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

三个大规模功能性大脑网络之间的动态协调-默认模式网络（DMN）， 中央执行网络（CEN）和显着网络（SN）-已被发现在许多异常 神经精神障碍，包括强迫症（OCD）。然而，这条研究路线 通常依赖于感兴趣网络的基于组的定义：标准化的解剖或功能 图谱或分块或基于组的独立成分分析（g-ICA）。这些群体方法 不允许在结构和这些之间的关系的个体差异的充分会计 大型网络。不考虑个体间的异质性，特别是空间变异（ 在几个临床人群中显示更大，尽管以前在强迫症中没有），可能导致失败 检测到显著的影响，不是因为它们不存在，而是因为我们没有针对正确的节点 每一个人。我们的目标是利用现有的患者数据，以系统的方式解决这一问题 强迫症患者和匹配的健康对照组。 已经提出了两种解决这个问题的方法。一种个性化的概率伊卡 方法为每个受试者单独定义大脑网络，然后输入个性化测量 进行二级随机效应分析分层概率群伊卡方法提供了 在人群和受试者水平上对脑功能网络进行基于模型的估计 同步数据的属性（即，受试者水平方差与总体水平方差的关系 在感兴趣的变量中）确定分层或非分层建模是否将产生上级 结果我们将分析先前收集的静息态fMRI数据从六个研究（共253人 被诊断为强迫症的148人没有服用任何药物，271名健康对照者没有服用任何药物。 精神病理学，HC）。我们将使用两个单独的网络映射和时间过程来获得特定主题的网络映射和时间过程。 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， 和非临床特征涉及（1）地形和功能组织的个体差异 的DMN，SN，和CEN和（2）这些网络之间的功能耦合的个体差异。 先前的研究已经表明，这些全脑网络的rs-fMRI指标在强迫症中各不相同 尺寸，可以作为治疗反应的预测因子。然而，这些指标的计算并不 通常解释跨学科拓扑异质性，这可能被误解为 偶合器.我们的研究将产生新的和更精确的跨患者异质性的标志物， 偏倚，有希望的治疗反应的现有生物标志物。

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.