kinetic Analysis on Causality of Triple Network in AD Patients[J]. 2022, 56(8): 168-177. DOI： 10.7652/xjtuxb202208018.

摘要

为深入探究阿尔茨海默症(Alzheimer's disease

AD)对患者大脑功能网络之间因果性连接的具体损伤情况

采用非线性的因果性分析方法——收敛交叉映射方法

区别于传统的基于线性模型的脑网络分析方法

研究在静息状态下阿尔茨海默症对默认模式网络(DMN)、中央执行网络(CEN)和显著网络(SN)中各个脑区之间因果性连接的影响

以及这种变化与临床认知水平指标之间的关联性。研究结果显示

静息状态下阿尔茨海默症患者的DMN、CEN和SN网络脑区之间的因果性连接普遍降低

而DMN和CEN网络中的因果性连接变化与认知行为相关性较强。本文研究从动力学角度探索了阿尔茨海默症对大脑3个重要功能网络因果性连接的损伤情况

可为深入理解静息态下AD大脑内在的动力学变化特征提供帮助。

Abstract

This study focuses on exploring the specific damage of causal connections between brain functional networks in patients with Alzheimer's disease(AD). A nonlinear causality analysis method

namely convergent cross mapping

which is different from the brain network analysis method based on the conventional linear model

is used to study the effects of Alzheimer's disease on the causal connections between brain regions in the default mode network(DMN)

central executive network(CEN)and salience network(SN)in the resting state

as well as the association between such effects and clinical indicators. The results show that the causal connections between brain regions in DMN

CEN and SN networks generally decreased in patients with Alzheimer's disease

and the altered causal connections in DMN and CEN networks were strongly correlated with subjects' cognitive level. This study examines the damage of the causal connections between three important functional networks of the brain caused by Alzheimer's disease from the perspective of kinetics

which is helpful for the in-depth understanding of the dynamic characteristics of the AD brain in the resting state.

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references

FRISTON K J, MECHELLI A, TURNER R, et al. Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics [J]. NeuroImage, 2000, 12(4): 466-477.

BRESSLER S L, MENON V. Large-scale brain networks in cognition: emerging methods and principles [J]. Trends in Cognitive Sciences, 2010, 14(6): 277-290.

SRIDHARAN D, LEVITIN D J, MENON V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks [J]. Proceedings of the National Academy of Sciences, 2008, 105(34): 12569-12574.

LI Youjun, YAO Hongxiang, LIN Pan, et al. Frequency-dependent altered functional connections of default mode network in Alzheimer's disease [J]. Frontiers in Aging Neuroscience, 2017, 9: 259.

TOUSSAINT P J, MAIZ S, COYNEL D, et al. Characteristics of the default mode functional connectivity in normal ageing and Alzheimer's disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements [J]. NeuroImage, 2014, 101: 778-786.

MENON V. Large-scale brain networks and psychopathology: a unifying triple network model [J]. Trends in Cognitive Sciences, 2011, 15(10): 483-506.

GULTEPE E, HE Bin. A linear/nonlinear characterization of resting state brain networks in fMRI time series [J]. Brain Topography, 2013, 26(1): 39-49.

WISMÜLLER A, ABIDIN A Z, D'SOUZA A M, et al. Nonlinear functional connectivity network recovery in the human brain with mutual connectivity analysis(MCA): convergent cross-mapping and non-metric clustering [C]∥Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging. Bellingham, WA, USA: SPIE, 2015: 94170M.

MCCANN K, HASTINGS A, HUXEL G R. Weak trophic interactions and the balance of nature [J]. Nature, 1998, 395(6704): 794-798.

SUGIHARA G, MAY R, YE Hao, et al. Detecting causality in complex ecosystems [J]. Science, 2012, 338(6106): 496-500.

黄文敏, 曹玲灿, 陈清坚, 等. 脑功能网络建模及分析研究进展 [J]. 中国科学(物理学·力学·天文学), 2020, 50(1): 81-93.

HUANG Wenmin, CAO Lingcan, CHEN Qingjian, et al. Modelling and analysis of brain functional network [J]. Scientia Sinica(Physica, Mechanica Astronomica), 2020, 50(1): 81-93.

姚楠, 苏春旺, 李尤君, 等. 人脑默认模式网络的动力学行为 [J]. 物理学报, 2020, 69(8): 134-145.

YAO Nan, SU Chunwang, LI Youjun, et al. Dynamics of the default mode network in human brain [J]. Acta Physica Sinica, 2020, 69(8): 134-145.

ASCIOTI F A, BELTRAMI E, CARROLL T O, et al. Is there chaos in plankton dynamics? [J]. Journal of Plankton Research, 1993, 15(6): 603-617.

WISMÜLLER A, ABIDIN A Z, DSOUZA A M, et al. Mutual connectivity analysis(MCA)for nonlinear functional connectivity network recovery in the human brain using convergent cross-mapping and non-metric clustering [C]∥Advances in Self-Organizing Maps and Learning Vector Quantization. Cham, Germany: Springer International Publishing, 2016: 217-226.

COX R W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages [J]. Computers and Biomedical Research, 1996, 29(3): 162-173.

DSOUZA A M, ABIDIN A Z, CHOCKANATHAN U, et al. Mutual connectivity analysis of resting-state functional MRI data with local models [J]. NeuroImage, 2018, 178: 210-223.

MIYASHITA Y. Inferior temporal cortex: where visual perception meets memory [J]. Annual Review of Neuroscience, 1993, 16: 245-263.

ISHIBASHI R, LAMBON RALPH M A, SAITO S, et al. Different roles of lateral anterior temporal lobe and inferior parietal lobule in coding function and manipulation tool knowledge: evidence from an rTMS study [J]. Neuropsychologia, 2011, 49(5): 1128-1135.

RADUA J, PHILLIPS M L, RUSSELL T, et al. Neural response to specific components of fearful faces in healthy and schizophrenic adults [J]. NeuroImage, 2010, 49(1): 939-946.

NIELSEN F Å, BALSLEV D, HANSEN L K. Mining the posterior cingulate: segregation between memory and pain components [J]. NeuroImage, 2005, 27(3): 520-532.

LEECH R, SHARP D J. The role of the posterior cingulate cortex in cognition and disease [J]. Brain, 2014, 137(1): 12-32.

WALLENTIN M, ROEPSTORFF A, GLOVER R, et al. Parallel memory systems for talking about location and age in precuneus, caudate and Broca's region [J]. NeuroImage, 2006, 32(4): 1850-1864.

LOU H C, LUBER B, CRUPAIN M, et al. Parietal cortex and representation of the mental self [J]. Proceedings of the National Academy of Sciences, 2004, 101(17): 6827-6832.

LUNDSTROM B N, INGVAR M, PETERSSON K M. The role of precuneus and left inferior frontal cortex during source memory episodic retrieval [J]. NeuroImage, 2005, 27(4): 824-834.

MYUNG W, NA K S, HAM B J, et al. Decreased medial frontal gyrus in patients with adjustment disorder [J]. Journal of Affective Disorders, 2016, 191: 36-40.

ZHOU Juan, GREICIUS M D, GENNATAS E D, et al. Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer's disease [J]. Brain, 2010, 133(5): 1352-1367.

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.

ZHONG Yufang, HUANG Liyu, CAI Suping, et al. Altered effective connectivity patterns of the default mode network in Alzheimer's disease: an fMRI study [J]. Neuroscience Letters, 2014, 578: 171-175.

YU Enyan, LIAO Zhengluan, TAN Yunfei, et al. High-sensitivity neuroimaging biomarkers for the identification of amnestic mild cognitive impairment based on resting-state fMRI and a triple network model [J]. Brain Imaging and Behavior, 2019, 13(1): 1-14.

XUE Jiayue, GUO Hao, GAO Yuan, et al. Altered directed functional connectivity of the hippocampus in mild cognitive impairment and Alzheimer's disease: a resting-state fMRI study [J]. Frontiers in Aging Neuroscience, 2019, 11: 326.

QI Huihui, HU Yang, LV Yingru, et al. Primarily disrupted default subsystems cause impairments in inter-system interactions and a higher regulatory burden in Alzheimer's disease [J]. Frontiers in Aging Neuroscience, 2020, 12: 593648.

YU Enyan, LIAO Zhengluan, MAO Dewang, et al. Directed functional connectivity of posterior cingulate cortex and whole brain in Alzheimer's disease and mild cognitive impairment [J]. Current Alzheimer Research, 2017, 14(6): 628-635.

CAI Suping, PENG Yanlin, CHONG Tao, et al. Differentiated effective connectivity patterns of the executive control network in progressive MCI: a potential biomarker for predicting AD [J]. Current Alzheimer Research, 2017, 14(9): 937-950.

WANG Mei, LIAO Zhengluan, MAO Dewang, et al. Application of granger causality analysis of the directed functional connection in Alzheimer's disease and mild cognitive impairment [J]. Journal of Visualized Experiments, 2017(126): e56015.

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