Examining individual differences in large scale brain networks in individuals with OCD and their relations to heterogeneity of obsessive compulsive symptoms.

三种大规模脑功能网络--默认模式网络(DMN)、 中央执行网络(CEN)和显著网络(SN)-在许多情况下被发现是异常的 神经精神障碍，包括强迫症(OCD)。然而，这一系列的研究 通常依赖于对感兴趣网络的基于组的定义：标准化的解剖或功能 图集或分类或基于组的独立成分分析(g-ICA)。这些小组方法 不允许对这些组织的结构和它们之间的关系的个体差异进行全面的核算 大规模网络。不考虑个体间的异质性，特别是空间差异(具有 在一些临床人群中被证明是更大的，尽管以前在强迫症中没有)，可能会导致失败 要检测重大影响，不是因为它们不存在，而是因为我们没有针对正确的节点 对每一个人来说。我们的目标是利用现有的患者数据，以系统的方式解决这个问题 强迫症患者和与之匹配的健康对照组。 已经提出了两种解决这个问题的方法。一种个性化的概率ICA算法 方法分别为每个受试者定义大脑网络，然后输入个性化的测量 转化为二级随机效应分析。分层概率群ICA方法提供了 基于模型的人群和受试者脑功能网络的估计 同时。数据的属性(即主题水平和总体水平差异之间的关系 在感兴趣的变量中)确定分层建模还是非分层建模将产生更好的 结果。我们将分析之前从六项研究(总计253人)收集的静息状态fMRI数据 被诊断为强迫症的148名患者没有服用任何药物，271名健康对照没有服用任何药物 精神病理学，HC)。我们将使用这两个个体得出特定于主题的网络地图和时间进程 和分层方法，并使用它们来检查受试者的诊断，连续的临床措施， 非临床特征与(1)局部和功能组织的个体变异性有关 DMN、SN和CEN之间的差异以及(2)这些网络之间功能耦合的个体差异。 先前的研究已经证明，这些全脑网络的rs-fmri指标在强迫症患者中是不同的。 并可作为治疗反应的预测因子。然而，对这些指标的计算不会 通常解释跨主题的拓扑异质性，这可能被误解为 耦合。我们的研究将产生新的、更准确的跨患者异质性标记，并消除 偏倚有望成为治疗反应的现有生物标记物。

Dynamic coordination among three large-scale functional brain networks – the default mode network (DMN), the central executive network (CEN), and the salience network (SN) – has been found to be aberrant in many neuropsychiatric disorders, including obsessive-compulsive disorder (OCD). However, this line of research typically relies on a group-based definition of the networks of interest: a standardized anatomical or functional atlas or parcellation or a group-based independent component analysis (g-ICA). These group approaches do not allow for a full accounting of individual variation in the structure of and relationships between these large-scale networks. Discounting inter-individual heterogeneity, especially spatial variation (which has been shown to be greater in several clinical populations, though not previously in OCD), may lead to failure to detect significant effects not because they are not present, but because we did not target the right nodes for every individual. We aim to address this problem in a systematic way, using existing data from patients with OCD and in matched healthy controls. Two approaches to remedy this problem have been proposed. An individualized probabilistic ICA approach defines brain networks individually for each subject and then enters the individualized measures into a second-level random effects analysis. A hierarchical probabilistic group ICA approach provides model‐based estimation of brain functional networks at both the population and subject level simultaneously. Properties of the data (i.e., relations between subject-level and population-level variance in variables of interest) determine whether hierarchical or non-hierarchical modeling will produce superior results. We will analyze previously collected resting-state fMRI data from six studies (total 253 individuals diagnosed with OCD, 148 of them were not on any medication, and 271 healthy controls without any psychopathology, HC). We will derive subject-specific network maps and time courses using both individual and hierarchical methods and use them to examine how subjects’ diagnosis, continuous clinical measures, and non-clinical characteristics relate to (1) individual variability in topographical and functional organization of DMN, SN, and CEN and (2) individual variability in functional coupling among these networks. Prior research has demonstrated that rs-fMRI metrics of these brain-wide networks vary across OCD dimensions and can serve as predictors of treatment response. However, calculation of these metrics does not generally account for cross-subject topological heterogeneity, which can be misinterpreted as variations in coupling. Our research will produce new and more precise markers of cross-patient heterogeneity, and de- bias promising existing biomarkers of treatment response.