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本文引用的文献

1
A framework for quantifying node-level community structure group differences in brain connectivity networks.一种用于量化脑连接网络中节点级社区结构组差异的框架。
Med Image Comput Comput Assist Interv. 2012;15(Pt 2):196-203. doi: 10.1007/978-3-642-33418-4_25.
2
Impaired inter-hemispheric integration in bipolar disorder revealed with brain network analyses.双相障碍的脑网络分析显示半球间整合受损。
Biol Psychiatry. 2013 Jan 15;73(2):183-93. doi: 10.1016/j.biopsych.2012.09.014. Epub 2012 Oct 31.
3
MEASURING INTER-HEMISPHERIC INTEGRATION IN BIPOLAR AFFECTIVE DISORDER USING BRAIN NETWORK ANALYSES AND HARDI.使用脑网络分析和高分辨率扩散成像测量双相情感障碍中的半球间整合
Proc IEEE Int Symp Biomed Imaging. 2012:5-8. doi: 10.1109/ISBI.2012.6235470.
4
Bipolar depressed patients show both failure to activate and failure to de-activate during performance of a working memory task.双相抑郁患者在执行工作记忆任务时既表现出激活失败,也表现出去激活失败。
J Affect Disord. 2013 Jun;148(2-3):170-8. doi: 10.1016/j.jad.2012.04.009. Epub 2012 Jul 30.
5
Rumination in bipolar disorder: evidence for an unquiet mind.双相情感障碍中的反刍思维:内心不安的证据。
Biol Mood Anxiety Disord. 2012 Jan 23;2:2. doi: 10.1186/2045-5380-2-2.
6
Patterns of autobiographical memory in bipolar disorder examined by psychometric and functional neuroimaging methods.采用心理测量和功能性神经影像学方法对双相情感障碍患者的自传体记忆模式进行研究。
J Nerv Ment Dis. 2012 Apr;200(4):296-304. doi: 10.1097/NMD.0b013e31824ceef7.
7
Exploring the psychosis functional connectome: aberrant intrinsic networks in schizophrenia and bipolar disorder.探索精神病性障碍的功能连接组:精神分裂症和双相情感障碍中异常的内在网络。
Front Psychiatry. 2012 Jan 10;2:75. doi: 10.3389/fpsyt.2011.00075. eCollection 2011.
8
The discovery of population differences in network community structure: new methods and applications to brain functional networks in schizophrenia.网络社区结构中种群差异的发现:新方法及其在精神分裂症脑功能网络中的应用。
Neuroimage. 2012 Feb 15;59(4):3889-900. doi: 10.1016/j.neuroimage.2011.11.035. Epub 2011 Nov 18.
9
Functional network organization of the human brain.人类大脑的功能网络组织。
Neuron. 2011 Nov 17;72(4):665-78. doi: 10.1016/j.neuron.2011.09.006.
10
Network centrality in the human functional connectome.人类功能连接组中的网络中心性。
Cereb Cortex. 2012 Aug;22(8):1862-75. doi: 10.1093/cercor/bhr269. Epub 2011 Oct 2.

使用与路径长度相关的社区估计方法研究双相情感障碍中的脑社区结构异常。

Investigating brain community structure abnormalities in bipolar disorder using path length associated community estimation.

作者信息

Gadelkarim Johnson J, Ajilore Olusola, Schonfeld Dan, Zhan Liang, Thompson Paul M, Feusner Jamie D, Kumar Anand, Altshuler Lori L, Leow Alex D

机构信息

Electrical and Computer Engineering department, University of Illinois at Chicago, Chicago, Illinois; Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois.

出版信息

Hum Brain Mapp. 2014 May;35(5):2253-64. doi: 10.1002/hbm.22324. Epub 2013 Jun 25.

DOI:10.1002/hbm.22324
PMID:23798337
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4264623/
Abstract

In this article, we present path length associated community estimation (PLACE), a comprehensive framework for studying node-level community structure. Instead of the well-known Q modularity metric, PLACE utilizes a novel metric, Ψ(PL), which measures the difference between intercommunity versus intracommunity path lengths. We compared community structures in human healthy brain networks generated using these two metrics and argued that Ψ(PL) may have theoretical advantages. PLACE consists of the following: (1) extracting community structure using top-down hierarchical binary trees, where a branch at each bifurcation denotes a collection of nodes that form a community at that level, (2) constructing and assessing mean group community structure, and (3) detecting node-level changes in community between groups. We applied PLACE and investigated the structural brain networks obtained from a sample of 25 euthymic bipolar I subjects versus 25 gender- and age-matched healthy controls. Results showed community structural differences in posterior default mode network regions, with the bipolar group exhibiting left-right decoupling.

摘要

在本文中,我们提出了与路径长度相关的社区估计(PLACE),这是一个用于研究节点级社区结构的综合框架。与著名的Q模块度指标不同,PLACE使用一种新的指标Ψ(PL),该指标衡量社区间路径长度与社区内路径长度之间的差异。我们比较了使用这两种指标生成的人类健康脑网络中的社区结构,并认为Ψ(PL)可能具有理论优势。PLACE包括以下内容:(1)使用自上而下的层次二叉树提取社区结构,其中每个分叉处的一个分支表示在该级别形成一个社区的节点集合,(2)构建和评估平均组社区结构,以及(3)检测组间社区的节点级变化。我们应用PLACE并研究了从25名处于双相情感障碍I型缓解期的受试者与25名性别和年龄匹配的健康对照样本中获得的脑结构网络。结果显示,在默认模式网络的后部区域存在社区结构差异,双相情感障碍组表现出左右解耦。