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）——已被发现在许多方面存在异常 神经精神疾病，包括强迫症（OCD）。然而，这一研究方向 通常依赖于感兴趣网络的基于组的定义：标准化的解剖学或功能 图集或分割或基于组的独立成分分析 (g-ICA)。这些团体方法 不允许全面考虑这些结构和之间关系的个体差异 大型网络。忽略个体间的异质性，尤其是空间变异（ 已被证明在几个临床人群中更大，尽管以前在强迫症中没有），可能会导致失败 检测显着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 对于每个人。我们的目标是利用患者的现有数据以系统的方式解决这个问题 患有强迫症和匹配的健康对照。 已经提出了两种解决该问题的方法。个性化概率 ICA 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 进入二级随机效应分析。分层概率组 ICA 方法提供 基于模型的人群和受试者水平的大脑功能网络估计 同时。数据的属性（即受试者水平和总体水平方差之间的关系 在感兴趣的变量中）确定分层或非分层建模是否会产生更好的结果 结果。我们将分析之前从六项研究（总共 253 名个体）中收集的静息态 fMRI 数据 被诊断患有强迫症的人中，有 148 人没有服用任何药物，而 271 名健康对照者则没有服用任何药物 精神病理学，HC）。我们将使用个人和个人来得出特定于主题的网络地图和时间课程 和分层方法，并使用它们来检查受试者的诊断、持续的临床测量、 非临床特征与 (1) 地形和功能组织的个体差异有关 DMN、SN 和 CEN 的影响；(2) 这些网络之间功能耦合的个体差异。 先前的研究表明，这些全脑网络的 rs-fMRI 指标在不同强迫症患者中有所不同 维度并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 耦合。我们的研究将产生新的、更精确的跨患者异质性标记，并消除 偏见有希望现有的治疗反应生物标志物。

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.