Organizing heterogeneous samples using community detection of GIMME-derived resting state functional networks.
Organizing heterogeneous samples using community detection of GIMME-derived resting state functional networks.
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DOI：
10.1371/journal.pone.0091322
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Fair DA
中科院分区：
文献类型：
--
作者：
Gates KM;Molenaar PC;Iyer SP;Nigg JT;Fair DA
许多神经精神障碍的临床研究依赖于这样的假设，即诊断类别和典型对照样本都具有组内同质性。然而，使用人类神经成像的研究表明，在临床和对照样本中，个体之间存在很大的异质性。这一现实要求研究人员识别和组织大脑生理学的潜在不同模式。我们介绍了一种分析方法，用于完全基于个人的大脑生理得出他们的子组。该方法首先使用组迭代多模型估计(GIMME)来评估个体有向功能连接图。GIMME是迄今为止仅有的可以在异质数据中恢复定向功能连接图的方向和存在的方法之一，这使其成为一个理想的起点，因为它解决了异质性问题。然后，使用用于社区检测的模块化方法，基于个人连接模式的相似性对个人进行分组。蒙特卡罗模拟表明，将Gimme与模块化算法结合使用效果非常好--平均超过97%的模拟个体被放置在精确的子群中，而没有关于功能结构或群体身份的先验信息。在证明了可靠性后，我们检查了来自典型发育中和注意力缺陷/多动障碍诊断儿童的样本(N = 80)的额顶区的休息状态数据。在这里，我们发现了5个子群。ADHD主要由两个亚组组成，这表明存在不止一个生物标记物可以根据儿童的大脑生理来识别他们患有ADHD。这里提供的经验证据支持ADHD和对照样本中的大脑生理学存在异质性的观点。从这里介绍的方法中获得的这种类型的信息可以帮助更好地描述患者的结果、最佳治疗策略、潜在的基因-环境相互作用，以及利用生物现象来帮助心理健康。
Clinical investigations of many neuropsychiatric disorders rely on the assumption that diagnostic categories and typical control samples each have within-group homogeneity. However, research using human neuroimaging has revealed that much heterogeneity exists across individuals in both clinical and control samples. This reality necessitates that researchers identify and organize the potentially varied patterns of brain physiology. We introduce an analytical approach for arriving at subgroups of individuals based entirely on their brain physiology. The method begins with Group Iterative Multiple Model Estimation (GIMME) to assess individual directed functional connectivity maps. GIMME is one of the only methods to date that can recover both the direction and presence of directed functional connectivity maps in heterogeneous data, making it an ideal place to start since it addresses the problem of heterogeneity. Individuals are then grouped based on similarities in their connectivity patterns using a modularity approach for community detection. Monte Carlo simulations demonstrate that using GIMME in combination with the modularity algorithm works exceptionally well - on average over 97% of simulated individuals are placed in the accurate subgroup with no prior information on functional architecture or group identity. Having demonstrated reliability, we examine resting-state data of fronto-parietal regions drawn from a sample (N = 80) of typically developing and attention-deficit/hyperactivity disorder (ADHD) -diagnosed children. Here, we find 5 subgroups. Two subgroups were predominantly comprised of ADHD, suggesting that more than one biological marker exists that can be used to identify children with ADHD based from their brain physiology. Empirical evidence presented here supports notions that heterogeneity exists in brain physiology within ADHD and control samples. This type of information gained from the approach presented here can assist in better characterizing patients in terms of outcomes, optimal treatment strategies, potential gene-environment interactions, and the use of biological phenomenon to assist with mental health.
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DOI：
10.1073/pnas.0504136102
发表时间：
2005-07-05
影响因子：
11.1
作者：
Fox, MD;Snyder, AZ;Raichle, ME
通讯作者：
Raichle, ME
影响因子：
4.4
作者：
Beltz, Adriene M.;Gates, Kathleen M.;Engels, Anna S.;Molenaar, Peter C. M.;Pulido, Carmen;Turrisi, Robert;Berenbaum, Sheri A.;Gilmore, Rick O.;Wilson, Stephen J.
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Wilson, Stephen J.
DOI：
10.1073/pnas.1115365109
发表时间：
2012-04-24
影响因子：
11.1
作者：
Fair, Damien A.;Bathula, Deepti;Nigg, Joel T.
通讯作者：
Nigg, Joel T.
影响因子：
11
作者：
Kapur, S.;Phillips, A. G.;Insel, T. R.
通讯作者：
Insel, T. R.
DOI：
10.1088/1742-5468/2008/10/p10008
发表时间：
2008-10-01
影响因子：
2.4
作者：
Blondel, Vincent D.;Guillaume, Jean-Loup;Lefebvre, Etienne
通讯作者：
Lefebvre, Etienne