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class="wi-authors at-ArticleAuthors"> <div class="al-authors-list"> <span class="al-author-name-more js-flyout-wrap"><a class="linked-name js-linked-name-trigger" href="javascript:;">Xi-Nian左</一个><span class="delimiter">,</span><span class="al-author-info-wrap arrow-up"> <div class="info-card-author authorInfo_OUP_ArticleTop_Info_Widget"> <div class="name-role-wrap"> <div class="info-card-name"> Xi-Nian左</d我v> </div> <div class="info-card-affilitation"> <div class="aff"> <span class="label">1</span>实验室功能连接体和发展行为科学重点实验室,中国科学院心理研究所100101年北京,中国</d我v> <div class="aff"> <span class="label">2</span>菲利斯绿色和伦道夫Cōwen儿科神经科学研究所,纽约大学Langone医学中心纽约,纽约10016,美国</d我v> </div> <div class="info-card-search-label"> 寻找其他作品的作者:</d我v> <div class="info-card-search info-card-search-internal"> <a href="//www.ez-admanager.com/cercor/search-results?f_Authors=Xi-Nian+Zuo" rel="nofollow">牛津大学的学术</一个> </div> <div class="info-card-search info-card-search-pubmed"> <a href="http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=Zuo X">PubMed</一个> </div> <div class="info-card-search info-card-search-google"> <a href="http://scholar.google.com/scholar?q=author:%22Zuo Xi-Nian%22">谷歌学术搜索</一个> </div> </div></span></span> <span class="al-author-name-more js-flyout-wrap"><a class="linked-name js-linked-name-trigger" href="javascript:;">罗斯Ehmke</一个><span class="delimiter">,</span><span class="al-author-info-wrap arrow-up"> <div class="info-card-author authorInfo_OUP_ArticleTop_Info_Widget"> <div class="name-role-wrap"> <div class="info-card-name"> 罗斯Ehmke</d我v> </div> <div class="info-card-affilitation"> <div class="aff"> <span class="label">3</span>心理和大脑科学部门,印第安纳大学布卢明顿,在47405年,美国</d我v> </div> <div class="info-card-search-label"> 寻找其他作品的作者:</d我v> <div class="info-card-search info-card-search-internal"> <a href="//www.ez-admanager.com/cercor/search-results?f_Authors=Ross+Ehmke" rel="nofollow">牛津大学的学术</一个> </div> <div class="info-card-search 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10962年纽约,美国</d我v> </div> <div class="info-card-search-label"> 寻找其他作品的作者:</d我v> <div class="info-card-search info-card-search-internal"> <a href="//www.ez-admanager.com/cercor/search-results?f_Authors=F.+Xavier+Castellanos" rel="nofollow">牛津大学的学术</一个> </div> <div class="info-card-search info-card-search-pubmed"> <a href="http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=Castellanos F">PubMed</一个> </div> <div class="info-card-search info-card-search-google"> <a href="http://scholar.google.com/scholar?q=author:%22Castellanos F. Xavier%22">谷歌学术搜索</一个> </div> </div></span></span> <span class="al-author-name-more js-flyout-wrap"><a class="linked-name js-linked-name-trigger" href="javascript:;">奥拉夫斯波恩</一个><span class="delimiter">,</span><span class="al-author-info-wrap arrow-up"> <div class="info-card-author authorInfo_OUP_ArticleTop_Info_Widget"> <div class="name-role-wrap"> <div class="info-card-name"> 奥拉夫斯波恩</d我v> </div> <div class="info-card-affilitation"> <div class="aff"> <span class="label">3</span>心理和大脑科学部门,印第安纳大学布卢明顿,在47405年,美国</d我v> </div> <div class="info-card-search-label"> 寻找其他作品的作者:</d我v> <div class="info-card-search info-card-search-internal"> <a href="//www.ez-admanager.com/cercor/search-results?f_Authors=Olaf+Sporns" rel="nofollow">牛津大学的学术</一个> </div> <div class="info-card-search info-card-search-pubmed"> <a href="http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=Sporns O">PubMed</一个> </div> <div class="info-card-search info-card-search-google"> <a href="http://scholar.google.com/scholar?q=author:%22Sporns Olaf%22">谷歌学术搜索</一个> </div> </div></span></span> <span class="al-author-name-more js-flyout-wrap"><a class="linked-name js-linked-name-trigger" href="javascript:;">迈克尔·p·Milham</一个><span class="delimiter"></span><span class="al-author-info-wrap arrow-up"> <div class="info-card-author authorInfo_OUP_ArticleTop_Info_Widget"> <div class="name-role-wrap"> <div class="info-card-name"> 迈克尔·p·Milham</d我v> </div> <div class="info-card-affilitation"> <div class="aff"> <span class="label">5</span>中心开发大脑,孩子的思想研究所,美国纽约,纽约,10022年</d我v> </div> <div class="info-card-search-label"> 寻找其他作品的作者:</d我v> <div class="info-card-search info-card-search-internal"> <a href="//www.ez-admanager.com/cercor/search-results?f_Authors=Michael+P.+Milham" rel="nofollow">牛津大学的学术</一个> </div> <div class="info-card-search info-card-search-pubmed"> <a href="http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=Milham M">PubMed</一个> </div> <div class="info-card-search 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js-splitscreen-abstract-title">文摘</h2> <section class="abstract"> <p class="chapter-para">在人类的大脑功能连接的网络体系结构的连接体了解甚少的体素的水平。这里,使用静息状态功能磁共振成像的数据来自1003名健康成年人,我们研究一个广泛的网络中心措施提供新颖的见解连接在整个大脑功能网络(即。功能连接体)。我们第一次组装和可视化voxel-wise(4毫米)功能连接体作为一个功能的网络。然后我们证明每个中心测量获取连接的不同方面,突出的重要性考虑全局和本地连接属性的功能连接体。除了检测功能中心,“我们治疗中心作为功能连通性的措施在大脑连接体和展示他们的可靠性和表型关联(即。、年龄和性别)。具体地说,我们的分析揭示老年性减少学位中心,但不是特征向量中心,楔前叶和后扣带区域内。这意味着,尽管本地或(直接)连接随着年龄降低,与枢纽内的大脑区域与年龄在全球水平保持稳定。总的来说,这些发现表明各种中心的非冗余性措施,提高其内在的生理机制问题,可能相关的神经退行性和精神疾病的研究。</p> </section> <div class="article-metadata-panel clearfix at-ArticleMetadata"></div> <div class="kwd-group"> <a class="kwd-part kwd-main" href="javascript:;" data-keyword=""functional connectome"">功能连接体</一个>,<一个class="kwd-part kwd-main" href="javascript:;" data-keyword=""network centrality"">网络中心</一个>,<一个class="kwd-part kwd-main" href="javascript:;" data-keyword=""resting-state fMRI"">静息状态功能磁共振成像</一个>,<一个class="kwd-part kwd-main" href="javascript:;" data-keyword=""test–retest reliability"">两次试验法的可靠性</一个>,<一个class="kwd-part kwd-main" href="javascript:;" data-keyword=""whole-brain connectivity"">整个大脑连通性</一个> </div> <h2 scrollto-destination="86050061" id="86050061" class="section-title js-splitscreen-section-title">介绍</h2> <p class="chapter-para">静息状态功能磁共振成像的出现(R-fMRI)已经向获得高分辨率的可行的人类地图功能连接体(<span class="xrefLink" id="jumplink-bib6"></span><a href="javascript:;" reveal-id="bib6" data-open="bib6" class="link link-ref link-reveal xref-bibr">Biswal et al . 1995</一个>;<span class="xrefLink" id="jumplink-bib68"></span><a href="javascript:;" reveal-id="bib68" data-open="bib68" class="link link-ref link-reveal xref-bibr">斯波恩et al . 2005</一个>;<span class="xrefLink" id="jumplink-bib30"></span><a href="javascript:;" reveal-id="bib30" data-open="bib30" class="link link-ref link-reveal xref-bibr">福克斯瑞克2007</一个>;<span class="xrefLink" id="jumplink-bib7"></span><a href="javascript:;" reveal-id="bib7" data-open="bib7" class="link link-ref link-reveal xref-bibr">Biswal et al . 2010</一个>)。然而,随着新进展新挑战(<span class="xrefLink" id="jumplink-bib23"></span><a href="javascript:;" reveal-id="bib23" data-open="bib23" class="link link-ref link-reveal xref-bibr">DeFelipe 2010</一个>;<span class="xrefLink" id="jumplink-bib34"></span><a href="javascript:;" reveal-id="bib34" data-open="bib34" class="link link-ref link-reveal xref-bibr">Friston和多兰2010</一个>)。特别是,大脑的功能连接体必然是动态的,因为它支撑着众多的大脑状态涉及情感,认知,行为,知觉和感觉(<span class="xrefLink" id="jumplink-bib14"></span><a href="javascript:;" reveal-id="bib14" data-open="bib14" class="link link-ref link-reveal xref-bibr">Carhart-Harris Friston 2010</一个>;<span class="xrefLink" id="jumplink-bib33"></span><a href="javascript:;" reveal-id="bib33" data-open="bib33" class="link link-ref link-reveal xref-bibr">Friston 2010</一个>;<span class="xrefLink" id="jumplink-bib22"></span><a href="javascript:;" reveal-id="bib22" data-open="bib22" class="link link-ref link-reveal xref-bibr">德科et al . 2011</一个>)。理解这些复杂功能的起源的大脑的功能连接,第一步由创建地图的潜在连接大脑和描述它们的功能相互作用的复杂性。</p> <p class="chapter-para">两种基本的方法主导功能连通性文学日期:seed-based相关分析和独立分量分析(<span class="xrefLink" id="jumplink-bib15"></span><a href="javascript:;" reveal-id="bib15" data-open="bib15" class="link link-ref link-reveal xref-bibr">科尔,史密斯,等。2010</一个>)。Seed-based方法估计成对的力量和意义之间的关系感兴趣的区域和其他所有体素在大脑中。相比之下,独立分量分析试图确定集的大脑区域分离的基础上动态时间序列的统计模式。这两种方法有着显著的成功导致大规模的发现和详细的映射大脑内在连接网络(icn) (<span class="xrefLink" id="jumplink-bib3"></span><a href="javascript:;" reveal-id="bib3" data-open="bib3" class="link link-ref link-reveal xref-bibr">贝克曼et al . 2005</一个>;<span class="xrefLink" id="jumplink-bib21"></span><a href="javascript:;" reveal-id="bib21" data-open="bib21" class="link link-ref link-reveal xref-bibr">Damoiseaux et al . 2006</一个>;<span class="xrefLink" id="jumplink-bib48"></span><a href="javascript:;" reveal-id="bib48" data-open="bib48" class="link link-ref link-reveal xref-bibr">马格里斯et al . 2007</一个>,<span class="xrefLink" id="jumplink-bib49"></span><a href="javascript:;" reveal-id="bib49" data-open="bib49" class="link link-ref link-reveal xref-bibr">2009年</一个>)。然而,这些方法都没有完全描述大脑的功能连接体。虽然seed-based方法可以用来映射功能连接体的,它只提供了一系列给定地区和其他地区之间的关系不考虑连接的完整模式。同样地,独立分量分析提供信息关于地区可能相关的大规模网络中但没有透露这些网络之间的连接模式。</p> <p class="chapter-para">最近,研究人员已经开始应用图基于理论的网络分析探索大脑连接(<span class="xrefLink" id="jumplink-bib13"></span><a href="javascript:;" reveal-id="bib13" data-open="bib13" class="link link-ref link-reveal xref-bibr">图像和2009年斯波恩</一个>;<span class="xrefLink" id="jumplink-bib58"></span><a href="javascript:;" reveal-id="bib58" data-open="bib58" class="link link-ref link-reveal xref-bibr">Rubinov和2010年斯波恩</一个>;<span class="xrefLink" id="jumplink-bib79"></span><a href="javascript:;" reveal-id="bib79" data-open="bib79" class="link link-ref link-reveal xref-bibr">王et al . 2010</一个>)。这种方法可以用来描述(即在整个大脑功能连接网络。功能连接体)。中心,一个类图基于理论的网络措施评估中心或功能的重要性,获得大量关注(<span class="xrefLink" id="jumplink-bib43"></span><a href="javascript:;" reveal-id="bib43" data-open="bib43" class="link link-ref link-reveal xref-bibr">Koschutzki et al . 2005</一个>)。与seed-based方法或独立分量分析,中心措施考虑给定区域与整个功能连接体的关系,而不仅仅是个别地区或较大的组件分离。因此,中心措施让我们捕捉功能连接体作为一个整体的复杂性。</p> <p class="chapter-para">最初的考试中心的功能连接体的识别主要关注“功能中心”(<span class="xrefLink" id="jumplink-bib1"></span><a href="javascript:;" reveal-id="bib1" data-open="bib1" class="link link-ref link-reveal xref-bibr">Achard et al . 2006</一个>;<span class="xrefLink" id="jumplink-bib40"></span><a href="javascript:;" reveal-id="bib40" data-open="bib40" class="link link-ref link-reveal xref-bibr">Hagmann et al . 2008</一个>;<span class="xrefLink" id="jumplink-bib12"></span><a href="javascript:;" reveal-id="bib12" data-open="bib12" class="link link-ref link-reveal xref-bibr">Buckner et al . 2009</一个>;<span class="xrefLink" id="jumplink-bib41"></span><a href="javascript:;" reveal-id="bib41" data-open="bib41" class="link link-ref link-reveal xref-bibr">他et al . 2009</一个>;<span class="xrefLink" id="jumplink-bib42"></span><a href="javascript:;" reveal-id="bib42" data-open="bib42" class="link link-ref link-reveal xref-bibr">乔伊斯et al . 2010</一个>;<span class="xrefLink" id="jumplink-bib46"></span><a href="javascript:;" reveal-id="bib46" data-open="bib46" class="link link-ref link-reveal xref-bibr">罗曼et al . 2010</一个>;<span class="xrefLink" id="jumplink-bib70"></span><a href="javascript:;" reveal-id="bib70" data-open="bib70" class="link link-ref link-reveal xref-bibr">预,Volkow 2010</一个>;<span class="xrefLink" id="jumplink-bib31"></span><a href="javascript:;" reveal-id="bib31" data-open="bib31" class="link link-ref link-reveal xref-bibr">Fransson et al . 2011</一个>;<span class="xrefLink" id="jumplink-bib71"></span><a href="javascript:;" reveal-id="bib71" data-open="bib71" class="link link-ref link-reveal xref-bibr">预和Volkow 2011</一个>)。虽然有些一致性的研究已经指出研究(如高中心后扣带,腹侧楔前叶,内侧前额叶/顶叶皮层、岛叶、颞叶皮层和海马旁回),也有一些值得注意的变化在特定的地区中心的识别。在某种程度上,这种差异可能反映了不同中心的具体措施或大规模采用分割模板(<span class="xrefLink" id="jumplink-bib78"></span><a href="javascript:;" reveal-id="bib78" data-open="bib78" class="link link-ref link-reveal xref-bibr">王et al . 2009</一个>)。而越来越多的研究已经开始采用特定的中心措施(<span class="xrefLink" id="jumplink-bib42"></span><a href="javascript:;" reveal-id="bib42" data-open="bib42" class="link link-ref link-reveal xref-bibr">乔伊斯et al . 2010</一个>;<span class="xrefLink" id="jumplink-bib46"></span><a href="javascript:;" reveal-id="bib46" data-open="bib46" class="link link-ref link-reveal xref-bibr">罗曼et al . 2010</一个>),主要集中在功能检测中心,我们缺乏一个更具体的了解多个中心的相关措施在功能连接体连接的不同方面。</p> <p class="chapter-para">因此,获得更全面的观点的本质连接大脑中独立于特定的分割模板,我们检查了voxel-wise功能连接体的多个中心措施的一致性(即参与者。voxel-wise网络中心地图[VNCM],在集团层面),他们两次试验法的可靠性(TRT)跨越时间,和他们的表型的关系。具体地说,我们进行了分析使用大样本(即。,One hundred.3R- - - - - -f先生I data sets from the 1000 Functional Connectomes Project: FCP,<一个class="link link-uri openInAnotherWindow" href="http://fcon_1000.projects.nitrc.org" target="_blank">http://fcon_1000.projects.nitrc.org</一个>)建立VNCM。我们使用了纽约大学(NYU)泰爱泰党数据集(<一个class="link link-uri openInAnotherWindow" href="http://www.nitrc.org/projects/nyu_trt" target="_blank">http://www.nitrc.org/projects/nyu_trt</一个>)来评估泰爱泰党VNCM的可靠性。最后,由于年龄和性有关的属性的差异地区被普遍认为是功能中心(<span class="xrefLink" id="jumplink-bib28"></span><a href="javascript:;" reveal-id="bib28" data-open="bib28" class="link link-ref link-reveal xref-bibr">公平et al . 2008</一个>;<span class="xrefLink" id="jumplink-bib7"></span><a href="javascript:;" reveal-id="bib7" data-open="bib7" class="link link-ref link-reveal xref-bibr">Biswal et al . 2010</一个>;<span class="xrefLink" id="jumplink-bib56"></span><a href="javascript:;" reveal-id="bib56" data-open="bib56" class="link link-ref link-reveal xref-bibr">权力et al . 2010</一个>;<span class="xrefLink" id="jumplink-bib85"></span><a href="javascript:;" reveal-id="bib85" data-open="bib85" class="link link-ref link-reveal xref-bibr">左,凯利,Di马蒂诺,等。2010</一个>;<span class="xrefLink" id="jumplink-bib72"></span><a href="javascript:;" reveal-id="bib72" data-open="bib72" class="link link-ref link-reveal xref-bibr">预,Volkow 2011 b</一个>),我们再次使用了冷冻铸造数据集在VNCM检查年龄和性有关的变化。</p> <h2 scrollto-destination="86050067" id="86050067" class="section-title js-splitscreen-section-title">材料和方法</h2> <h3 scrollto-destination="86050068" id="86050068" class="section-title js-splitscreen-section-title">参与者和脑成像过程</h3> <h4 scrollto-destination="86050069" id="86050069" class="section-title js-splitscreen-section-title">纽约大学泰爱泰党数据</h4> <p class="chapter-para">共有25人参加了这个泰爱泰党实验男性(平均年龄30.7±8.8,9)。三个静止状态扫描获得每个参与者使用西门子爱兰歌娜3.0 T扫描仪。每个扫描由197相邻回波平面成像(EPI)功能。扫描2和3进行会话,扫描一次45分钟,获得5-16个月(平均11±4)后扫描1。可以找到更多的细节在我们先前的研究使用此数据集(<span class="xrefLink" id="jumplink-bib62"></span><a href="javascript:;" reveal-id="bib62" data-open="bib62" class="link link-ref link-reveal xref-bibr">Shehzad et al . 2009</一个>;<span class="xrefLink" id="jumplink-bib83"></span><a href="javascript:;" reveal-id="bib83" data-open="bib83" class="link link-ref link-reveal xref-bibr">左,Di马蒂诺,等。2010</一个>;<span class="xrefLink" id="jumplink-bib84"></span><a href="javascript:;" reveal-id="bib84" data-open="bib84" class="link link-ref link-reveal xref-bibr">左,凯利,艾德斯坦,等。2010</一个>;<span class="xrefLink" id="jumplink-bib85"></span><a href="javascript:;" reveal-id="bib85" data-open="bib85" class="link link-ref link-reveal xref-bibr">左,凯利,Di马蒂诺,等。2010</一个>)。所有的参与者的历史证实了精神或神经系统疾病的临床评估。之前获得知情同意参与。数据是根据收集的机构审查委员会批准的协议的纽约大学和纽约大学医学院的。这些泰爱泰党数据集是完全可以从网站下载NITRC (<一个class="link link-uri openInAnotherWindow" href="http://www.nitrc.org/projects/nyu_trt" target="_blank">http://www.nitrc.org/projects/nyu_trt</一个>)。</p> <h4 scrollto-destination="86050071" id="86050071" class="section-title js-splitscreen-section-title">1000年铸造数据</h4> <p class="chapter-para">共有1003名参与者(平均年龄28.1±12.7年;434男性)21中心,包括在1000年的铸造,R-fMRI扫描完成。与原始铸造出版物(<span class="xrefLink" id="jumplink-bib7"></span><a href="javascript:;" reveal-id="bib7" data-open="bib7" class="link link-ref link-reveal xref-bibr">Biswal et al . 2010</一个>),我们只包括21中心在这项研究中,以确保均匀brain-wide报道。<span class="xrefLink" id="jumplink-tbl1"></span><a href="javascript:;" reveal-id="tbl1" data-open="tbl1" class="link link-reveal link-table xref-fig">表1</一个>21中心提供信息。每个中心的各自获得伦理委员会批准提交消除识别信息数据从每个参与者以书面知情同意。每个中心的R-fMRI细节数据集可以铸造网站上找到<一个class="link link-uri openInAnotherWindow" href="http://www.nitrc.org/projects/fcon_1000" target="_blank">http://www.nitrc.org/projects/fcon_1000</一个>。</p> <a id="86050073" scrollto-destination="86050073"></a> <div content-id="tbl1" class="table-modal table-full-width-wrap"> <div class="table-wrap table-wide"> <div class="table-wrap-title" id="tbl1" data-id="tbl1"> <span class="label">表1</span> <div class="caption"> <p class="chapter-para">“1000功能连接体”为当前研究项目数据</p> </div> </div> <div class="table-overflow"> <table role="table"> <thead> <tr> <td></td> <td>中心</td> <td>π</td> <td><em>N</e米></td> </tr> </thead> <tbody> <tr> <td>1。</td> <td>美国马里兰州巴尔的摩</td> <td>詹姆斯j . Stewart h . Mostofsky之中/</td> <td char=".">23</td> </tr> <tr> <td>2。</td> <td>英国班戈</td> <td>斯坦Colcombe</td> <td char=".">20.</td> </tr> <tr> <td>3所示。</td> <td>柏林,德国</td> <td>丹尼尔·格里斯</td> <td char=".">26</td> </tr> <tr> <td>4所示。</td> <td>中国,北京</td> <td>于枫藏</td> <td char=".">192年</td> </tr> <tr> <td>5。</td> <td>美国剑桥,马</td> <td>兰迪·l·巴克纳</td> <td char=".">198年</td> </tr> <tr> <td>6。</td> <td>美国克利夫兰哦</td> <td>马克·j·劳</td> <td char=".">31日</td> </tr> <tr> <td>7所示。</td> <td>美国达拉斯,TX</td> <td>巴特Rypma</td> <td char=".">24</td> </tr> <tr> <td>8。</td> <td>荷兰莱顿</td> <td>哔叽R b . Rombouts</td> <td char=".">31日</td> </tr> <tr> <td>9。</td> <td>德国莱比锡</td> <td>阿诺Villringer</td> <td char=".">37</td> </tr> <tr> <td>10。</td> <td>美国密尔沃基,WI</td> <td>Shi-Jiang李</td> <td char=".">46</td> </tr> <tr> <td>11。</td> <td>加拿大蒙特利尔</td> <td>艾伦·c·埃文斯</td> <td char=".">51</td> </tr> <tr> <td>12。</td> <td>德国慕尼黑</td> <td>基督教Sorg Valentin Riedl</td> <td char=".">15</td> </tr> <tr> <td>13。</td> <td>纽约,纽约,美国</td> <td>迈克尔Milham / F。泽维尔卡斯特罗</td> <td char=".">59</td> </tr> <tr> <td>14。</td> <td>纽约,纽约,美国</td> <td>迈克尔Milham / F。泽维尔卡斯特罗</td> <td char=".">20.</td> </tr> <tr> <td>15。</td> <td>美国新泽西纽瓦克</td> <td>巴拉特b Biswal</td> <td char=".">19</td> </tr> <tr> <td>16。</td> <td>Orangeburg,纽约,美国</td> <td>马修·j·Hoptman</td> <td char=".">20.</td> </tr> <tr> <td>17所示。</td> <td>芬兰奥卢</td> <td>视频电子设备标准协会j·基维涅米/ Juha Veijola</td> <td char=".">103年</td> </tr> <tr> <td>18岁。</td> <td>牛津大学,英国</td> <td>史蒂夫·m·史密斯/克莱尔麦凯</td> <td char=".">22</td> </tr> <tr> <td>19所示。</td> <td>帕洛阿尔托,加州,美国</td> <td>迈克尔·格雷丘斯</td> <td char=".">17</td> </tr> <tr> <td>20.</td> <td>澳大利亚昆士兰州</td> <td>凯蒂·麦克马洪</td> <td char=".">18</td> </tr> <tr> <td>21。</td> <td>圣路易斯,密苏里州,美国</td> <td>布拉德利l . Schlaggar /史蒂文·e·彼得森</td> <td char=".">31日</td> </tr> </tbody> </table> </div> <div class="table-modal"> <table> <thead> <tr> <td></td> <td>中心</td> <td>π</td> <td><em>N</e米></td> </tr> </thead> <tbody> <tr> <td>1。</td> <td>美国马里兰州巴尔的摩</td> <td>詹姆斯j . Stewart h . Mostofsky之中/</td> <td char=".">23</td> </tr> <tr> <td>2。</td> <td>英国班戈</td> <td>斯坦Colcombe</td> <td char=".">20.</td> </tr> <tr> <td>3所示。</td> <td>柏林,德国</td> <td>丹尼尔·格里斯</td> <td char=".">26</td> </tr> <tr> <td>4所示。</td> <td>中国,北京</td> <td>于枫藏</td> <td char=".">192年</td> </tr> <tr> <td>5。</td> <td>美国剑桥,马</td> <td>兰迪·l·巴克纳</td> <td char=".">198年</td> </tr> <tr> <td>6。</td> <td>美国克利夫兰哦</td> <td>马克·j·劳</td> <td char=".">31日</td> </tr> <tr> <td>7所示。</td> <td>美国达拉斯,TX</td> <td>巴特Rypma</td> <td char=".">24</td> </tr> <tr> <td>8。</td> <td>荷兰莱顿</td> <td>哔叽R b . Rombouts</td> <td char=".">31日</td> </tr> <tr> <td>9。</td> <td>德国莱比锡</td> <td>阿诺Villringer</td> <td char=".">37</td> </tr> <tr> <td>10。</td> <td>美国密尔沃基,WI</td> <td>Shi-Jiang李</td> <td char=".">46</td> </tr> <tr> <td>11。</td> <td>加拿大蒙特利尔</td> <td>艾伦·c·埃文斯</td> <td char=".">51</td> </tr> <tr> <td>12。</td> <td>德国慕尼黑</td> <td>基督教Sorg Valentin Riedl</td> <td char=".">15</td> </tr> <tr> <td>13。</td> <td>纽约,纽约,美国</td> <td>迈克尔Milham / F。泽维尔卡斯特罗</td> <td char=".">59</td> </tr> <tr> <td>14。</td> <td>纽约,纽约,美国</td> <td>迈克尔Milham / F。泽维尔卡斯特罗</td> <td char=".">20.</td> </tr> <tr> <td>15。</td> <td>美国新泽西纽瓦克</td> <td>巴拉特b Biswal</td> <td char=".">19</td> </tr> <tr> <td>16。</td> <td>Orangeburg,纽约,美国</td> <td>马修·j·Hoptman</td> <td char=".">20.</td> </tr> <tr> <td>17所示。</td> <td>芬兰奥卢</td> <td>视频电子设备标准协会j·基维涅米/ Juha Veijola</td> <td char=".">103年</td> </tr> <tr> <td>18岁。</td> <td>牛津大学,英国</td> <td>史蒂夫·m·史密斯/克莱尔麦凯</td> <td char=".">22</td> </tr> <tr> <td>19所示。</td> <td>帕洛阿尔托,加州,美国</td> <td>迈克尔·格雷丘斯</td> <td char=".">17</td> </tr> <tr> <td>20.</td> <td>澳大利亚昆士兰州</td> <td>凯蒂·麦克马洪</td> <td char=".">18</td> </tr> <tr> <td>21。</td> <td>圣路易斯,密苏里州,美国</td> <td>布拉德利l . Schlaggar /史蒂文·e·彼得森</td> <td char=".">31日</td> </tr> </tbody> </table> </div> <div class=" graphic-wrap "> <a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" target="_blank" href="//www.ez-admanager.com/view-large/86050073">打开新标签</一个> </div> </div> </div> <div class="table-full-width-wrap"> <div class="table-wrap table-wide"> <div class="table-wrap-title" id="tbl1" data-id="tbl1"> <span class="label">表1</span> <div class="caption"> <p class="chapter-para">“1000功能连接体”为当前研究项目数据</p> </div> </div> <div class="table-overflow"> <table role="table"> <thead> <tr> <td></td> <td>中心</td> <td>π</td> <td><em>N</e米></td> </tr> </thead> <tbody> <tr> <td>1。</td> <td>美国马里兰州巴尔的摩</td> <td>詹姆斯j . Stewart h . Mostofsky之中/</td> <td char=".">23</td> </tr> <tr> <td>2。</td> <td>英国班戈</td> <td>斯坦Colcombe</td> <td char=".">20.</td> </tr> <tr> <td>3所示。</td> <td>柏林,德国</td> <td>丹尼尔·格里斯</td> <td char=".">26</td> </tr> <tr> <td>4所示。</td> <td>中国,北京</td> <td>于枫藏</td> <td char=".">192年</td> </tr> <tr> <td>5。</td> <td>美国剑桥,马</td> <td>兰迪·l·巴克纳</td> <td char=".">198年</td> </tr> <tr> <td>6。</td> <td>美国克利夫兰哦</td> <td>马克·j·劳</td> <td char=".">31日</td> </tr> <tr> <td>7所示。</td> <td>美国达拉斯,TX</td> <td>巴特Rypma</td> <td char=".">24</td> </tr> <tr> <td>8。</td> <td>荷兰莱顿</td> <td>哔叽R b . Rombouts</td> <td char=".">31日</td> </tr> <tr> <td>9。</td> <td>德国莱比锡</td> <td>阿诺Villringer</td> <td char=".">37</td> </tr> <tr> <td>10。</td> <td>美国密尔沃基,WI</td> <td>Shi-Jiang李</td> <td char=".">46</td> </tr> <tr> <td>11。</td> <td>加拿大蒙特利尔</td> <td>艾伦·c·埃文斯</td> <td char=".">51</td> </tr> <tr> <td>12。</td> <td>德国慕尼黑</td> <td>基督教Sorg Valentin Riedl</td> <td char=".">15</td> </tr> <tr> <td>13。</td> <td>纽约,纽约,美国</td> <td>迈克尔Milham / F。泽维尔卡斯特罗</td> <td char=".">59</td> </tr> <tr> <td>14。</td> <td>纽约,纽约,美国</td> <td>迈克尔Milham / F。泽维尔卡斯特罗</td> <td char=".">20.</td> </tr> <tr> <td>15。</td> <td>美国新泽西纽瓦克</td> <td>巴拉特b Biswal</td> <td char=".">19</td> </tr> <tr> <td>16。</td> <td>Orangeburg,纽约,美国</td> <td>马修·j·Hoptman</td> <td char=".">20.</td> </tr> <tr> <td>17所示。</td> <td>芬兰奥卢</td> <td>视频电子设备标准协会j·基维涅米/ Juha Veijola</td> <td char=".">103年</td> </tr> <tr> <td>18岁。</td> <td>牛津大学,英国</td> <td>史蒂夫·m·史密斯/克莱尔麦凯</td> <td char=".">22</td> </tr> <tr> <td>19所示。</td> <td>帕洛阿尔托,加州,美国</td> <td>迈克尔·格雷丘斯</td> <td char=".">17</td> </tr> <tr> <td>20.</td> <td>澳大利亚昆士兰州</td> <td>凯蒂·麦克马洪</td> <td char=".">18</td> </tr> <tr> <td>21。</td> <td>圣路易斯,密苏里州,美国</td> <td>布拉德利l . Schlaggar /史蒂文·e·彼得森</td> <td char=".">31日</td> </tr> </tbody> </table> </div> <div class="table-modal"> <table> <thead> <tr> <td></td> <td>中心</td> <td>π</td> <td><em>N</e米></td> </tr> </thead> <tbody> <tr> <td>1。</td> <td>美国马里兰州巴尔的摩</td> <td>詹姆斯j . Stewart h . Mostofsky之中/</td> <td char=".">23</td> </tr> <tr> <td>2。</td> <td>英国班戈</td> <td>斯坦Colcombe</td> <td char=".">20.</td> </tr> <tr> <td>3所示。</td> <td>柏林,德国</td> <td>丹尼尔·格里斯</td> <td char=".">26</td> </tr> <tr> <td>4所示。</td> <td>中国,北京</td> <td>于枫藏</td> <td char=".">192年</td> </tr> <tr> <td>5。</td> <td>美国剑桥,马</td> <td>兰迪·l·巴克纳</td> <td char=".">198年</td> </tr> <tr> <td>6。</td> <td>美国克利夫兰哦</td> <td>马克·j·劳</td> <td char=".">31日</td> </tr> <tr> <td>7所示。</td> <td>美国达拉斯,TX</td> <td>巴特Rypma</td> <td char=".">24</td> </tr> <tr> <td>8。</td> <td>荷兰莱顿</td> <td>哔叽R b . Rombouts</td> <td char=".">31日</td> </tr> <tr> <td>9。</td> <td>德国莱比锡</td> <td>阿诺Villringer</td> <td char=".">37</td> </tr> <tr> <td>10。</td> <td>美国密尔沃基,WI</td> <td>Shi-Jiang李</td> <td char=".">46</td> </tr> <tr> <td>11。</td> <td>加拿大蒙特利尔</td> <td>艾伦·c·埃文斯</td> <td char=".">51</td> </tr> <tr> <td>12。</td> <td>德国慕尼黑</td> <td>基督教Sorg Valentin Riedl</td> <td char=".">15</td> </tr> <tr> <td>13。</td> <td>纽约,纽约,美国</td> <td>迈克尔Milham / F。泽维尔卡斯特罗</td> <td char=".">59</td> </tr> <tr> <td>14。</td> <td>纽约,纽约,美国</td> <td>迈克尔Milham / F。泽维尔卡斯特罗</td> <td char=".">20.</td> </tr> <tr> <td>15。</td> <td>美国新泽西纽瓦克</td> <td>巴拉特b Biswal</td> <td char=".">19</td> </tr> <tr> <td>16。</td> <td>Orangeburg,纽约,美国</td> <td>马修·j·Hoptman</td> <td char=".">20.</td> </tr> <tr> <td>17所示。</td> <td>芬兰奥卢</td> <td>视频电子设备标准协会j·基维涅米/ Juha Veijola</td> <td char=".">103年</td> </tr> <tr> <td>18岁。</td> <td>牛津大学,英国</td> <td>史蒂夫·m·史密斯/克莱尔麦凯</td> <td char=".">22</td> </tr> <tr> <td>19所示。</td> <td>帕洛阿尔托,加州,美国</td> <td>迈克尔·格雷丘斯</td> <td char=".">17</td> </tr> <tr> <td>20.</td> <td>澳大利亚昆士兰州</td> <td>凯蒂·麦克马洪</td> <td char=".">18</td> </tr> <tr> <td>21。</td> <td>圣路易斯,密苏里州,美国</td> <td>布拉德利l . Schlaggar /史蒂文·e·彼得森</td> <td char=".">31日</td> </tr> </tbody> </table> </div> <div class=" graphic-wrap "> <a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" target="_blank" href="//www.ez-admanager.com/view-large/86050073">打开新标签</一个> </div> </div> </div> <h3 scrollto-destination="86050074" id="86050074" class="section-title js-splitscreen-section-title">核磁共振成像预处理</h3> <h4 scrollto-destination="86050075" id="86050075" class="section-title js-splitscreen-section-title">1000年铸造数据</h4> <p class="chapter-para">一个标准的数据预处理策略是使用目前(<一个class="link link-uri openInAnotherWindow" href="http://www.fmrib.ox.ac.uk/fsl/" target="_blank">http://www.fmrib.ox.ac.uk/fsl/</一个>)和AFNI (<一个class="link link-uri openInAnotherWindow" href="http://afni.nimh.nih.gov/afni" target="_blank">http://afni.nimh.nih.gov/afni</一个>)。这里采用脚本包含处理命令已经作为铸造的一部分发布。具体地说,它包括:1)丢弃前5 EPI卷从每个静息状态扫描允许信号平衡,2)3 d运动校正,3)时间序列削峰,4)4 d mean-based强度归一化,5)带通时间过滤(0.01 -0.1赫兹),6)消除线性和二次趋势,7)估计12个参数仿射线性变换从各个功能空间到蒙特利尔神经学研究所(MNI) 152的空间,和8)回归9妨害信号(全球的意思是,白质,脑脊液信号,和6运动参数)。这些预处理步骤的输出是一个4 d残留功能在本机功能空间,为每一个参与者。4 d本地数据注册与4毫米MNI152空间分辨率基于仿射变换。</p> <h4 scrollto-destination="86050077" id="86050077" class="section-title js-splitscreen-section-title">纽约大学泰爱泰党数据</h4> <p class="chapter-para">上述预处理步骤是相似的,除了跳过步骤1 (EPI丢弃前5卷)由于扫描仪的默认设置,无视第一4卷。Slice-timing交叉使用Sinc收购插值校正与汉宁窗口内核之前另外添加3 d运动校正(步骤2)。第七步是由b样条的基础上提炼非线性变换从个体功能空间MNI152标准大脑空间中实现目前FNIRT (<一个class="link link-uri openInAnotherWindow" href="http://www.fmrib.ox.ac.uk/fsl/fnirt/index.html" target="_blank">http://www.fmrib.ox.ac.uk/fsl/fnirt/index.html</一个>)。最后,残差注册MNI152标准4毫米的空间分辨率。有两个因素考虑在选择4毫米作为我们的体素大小:1)的平均体素的大小EPI(即原始数据。中,最大<e米>x</e米>/<e米>y</e米>/<e米>z</e米>决议)21个网站大约是4毫米(3.9497毫米),因此死亡的潜在效用使用一个更小的体素的大小和图2)42 835节点3毫米立体像素大小,大大增加物理内存的需求(例如,∼50 GB的子图中心(SC))中心的估计和计算时间。</p> <p class="chapter-para">1000年铸造数据和纽约大学泰爱泰党数据,所有空间平滑预处理不包括因为3因素(<span class="xrefLink" id="jumplink-bib75"></span><a href="javascript:;" reveal-id="bib75" data-open="bib75" class="link link-ref link-reveal xref-bibr">van den Heuvel,斯塔姆,等。2008</一个>):1)4 d剩余工资的空间登记涉及EPI的插值像素点(即。,somewhat like spatial smoothing), 2) spatial smoothing could introduce artifactual local correlations between voxels unrelated to their functional connectivity, and 3) spatial smoothing can blur the boundaries and artificially increase the correlations between 2 regions with respect to their time series.</p> <h3 scrollto-destination="86050080" id="86050080" class="section-title js-splitscreen-section-title">图的形成icn</h3> <div class=" block-child-p "> 之前任何中心或其他图形理论分析,图表必须从真实数据抽象和格式化。这一步是至关重要的在指挥中心分析(<span class="xrefLink" id="jumplink-bib65"></span> <a href="javascript:;" reveal-id="bib65" data-open="bib65" class="link link-ref link-reveal xref-bibr">史密斯et al . 2011</一个>)。我们构造图icn在体素水平。每个立体像素的时间序列提取的预处理R-fMRI数据计算相关矩阵<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <mi> R</米我> <mo> =</米o> <mo> (</米o> <msub> <mi> r</米我> <mrow> <mi> 我</米我> <mi> j</米我> </mrow> </msub> <mo> )</米o> <mo> ,</米o> <mtext> 1</米text> <mo> ≤</米o> <mi> 我</米我> <mo> ,</米o> <mi> j</米我> <mo> ≤</米o> <mi> N</米我> </mrow> </math></span>(<e米>N</e米>是像素点的数量),在哪里<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <msub> <mi> r</米我> <mrow> <mi> 我</米我> <mi> j</米我> </mrow> </msub> </mrow> </math></span>是颞皮尔逊相关性之间的时间序列<e米>我</e米>- - -<e米>j</e米>th体素和措施的相似性R-fMRI 2像素点之间的信号。建立一个完全连通图,一个阈值的相关性<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <msub> <mi> r</米我> <mn> 0</米n> </msub> </mrow> </math></span>估计有统计学意义<e米>P</e米>= 0.0001(未调整的)。这是用于阈值相关矩阵为邻接矩阵<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <mi> 一个</米我> <mo> =</米o> <msub> <mrow> <mrow> <mo> (</米o> <mrow> <msub> <mi> 一个</米我> <mrow> <mi> 我</米我> <mi> j</米我> </mrow> </msub> </mrow> <mo> )</米o> </mrow> </mrow> <mrow> <mn> 1</米n> <mo> ≤</米o> <mi> 我</米我> <mo> ≤</米o> <mi> N</米我> <mo> ,</米o> <mtext> </mtext> <mn> 1</米n> <mo> ≤</米o> <mi> j</米我> <mo> ≤</米o> <mi> N</米我> </mrow> </msub> </mrow> </math></span>的图<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mi xmlns=""> G</米我> </math>⁠</span>:<d我vclass="formula-wrap"> <div class="disp-formula" id="fd1a" content-id="fd1a"> <span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <msub> <mi> 一个</米我> <mrow> <mi> 我</米我> <mi> j</米我> </mrow> </msub> <mo> =</米o> <mrow> <mo> {</米o> <mrow> <mrow> <mn> 0</米n> <mo> ,</米o> <mtext></mtext> <msub> <mi> r</米我> <mrow> <mi> 我</米我> <mi> j</米我> </mrow> </msub> <mo> <</米o> <msub> <mi> r</米我> <mn> 0</米n> </msub> </mrow> <mrow> <mn> 1</米n> <mo> ,</米o> <mtext></mtext> <msub> <mi> r</米我> <mrow> <mi> 我</米我> <mi> j</米我> </mrow> </msub> <mo> ≥</米o> <msub> <mi> r</米我> <mn> 0</米n> </msub> </mrow> </mrow> </mrow> <mo> 。</米o> </mrow> </math> </div> <span class="label">(1)</span> </div> <div class="formula-wrap"> <div class="disp-formula" id="fd1b" content-id="fd1b"> <span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <msub> <mi> 一个</米我> <mrow> <mi> 我</米我> <mi> j</米我> </mrow> </msub> <mo> =</米o> <mrow> <mo> {</米o> <mrow> <mrow> <mn> 0</米n> <mo> ,</米o> <mtext></mtext> <msub> <mi> r</米我> <mrow> <mi> 我</米我> <mi> j</米我> </mrow> </msub> <mo> <</米o> <msub> <mi> r</米我> <mn> 0</米n> </msub> </mrow> <msub> <mi> r</米我> <mrow> <mi> 我</米我> <mi> j</米我> </mrow> </msub> <mo> ,</米o> <mtext></mtext> <msub> <mi> r</米我> <mrow> <mi> 我</米我> <mi> j</米我> </mrow> </msub> <mo> ≥</米o> <msub> <mi> r</米我> <mn> 0</米n> </msub> </mrow> <mo> 。</米o> </mrow> </mrow> </math> </div> <span class="label">(1 b)</span> </div> </div> <p class="chapter-para">一个二进制图像被构造成在方程(<span class="xrefLink" id="jumplink-fd1a"></span><a href="#fd1a" class="sectionLink xref-sec js-xref-sec">1</一个>);它的加权版本是方程(<span class="xrefLink" id="jumplink-fd1b"></span><a href="#fd1b" class="sectionLink xref-sec js-xref-sec">1 b</一个>)。排除artifactual从非灰色物质像素点的相关性,我们限制voxel-wise中心分析一个预定义的灰质面具与灰质组织概率超过20%。灰质组织模板的一部分发布组织先验概率SPM8 (<一个class="link link-uri openInAnotherWindow" href="http://www.fil.ion.ucl.ac.uk/spm/software/spm8" target="_blank">http://www.fil.ion.ucl.ac.uk/spm/software/spm8</一个>)。</p> <p class="chapter-para">探索其他不那么保守的方式构建图,我们也使用相关性意义标准的阈值<e米>P</e米>= 0.005立体像素明智的分析。结果从这些图十分相似,从而获得不了。</p> <h3 scrollto-destination="86050084" id="86050084" class="section-title js-splitscreen-section-title">核心指标</h3> <p class="chapter-para">给定一个图<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <mi> G</米我> <mo> =</米o> <mrow> <mo> (</米o> <mrow> <mi> V</米我> <mo> ,</米o> <mi> E</米我> </mrow> <mo> )</米o> </mrow> <mo> ,</米o> <mtext> </mtext> <mi> V</米我> <mo> =</米o> <msub> <mrow> <mrow> <mo> (</米o> <mrow> <msub> <mi> v</米我> <mi> 我</米我> </msub> </mrow> <mo> )</米o> </mrow> </mrow> <mrow> <mn> 1</米n> <mo> ≤</米o> <mi> 我</米我> <mo> ≤</米o> <mi> N</米我> </mrow> </msub> </mrow> </math></span>定义了所有节点和<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <mi> 一个</米我> <mo> =</米o> <msub> <mrow> <mo> (</米o> <msub> <mi> 一个</米我> <mrow> <mi> 我</米我> <mi> j</米我> </mrow> </msub> <mo> )</米o> </mrow> <mrow> <mn> 1</米n> <mo> ≤</米o> <mi> 我</米我> <mo> ≤</米o> <mi> N</米我> <mo> ,</米o> <mtext> </mtext> <mn> 1</米n> <mo> ≤</米o> <mi> j</米我> <mo> ≤</米o> <mi> N</米我> </mrow> </msub> </mrow> </math></span>代表图的邻接矩阵,所有非零元素定义图<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mi xmlns=""> G</米我> </math></span>的边缘设置<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mi xmlns=""> E</米我> </math>⁠</span>。的元素<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <msub> <mi> 一个</米我> <mrow> <mi> 我</米我> <mi> j</米我> </mrow> </msub> </mrow> </math></span>邻接矩阵表示连接或边缘的节点<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <msub> <mi> v</米我> <mi> 我</米我> </msub> </mrow> </math></span>到节点<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <msub> <mi> v</米我> <mi> j</米我> </msub> </mrow> </math>⁠</span>,如果不存在边缘和非零重量的优势<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <msub> <mi> 一个</米我> <mrow> <mi> 我</米我> <mi> j</米我> </mrow> </msub> </mrow> </math>⁠</span>。在当前的工作中,我们只专注于无向图,也就是说,邻接矩阵<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mi xmlns=""> 一个</米我> </math></span>是对称的。图0和1是二进制要是出现在它的邻接矩阵;否则,它是一个加权图。(即量化节点的贡献。,nodal centrality) for a graph, we examined 4 different centrality measures: degree, eigenvector, page-rank, and subgraph. They can be categorized into local (degree), mesoscale (subgraph), and global centralities (eigenvector and page-rank). Each of them is a function of nodes<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <mi> f</米我> <mo> (</米o> <mi> 我</米我> <mo> )</米o> <mo> =</米o> <mi> f</米我> <mrow> <mo> (</米o> <mrow> <msub> <mi> v</米我> <mi> 我</米我> </msub> </mrow> <mo> )</米o> </mrow> <mo> ,</米o> <mtext> </mtext> <mtext> 1</米text> <mo> ≤</米o> <mi> 我</米我> <mo> ≤</米o> <mi> N</米我> </mrow> </math>⁠</span>。</p> <h4 scrollto-destination="86050086" id="86050086" class="section-title js-splitscreen-section-title">学位中心</h4> <div class=" block-child-p "> 二进制图像,学位中心(DC)是边缘连接到一个节点的数量。加权图,它被定义为从边缘连接到节点权重的总和(有时也称为节点强度)。根据图的邻接矩阵,直流方程可以计算(<span class="xrefLink" id="jumplink-fd3"></span> <a href="#fd3" class="sectionLink xref-sec js-xref-sec">3</一个>)。它代表了大多数地方和直接量化中心措施。这种方法被广泛用于检查节点icn特性(<span class="xrefLink" id="jumplink-bib12"></span> <a href="javascript:;" reveal-id="bib12" data-open="bib12" class="link link-ref link-reveal xref-bibr">Buckner et al . 2009</一个>;<span class="xrefLink" id="jumplink-bib13"></span> <a href="javascript:;" reveal-id="bib13" data-open="bib13" class="link link-ref link-reveal xref-bibr">图像和2009年斯波恩</一个>;<span class="xrefLink" id="jumplink-bib41"></span> <a href="javascript:;" reveal-id="bib41" data-open="bib41" class="link link-ref link-reveal xref-bibr">他et al . 2009</一个>;<span class="xrefLink" id="jumplink-bib17"></span> <a href="javascript:;" reveal-id="bib17" data-open="bib17" class="link link-ref link-reveal xref-bibr">科尔,帕沙克,等。2010</一个>;<span class="xrefLink" id="jumplink-bib79"></span> <a href="javascript:;" reveal-id="bib79" data-open="bib79" class="link link-ref link-reveal xref-bibr">王et al . 2010</一个>;<span class="xrefLink" id="jumplink-bib31"></span> <a href="javascript:;" reveal-id="bib31" data-open="bib31" class="link link-ref link-reveal xref-bibr">Fransson et al . 2011</一个>)。<d我vclass="formula-wrap"> <div class="disp-formula" id="fd3" content-id="fd3"> <span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <mtext> 直流</米text> <mo> (</米o> <mi> 我</米我> <mo> )</米o> <mo> =</米o> <mstyle displaystyle="true"> <munderover> <mo> ∑</米o> <mrow> <mi> j</米我> <mo> =</米o> <mn> 1</米n> </mrow> <mi> N</米我> </munderover> <mrow> <msub> <mi> 一个</米我> <mrow> <mi> 我</米我> <mi> j</米我> </mrow> </msub> </mrow> </mstyle> <mo> 。</米o> </mrow> </math> </div> <span class="label">(3)</span> </div> </div> <h4 scrollto-destination="86050088" id="86050088" class="section-title js-splitscreen-section-title">子图中心</h4> <div class=" block-child-p "> SC特征节点参与所有子图包含在一个网络(<span class="xrefLink" id="jumplink-bib27"></span> <a href="javascript:;" reveal-id="bib27" data-open="bib27" class="link link-ref link-reveal xref-bibr">埃斯特拉达Rodriguez-Velazquez 2005</一个>)。“中尺度”这个指数是反映在规模较小的子图的性质获得更多的重量比大的子图。SC通常是基于计算的谱表示图,也就是说,邻接矩阵的特征值和特征向量的图,<d我vclass="formula-wrap"> <div class="disp-formula" id="fd4" content-id="fd4"> <span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <mi mathvariant="normal"> SC</米我> <mo> (</米o> <mi> 我</米我> <mo> )</米o> <mo> =</米o> <mstyle displaystyle="true"> <munderover> <mo> ∑</米o> <mrow> <mi> j</米我> <mo> =</米o> <mn> 1</米n> </mrow> <mi> N</米我> </munderover> <mrow> <msup> <mrow> <mo> (</米o> <msub> <mi mathvariant="normal"> μ</米我> <mi> j</米我> </msub> <mo> (</米o> <mi> 我</米我> <mo> )</米o> <mo> ]</米o> </mrow> <mn> 2</米n> </msup> <mi> sinh</米我> <mo> (</米o> <msub> <mi mathvariant="normal"> λ</米我> <mi> j</米我> </msub> <mo> )</米o> </mrow> </mstyle> <mo> 。</米o> </mrow> </math> </div> <span class="label">(4)</span> </div> </div> <div class=" block-child-p "> 在方程(<span class="xrefLink" id="jumplink-fd4"></span> <a href="#fd4" class="sectionLink xref-sec js-xref-sec">4</一个>),<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <msub> <mi mathvariant="normal"> μ</米我> <mi> j</米我> </msub> <mo> (</米o> <mi> 我</米我> <mo> )</米o> </mrow> </math></span>是<e米>我</e米>th组成部分<e米>j</e米>th邻接矩阵的特征向量<e米>一个</e米>,<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <msub> <mi mathvariant="normal"> λ</米我> <mi> j</米我> </msub> </mrow> </math></span>是相应的<e米>j</e米>th特征值,<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <mi> 一个</米我> <msub> <mi mathvariant="normal"> μ</米我> <mi> j</米我> </msub> <mo> =</米o> <msub> <mi mathvariant="normal"> λ</米我> <mi> j</米我> </msub> <msub> <mi mathvariant="normal"> μ</米我> <mi> j</米我> </msub> </mrow> </math>⁠</span>。SC可以视为一个中尺度的网络订购,这是odd-cyclic子图或关闭走的中心<e米>我</e米>th大脑网络的节点。主题和聚类系数2网络指标基于子图丰度,被广泛用于描述一个复杂的网络(<span class="xrefLink" id="jumplink-bib66"></span> <a href="javascript:;" reveal-id="bib66" data-open="bib66" class="link link-ref link-reveal xref-bibr">2006年斯波恩</一个>;<span class="xrefLink" id="jumplink-bib13"></span> <a href="javascript:;" reveal-id="bib13" data-open="bib13" class="link link-ref link-reveal xref-bibr">图像和2009年斯波恩</一个>)(例如,其small-worldness)。尽管icn的中尺度属性可能会感兴趣的(<span class="xrefLink" id="jumplink-bib68"></span> <a href="javascript:;" reveal-id="bib68" data-open="bib68" class="link link-ref link-reveal xref-bibr">斯波恩et al . 2005</一个>),SC尚未广泛用于调查icn。SC考虑所有可能的子图。一个加权图的邻接矩阵<e米>一个</e米>被转换为<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <msup> <mi> D</米我> <mrow> <mo> −</米o> <mn> 1</米n> <mo> /</米o> <mn> 2</米n> </mrow> </msup> <mi> 一个</米我> <msup> <mi> D</米我> <mrow> <mo> −</米o> <mn> 1</米n> <mo> /</米o> <mn> 2</米n> </mrow> </msup> </mrow> </math></span>使用度对角矩阵<e米>D</e米>之前计算SC (<span class="xrefLink" id="jumplink-bib20"></span> <a href="javascript:;" reveal-id="bib20" data-open="bib20" class="link link-ref link-reveal xref-bibr">园地和海厄姆2009</一个>)。计算SC在体元层面,我们只使用第一个20个最大特征值(即,<e米>j</e米>= 1…20<span class="xrefLink" id="jumplink-fd4"></span> <a href="#fd4" class="sectionLink xref-sec js-xref-sec">eq。4</一个>)由于计算限制解决大型稀疏的eigensystem邻接矩阵。MATLAB函数“eigs”是用来计算大型稀疏矩阵的特征值和特征向量,实现通货紧缩为隐式重启Arnoldi迭代技术(<span class="xrefLink" id="jumplink-bib45"></span> <a href="javascript:;" reveal-id="bib45" data-open="bib45" class="link link-ref link-reveal xref-bibr">Lehoucq索伦森1996</一个>)。我们也修改(<span class="xrefLink" id="jumplink-fd4"></span> <a href="#fd4" class="sectionLink xref-sec js-xref-sec">eq。4</一个>)其庞大的网络版本,因为重量的散步可以产生无限SC(即关闭。除了机器精度):<d我vclass="formula-wrap"> <div class="disp-formula" id="" content-id=""> <span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <mi mathvariant="normal"> SC</米我> <mo> (</米o> <mi> 我</米我> <mo> )</米o> <mo> =</米o> <mstyle displaystyle="true"> <munderover> <mo> ∑</米o> <mrow> <mi> j</米我> <mo> =</米o> <mn> 1</米n> </mrow> <mrow> <mn> 20.</米n> </mrow> </munderover> <mrow> <msup> <mrow> <mo> (</米o> <msub> <mi mathvariant="normal"> μ</米我> <mi> j</米我> </msub> <mo> (</米o> <mi> 我</米我> <mo> )</米o> <mo> ]</米o> </mrow> <mn> 2</米n> </msup> <mrow> <mo> (</米o> <mrow> <mfrac> <mrow> <mi> N</米我> <mi mathvariant="normal"> −</米我> <mn> 1</米n> </mrow> <mrow> <mi> N</米我> <mi mathvariant="normal"> −</米我> <mn> 1</米n> <mi mathvariant="normal"> −</米我> <msub> <mi mathvariant="normal"> λ</米我> <mi> j</米我> </msub> </mrow> </mfrac> </mrow> <mo> )</米o> </mrow> </mrow> </mstyle> <mo> 。</米o> </mrow> </math> </div> </div> </div> <p class="chapter-para">这个一般SC,名叫消散的中心,最近提出了揭示网络属性矩阵的函数(<span class="xrefLink" id="jumplink-bib26"></span><a href="javascript:;" reveal-id="bib26" data-open="bib26" class="link link-ref link-reveal xref-bibr">埃斯特拉达,海厄姆2010</一个>)。</p> <h4 scrollto-destination="86050092" id="86050092" class="section-title js-splitscreen-section-title">特征向量中心</h4> <div class=" block-child-p "> 介绍了由Bonacich (<span class="xrefLink" id="jumplink-bib10"></span> <a href="javascript:;" reveal-id="bib10" data-open="bib10" class="link link-ref link-reveal xref-bibr">Bonacich 1972</一个>),特征向量中心(EC)是第一个邻接矩阵的特征向量,这对应于最大的特征值<span class="inline-formula no-formula-id"><span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <msub> <mi mathvariant="normal"> λ</米我> <mn> 1</米n> </msub> </mrow> </math></span>(称为主要特征值):<d我vclass="formula-wrap"> <div class="disp-formula" id="fd5" content-id="fd5"> <span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <mi mathvariant="normal"> 电子商务</米我> <mo> (</米o> <mi> 我</米我> <mo> )</米o> <mo> =</米o> <msub> <mi mathvariant="normal"> μ</米我> <mn> 1</米n> </msub> <mo> (</米o> <mi> 我</米我> <mo> )</米o> <mo> =</米o> <mfrac> <mn> 1</米n> <mrow> <msub> <mi mathvariant="normal"> λ</米我> <mn> 1</米n> </msub> </mrow> </mfrac> <mi> 一个</米我> <msub> <mi mathvariant="normal"> μ</米我> <mn> 1</米n> </msub> <mo> =</米o> <mfrac> <mn> 1</米n> <mrow> <msub> <mi mathvariant="normal"> λ</米我> <mn> 1</米n> </msub> </mrow> </mfrac> <mstyle displaystyle="true"> <munderover> <mo> ∑</米o> <mrow> <mi> j</米我> <mo> =</米o> <mn> 1</米n> </mrow> <mi> N</米我> </munderover> <mrow> <msub> <mi> 一个</米我> <mrow> <mi> 我</米我> <mi> j</米我> </mrow> </msub> <msub> <mi mathvariant="normal"> μ</米我> <mn> 1</米n> </msub> <mo> (</米o> <mi> j</米我> <mo> )</米o> </mrow> </mstyle> <mo> 。</米o> </mrow> </math> </div> <span class="label">(5)</span> </div> </div> <p class="chapter-para">显然,电子商务不仅取决于所有相邻节点的程度还在EC。因为这个递归性质,电子商务能够捕获中心,延伸到全球的一个方面的特征图。值得注意的是,类似于SC,其实现也基于MATLAB eigs。神经科学家已经开始采用EC测量功能连通性在休息和大脑活动的任务状态(<span class="xrefLink" id="jumplink-bib46"></span><a href="javascript:;" reveal-id="bib46" data-open="bib46" class="link link-ref link-reveal xref-bibr">罗曼et al . 2010</一个>)。</p> <h4 scrollto-destination="86050095" id="86050095" class="section-title js-splitscreen-section-title">网页排名中心</h4> <div class=" block-child-p "> 著名的谷歌网页排名中心(PC)算法是电子商务的一个变种。它引入了一个小概率(1−<e米>d</e米>= 0.15)的随机阻尼(即。,d一个米p我ng factor) to handle walking traps on a graph (<span class="xrefLink" id="jumplink-bib9"></span> <a href="javascript:;" reveal-id="bib9" data-open="bib9" class="link link-ref link-reveal xref-bibr">Boldi et al . 2009</一个>):<d我vclass="formula-wrap"> <div class="disp-formula" id="fd6" content-id="fd6"> <span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <mi mathvariant="normal"> 个人电脑</米我> <mo> (</米o> <mi> 我</米我> <mo> )</米o> <mo> =</米o> <mi> r</米我> <mo> (</米o> <mi> 我</米我> <mo> )</米o> <mo> =</米o> <mn> 1</米n> <mo> −</米o> <mi> d</米我> <mo> +</米o> <mi> d</米我> <mstyle displaystyle="true"> <munderover> <mo> ∑</米o> <mrow> <mi> j</米我> <mo> =</米o> <mn> 1</米n> </mrow> <mi> N</米我> </munderover> <mrow> <mfrac> <mrow> <msub> <mi> 一个</米我> <mrow> <mi> 我</米我> <mi> j</米我> </mrow> </msub> <mi> r</米我> <mo> (</米o> <mi> j</米我> <mo> )</米o> </mrow> <mrow> <mi mathvariant="normal"> 直流</米我> <mo> (</米o> <mi> j</米我> <mo> )</米o> </mrow> </mfrac> </mrow> </mstyle> <mo> 。</米o> </mrow> </math> </div> <span class="label">(6)</span> </div> </div> <p class="chapter-para">电脑已被证明是类似于直流无向图(<span class="xrefLink" id="jumplink-bib29"></span><a href="javascript:;" reveal-id="bib29" data-open="bib29" class="link link-ref link-reveal xref-bibr">Fortunato et al . 2008</一个>)。然而,没有研究直接检查电脑icn(即。R-fMRI网络),神经生物学的背景。电子商务和电脑都是可以解决的,二进制和加权图。加快电脑的计算在体元层面,我们使用一个inner-outer迭代策略(<span class="xrefLink" id="jumplink-bib37"></span><a href="javascript:;" reveal-id="bib37" data-open="bib37" class="link link-ref link-reveal xref-bibr">Gleich et al . 2010</一个>)。</p> <h3 scrollto-destination="86050098" id="86050098" class="section-title js-splitscreen-section-title">个体层面的VNCM分析</h3> <p class="chapter-para">为每个网站21日,一群面具第一次生成包含所有体素网站中所有科目。具体地说,一个人面具包括所有体素与积极的标准差(SD)的剩余时间序列。个人面具都是增加网站的面具。所有21个站点的面具被组合成一个最终的面具包括体素目前所有1003名调查对象来自21个站点。优化网络规模的相似性在21个站点,所有单个网络分析限制在最后一组的面具在体素水平。具体来说,只有灰质压(即。,>20% gray matter tissue probability) within the final mask were employed to calculate individual centralities.</p> <div class=" block-child-p "> 为每个主题分布图表生成。根据图4中心措施是在个体层面计算。这些个人中心voxel-wise地图被标准化<e米>z</e米>跨科目成绩让他们可比,网站和中心(<span class="xrefLink" id="jumplink-bib12"></span> <a href="javascript:;" reveal-id="bib12" data-open="bib12" class="link link-ref link-reveal xref-bibr">Buckner et al . 2009</一个>;<span class="xrefLink" id="jumplink-bib83"></span> <a href="javascript:;" reveal-id="bib83" data-open="bib83" class="link link-ref link-reveal xref-bibr">左,Di马蒂诺,等。2010</一个>)。的<e米>z</e米>评分标准是:<d我vclass="formula-wrap"> <div class="disp-formula" id="" content-id=""> <span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <msub> <mi> z</米我> <mi> 我</米我> </msub> <mo> =</米o> <mfrac> <mrow> <msub> <mi> x</米我> <mi> 我</米我> </msub> <mo> −</米o> <mi mathvariant="normal"> μ</米我> </mrow> <mi mathvariant="normal"> σ</米我> </mfrac> <mo> ,</米o> <mtext> </mtext> <mtext> 1</米text> <mo> ≤</米o> <mi> 我</米我> <mo> ≤</米o> <mi> N</米我> <mo> 。</米o> </mrow> </math> </div> </div>μ和σ和SD的中心措施意味着什么<e米>x</e米>在所有<e米>N</e米>节点(即。,体素s in the group mask).</d我v> <h3 scrollto-destination="86050101" id="86050101" class="section-title js-splitscreen-section-title">组级别VNCM分析</h3> <p class="chapter-para">所有的个人中心<e米>z</e米>分数地图第一次空间平滑和高斯平滑的内核(宽屏半峰= 6毫米)和用作输入后续组级别的分析。</p> <h4 scrollto-destination="86050103" id="86050103" class="section-title js-splitscreen-section-title">1000年铸造数据:单向方差分析</h4> <p class="chapter-para">在我们之前的研究(<span class="xrefLink" id="jumplink-bib7"></span><a href="javascript:;" reveal-id="bib7" data-open="bib7" class="link link-ref link-reveal xref-bibr">Biswal et al . 2010</一个>),同时检查感兴趣的因素,我们开发了一个统一的一般线性模型框架center-level统计分析。统一的统计模型是一个单向方差分析(方差分析)between-factor治疗中心。F-contrasts被用来测量中心的影响。整体组织意味着对比各中心也被建模。具体来说,我们使用1-factor 21-level方差分析(因素:中心;1003名参与者)与年龄和性别不检查年龄的影响,性别和中心中心的措施。在考虑与年龄相关的潜在敏感性差异运动中心领导和注册质量,根的意思是整体头位移/旋转的广场,他们的时间导数,登记错误包括调整头运动和登记。头部运动和配准误差的计算细节可以在我们之前的研究(<span class="xrefLink" id="jumplink-bib85"></span><a href="javascript:;" reveal-id="bib85" data-open="bib85" class="link link-ref link-reveal xref-bibr">左,凯利,Di马蒂诺,等。2010</一个>)。多重比较时纠正集群级别使用高斯随机场理论(min Z > 2.3;集群的意义:<e米>P</e米>< 0.05,纠正)。</p> <h4 scrollto-destination="86050105" id="86050105" class="section-title js-splitscreen-section-title">纽约大学泰爱泰党数据:泰爱泰党可靠性分析</h4> <div class=" block-child-p "> 评估每个中心的泰爱泰党可靠性措施,我们使用了组内相关系数(可以),在我们以前的工作(<span class="xrefLink" id="jumplink-bib84"></span> <a href="javascript:;" reveal-id="bib84" data-open="bib84" class="link link-ref link-reveal xref-bibr">左,凯利,艾德斯坦,等。2010</一个>):<d我vclass="formula-wrap"> <div class="disp-formula" id="" content-id=""> <span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <msub> <mi> r</米我> <mrow> <mi mathvariant="normal"> 泰爱泰党</米我> </mrow> </msub> <mo> =</米o> <mfrac> <mrow> <mi mathvariant="normal"> 米</米我> <msub> <mi mathvariant="normal"> 年代</米我> <mi mathvariant="normal"> p</米我> </msub> <mi mathvariant="normal"> −米</米我> <msub> <mi mathvariant="normal"> 年代</米我> <mi mathvariant="normal"> e</米我> </msub> </mrow> <mrow> <mi mathvariant="normal"> 米</米我> <msub> <mi mathvariant="normal"> 年代</米我> <mi mathvariant="normal"> p</米我> </msub> <mi mathvariant="normal"> + M</米我> <msub> <mi mathvariant="normal"> 年代</米我> <mi mathvariant="normal"> e</米我> </msub> </mrow> </mfrac> <mo> 。</米o> </mrow> </math> </div> </div> </div> <div class=" block-child-p "> 在这个等式,女士<sub>p</sub>是参与者之间的均方和女士吗<sub>e</sub>均方误差。我们检查了intrasession(即。,short term) and intersession (i.e., long term) ICC values based upon centralities from scans 2/3 and scans 1/2, respectively. To estimate significantly reliable centralities, ICC values were converted to<e米>Z</e米>统计:<d我vclass="formula-wrap"> <div class="disp-formula" id="" content-id=""> <span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <mi> Z</米我> <mo> =</米o> <msqrt> <mrow> <mi> N</米我> <mi mathvariant="normal"> −</米我> <mn> 3</米n> </mrow> </msqrt> <mi mathvariant="normal"> ×</米我> <msup> <mrow> <mi> 双曲正切</米我> </mrow> <mrow> <mo> −</米o> <mn> 1</米n> </mrow> </msup> <mrow> <mo> (</米o> <mrow> <msub> <mi> r</米我> <mrow> <mi mathvariant="normal"> 泰爱泰党</米我> </mrow> </msub> </mrow> <mo> )</米o> </mrow> <mo> ,</米o> </mrow> </math> </div> </div>在哪里<e米>N</e米>参与者的数量。一个最低<e米>Z</e米>= 2.3 (<e米>P</e米>< 0.01)应用于阈值<e米>Z</e米>统计值。Voxel-wise<e米>Z</e米>地图纠正了多个集群级别的比较使用高斯随机场理论(最低<e米>Z</e米>> 2.3;集群的意义:<e米>P</e米>< 0.05,纠正)。</d我v> <h4 scrollto-destination="86050108" id="86050108" class="section-title js-splitscreen-section-title">人类的大脑功能连接体</h4> <p class="chapter-para">可视化完整的大脑功能连接体和进一步检查不同的中心之间的关系,对于所有21个站点,我们构建了一个的意思是脑功能网络。具体来说,先从参与者所有相关矩阵转化为费舍尔<e米>z</e米>值,然后在参与者平均。平均费雪<e米>z</e米>矩阵是back-transformed平均关联矩阵,利用逆费舍尔<e米>z</e米>的转型。基于平均相关矩阵,同样的图或网络建设方法如上所述(参见“图形成icn”)被用来生成意味着功能网络。相关的均值阈值在所有1003名被试被用来阈值均值相关矩阵。具体来说,对应相同的<e米>P</e米>值= 0.0001,相关阈值的均值为0.2982 voxel-derived图。</p> <p class="chapter-para">我们评估的可视化1)完整的大脑功能连接体基于MATLAB的能力(<一个class="link link-uri openInAnotherWindow" href="http://www.mathworks.com" target="_blank">http://www.mathworks.com</一个>)处理大型稀疏矩阵和Gephi (<一个class="link link-uri openInAnotherWindow" href="http://gephi.org" target="_blank">http://gephi.org</一个>探索一个大型网络;2)分布的中心措施,3)中心之间的相关性的措施。</p> <div class=" block-child-p "> 最后,对于每个体素,描述在1003例中心措施的一致性,我们提出另一个statistic-Kendall的摘要<e米>W</e米>(<span class="xrefLink" id="jumplink-bib44"></span> <a href="javascript:;" reveal-id="bib44" data-open="bib44" class="link link-ref link-reveal xref-bibr">勒让德2005</一个>):<d我vclass="formula-wrap"> <div class="disp-formula" id="fd7" content-id="fd7"> <span class="mathFormula"></span> <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow xmlns=""> <mi> W</米我> <mo> =</米o> <mfrac> <mrow> <mn> 12</米n> <mi> 年代</米我> </mrow> <mrow> <msup> <mi> 米</米我> <mn> 2</米n> </msup> <mrow> <mo> (</米o> <mrow> <msup> <mi> n</米我> <mn> 3</米n> </msup> <mo> −</米o> <mi> n</米我> </mrow> <mo> )</米o> </mrow> </mrow> </mfrac> <mo> ,</米o> <mi> E</米我> <mo> =</米o> <mo> −</米o> <mi> W</米我> <mi> ln (</米我> <mi> W</米我> <mi> )</米我> <mi> 。</米我> </mrow> </math> </div> <span class="label">(7)</span> </div> </div> <p class="chapter-para">熵<e米>E</e米>是用来测量信息的丰富性编码在中心的措施。在这里,<e米>米</e米>是中心的数量,<e米>n</e米>是对象的数量,和<e米>年代</e米>的平方SD行列。</p> <h2 scrollto-destination="86050113" id="86050113" class="section-title js-splitscreen-section-title">结果</h2> <p class="chapter-para">在目前的工作中,中心分析二进制和加权图代表功能的连接体。我们主要是报告的发现基于关键网络除了加权中心分析揭示了重要的不同结果的情况。</p> <h3 scrollto-destination="86050115" id="86050115" class="section-title js-splitscreen-section-title">人类的大脑功能连接体:社区和网络布局</h3> <p class="chapter-para">鉴于可视化的挑战(即voxel-wise连接矩阵图。,f或the entire brain functional connectome, 59 960 050 connections were detected on the mean connectivity matrix, representing a sparsity of 1.2%), we first depict the sparse adjacency matrix for the entire functional connectome (<span class="link link-data-supplement" data-supplement-target="sup1"></span><span class="content-section supplementary-material"><a path-from-xml="sup1" href="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/bhr269_Supplementary_Data.zip?Expires=1689100713&Signature=jCGM8AkNKnB1sOgZYkUARHF3vflZlpTNiKXdBa34yFFEUcxEg-TtXHnSc8h26d8-mWnezgAbDy06EAJrTUM2LAp3Q7vDDOe6FFqeqjb~0NeLbqNh21x8uqH-G2CC9YaXIs8orx78MFcPLMiFkDZVhyJ~21fdZGeXDVxozt~FFJSW0JCH2jcURiNK1jrZdL-9A1yDLbsCtOJDcqE4sDqrcx9sxATy999i7nCikg9i5eiR8djYxVJfAJY0BPKeN181CI6WK5ZjEWpKcGupL2kiReS9bhGMLl6L2dpgDC1FhSBrVXBljoQlcbGoQxLO6DnZf9Qg0ejQhraZ-r4Z5TDzWg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA">补充图S1A</一个></span>)及其记录版本(<span class="link link-data-supplement" data-supplement-target="sup1"></span><span class="content-section supplementary-material"><a path-from-xml="sup1" href="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/bhr269_Supplementary_Data.zip?Expires=1689100713&Signature=jCGM8AkNKnB1sOgZYkUARHF3vflZlpTNiKXdBa34yFFEUcxEg-TtXHnSc8h26d8-mWnezgAbDy06EAJrTUM2LAp3Q7vDDOe6FFqeqjb~0NeLbqNh21x8uqH-G2CC9YaXIs8orx78MFcPLMiFkDZVhyJ~21fdZGeXDVxozt~FFJSW0JCH2jcURiNK1jrZdL-9A1yDLbsCtOJDcqE4sDqrcx9sxATy999i7nCikg9i5eiR8djYxVJfAJY0BPKeN181CI6WK5ZjEWpKcGupL2kiReS9bhGMLl6L2dpgDC1FhSBrVXBljoQlcbGoQxLO6DnZf9Qg0ejQhraZ-r4Z5TDzWg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA">补充图。印地</一个></span>)。建立一个网络布局的整个功能连接体,我们采用快速指定布局algorithm-OpenOrd (<span class="xrefLink" id="jumplink-bib50"></span><a href="javascript:;" reveal-id="bib50" data-open="bib50" class="link link-ref link-reveal xref-bibr">马丁et al . 2011</一个>)——强调不同的集群或层次结构的连接体,导致20发现社区(<span class="xrefLink" id="jumplink-fig1"></span><a href="javascript:;" data-modal-source-id="fig1" class="link xref-fig">图1一个</一个>;看到<span class="xrefLink" id="jumplink-tbl2"></span><a href="javascript:;" reveal-id="tbl2" data-open="tbl2" class="link link-reveal link-table xref-fig">表2</一个>大脑区域的细节)。我们应用一个反复迭代算法在大型网络快速展开社区(<span class="xrefLink" id="jumplink-bib8"></span><a href="javascript:;" reveal-id="bib8" data-open="bib8" class="link link-ref link-reveal xref-bibr">他们批判et al . 2008</一个>)。具体地说,每次迭代包括两个阶段:一个模块化优化通过只允许本地社区的变化;一个社区发现的聚合,以建立一个新的网络社区。这些阶段是重复迭代,直到没有模块化的增加是可能的。最后社区的数量因此被发现在这样的自动优化方法。见的voxel-wise全脑网络布局<span class="xrefLink" id="jumplink-fig1"></span><a href="javascript:;" data-modal-source-id="fig1" class="link xref-fig">图1 b</一个>(见其全部解决<一个class="link link-uri openInAnotherWindow" href="http://lfcd.psych.ac.cn/pdfs/kfc_layout_22knodes.pdf" target="_blank">http://lfcd.psych.ac.cn/pdfs/kfc_layout_22knodes.pdf</一个>)。所有可视化程序完成图形工作站(<e米>联想</e米>ThinkStation D20开头配备16 CPU核心,16 GB物理内存,今年,月份,nvidia方形住宅区FX4800视频卡)神经心理学和应用心理学研究所认知神经科学实验室中国科学院。为探索提供一种公共资源进一步大规模脑网络的性质,我们上传的邻接矩阵整个功能连接体(<e米>N</e米>= 1003)LFCD网站(<一个class="link link-uri openInAnotherWindow" href="http://lfcd.psych.ac.cn/vncm/sp_fullbrain_4mm_1003subs_p0001.mat" target="_blank">http://lfcd.psych.ac.cn/vncm/sp_fullbrain_4mm_1003subs_p0001.mat</一个>)。</p> <a id="86050117" scrollto-destination="86050117"></a> <div content-id="tbl2" class="table-modal table-full-width-wrap"> <div class="table-wrap table-wide"> <div class="table-wrap-title" id="tbl2" data-id="tbl2"> <span class="label">表2</span> <div class="caption"> <p class="chapter-para">功能分割包括20个功能社区</p> </div> </div> <div class="table-overflow"> <table role="table"> <thead> <tr> <td></td> <td>功能社区</td> </tr> </thead> <tbody> <tr> <td>1。</td> <td>海马体(B)脑干(B)小脑前Loble (B)</td> </tr> <tr> <td>2。</td> <td>杏仁核(B) -Parahippocampus (B)后颞回(右)</td> </tr> <tr> <td>3所示。</td> <td>眶额叶皮质(B)</td> </tr> <tr> <td>4所示。</td> <td>纹状体(B)内侧前额叶皮层(B)</td> </tr> <tr> <td>5。</td> <td>前颞回(右)</td> </tr> <tr> <td>6。</td> <td>颞回(左)</td> </tr> <tr> <td>7</td> <td>视觉皮层(B)</td> </tr> <tr> <td>8。</td> <td>丘脑(B)</td> </tr> <tr> <td>9。</td> <td>后扣带皮层(B)楔前叶(B)角皮层(B)优越的额叶脑回(B)海马旁回(B)内侧前额叶皮质(B)中颞回(B)</td> </tr> <tr> <td>10。</td> <td>背外侧前额叶皮层(bl)</td> </tr> <tr> <td>11。</td> <td>额中回(R)糖尿病前期(右)</td> </tr> <tr> <td>12。</td> <td>额中回(L)糖尿病前期(左)</td> </tr> <tr> <td>13。</td> <td>辅助运动皮层(B)中间扣带回(B)</td> </tr> <tr> <td>14。</td> <td>中央前回(B)中央后回(B)</td> </tr> <tr> <td>15。</td> <td>前扣带皮层(B) -Supramarginal回(B)下顶叶小叶(B)上颞回(B)岛叶(B)</td> </tr> <tr> <td>16。</td> <td>额上回(L)中间额叶皮质(左)</td> </tr> <tr> <td>17所示。</td> <td>额中回(R)下顶叶小叶(B)</td> </tr> <tr> <td>18岁。</td> <td>侧枕叶皮质区(B) -Pecuneus (B)</td> </tr> <tr> <td>19所示。</td> <td>颞上回(L)中间颞回(右)</td> </tr> <tr> <td>20.</td> <td>颞下回(B)</td> </tr> </tbody> </table> </div> <div class="table-modal"> <table> <thead> <tr> <td></td> <td>功能社区</td> </tr> </thead> <tbody> <tr> <td>1。</td> <td>海马体(B)脑干(B)小脑前Loble (B)</td> </tr> <tr> <td>2。</td> <td>杏仁核(B) -Parahippocampus (B)后颞回(右)</td> </tr> <tr> <td>3所示。</td> <td>眶额叶皮质(B)</td> </tr> <tr> <td>4所示。</td> <td>纹状体(B)内侧前额叶皮层(B)</td> </tr> <tr> <td>5。</td> <td>前颞回(右)</td> </tr> <tr> <td>6。</td> <td>颞回(左)</td> </tr> <tr> <td>7</td> <td>视觉皮层(B)</td> </tr> <tr> <td>8。</td> <td>丘脑(B)</td> </tr> <tr> <td>9。</td> <td>后扣带皮层(B)楔前叶(B)角皮层(B)优越的额叶脑回(B)海马旁回(B)内侧前额叶皮质(B)中颞回(B)</td> </tr> <tr> <td>10。</td> <td>背外侧前额叶皮层(bl)</td> </tr> <tr> <td>11。</td> <td>额中回(R)糖尿病前期(右)</td> </tr> <tr> <td>12。</td> <td>额中回(L)糖尿病前期(左)</td> </tr> <tr> <td>13。</td> <td>辅助运动皮层(B)中间扣带回(B)</td> </tr> <tr> <td>14。</td> <td>中央前回(B)中央后回(B)</td> </tr> <tr> <td>15。</td> <td>前扣带皮层(B) -Supramarginal回(B)下顶叶小叶(B)上颞回(B)岛叶(B)</td> </tr> <tr> <td>16。</td> <td>额上回(L)中间额叶皮质(左)</td> </tr> <tr> <td>17所示。</td> <td>额中回(R)下顶叶小叶(B)</td> </tr> <tr> <td>18岁。</td> <td>侧枕叶皮质区(B) -Pecuneus (B)</td> </tr> <tr> <td>19所示。</td> <td>颞上回(L)中间颞回(右)</td> </tr> <tr> <td>20.</td> <td>颞下回(B)</td> </tr> </tbody> </table> </div> <div class="table-wrap-foot"> <span id="fn-tblfn1"></span> <div content-id="tblfn1" class="footnote"> <span class="fn"><p class="chapter-para">注意:B,两国;L,离开;和R。</p></span> </div> </div> <div class=" graphic-wrap "> <a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" target="_blank" href="//www.ez-admanager.com/view-large/86050117">打开新标签</一个> </div> </div> </div> <div class="table-full-width-wrap"> <div class="table-wrap table-wide"> <div class="table-wrap-title" id="tbl2" data-id="tbl2"> <span class="label">表2</span> <div class="caption"> <p class="chapter-para">功能分割包括20个功能社区</p> </div> </div> <div class="table-overflow"> <table role="table"> <thead> <tr> <td></td> <td>功能社区</td> </tr> </thead> <tbody> <tr> <td>1。</td> <td>海马体(B)脑干(B)小脑前Loble (B)</td> </tr> <tr> <td>2。</td> <td>杏仁核(B) -Parahippocampus (B)后颞回(右)</td> </tr> <tr> <td>3所示。</td> <td>眶额叶皮质(B)</td> </tr> <tr> <td>4所示。</td> <td>纹状体(B)内侧前额叶皮层(B)</td> </tr> <tr> <td>5。</td> <td>前颞回(右)</td> </tr> <tr> <td>6。</td> <td>颞回(左)</td> </tr> <tr> <td>7</td> <td>视觉皮层(B)</td> </tr> <tr> <td>8。</td> <td>丘脑(B)</td> </tr> <tr> <td>9。</td> <td>后扣带皮层(B)楔前叶(B)角皮层(B)优越的额叶脑回(B)海马旁回(B)内侧前额叶皮质(B)中颞回(B)</td> </tr> <tr> <td>10。</td> <td>背外侧前额叶皮层(bl)</td> </tr> <tr> <td>11。</td> <td>额中回(R)糖尿病前期(右)</td> </tr> <tr> <td>12。</td> <td>额中回(L)糖尿病前期(左)</td> </tr> <tr> <td>13。</td> <td>辅助运动皮层(B)中间扣带回(B)</td> </tr> <tr> <td>14。</td> <td>中央前回(B)中央后回(B)</td> </tr> <tr> <td>15。</td> <td>前扣带皮层(B) -Supramarginal回(B)下顶叶小叶(B)上颞回(B)岛叶(B)</td> </tr> <tr> <td>16。</td> <td>额上回(L)中间额叶皮质(左)</td> </tr> <tr> <td>17所示。</td> <td>额中回(R)下顶叶小叶(B)</td> </tr> <tr> <td>18岁。</td> <td>侧枕叶皮质区(B) -Pecuneus (B)</td> </tr> <tr> <td>19所示。</td> <td>颞上回(L)中间颞回(右)</td> </tr> <tr> <td>20.</td> <td>颞下回(B)</td> </tr> </tbody> </table> </div> <div class="table-modal"> <table> <thead> <tr> <td></td> <td>功能社区</td> </tr> </thead> <tbody> <tr> <td>1。</td> <td>海马体(B)脑干(B)小脑前Loble (B)</td> </tr> <tr> <td>2。</td> <td>杏仁核(B) -Parahippocampus (B)后颞回(右)</td> </tr> <tr> <td>3所示。</td> <td>眶额叶皮质(B)</td> </tr> <tr> <td>4所示。</td> <td>纹状体(B)内侧前额叶皮层(B)</td> </tr> <tr> <td>5。</td> <td>前颞回(右)</td> </tr> <tr> <td>6。</td> <td>颞回(左)</td> </tr> <tr> <td>7</td> <td>视觉皮层(B)</td> </tr> <tr> <td>8。</td> <td>丘脑(B)</td> </tr> <tr> <td>9。</td> <td>后扣带皮层(B)楔前叶(B)角皮层(B)优越的额叶脑回(B)海马旁回(B)内侧前额叶皮质(B)中颞回(B)</td> </tr> <tr> <td>10。</td> <td>背外侧前额叶皮层(bl)</td> </tr> <tr> <td>11。</td> <td>额中回(R)糖尿病前期(右)</td> </tr> <tr> <td>12。</td> <td>额中回(L)糖尿病前期(左)</td> </tr> <tr> <td>13。</td> <td>辅助运动皮层(B)中间扣带回(B)</td> </tr> <tr> <td>14。</td> <td>中央前回(B)中央后回(B)</td> </tr> <tr> <td>15。</td> <td>前扣带皮层(B) -Supramarginal回(B)下顶叶小叶(B)上颞回(B)岛叶(B)</td> </tr> <tr> <td>16。</td> <td>额上回(L)中间额叶皮质(左)</td> </tr> <tr> <td>17所示。</td> <td>额中回(R)下顶叶小叶(B)</td> </tr> <tr> <td>18岁。</td> <td>侧枕叶皮质区(B) -Pecuneus (B)</td> </tr> <tr> <td>19所示。</td> <td>颞上回(L)中间颞回(右)</td> </tr> <tr> <td>20.</td> <td>颞下回(B)</td> </tr> </tbody> </table> </div> <div class="table-wrap-foot"> <span id="fn-tblfn1"></span> <div content-id="tblfn1" class="footnote"> <span class="fn"><p class="chapter-para">注意:B,两国;L,离开;和R。</p></span> </div> </div> <div class=" graphic-wrap "> <a class="fig-view-orig at-tableViewLarge openInAnotherWindow btn js-view-large" target="_blank" href="//www.ez-admanager.com/view-large/86050117">打开新标签</一个> </div> </div> </div> <a id="86050118" scrollto-destination="86050118"></a> <div data-id="fig1" data-content-id="fig1" class="fig fig-section js-fig-section" swap-content-for-modal="true"> <div class="label fig-label"> 图1所示。</d我v> <div class="graphic-wrap"> <img class="content-image" src="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/m_cercorbhr269f01_4c.jpeg?Expires=1689100713&Signature=1nJPThDgQ1Fju225NxPXbAFQT8RpyWzn1DxjWyr16PhYEbahOsIsfdDJjndJFe24k~NeE8jF-GhPpss8JcZvyLqQJ4kHlxSzF5BqJdPzRtBr9fVxyfFjjulUHJJT35gmlt1bAo1ofk8cJPOwFXaawHpM1sUSlu9kQtXC7A9s4mnGMXk7J~Hi4bl957DhM~-PEUxqtm5L9bxdD5DAG-gtqUtg407l0cBIK0flW5RA9Val1Tmm-fBnPgFjlcy58LE-muwhB5ABwjXNJ1x2xC2sDqLp2sHN5qMnFI3Yz-XcbY7zvSq~AhLlmZACMb-Kg3rsd7kkXGg4edOb7rYgCBhLaQ__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" alt="全脑功能连接体:社区和布局。完整的大脑含有约22 387 4毫米立方体素。我们发现20功能社区(),它在多个轴向片和彩色明显呈现MNI152标准大脑表面上(有关详细信息,请参阅表2在社区和地区)。强调整体布局,voxel-wise连接体进一步可视化网络布局相同的颜色(B)。" data-path-from-xml="cercorbhr269f01_4c.jpeg"> <div class="fig-orig original-slide"> <a class="fig-view-orig js-view-large at-figureViewLarge openInAnotherWindow" href="//www.ez-admanager.com/view-large/figure/86050118/cercorbhr269f01_4c.jpeg" data-path-from-xml="cercorbhr269f01_4c.jpeg" target="_blank">打开新标签</一个> <a data-section="86050118" href="//www.ez-admanager.com/DownloadFile/DownloadImage.aspx?image=https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/cercorbhr269f01_4c.jpeg?Expires=1689100713&Signature=tD6tsH8KV5lHDpv35Qsen39tKFYDqdXpZTsv9Iau6CWVX~gDxsZjaj-YSPgsZ005dbYCPopnpz-kJoq8UhlJx5azBQ6JV1P-B-45n8aqeD9j000RgngyIWbkNwdDAcMGCQoIX5JtIIbpzEDTuNmmqZGwgS0arJ2AUEb5-EPWprz1iCM-dhijbmAH3OYMB5nhWF8he~LeR-~fY8Irw1fhCTRtrQcDWOxG9JsLYnWiir8GWppa~WjKaS5vrIzMA7anJomiuNFdtgUIgz1ba5vS8zlVV2U1zUcSW5OHrQnkBeeAeI-YWMN7ygVcR1mnFPFOchGzQVfW2PC0-fYn1ImPog__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA&sec=86050118&ar=321860&xsltPath=~/UI/app/XSLT&imagename=&siteId=5122" class="download-slide" data-path-from-xml="cercorbhr269f01_4c.jpeg">下载幻灯片</一个> </div> </div> <div class="caption fig-caption"> <p class="chapter-para">全脑功能连接体:社区和布局。完整的大脑含有约22 387 4毫米立方体素。我们发现20功能社区(<e米>一个</e米>),它在多个轴向片和彩色明显呈现在MNI152大脑表面(见标准<span class="xrefLink" id="jumplink-tbl2"></span><a href="javascript:;" reveal-id="tbl2" data-open="tbl2" class="link link-reveal link-table xref-fig">表2</一个>有关社区和地区)。强调整体布局,voxel-wise连接体进一步可视化为网络布局相同的颜色(<e米>B</e米>)。</p> </div> </div> <h3 scrollto-destination="86050119" id="86050119" class="section-title js-splitscreen-section-title">Voxel-wise中心优势</h3> <p class="chapter-para">Voxel-wise中心分析允许我们描述完整的大脑模式4中心措施在4毫米级。所示<span class="xrefLink" id="jumplink-fig2"></span><a href="javascript:;" data-modal-source-id="fig2" class="link xref-fig">图2</一个>体素,脑岛表现出高中心和高跨中心相似的措施。我们绘制像素点反对另一个关于中心的评分(<span class="xrefLink" id="jumplink-fig3"></span><a href="javascript:;" data-modal-source-id="fig3" class="link xref-fig">图3</一个>)。除了直流和PC,再次是高度相关的,我们观察到更复杂的人际关系,与亚种群的体素表现出明显不同的关系。例如,当一个人口体素表现出相对较高的相关性EC直流和PC,体素表现出的另一个人口没有EC和这两种措施之间的关系。这样的体素之间的区别可能反映差异对给定的体素是否有高度的全球连通性,EC是特别敏感的一个特性。地图EC和华盛顿之间的区别<span class="link link-data-supplement" data-supplement-target="sup1"></span><span class="content-section supplementary-material"><a path-from-xml="sup1" href="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/bhr269_Supplementary_Data.zip?Expires=1689100713&Signature=jCGM8AkNKnB1sOgZYkUARHF3vflZlpTNiKXdBa34yFFEUcxEg-TtXHnSc8h26d8-mWnezgAbDy06EAJrTUM2LAp3Q7vDDOe6FFqeqjb~0NeLbqNh21x8uqH-G2CC9YaXIs8orx78MFcPLMiFkDZVhyJ~21fdZGeXDVxozt~FFJSW0JCH2jcURiNK1jrZdL-9A1yDLbsCtOJDcqE4sDqrcx9sxATy999i7nCikg9i5eiR8djYxVJfAJY0BPKeN181CI6WK5ZjEWpKcGupL2kiReS9bhGMLl6L2dpgDC1FhSBrVXBljoQlcbGoQxLO6DnZf9Qg0ejQhraZ-r4Z5TDzWg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA">补充图S3</一个></span>说明这两个种群之间的差异(例如,内侧视觉皮层和后扣带皮层)。最后,EC和SC是抛物线的关系(即。,“U”形),表明SC是中间直流和电子商务之间的关系。</p> <a id="86050121" scrollto-destination="86050121"></a> <div data-id="fig2" data-content-id="fig2" class="fig fig-section js-fig-section" swap-content-for-modal="true"> <div class="label fig-label"> 图2。</d我v> <div class="graphic-wrap"> <img class="content-image" src="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/m_cercorbhr269f02_4c.jpeg?Expires=1689100713&Signature=CtNO~nfG-NEA027YqPUwANKlLDrM75MHvRE6XJGYZyA~CFKCOSzC2F-eetF0qwqvWb3h9CPFGMDxntm-KEMzWT5iv3Zg8axNfz~tM4b6D4VN944OjAARFLJfHMFXPom6~s-WyKc44xW-vhBAevBfxt8LPWzDvWxGissuFf0HJjsnuF4t3r9~nBNKrwAr8XupPDXSkH46PajuKta1UukcDzM-4TzAHvOFCfAekatku59N~umuxL2TRYnUXHpKtCItmZ3c78FQBN1iBeTjps7z4PuWAsWGKwEGPLFZymPCjycVIOhttyb3QRtwUeTVRXz1hTLQGdXx3QfXy3Z8QzbRUA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" alt="VNCM。为每个4中心包括直流、SC、EC, PC,单向方差分析模型包括中心,年龄、性别、头部运动,和配准误差不感兴趣的是用来计算voxel-wise中心地图在1003名参与者。多重比较时纠正集群级别使用高斯随机场理论(min Z > 2.3;集群的意义:P < 0.05,纠正)。最后统计地图可视化为4半球表面(左半球皮层区域:2 (L)和2右半球[R])和8冠状切片(皮质下区域:y坐标从23.5−11.5毫米和5毫米空间)。" data-path-from-xml="cercorbhr269f02_4c.jpeg"> <div class="fig-orig original-slide"> <a class="fig-view-orig js-view-large at-figureViewLarge openInAnotherWindow" href="//www.ez-admanager.com/view-large/figure/86050121/cercorbhr269f02_4c.jpeg" data-path-from-xml="cercorbhr269f02_4c.jpeg" target="_blank">打开新标签</一个> <a data-section="86050121" href="//www.ez-admanager.com/DownloadFile/DownloadImage.aspx?image=https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/cercorbhr269f02_4c.jpeg?Expires=1689100713&Signature=F62e57BI1CnDHTjgMiB2yZZzRQEXEfH2hsrexcdXOekk3YG6OuLEBUovNp74t3NFEtTKHdljdzts8hDZDJJzSS9zkk4mDe4QL4xcNwp2fNnHfh3L0xiI0wX0R9Za2iOUb4EwXz2ZBUHRvAVK-XH5jl2Zbh5keSpbK8OALM8cjnc02P4gl2EyiRHW1iogfwNRGNiR-zYEJtTpIsMiAJ-SN5JwIQRjC34xgqsVdmfwBGwkYRSms0lOkoXHjYzV0rk~MC8P1~8u3oxWy2ERj5WagKeqLBU~tCmBUQDCcRbWt7JP~hkCT6GS7aCiI19qImmgLO4dk3cbopL15aKxOmClwg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA&sec=86050121&ar=321860&xsltPath=~/UI/app/XSLT&imagename=&siteId=5122" class="download-slide" data-path-from-xml="cercorbhr269f02_4c.jpeg">下载幻灯片</一个> </div> </div> <div class="caption fig-caption"> <p class="chapter-para">VNCM。为每个4中心包括直流、SC、EC, PC,单向方差分析模型包括中心,年龄、性别、头部运动,和配准误差不感兴趣的是用来计算voxel-wise中心地图在1003名参与者。多重比较时纠正集群级别使用高斯随机场理论(最低<e米>Z</e米>> 2.3;集群的意义:<e米>P</e米>< 0.05,纠正)。最后统计地图可视化为4半球表面(左半球皮层区域:2 (L)和2右半球[R])和8冠状切片(皮质下区域:<e米>y</e米>坐标从23.5−11.5毫米和5毫米空间)。</p> </div> </div> <a id="86050122" scrollto-destination="86050122"></a> <div data-id="fig3" data-content-id="fig3" class="fig fig-section js-fig-section" swap-content-for-modal="true"> <div class="label fig-label"> 图3。</d我v> <div class="graphic-wrap"> <img class="content-image" src="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/m_cercorbhr269f03_4c.jpeg?Expires=1689100713&Signature=JcPpNEM3qGbN3oy3G3kTUIUlhy64CsJN-gFHkY11q2kQGkgkrXYOkhaZ-NITpNZlHgmbTW3cc4fZHCsAp7KXFgz3lvqw0IEtjWxHYGmuG~x-BGdhL1F1VsZ7iYL5QXHqijZUipru6XSJGZDFG5KNZQakIvMy~R5pX2LfCOYxz60wjYd~XJMNPm8Pil7tDj~KGt7oXYY5nTulZn7eugEqCgOvfzBgrNz4FTFFoONXTnb4Sm3zeIVPRjFrIQVmfsDjWb~ovXVe14aXt2oAV3NVXMEQkffNtKzZPtXMr6PSNJEspSNwoQjatDsQAug1A7jXP5XR4jgtV7~BFSuNVeJ19w__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" alt="散点图voxel-wise网络中心的措施。每一对4中心措施包括直流、SC、EC、和电脑,所有体素的subscatter情节中心成绩是用来显示双中心措施之间的关系。安装线也被绘制在红色的最佳最小二乘法适合中心的分数。每个中心的直方图分数被描绘成对角线的散点图。" data-path-from-xml="cercorbhr269f03_4c.jpeg"> <div class="fig-orig original-slide"> <a class="fig-view-orig js-view-large at-figureViewLarge openInAnotherWindow" href="//www.ez-admanager.com/view-large/figure/86050122/cercorbhr269f03_4c.jpeg" data-path-from-xml="cercorbhr269f03_4c.jpeg" target="_blank">打开新标签</一个> <a data-section="86050122" href="//www.ez-admanager.com/DownloadFile/DownloadImage.aspx?image=https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/cercorbhr269f03_4c.jpeg?Expires=1689100713&Signature=pbOU43OS1Z0KeQvKn~AtH6~gIFxhO9rQNH~1kE25BJsTqTYORxs2oK-MLs~FFKdeTwH3tu4ZTfuGPONDKqWHrX2D-3GErP6F4VxYqXaUeCVriSLAULlx6e1WuosXGzR7TXZEU1AM7-kiiixC55JhDO7LZ0BYSiLBt0ur3dHXm9GipsrhTFtnP4VDgmRnrYhk86CZqblm3NPSIQFaq5lMWPRRM8t~wcZo5vBxA4WABv5MKsuX8oVRrTGtuuONzZXqBo9xYRCSB68C5SQqu0VDbEaTbXsTH0-Ntyq4PrID8SpNC6naOUVAKOcHVP1iLbwiEQOoy~xFgZz8lLHt7Kti4g__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA&sec=86050122&ar=321860&xsltPath=~/UI/app/XSLT&imagename=&siteId=5122" class="download-slide" data-path-from-xml="cercorbhr269f03_4c.jpeg">下载幻灯片</一个> </div> </div> <div class="caption fig-caption"> <p class="chapter-para">散点图voxel-wise网络中心的措施。每一对4中心措施包括直流、SC、EC、和电脑,所有体素的subscatter情节中心成绩是用来显示双中心措施之间的关系。安装线也被绘制在红色的最佳最小二乘法适合中心的分数。每个中心的直方图分数被描绘成对角线的散点图。</p> </div> </div> <h3 scrollto-destination="86050123" id="86050123" class="section-title js-splitscreen-section-title">区域中心的一致性</h3> <p class="chapter-para">两种分析方法被用来评估和谐中心措施分配到一个给定的地区。首先,我们肯德尔的计算<e米>W</e米>及其熵版本通过治疗不同的中心措施评级机构和对象作为样本。默认网络的核心区域和背侧注意网络表现出一致性较低(<span class="xrefLink" id="jumplink-fig4"></span><a href="javascript:;" data-modal-source-id="fig4" class="link xref-fig">图4</一个>)和较高的熵(<span class="xrefLink" id="jumplink-fig4"></span><a href="javascript:;" data-modal-source-id="fig4" class="link xref-fig">图4 b</一个>与低阶地区相比)。值得注意的是,所有的中心措施是高度整合对区分灰质和白质边界。</p> <a id="86050125" scrollto-destination="86050125"></a> <div data-id="fig4" data-content-id="fig4" class="fig fig-section js-fig-section" swap-content-for-modal="true"> <div class="label fig-label"> 图4。</d我v> <div class="graphic-wrap"> <img class="content-image" src="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/m_cercorbhr269f04_4c.jpeg?Expires=1689100713&Signature=3~GDP5hrDB~qjpXZr-lVg53CVmIYR~jo8aX3~T8XnpsLhLvnvPptyIkfSTzkofS9vBh-2YElU30jI166EnhcelpqCajsltM-YNG-sugY2LDxV9RxKQwt0vA1f84k95XeN4~LJ2UScpkfJNVzGfsWjq8I22X3CMU1TEHH6cCJx9n~Mr7rFa-JIzLUu~G3qi2oxGO6m61O-JhkYmEZb1VfqFwh9W~yiNmBrMQfYabnOVe9CB9hlP45trK1VIQNjtRq~ffgKbGPJN6BxV166tzr7Ahus9xvNfvv4wXYMxsg4yD8Shv8d3ijs6ovpK4CiEYVMDyh10e~fAroT5sCXSNwag__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" alt="区域变化之间的和谐中心措施跨越1003名参与者。肯德尔W熵(A)和(B)计算来衡量不同中心措施之间的一致性。最后统计地图可视化为4半球表面(左半球皮层区域:2 (L)和2右半球[R])和8冠状切片(皮质下区域:y坐标从23.5−11.5毫米和5毫米空间)。" data-path-from-xml="cercorbhr269f04_4c.jpeg"> <div class="fig-orig original-slide"> <a class="fig-view-orig js-view-large at-figureViewLarge openInAnotherWindow" href="//www.ez-admanager.com/view-large/figure/86050125/cercorbhr269f04_4c.jpeg" data-path-from-xml="cercorbhr269f04_4c.jpeg" target="_blank">打开新标签</一个> <a data-section="86050125" href="//www.ez-admanager.com/DownloadFile/DownloadImage.aspx?image=https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/cercorbhr269f04_4c.jpeg?Expires=1689100713&Signature=OioNyiVRy~HlmnKDyZCfQ3yvzzNv6vWbgYhGPvJphH0u5p1F9TYY-Rrdbe1bwHEngmxP0a5zSwZMRKBJl0mkftwWKFbFTsNg9QFGh6cg2dejn03MczWhj7GFNad8RMukWQM70J6FJ3cDyw9P8ltJR7ASmnj77IAuplWaGYxPuaGM3YbnmHtapRqwxFZrfJ8NKIi4bK7~poIMdz3PSjMfqRGrS0G49odOBobIT-7O4bIjo~dJt8sCOk~PLhQw5Rh6ose~4jJzqI~yYYCWnEcjZOLjqZc~lu6oOUG8nPVAYfWlyC8O9LBfFlqbcqRCzYCvFR5eb~A7oNNyehfYUlsf7g__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA&sec=86050125&ar=321860&xsltPath=~/UI/app/XSLT&imagename=&siteId=5122" class="download-slide" data-path-from-xml="cercorbhr269f04_4c.jpeg">下载幻灯片</一个> </div> </div> <div class="caption fig-caption"> <p class="chapter-para">区域变化之间的和谐中心措施跨越1003名参与者。肯德尔的<e米>W</e米>(<e米>一个</e米>)和熵(<e米>B</e米>)计算来衡量不同中心措施之间的一致性。最后统计地图可视化为4半球表面(左半球皮层区域:2 (L)和2右半球[R])和8冠状切片(皮质下区域:<e米>y</e米>坐标从23.5−11.5毫米和5毫米空间)。</p> </div> </div> <p class="chapter-para">了解独特的功能连通性方面被不同的中心措施,我们将获得的结果与每个中心的措施与获得,使用voxel-wise (<span class="xrefLink" id="jumplink-fig5"></span><a href="javascript:;" data-modal-source-id="fig5" class="link xref-fig">图5</一个>)配对<e米>t</e米>测试参与者进行。我们使用直流参考测量由于其相对简单和广泛使用。相对于直流,EC明显高于皮质下区域的中心,这说明在这些区域可能不是广泛连接整个大脑,它们与关键的枢纽,从而增加自己的电子商务。相比之下,DC在皮质区域更大,建议更加分散的连接模式。值得注意的是,PC,通过定义中间更多全球集中EC和本地集中直流措施,结果中间EC和直流。和电子商务一样,PC是明显更敏感的皮质下区域的中心。电脑也表现出显著降低皮质区域的中心,虽然不是电子商务的程度。</p> <a id="86050127" scrollto-destination="86050127"></a> <div data-id="fig5" data-content-id="fig5" class="fig fig-section js-fig-section" swap-content-for-modal="true"> <div class="label fig-label"> 图5。</d我v> <div class="graphic-wrap"> <img class="content-image" src="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/m_cercorbhr269f05_4c.jpeg?Expires=1689100713&Signature=xprU8tdD2DIdMYNehJQpBdo8CTtkME~dFGlpeaIb4tmxV-8G2T70uWldVDn1l-4JrB9x0vFqsZ~XoaVQ0YamEWKqSPz9eCi1CGhjzurqRAvSpOm8z6JMEz7UPOTTsc4oXQumXueNZ-PwoUhFpKwctxY8MBcVTYo10ZWukPJLWO-3UgILx~E1WZhIq~C4slIkvntTnyzUvieEvzjyy0E-KLcHCZzDVAupp4MumSul24WY1zquG5ZRQhwm1PWN8FIYyY8R9SKvcwWyZwOgy5WebFkqjlkBiCMB60NDMASGBXSGvBhtG5ezPvGuDeAOu-RSUWa-0dpN5sFaLkxzFtxfOw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" alt="区分voxel-wise网络中心的措施。使用直流参考中心测量,两个示例进行配对t检验中心成绩对于每个体素之间的直流和每个其他3中心成绩:SC, EC和PC 1003名参与者。多重比较时纠正集群级别使用高斯随机场理论(min Z > 2.3;集群的意义:P < 0.05,纠正)。最后统计地图可视化为4半球表面(左半球皮层区域:2 (L)和2右半球[R])和8冠状切片(皮质下区域:y坐标从23.5−11.5毫米和5毫米空间)。" data-path-from-xml="cercorbhr269f05_4c.jpeg"> <div class="fig-orig original-slide"> <a class="fig-view-orig js-view-large at-figureViewLarge openInAnotherWindow" href="//www.ez-admanager.com/view-large/figure/86050127/cercorbhr269f05_4c.jpeg" data-path-from-xml="cercorbhr269f05_4c.jpeg" target="_blank">打开新标签</一个> <a data-section="86050127" href="//www.ez-admanager.com/DownloadFile/DownloadImage.aspx?image=https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/cercorbhr269f05_4c.jpeg?Expires=1689100713&Signature=2scg1Gx7mbQKXdZiypOC4UhK~Q-FcMT0DI8P36894merLopqXTZKVgSpZYaiJDst0TpXrzhryw6Y7NO6kspqa8rHajhSFSs7pNm6HE1GoODPpQ4NiAf1qGYJS-mRD4LeG3l5kU4HvJmJMISlgFWlehXsHAzVPePLsKKrDvoYKEkmTHd734296MjzLrIm6hQnZYtqbYxQ0nJZYeHzgn0bPdm0gHSmipcaKwSOx3qa~v1f-Feo8~qxVrbF9~-jM1z6Fb-s7emkpK0fPg5aev-ZFKdGpfkUvt0Sgq7WnL4fK6vv~WtyddVhKsB4ipmLeCBTHJMIT~zs4FP5TQ5zmfuVKQ__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA&sec=86050127&ar=321860&xsltPath=~/UI/app/XSLT&imagename=&siteId=5122" class="download-slide" data-path-from-xml="cercorbhr269f05_4c.jpeg">下载幻灯片</一个> </div> </div> <div class="caption fig-caption"> <p class="chapter-para">区分voxel-wise网络中心的措施。使用直流参考中心测量,两个示例配对<e米>t</e米>以及在中心分数为每个执行体素之间的直流和每个其他3中心成绩:SC, EC和PC 1003名参与者。多重比较时纠正集群级别使用高斯随机场理论(最低<e米>Z</e米>> 2.3;集群的意义:<e米>P</e米>< 0.05,纠正)。最后统计地图可视化为4半球表面(左半球皮层区域:2 (L)和2右半球[R])和8冠状切片(皮质下区域:<e米>y</e米>坐标从23.5−11.5毫米和5毫米空间)。</p> </div> </div> <p class="chapter-para">SC进行对比,发现是明显不同于那些获得电子商务和电脑。特别是,SC强调顶叶和额叶区域的中心通常涉及注意力控制(<span class="xrefLink" id="jumplink-bib4"></span><a href="javascript:;" reveal-id="bib4" data-open="bib4" class="link link-ref link-reveal xref-bibr">代表et al . 2004</一个>),尤其是后额下回(IFG)已知注意力偏压和选择(<span class="xrefLink" id="jumplink-bib47"></span><a href="javascript:;" reveal-id="bib47" data-open="bib47" class="link link-ref link-reveal xref-bibr">麦克唐纳et al . 2000</一个>;<span class="xrefLink" id="jumplink-bib53"></span><a href="javascript:;" reveal-id="bib53" data-open="bib53" class="link link-ref link-reveal xref-bibr">米勒和科恩2001年</一个>;<span class="xrefLink" id="jumplink-bib52"></span><a href="javascript:;" reveal-id="bib52" data-open="bib52" class="link link-ref link-reveal xref-bibr">Milham et al . 2003</一个>;<span class="xrefLink" id="jumplink-bib2"></span><a href="javascript:;" reveal-id="bib2" data-open="bib2" class="link link-ref link-reveal xref-bibr">Banich 2009</一个>)。</p> <h3 scrollto-destination="86050129" id="86050129" class="section-title js-splitscreen-section-title">泰爱泰党可靠性</h3> <p class="chapter-para">所有voxel-wise中心措施产生了空间扩展短期(<span class="link link-data-supplement" data-supplement-target="sup1"></span><span class="content-section supplementary-material"><a path-from-xml="sup1" href="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/bhr269_Supplementary_Data.zip?Expires=1689100713&Signature=jCGM8AkNKnB1sOgZYkUARHF3vflZlpTNiKXdBa34yFFEUcxEg-TtXHnSc8h26d8-mWnezgAbDy06EAJrTUM2LAp3Q7vDDOe6FFqeqjb~0NeLbqNh21x8uqH-G2CC9YaXIs8orx78MFcPLMiFkDZVhyJ~21fdZGeXDVxozt~FFJSW0JCH2jcURiNK1jrZdL-9A1yDLbsCtOJDcqE4sDqrcx9sxATy999i7nCikg9i5eiR8djYxVJfAJY0BPKeN181CI6WK5ZjEWpKcGupL2kiReS9bhGMLl6L2dpgDC1FhSBrVXBljoQlcbGoQxLO6DnZf9Qg0ejQhraZ-r4Z5TDzWg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA">补充图S2</一个></span>)和长期(<span class="xrefLink" id="jumplink-fig6"></span><a href="javascript:;" data-modal-source-id="fig6" class="link xref-fig">图6</一个>泰爱泰党可靠性,短期可靠性优于长期可靠性。4中心的措施显示高度相似的模式的泰爱泰党可靠性的关键(<span class="xrefLink" id="jumplink-fig6"></span><a href="javascript:;" data-modal-source-id="fig6" class="link xref-fig">图6</一个>:intersession;<span class="link link-data-supplement" data-supplement-target="sup1"></span><span class="content-section supplementary-material"><a path-from-xml="sup1" href="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/bhr269_Supplementary_Data.zip?Expires=1689100713&Signature=jCGM8AkNKnB1sOgZYkUARHF3vflZlpTNiKXdBa34yFFEUcxEg-TtXHnSc8h26d8-mWnezgAbDy06EAJrTUM2LAp3Q7vDDOe6FFqeqjb~0NeLbqNh21x8uqH-G2CC9YaXIs8orx78MFcPLMiFkDZVhyJ~21fdZGeXDVxozt~FFJSW0JCH2jcURiNK1jrZdL-9A1yDLbsCtOJDcqE4sDqrcx9sxATy999i7nCikg9i5eiR8djYxVJfAJY0BPKeN181CI6WK5ZjEWpKcGupL2kiReS9bhGMLl6L2dpgDC1FhSBrVXBljoQlcbGoQxLO6DnZf9Qg0ejQhraZ-r4Z5TDzWg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA">补充图S2</一个></span>:intrasession)和加权网络(<span class="link link-data-supplement" data-supplement-target="sup1"></span><span class="content-section supplementary-material"><a path-from-xml="sup1" href="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/bhr269_Supplementary_Data.zip?Expires=1689100713&Signature=jCGM8AkNKnB1sOgZYkUARHF3vflZlpTNiKXdBa34yFFEUcxEg-TtXHnSc8h26d8-mWnezgAbDy06EAJrTUM2LAp3Q7vDDOe6FFqeqjb~0NeLbqNh21x8uqH-G2CC9YaXIs8orx78MFcPLMiFkDZVhyJ~21fdZGeXDVxozt~FFJSW0JCH2jcURiNK1jrZdL-9A1yDLbsCtOJDcqE4sDqrcx9sxATy999i7nCikg9i5eiR8djYxVJfAJY0BPKeN181CI6WK5ZjEWpKcGupL2kiReS9bhGMLl6L2dpgDC1FhSBrVXBljoQlcbGoQxLO6DnZf9Qg0ejQhraZ-r4Z5TDzWg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA">补充图S8</一个></span>:intersession;<span class="link link-data-supplement" data-supplement-target="sup1"></span><span class="content-section supplementary-material"><a path-from-xml="sup1" href="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/bhr269_Supplementary_Data.zip?Expires=1689100713&Signature=jCGM8AkNKnB1sOgZYkUARHF3vflZlpTNiKXdBa34yFFEUcxEg-TtXHnSc8h26d8-mWnezgAbDy06EAJrTUM2LAp3Q7vDDOe6FFqeqjb~0NeLbqNh21x8uqH-G2CC9YaXIs8orx78MFcPLMiFkDZVhyJ~21fdZGeXDVxozt~FFJSW0JCH2jcURiNK1jrZdL-9A1yDLbsCtOJDcqE4sDqrcx9sxATy999i7nCikg9i5eiR8djYxVJfAJY0BPKeN181CI6WK5ZjEWpKcGupL2kiReS9bhGMLl6L2dpgDC1FhSBrVXBljoQlcbGoQxLO6DnZf9Qg0ejQhraZ-r4Z5TDzWg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA">补充图S9</一个></span>:intrasession)。这些voxel-level发现是一致的与最近的一项研究对泰爱泰党大规模的脑功能网络的可靠性<span class="xrefLink" id="jumplink-bib80"></span><a href="javascript:;" reveal-id="bib80" data-open="bib80" class="link link-ref link-reveal xref-bibr">王et al . 2011</一个>)。再一次,具体的中心措施是并不是所有的等价对地区表现出更高或更低的可靠性。直流和PC显示附近最大相关性彼此对短期(<e米>r</e米>= 0.99)和长期(<e米>r</e米>= 0.99)刑事法庭在体素值(即。,spatially correlating the ICC maps for DC and PC). This implies that DC and PC have nearly identical reliability. The other correlations with DC were much lower (for EC, short term:<e米>r</e米>= 0.69;长期:<e米>r</e米>= 0.70;SC,短期内:<e米>r</e米>= 0.82;长期:<e米>r</e米>= 0.84)。SC展出ICC的最低程度的相关性值3其他中心措施(短期:平均水平<e米>r</e米>= 0.61;长期:平均<e米>r</e米>= 0.62)。</p> <a id="86050131" scrollto-destination="86050131"></a> <div data-id="fig6" data-content-id="fig6" class="fig fig-section js-fig-section" swap-content-for-modal="true"> <div class="label fig-label"> 图6。</d我v> <div class="graphic-wrap"> <img class="content-image" src="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/m_cercorbhr269f06_4c.jpeg?Expires=1689100713&Signature=wfkIILIlEsKnWt1Awlf58EEESRaiAYo938lkiTlAO-5qtOQK1xh7-7Kkw2E7LGf8tN~BBYQqLjGX2BGnVaUH~vOpACguEAlinCfoAmyfjYLltQW9clIuLsj8sFWFKko84IS1ggJLifpK0bhs0zf5JFW6XC3rx4oiU6puJ5ziTjrWDSUHF7TRnvOgj2MpvErYE7XBMB7p26NaWm9WMAarQmIs13SLhMX3Qt-TCecYzuIM7sxlevtothR-~1WV23-iSDRA7wIvV9GFjZA3Q46SDK5X1dJLMX0KLmeMayFBwexsTeNdVsmLszw1RKdL-r0UgVCTNsLOAP8Tr04Vpkm6Wg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" alt="泰爱泰党voxel-wise网络中心的可靠性措施。为每个4中心措施包括直流、SC、EC、PC, intersession或长期为每个立体像素可以被计算。对于每个中心测量,明显可靠的体素都是可视化为多个轴向片(集群的意义:为多个比较P < 0.05,纠正)。最后统计地图可视化为4半球表面(左半球皮层区域:2 (L)和2右半球[R])和8冠状切片(皮质下区域:y坐标从23.5−11.5毫米和5毫米空间)。" data-path-from-xml="cercorbhr269f06_4c.jpeg"> <div class="fig-orig original-slide"> <a class="fig-view-orig js-view-large at-figureViewLarge openInAnotherWindow" href="//www.ez-admanager.com/view-large/figure/86050131/cercorbhr269f06_4c.jpeg" data-path-from-xml="cercorbhr269f06_4c.jpeg" target="_blank">打开新标签</一个> <a data-section="86050131" href="//www.ez-admanager.com/DownloadFile/DownloadImage.aspx?image=https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/cercorbhr269f06_4c.jpeg?Expires=1689100713&Signature=F3Aj5-3bhm9KVgoOCtpE7cOi8OttrX8dVz4WSH7cFTfH7YxTUF-8jGUdalAWepHjHAoygebkEx1YAqMIV-ZLf3wDDEbGokfmqO-S47JP4RUz9J-qd36~35qw5namdvQ7-zgRNYBdZC32B5Tc-ZIz4Yizmu7Nuns0ImMs36JlLnPElXBohfytMXs2-mv2z1xKXzWtU7sEwRMKLo2M9cKj6eARt8U2e0HcqEZkRQY5eVTgr2PTM7ZFjH3NlWow8zMQj9bNYmigEXdKe2WlxE0W1Nj9HOmt~Db-HaCVG0ch~Xbnr9CBvLLrkqg~wFEofYswcwrq3xQj53ibp0jotDntlg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA&sec=86050131&ar=321860&xsltPath=~/UI/app/XSLT&imagename=&siteId=5122" class="download-slide" data-path-from-xml="cercorbhr269f06_4c.jpeg">下载幻灯片</一个> </div> </div> <div class="caption fig-caption"> <p class="chapter-para">泰爱泰党voxel-wise网络中心的可靠性措施。为每个4中心措施包括直流、SC、EC、PC, intersession或长期为每个立体像素可以被计算。对于每个中心测量,明显可靠的体素都是可视化为多个轴向片(集群的意义:<e米>P</e米>< 0.05,纠正多个比较)。最后统计地图可视化为4半球表面(左半球皮层区域:2 (L)和2右半球[R])和8冠状切片(皮质下区域:<e米>y</e米>坐标从23.5−11.5毫米和5毫米空间)。</p> </div> </div> <h3 scrollto-destination="86050132" id="86050132" class="section-title js-splitscreen-section-title">年龄和性别的影响</h3> <p class="chapter-para">检查特定的结果之前,我们首先计算4措施之间的一致性对年龄和性别的统计量产生影响。对年龄和性别,总体统计地图中表现出高度的一致性,EC, PC(年龄:平均肯德尔的措施<e米>W</e米>= 0.93;性:肯德尔的平均水平<e米>W</e米>= 0.92);观察更为温和的一致性之间SC和其他措施,尤其是对年龄(DC:肯德尔的影响<e米>W</e米>= 0.85;电子商务:肯德尔的<e米>W</e米>= 0.72;PC:肯德尔的<e米>W</e米>= 0.87)。</p> <p class="chapter-para">当检查voxel-level发现相关年龄(<span class="xrefLink" id="jumplink-fig7"></span><a href="javascript:;" data-modal-source-id="fig7" class="link xref-fig">图7</一个>),年龄增长中心内侧颞叶和尾状区域内所有中心高度整合的措施。此外,与年龄相关的减少中心被发现在顶叶和后扣带地区,尽管只有直流,SC,电脑。EC相对不敏感的年龄,暗示全球组织的保存在我们检查的年龄范围。除了高度类似的发现,对直流和SC,加权网络(<span class="link link-data-supplement" data-supplement-target="sup1"></span><span class="content-section supplementary-material"><a path-from-xml="sup1" href="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/bhr269_Supplementary_Data.zip?Expires=1689100713&Signature=jCGM8AkNKnB1sOgZYkUARHF3vflZlpTNiKXdBa34yFFEUcxEg-TtXHnSc8h26d8-mWnezgAbDy06EAJrTUM2LAp3Q7vDDOe6FFqeqjb~0NeLbqNh21x8uqH-G2CC9YaXIs8orx78MFcPLMiFkDZVhyJ~21fdZGeXDVxozt~FFJSW0JCH2jcURiNK1jrZdL-9A1yDLbsCtOJDcqE4sDqrcx9sxATy999i7nCikg9i5eiR8djYxVJfAJY0BPKeN181CI6WK5ZjEWpKcGupL2kiReS9bhGMLl6L2dpgDC1FhSBrVXBljoQlcbGoQxLO6DnZf9Qg0ejQhraZ-r4Z5TDzWg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA">补充图S5</一个></span>)似乎更强大的比的关键年龄效应的检测网络(<span class="xrefLink" id="jumplink-fig7"></span><a href="javascript:;" data-modal-source-id="fig7" class="link xref-fig">图7</一个>:关键网络)。</p> <a id="86050135" scrollto-destination="86050135"></a> <div data-id="fig7" data-content-id="fig7" class="fig fig-section js-fig-section" swap-content-for-modal="true"> <div class="label fig-label"> 图7。</d我v> <div class="graphic-wrap"> <img class="content-image" src="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/m_cercorbhr269f07_4c.jpeg?Expires=1689100713&Signature=ODQtZgPVaY0~GqMNahsy~BtVe1DmgUXToo3LBKML9mZEnFAeaoOHAurYVZb48rtXh8LPrk3vr6Xxo8uKSQ0YDZo4M1jzETPEw-t-4pmUGlqglo26f7TyaCh5AVYI1~In8wEXeZG2cAyGq~551tddYZat8Phq1-tX6QSc7~nC8sRfBIrE0TnSlAnXPxL7gIXuatQ5NRDB6~YQMpyAP8ZwxEm7lTQKlZFH2vKGNQoSu6HiImJodIvdib494QBTYgjpIRUxdC-1DjP5t0KCx6eq-7DWSYIT6kK1BezR9WLUZOe~KOMpX3XcMpTiS1du0IOkRqJzUqMby8UChDhoGsDXMA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" alt="领域表现出显著的与年龄相关的变化voxel-wise网络中心的措施。组级别的地图来自单向方差分析1003名参与者对面21中心(因素:中心;协变量:年龄、性别、头部运动,和登记错误)。所有组级别地图描绘的是纠正多个集群级别的比较使用高斯随机场理论(min Z > 2.3;集群的意义P < 0.05,纠正)。对于每个4中心措施,包括直流,SC, EC,年龄和PC,体素表现出显著的影响发现的单向方差分析进行描述。最后统计地图可视化为4半球表面(左半球皮层区域:2 (L)和2右半球[R])和8冠状切片(皮质下区域:y坐标从23.5−11.5毫米和5毫米空间)。" data-path-from-xml="cercorbhr269f07_4c.jpeg"> <div class="fig-orig original-slide"> <a class="fig-view-orig js-view-large at-figureViewLarge openInAnotherWindow" href="//www.ez-admanager.com/view-large/figure/86050135/cercorbhr269f07_4c.jpeg" data-path-from-xml="cercorbhr269f07_4c.jpeg" target="_blank">打开新标签</一个> <a data-section="86050135" href="//www.ez-admanager.com/DownloadFile/DownloadImage.aspx?image=https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/cercorbhr269f07_4c.jpeg?Expires=1689100713&Signature=F4lyMsboGNCrtFhgWAvJL1MRHoR6ctEwtOWOX7nrP2bkhgExplqhDQO2AeQrvW4ukMrulMWKYz~1FBJvoiuM2NXGCeCFhLCTEj9PGL1YbTkSBo52iF8O~F7jok5I3tkA~MY7N515kfF-aN3ixP4V6pabXtjYfE0noTgu8K5O-E5P-CcW00ks7h6sYx41QoiamchQvuWRB58ofAidRQuNdShgpoIi-34J6ROoGD4WTZtcIyiYXkz1A5TXiSxnd8CiwxiZO6zaX~s~XE-WuBAXi9jRFpXkOniWCzgHqiUmMT05uK0cKeqab3rwtHaog3cz8XILPu0dMjRCa5RwbOhjfg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA&sec=86050135&ar=321860&xsltPath=~/UI/app/XSLT&imagename=&siteId=5122" class="download-slide" data-path-from-xml="cercorbhr269f07_4c.jpeg">下载幻灯片</一个> </div> </div> <div class="caption fig-caption"> <p class="chapter-para">领域表现出显著的与年龄相关的变化voxel-wise网络中心的措施。组级别的地图来自单向方差分析1003名参与者对面21中心(因素:中心;协变量:年龄、性别、头部运动,和登记错误)。所有组级别地图描绘的是纠正多个集群级别的比较使用高斯随机场理论(最低<e米>Z</e米>> 2.3;集群的意义<e米>P</e米>< 0.05,纠正)。对于每个4中心措施,包括直流,SC, EC,年龄和PC,体素表现出显著的影响发现的单向方差分析进行描述。最后统计地图可视化为4半球表面(左半球皮层区域:2 (L)和2右半球[R])和8冠状切片(皮质下区域:<e米>y</e米>坐标从23.5−11.5毫米和5毫米空间)。</p> </div> </div> <p class="chapter-para">只有海马体显示一致的性别差异在所有4措施与中心被观察到在雌性比雄性(<span class="xrefLink" id="jumplink-fig8"></span><a href="javascript:;" data-modal-source-id="fig8" class="link xref-fig">图8</一个>)。一致的影响是在多个区域中发现3 4中心措施。的特定组合措施有所不同。例如,横向中央前和中央后小叶地区表现出更大的直流,EC,和PC,但不是SC在男性中心。相比之下,内侧枕区域表现出更强的直流,SC,和PC,但不是电子商务中心的女性。有趣的是,后扣带皮层显示检测结果不一致的性在3 4措施——的发现,女性表现出更大的直流和EC,但较低的SC。相比之下,加权SC没有检测到显著高于中心PCC的女性比男性(<span class="link link-data-supplement" data-supplement-target="sup1"></span><span class="content-section supplementary-material"><a path-from-xml="sup1" href="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/bhr269_Supplementary_Data.zip?Expires=1689100713&Signature=jCGM8AkNKnB1sOgZYkUARHF3vflZlpTNiKXdBa34yFFEUcxEg-TtXHnSc8h26d8-mWnezgAbDy06EAJrTUM2LAp3Q7vDDOe6FFqeqjb~0NeLbqNh21x8uqH-G2CC9YaXIs8orx78MFcPLMiFkDZVhyJ~21fdZGeXDVxozt~FFJSW0JCH2jcURiNK1jrZdL-9A1yDLbsCtOJDcqE4sDqrcx9sxATy999i7nCikg9i5eiR8djYxVJfAJY0BPKeN181CI6WK5ZjEWpKcGupL2kiReS9bhGMLl6L2dpgDC1FhSBrVXBljoQlcbGoQxLO6DnZf9Qg0ejQhraZ-r4Z5TDzWg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA">补充图S7</一个></span>)。</p> <a id="86050137" scrollto-destination="86050137"></a> <div data-id="fig8" data-content-id="fig8" class="fig fig-section js-fig-section" swap-content-for-modal="true"> <div class="label fig-label"> 图8。</d我v> <div class="graphic-wrap"> <img class="content-image" src="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/m_cercorbhr269f08_4c.jpeg?Expires=1689100713&Signature=BtN2nByX9WJZbdMnlcPqxOAiT4qsPlDOszWiqwIq6fA1xnUkCWk9~yZAxIf3Hqwd04EHTL8OLbq9E~u20aP7hT1VVHqsC5ciDUyoCJw-BL7~yf15UzfxkP4TQOElyA7hZex7g9BfkD6NkcJvO1T9RwdTrhQ3lP0s2xT-Us2CIJF~yEdL0nSIprSijYxZp4N~CbKvlMGbLniL87rqF0jBSASML0Vlfd4KQcGmNddQ9jmtKB89PX7rCQ9RJCBwlWM4A0Z3DuiJgRHV-OsL2E5XJWzD~fl9cM82k08EcoXzhpIIDxRXxP5N4R20rlDrAsyKqWkUAJaz1ol5fdEaCeWvnQ__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" alt="领域表现出显著性差异voxel-wise网络中心的措施。组级别的地图来自单向方差分析1003名参与者对面21中心(因素:中心;协变量:年龄、性别、头部运动,和登记错误)。所有组级别地图描绘的是纠正多个集群级别的比较使用高斯随机场理论(min Z > 2.3;集群的意义P < 0.05,纠正)。对于每个4中心措施,包括直流,SC, EC, PC,体素表现出显著性的影响,检测到单向方差分析进行描述。最后统计地图可视化为4半球表面(左半球皮层区域:2 (L)和2右半球[R])和8冠状切片(皮质下区域:y坐标从23.5−11.5毫米和5毫米空间)。" data-path-from-xml="cercorbhr269f08_4c.jpeg"> <div class="fig-orig original-slide"> <a class="fig-view-orig js-view-large at-figureViewLarge openInAnotherWindow" href="//www.ez-admanager.com/view-large/figure/86050137/cercorbhr269f08_4c.jpeg" data-path-from-xml="cercorbhr269f08_4c.jpeg" target="_blank">打开新标签</一个> <a data-section="86050137" href="//www.ez-admanager.com/DownloadFile/DownloadImage.aspx?image=https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/cercorbhr269f08_4c.jpeg?Expires=1689100713&Signature=SER5~TSMx8KBwCcs1xDy7voASN8TD2OmHhDoBxEHf9woNY7~onXouugFFY8dXO-TVz-GG7TqocfJkcwRb4UmHjmHDmTlDKMDCOFr57uy5DZIvEd8Cn8BwpPqOqbgw61eb-lm1MQYPxYqAF2Uk268z8h2gxEum2wgpVsUOwcEt7BsaBDxkoUPkweSRr9lxlopxGY9Xm4556xPJfI0h0WeXCoV-Z3B0WV~dJf97Z1S3vzuQME~Wb7YaRtRMkYCYfK~AWSfZ78Ch1WbKBJ1q7GYdtlcwABBDYtjZL-kl8ztaWvPnizArMgw4fXrJ5EZX54f6hoXSRIceO1bios7XDnMpw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA&sec=86050137&ar=321860&xsltPath=~/UI/app/XSLT&imagename=&siteId=5122" class="download-slide" data-path-from-xml="cercorbhr269f08_4c.jpeg">下载幻灯片</一个> </div> </div> <div class="caption fig-caption"> <p class="chapter-para">领域表现出显著性差异voxel-wise网络中心的措施。组级别的地图来自单向方差分析1003名参与者对面21中心(因素:中心;协变量:年龄、性别、头部运动,和登记错误)。所有组级别地图描绘的是纠正多个集群级别的比较使用高斯随机场理论(最低<e米>Z</e米>> 2.3;集群的意义<e米>P</e米>< 0.05,纠正)。对于每个4中心措施,包括直流,SC, EC, PC,体素表现出显著性的影响,检测到单向方差分析进行描述。最后统计地图可视化为4半球表面(左半球皮层区域:2 (L)和2右半球[R])和8冠状切片(皮质下区域:<e米>y</e米>坐标从23.5−11.5毫米和5毫米空间)。</p> </div> </div> <p class="chapter-para">我们的方差分析模型不包括头部运动和登记错误。正如所料,年龄影响的空间范围明显减少,暗示两因素的潜在影响中心随着年龄的变化。然而,还观察到显著的age-centrality关系(<span class="xrefLink" id="jumplink-fig7"></span><a href="javascript:;" data-modal-source-id="fig7" class="link xref-fig">图7</一个>:头运动和登记错误不;<span class="link link-data-supplement" data-supplement-target="sup1"></span><span class="content-section supplementary-material"><a path-from-xml="sup1" href="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/bhr269_Supplementary_Data.zip?Expires=1689100713&Signature=jCGM8AkNKnB1sOgZYkUARHF3vflZlpTNiKXdBa34yFFEUcxEg-TtXHnSc8h26d8-mWnezgAbDy06EAJrTUM2LAp3Q7vDDOe6FFqeqjb~0NeLbqNh21x8uqH-G2CC9YaXIs8orx78MFcPLMiFkDZVhyJ~21fdZGeXDVxozt~FFJSW0JCH2jcURiNK1jrZdL-9A1yDLbsCtOJDcqE4sDqrcx9sxATy999i7nCikg9i5eiR8djYxVJfAJY0BPKeN181CI6WK5ZjEWpKcGupL2kiReS9bhGMLl6L2dpgDC1FhSBrVXBljoQlcbGoQxLO6DnZf9Qg0ejQhraZ-r4Z5TDzWg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA">补充图。S4</一个></span>:没有头运动和登记错误不)。值得注意的是,中心的加权版本看起来更健壮的对这些2的混杂因素(<span class="xrefLink" id="jumplink-fig7"></span><a href="javascript:;" data-modal-source-id="fig7" class="link xref-fig">图7</一个>:二元网络;<span class="link link-data-supplement" data-supplement-target="sup1"></span><span class="content-section supplementary-material"><a path-from-xml="sup1" href="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/bhr269_Supplementary_Data.zip?Expires=1689100713&Signature=jCGM8AkNKnB1sOgZYkUARHF3vflZlpTNiKXdBa34yFFEUcxEg-TtXHnSc8h26d8-mWnezgAbDy06EAJrTUM2LAp3Q7vDDOe6FFqeqjb~0NeLbqNh21x8uqH-G2CC9YaXIs8orx78MFcPLMiFkDZVhyJ~21fdZGeXDVxozt~FFJSW0JCH2jcURiNK1jrZdL-9A1yDLbsCtOJDcqE4sDqrcx9sxATy999i7nCikg9i5eiR8djYxVJfAJY0BPKeN181CI6WK5ZjEWpKcGupL2kiReS9bhGMLl6L2dpgDC1FhSBrVXBljoQlcbGoQxLO6DnZf9Qg0ejQhraZ-r4Z5TDzWg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA">补充图S5</一个></span>:加权网络)。相比之下,2因素对sex-centrality关系没有明显的影响(<span class="xrefLink" id="jumplink-fig8"></span><a href="javascript:;" data-modal-source-id="fig8" class="link xref-fig">图8</一个>:头运动和登记错误不;<span class="link link-data-supplement" data-supplement-target="sup1"></span><span class="content-section supplementary-material"><a path-from-xml="sup1" href="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/bhr269_Supplementary_Data.zip?Expires=1689100713&Signature=jCGM8AkNKnB1sOgZYkUARHF3vflZlpTNiKXdBa34yFFEUcxEg-TtXHnSc8h26d8-mWnezgAbDy06EAJrTUM2LAp3Q7vDDOe6FFqeqjb~0NeLbqNh21x8uqH-G2CC9YaXIs8orx78MFcPLMiFkDZVhyJ~21fdZGeXDVxozt~FFJSW0JCH2jcURiNK1jrZdL-9A1yDLbsCtOJDcqE4sDqrcx9sxATy999i7nCikg9i5eiR8djYxVJfAJY0BPKeN181CI6WK5ZjEWpKcGupL2kiReS9bhGMLl6L2dpgDC1FhSBrVXBljoQlcbGoQxLO6DnZf9Qg0ejQhraZ-r4Z5TDzWg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA">补充图S6</一个></span>:没有头运动和登记错误不)。</p> <h2 scrollto-destination="86050140" id="86050140" class="section-title js-splitscreen-section-title">讨论</h2> <p class="chapter-para">调查voxel-wise中心地图提供了新颖的见解功能连通性的模式和复杂性在整个巨大的人类功能的连接体。尽管标记定义的差异中心措施,之前的研究中心的治疗他们,仿佛他们是可互换的,和一般来说,类似的模式已报告的结果(<span class="xrefLink" id="jumplink-bib32"></span><a href="javascript:;" reveal-id="bib32" data-open="bib32" class="link link-ref link-reveal xref-bibr">Fransson Marrelec 2008</一个>;<span class="xrefLink" id="jumplink-bib40"></span><a href="javascript:;" reveal-id="bib40" data-open="bib40" class="link link-ref link-reveal xref-bibr">Hagmann et al . 2008</一个>;<span class="xrefLink" id="jumplink-bib12"></span><a href="javascript:;" reveal-id="bib12" data-open="bib12" class="link link-ref link-reveal xref-bibr">Buckner et al . 2009</一个>;<span class="xrefLink" id="jumplink-bib41"></span><a href="javascript:;" reveal-id="bib41" data-open="bib41" class="link link-ref link-reveal xref-bibr">他et al . 2009</一个>;<span class="xrefLink" id="jumplink-bib31"></span><a href="javascript:;" reveal-id="bib31" data-open="bib31" class="link link-ref link-reveal xref-bibr">Fransson et al . 2011</一个>)。在目前的工作,我们首先组装和可视化4毫米分辨率功能连接体与22 387功能网络节点,然后分析了每个节点的贡献connectome-wide功能连通性。我们的研究结果强调的重要性考虑多个(即。,local, meso, and global) scales of connectivity properties in the functional connectome and distinguishing them in terms of their interindividual variability, TRT reliability, and phenotypic sensitivity. We argue that using voxel-wise centrality as measures of whole-brain functional connectivity will facilitate discovery of physiological mechanisms underlying the brain’s intrinsic functional organization.</p> <h3 scrollto-destination="86050144" id="86050144" class="section-title js-splitscreen-section-title">功能社区内的功能连接体</h3> <p class="chapter-para">建设的整个大脑功能网络计算中心的第一步措施。尤其是,在体元层面,网络及其组织并不常见的可视化由于其大小。重要的是说明了复杂网络及其组织图的读者得到一个升值的中心和/或功能连通性的措施将计算并指导其进一步的探索。我们首先构建和可视化的网络布局功能连接体在4毫米的空间分辨率,显示网络的高复杂性。社区检测了20个功能模块,parcellating大脑的功能之间的交互体素的基础上自发的低频波动。</p> <p class="chapter-para">超出了网络符合那些在之前的研究发现脑功能分割(<span class="xrefLink" id="jumplink-bib55"></span><a href="javascript:;" reveal-id="bib55" data-open="bib55" class="link link-ref link-reveal xref-bibr">珀尔帖et al . 2003</一个>;<span class="xrefLink" id="jumplink-bib5"></span><a href="javascript:;" reveal-id="bib5" data-open="bib5" class="link link-ref link-reveal xref-bibr">Bellec et al . 2006</一个>;<span class="xrefLink" id="jumplink-bib74"></span><a href="javascript:;" reveal-id="bib74" data-open="bib74" class="link link-ref link-reveal xref-bibr">van den Heuvel Mandl, et al . 2008</一个>;<span class="xrefLink" id="jumplink-bib41"></span><a href="javascript:;" reveal-id="bib41" data-open="bib41" class="link link-ref link-reveal xref-bibr">他et al . 2009</一个>;<span class="xrefLink" id="jumplink-bib51"></span><a href="javascript:;" reveal-id="bib51" data-open="bib51" class="link link-ref link-reveal xref-bibr">meze et al . 2009</一个>)(例如,默认网络和控制网络),社区发现似乎反映的基本组织功能的连接体。算法产生20社区或模块生成40个不同的大脑区域。显然,这样的功能分割不同于结构分割(<span class="xrefLink" id="jumplink-bib73"></span><a href="javascript:;" reveal-id="bib73" data-open="bib73" class="link link-ref link-reveal xref-bibr">Tzourio-Mazoyer et al . 2002</一个>),反映出拓扑和分层大脑的结构和功能之间的区别。相比之下,3最近的研究在人类大脑的功能分割(<span class="xrefLink" id="jumplink-bib19"></span><a href="javascript:;" reveal-id="bib19" data-open="bib19" class="link link-ref link-reveal xref-bibr">克拉多克et al . 2011</一个>;<span class="xrefLink" id="jumplink-bib25"></span><a href="javascript:;" reveal-id="bib25" data-open="bib25" class="link link-ref link-reveal xref-bibr">Doucet et al . 2011</一个>;<span class="xrefLink" id="jumplink-bib82"></span><a href="javascript:;" reveal-id="bib82" data-open="bib82" class="link link-ref link-reveal xref-bibr">杨et al . 2011</一个>),有明显的收敛有关大脑的基本结构。例如,这些研究显示一些常见的感觉运动功能模块,听觉、和视觉皮层以及默认网络和控制网络的关注,这是双边分布(即。等位的社区)。注意,更精确的比较与先前的研究是困难的,因为他们使用各种聚类方法建立模块化组织,而我们认为社区检测算法从网络科学。在算法层次的感觉,的工作<span class="xrefLink" id="jumplink-bib25"></span><a href="javascript:;" reveal-id="bib25" data-open="bib25" class="link link-ref link-reveal xref-bibr">Doucet et al。(2011)</一个>更与我们类似。我们找到了一个匹配的模块(20)的(23)。然而,我们的方法揭示了一些独特的单侧性的社区(即。,nonhomotopic communities) that are commonly associated with functions, such as language, working memory, and biasing attention (<span class="xrefLink" id="jumplink-bib69"></span><a href="javascript:;" reveal-id="bib69" data-open="bib69" class="link link-ref link-reveal xref-bibr">宽外袍和汤普森2003</一个>;<span class="xrefLink" id="jumplink-bib81"></span><a href="javascript:;" reveal-id="bib81" data-open="bib81" class="link link-ref link-reveal xref-bibr">燕et al . 2009</一个>;<span class="xrefLink" id="jumplink-bib85"></span><a href="javascript:;" reveal-id="bib85" data-open="bib85" class="link link-ref link-reveal xref-bibr">左,凯利,Di马蒂诺,等。2010</一个>;<span class="xrefLink" id="jumplink-bib36"></span><a href="javascript:;" reveal-id="bib36" data-open="bib36" class="link link-ref link-reveal xref-bibr">哎呀et al . 2011</一个>)。我们认为,这种差异可能是导致一个方法论的选择。总之,当前功能分割基于社区检测算法提供了一个独特的映射方式,深化理解人类大脑内的功能层次结构(<span class="xrefLink" id="jumplink-bib64"></span><a href="javascript:;" reveal-id="bib64" data-open="bib64" class="link link-ref link-reveal xref-bibr">史密斯et al . 2009</一个>)。</p> <h3 scrollto-destination="86050149" id="86050149" class="section-title js-splitscreen-section-title">多尺度的连接属性的重要性:网络中心的映射</h3> <p class="chapter-para">虽然直流,衡量当地(直接)网络连接,凸显出高阶皮质协会地区的重要性,对旁边缘和皮质下区域不太敏感。然而,当考虑到全局属性的中心措施,如EC和PC、海马、纹状体、丘脑的地区变得突出,可能由于其广泛的全球协会(<span class="xrefLink" id="jumplink-bib1"></span><a href="javascript:;" reveal-id="bib1" data-open="bib1" class="link link-ref link-reveal xref-bibr">Achard et al . 2006</一个>;<span class="xrefLink" id="jumplink-bib67"></span><a href="javascript:;" reveal-id="bib67" data-open="bib67" class="link link-ref link-reveal xref-bibr">斯波恩et al . 2007</一个>)。</p> <p class="chapter-para">在中尺度连接性,SC特别强调顶叶和额叶区域的中心通常涉及注意力控制(<span class="xrefLink" id="jumplink-bib4"></span><a href="javascript:;" reveal-id="bib4" data-open="bib4" class="link link-ref link-reveal xref-bibr">代表et al . 2004</一个>)。后IFG的中心,有趣的是,一个关键地区涉及自上而下控制的关注(<span class="xrefLink" id="jumplink-bib52"></span><a href="javascript:;" reveal-id="bib52" data-open="bib52" class="link link-ref link-reveal xref-bibr">Milham et al . 2003</一个>;<span class="xrefLink" id="jumplink-bib4"></span><a href="javascript:;" reveal-id="bib4" data-open="bib4" class="link link-ref link-reveal xref-bibr">代表et al . 2004</一个>;<span class="xrefLink" id="jumplink-bib2"></span><a href="javascript:;" reveal-id="bib2" data-open="bib2" class="link link-ref link-reveal xref-bibr">Banich 2009</一个>),只是升值SC。进一步的研究将会显示这种发散结果是否反映信息的重要性减弱这一地区或者说一些独特性质的信息流动。</p> <h3 scrollto-destination="86050155" id="86050155" class="section-title js-splitscreen-section-title">多尺度的连接属性的重要性:相关的表型信息网络中心</h3> <p class="chapter-para">中心措施是他们固有的探索性的主要特征——先天的预测是必需的。这使得它们适合探索神经关联的维度和类别表型数据(<span class="xrefLink" id="jumplink-bib24"></span><a href="javascript:;" reveal-id="bib24" data-open="bib24" class="link link-ref link-reveal xref-bibr">Di马蒂诺et al . 2009</一个>;<span class="xrefLink" id="jumplink-bib18"></span><a href="javascript:;" reveal-id="bib18" data-open="bib18" class="link link-ref link-reveal xref-bibr">考克斯et al . 2010</一个>)。在这里,我们可以检测强劲重要如果年龄和性别的复杂关联。</p> <p class="chapter-para">即使焦点是有限的大脑皮层,新兴的信息从全球和当地的措施可以大大不同。例如,当检查年龄影响特区透露年龄相关性减少中心内楔前叶和后扣带地区,但是电子商务却没有。尽管这种差异似乎有问题乍一看,它们是有益的。在这种情况下,我们解释这些发现支持的假设,而这些地区的本地或(直接)连通性下降随着年龄的增长,他们保持联系枢纽内的大脑区域,因此他们的中心在全球层面(<span class="xrefLink" id="jumplink-bib54"></span><a href="javascript:;" reveal-id="bib54" data-open="bib54" class="link link-ref link-reveal xref-bibr">公园和Reuter-Lorenz 2009</一个>)。有趣的是,个人电脑测量可能是最有效的捕捉中心在全球和当地的水平,可能由于包含“随机跳跃”,允许否则全球算法更好地了解当地的区别。</p> <p class="chapter-para">评估中心与性有关的差异显示一个复杂的中尺度范围的结果。女性比男性表现出更强的中心为所有4海马的措施。然而,对于其他的皮层区域,与性有关的方向差异取决于特定的中心措施。如果独立复制我们的结果,他们会激发一种改进的理解如何将高阶措施并在多尺度模型。</p> <h3 scrollto-destination="86050163" id="86050163" class="section-title js-splitscreen-section-title">展望未来功能中心</h3> <p class="chapter-para">结构的文献后,功能连通性社区已经开始自己的搜寻中心了<span class="xrefLink" id="jumplink-bib79"></span><a href="javascript:;" reveal-id="bib79" data-open="bib79" class="link link-ref link-reveal xref-bibr">王et al . 2010</一个>)。然而,实施功能中心的定义是糟糕,至少在功能连通性是基于简单的互相关。我们的研究结果强调的潜在效用使用中心的描述信息流通过完整的大脑功能连接体而不是试图识别功能的枢纽。我们没有发现功能中心新兴跨中心的措施,至少不是在个别学科的水平。Heteromodal协会在大脑皮层区域显示最低的一致性(即。,最高熵)中心的措施。这个证明的复杂性和丰富性这些regions-presumably不是他们缺乏信息流动的重要性。</p> <h3 scrollto-destination="86050166" id="86050166" class="section-title js-splitscreen-section-title">局限性和未来的发展方向</h3> <p class="chapter-para">我们注意到一些局限性时要考虑解释我们的发现。潜在的批评关注差异分析和图论方法策略。例如,各种策略可以用于阈值功能连通性矩阵格式图邻接矩阵(<span class="xrefLink" id="jumplink-bib79"></span><a href="javascript:;" reveal-id="bib79" data-open="bib79" class="link link-ref link-reveal xref-bibr">王et al . 2010</一个>):1)相关标准为所有科目使用一个固定的相关价值,2)稀疏准则使用固定稀疏值对所有科目,或3)曲线下的面积标准基于多个阈值结合的结果。在当前的研究中,我们使用第四个选择我们认为“意义标准”- - -也就是说,保持固定概率的假阳性样本。这标准是出于我们使用1003 R-fMRI数据集来自21个中心,每一个由不同数量的扫描卷。虽然我们在两个不同阈值的意义进行分析,我们的结果的程度将健壮的标准需要额外的调查更广泛的意义。值得注意的是,更可获得一致的结果如果图分析可以进行完全加权(即。nonthresholded)功能网络。最初的努力在这方面针对描述功能模块(<span class="xrefLink" id="jumplink-bib59"></span><a href="javascript:;" reveal-id="bib59" data-open="bib59" class="link link-ref link-reveal xref-bibr">Rubinov和2011年斯波恩</一个>),可能是可扩展的评估节点中心模块和节点影响分区。</p> <p class="chapter-para">中心关于结构关系分析也提出具有挑战性的问题。虽然高中心区域(即。,network hubs) have been consistently identified by both structural and functional data (<span class="xrefLink" id="jumplink-bib32"></span><a href="javascript:;" reveal-id="bib32" data-open="bib32" class="link link-ref link-reveal xref-bibr">Fransson Marrelec 2008</一个>;<span class="xrefLink" id="jumplink-bib40"></span><a href="javascript:;" reveal-id="bib40" data-open="bib40" class="link link-ref link-reveal xref-bibr">Hagmann et al . 2008</一个>;<span class="xrefLink" id="jumplink-bib12"></span><a href="javascript:;" reveal-id="bib12" data-open="bib12" class="link link-ref link-reveal xref-bibr">Buckner et al . 2009</一个>;<span class="xrefLink" id="jumplink-bib38"></span><a href="javascript:;" reveal-id="bib38" data-open="bib38" class="link link-ref link-reveal xref-bibr">龚et al . 2009</一个>;<span class="xrefLink" id="jumplink-bib31"></span><a href="javascript:;" reveal-id="bib31" data-open="bib31" class="link link-ref link-reveal xref-bibr">Fransson et al . 2011</一个>),差异也被指出(<span class="xrefLink" id="jumplink-bib41"></span><a href="javascript:;" reveal-id="bib41" data-open="bib41" class="link link-ref link-reveal xref-bibr">他et al . 2009</一个>)。这样的差异是会由于结构和功能连通性不显示简单的1:1的对应关系(<span class="xrefLink" id="jumplink-bib77"></span><a href="javascript:;" reveal-id="bib77" data-open="bib77" class="link link-ref link-reveal xref-bibr">文森特et al . 2007</一个>;<span class="xrefLink" id="jumplink-bib49"></span><a href="javascript:;" reveal-id="bib49" data-open="bib49" class="link link-ref link-reveal xref-bibr">马格里斯et al . 2009</一个>;<span class="xrefLink" id="jumplink-bib57"></span><a href="javascript:;" reveal-id="bib57" data-open="bib57" class="link link-ref link-reveal xref-bibr">罗伊et al . 2009</一个>)。此外,图指标应用于结构和功能网络是适合不同的神经生物学解释。未来的研究应该阐明中心通过结合结构和功能的关系基于高分辨率影像数据的连接体。</p> <p class="chapter-para">有两个潜在的混淆分析多点神经影像数据和检查年龄的影响。首先,可以引入人工影响如果年龄不是同样分布在不同的站点,这是特别相关的在当前形势下,大多数网站年轻参与者只有几个网站有少量的老参与者。这样的网站效果不能完全排除了包括中心模型的一个因素。我们因此也做了相同的分析基于数据从一个网站(蒙特利尔),最大年龄跨度(19 - 85年)。有限的样本的数量(51科目),一个自由未修正的<e米>Z</e米>统计阈值(<e米>Z</e米>> 1.0)被用来允许可视化阈下的趋势呈现年龄效应的中心。表示在<span class="link link-data-supplement" data-supplement-target="sup1"></span><span class="content-section supplementary-material"><a path-from-xml="sup1" href="https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercor/22/8/10.1093_cercor_bhr269/2/bhr269_Supplementary_Data.zip?Expires=1689100713&Signature=jCGM8AkNKnB1sOgZYkUARHF3vflZlpTNiKXdBa34yFFEUcxEg-TtXHnSc8h26d8-mWnezgAbDy06EAJrTUM2LAp3Q7vDDOe6FFqeqjb~0NeLbqNh21x8uqH-G2CC9YaXIs8orx78MFcPLMiFkDZVhyJ~21fdZGeXDVxozt~FFJSW0JCH2jcURiNK1jrZdL-9A1yDLbsCtOJDcqE4sDqrcx9sxATy999i7nCikg9i5eiR8djYxVJfAJY0BPKeN181CI6WK5ZjEWpKcGupL2kiReS9bhGMLl6L2dpgDC1FhSBrVXBljoQlcbGoQxLO6DnZf9Qg0ejQhraZ-r4Z5TDzWg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA">补充无花果S10和S11</一个></span>观察age-centrality关系的一个总体趋势,减轻这个问题。第二,越来越多的人意识到运动影响icn (<span class="xrefLink" id="jumplink-bib76"></span><a href="javascript:;" reveal-id="bib76" data-open="bib76" class="link link-ref link-reveal xref-bibr">凡戴克et al . 2011</一个>)。我们有了类似的头部运动的影响和登记质量寿命发展轨迹研究(<span class="xrefLink" id="jumplink-bib85"></span><a href="javascript:;" reveal-id="bib85" data-open="bib85" class="link link-ref link-reveal xref-bibr">左,凯利,Di马蒂诺,等。2010</一个>)。同样,我们更新了我们的中心分析,不包括头部运动和登记错误。正如所料,年龄影响的空间范围明显减少,这意味着潜在的影响这两个因素对中心随着年龄的变化。然而,一直显著age-centrality关系,暗示这种效果不太可能完全解释为2的混杂因素。</p> <p class="chapter-para">未来的工作与定向图(即中心分析。,graphs in which temporal order or causal relationships can be inferred or demonstrated) derived from R-fMRI data would yield crucial insights into the information flow through the functional connectome. We performed our analyses using undirected graphs. Although establishing a “true” network model for the brain connectome is difficult (<span class="xrefLink" id="jumplink-bib65"></span><a href="javascript:;" reveal-id="bib65" data-open="bib65" class="link link-ref link-reveal xref-bibr">史密斯et al . 2011</一个>),最近的研究表明,它可能会制定一个有向图形表示大脑区域之间的因果关系使用fMRI数据(休息<span class="xrefLink" id="jumplink-bib35"></span><a href="javascript:;" reveal-id="bib35" data-open="bib35" class="link link-ref link-reveal xref-bibr">Friston et al . 2011</一个>)。我们的方法检测数量有限的中心社区可以提供候选人直接图形分析使用数据驱动的动态因果模型(<span class="xrefLink" id="jumplink-bib35"></span><a href="javascript:;" reveal-id="bib35" data-open="bib35" class="link link-ref link-reveal xref-bibr">Friston et al . 2011</一个>)。这种分析可以为地区首次进行的设立网络。值得注意的是,voxel-level中心分析有向图是特别具有挑战性。地区的基于兴趣的分析,另一方面,可以打开门图理论分析,利用先进的centrality-based定向网络中信息流的特点(<span class="xrefLink" id="jumplink-bib63"></span><a href="javascript:;" reveal-id="bib63" data-open="bib63" class="link link-ref link-reveal xref-bibr">Shepelyansky Zhirov 2010</一个>)。</p> <h2 scrollto-destination="86050175" id="86050175" class="section-title js-splitscreen-section-title">结论</h2> <p class="chapter-para">我们证明,每个4中心措施捕捉功能连通性的不同方面,屈服在他们的相对敏感性显著差异(例如,全局与局部属性)和工具(例如,泰爱泰党可靠性)。我们的研究结果强调的重要性考虑多尺度的连接属性时检查功能的连接体。在超越检测功能的中心,我们治疗中心作为一种信息流图表的功能连接体和捕获表型关联(即展示其力量。、年龄和性别)。总之,我们的工作表明,组合多个中心措施允许一个更全面的描述复杂的功能连接体和可以用来激励研究功能性组织的潜在生理机制在神经退行性和精神疾病(<span class="xrefLink" id="jumplink-bib39"></span><a href="javascript:;" reveal-id="bib39" data-open="bib39" class="link link-ref link-reveal xref-bibr">格雷丘斯2008</一个>;<span class="xrefLink" id="jumplink-bib11"></span><a href="javascript:;" reveal-id="bib11" data-open="bib11" class="link link-ref link-reveal xref-bibr">Broyd et al . 2009</一个>;<span class="xrefLink" id="jumplink-bib61"></span><a href="javascript:;" reveal-id="bib61" data-open="bib61" class="link link-ref link-reveal xref-bibr">斯利et al . 2009</一个>;<span class="xrefLink" id="jumplink-bib60"></span><a href="javascript:;" reveal-id="bib60" data-open="bib60" class="link link-ref link-reveal xref-bibr">斯利2010</一个>;<span class="xrefLink" id="jumplink-bib16"></span><a href="javascript:;" reveal-id="bib16" data-open="bib16" class="link link-ref link-reveal xref-bibr">科尔et al . 2011</一个>)。</p> <h2 scrollto-destination="86050178" id="86050178" class="section-title js-splitscreen-section-title">资金</h2> <p class="chapter-para">X.-N.Z.博士承认创业杰出研究基金会的支持学院心理学教授(Y0CX492S03),中国的自然科学基金(81171409),和她身上博士承认j.s麦克唐奈基金会的支持。</p> <p class="chapter-para">作者要感谢博士埃内斯托埃斯特拉达有价值的讨论非常大的图子图中心,Drs阿德里亚娜迪马蒂诺和斯坦Colcombe编辑评论,克莱尔和Drs凯利和克里斯汀•考克斯使用中心的反馈分析脚本。<e米>的利益冲突</e米>:没有宣布。</p> <h2 scrollto-destination="86050183" id="86050183" class="backreferences-title js-splitscreen-backreferences-title">引用</h2> <div class="ref-list js-splitview-ref-list"> <div content-id="bib1" class="js-splitview-ref-item" data-legacy-id="bib1"> <div class="refLink-parent"> <span class="refLink"><a name="jumplink-bib1" href="javascript:;" aria-label="jumplink-bib1" data-id=""></a></span> </div> <div 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