Lithium Effects on the Brain's Functional and Structural Connectome in the Treatment of Bipolar Disorder

摘要 锂是治疗双相情感障碍(BD)最特异、最有效的方法之一，但其作用机制尚不清楚 具体的行动仍不清楚。这阻碍了监测其影响的方法和新的 具有相似疗效和特异性的药物。锂治疗双相情感障碍的临床疗效及其临床意义 对多种大脑生理功能的复杂影响，可能是使用网络破译的最好方法 属性-度量法。此方法非常关键，因为它提供了对 大脑网络(例如，对中断的恢复能力、中央中枢)，这可能与 行为结果。此外，锂的神经影像效应与其之间的关系 对人类分子效应的研究就更少了。该提案针对这些重要的问题 急性发作前后测量脑功能和结构连接的知识差距 以及长期的锂治疗。我们将研究90例无药物治疗的抑郁双相情感障碍 (BDD)II型受试者在基线和锂单一治疗2、8和26周后。我们还将 研究30名接近匹配的健康对照组，他们将在相同的时间点进行成像，但不会 接受任何治疗。BDD II受试者将接受静息状态的功能磁共振 成像(FMRI)扫描和弥散加权结构成像扫描。除了成像，我们还将 还采集外周血淋巴细胞RNA样本，探索锂诱导的外周基因 表情发生了变化。功能和结构扫描将使用图论指标进行分析 (GTM)和独立成分分析(ICA)。图论度量超越了简单 描述大脑连通性并提供对网络功能的洞察(例如，对中断的恢复能力， 区域枢纽)。ICA分析已经确定了一致且定义良好的独立成分 休眠状态数据，例如默认模式和显著网络。我们研究的初步数据 表明锂对GTM和ICA指标的显著影响以及这些指标变化的相关性 临床改善的指标。在这项计划中，我们将在最先进的7-T上获取图像 扫描仪，进行严格的运动校正，并应用尖端的GTM和ICA分析。我们会 GTA和ICA指标的变化与短期内抑郁和情绪改善的相关性 BDD II受试者的长期稳定性。此外，我们还将进行探索性分析 关于外周基因表达的变化与疾病的改善之间的关系 使用GTA指标变化作为中介并使用ICA的症状学和情绪稳定性 多模式融合分析。这将是一种探索性分析，为未来提供更具确定性的数据 锂效应的人体和基础科学研究。因此，这项研究将提供独特的数据 关于锂单一疗法的影像和分子相关性及其与锂的关系 对双相情感障碍的疗效。此外，这项研究还将提供一种新的范式来研究 锂和其他精神药物使用网络属性指标作为生物标记物，可以用于 治疗监测和药物开发。

ABSTRACT Lithium is one of the most specific and effective treatments for bipolar disorder (BD) yet its mechanism of action remains unclear. This has impeded methods to monitor its effects and the development of new medications with similar efficacy and specificity. Clinical efficacy of lithium in bipolar disorder, and its complex effects on multiple brain physiological functions, may be best deciphered using a network properties-metric approach. This approach is critical because it provides insight into the function of brain networks (e.g., resilience to disruption, central hubs), which is likely to be more closely linked to behavioral outcomes. Furthermore, the relationship of the neuroimaging effects of lithium and its molecular effects in humans has been studied even less. This proposal addresses these important gaps in knowledge by measuring brain functional and structural connectomics before and after acute and longer term treatment with lithium. We will study 90 medication free bipolar disorder depressed (BDD) type II subjects at baseline and after 2, 8 and 26 weeks of lithium monotherapy. We will also study 30 closely matched healthy controls who will be imaged at the same time points but will not receive any treatment. BDD II subjects will undergo resting state functional magnetic resonance imaging (fMRI) scans and diffusion weighted structural imaging scans. In addition to imaging, we will also collect RNA samples from peripheral blood lymphocytes to explore lithium induced peripheral gene expression changes. Functional and structural scans will be analyzed using graph theory metrics (GTM) and Independent Component Analysis (ICA). Graph theory metrics move beyond simple description of brain connectivity and provide insight into network function (e.g., resilience to disruption, regional hubs). ICA analysis has identified consistent and well defined independent components in resting state data e.g the default mode and salience networks. Preliminary data from our studies indicates significant effect of lithium on GTM and ICA metrics and correlation of the changes in these metrics to clinical improvement. In this proposal, we will acquire images on a state-of-the-art 7-T scanner, conduct rigorous motion correction and apply cutting-edge GTM and ICA analytics. We will correlate changes in GTA and ICA metrics to improvement in depression in the short term and mood stability in the longer term in BDD II subjects. Furthermore, we will conduct an exploratory analysis regarding association between changes in peripheral gene expression and improvement in symptomatology and mood stability using the GTA metrics change as mediators and using ICA multimodality fusion analysis. This will be an exploratory analysis to yield data for future more definitive human and basic science studies of lithium effect. This study will therefore provide unique data regarding imaging and molecular correlates of lithium monotherapy and its relationship to lithium efficacy in bipolar disorder. In addition, this study will provide a novel paradigm to investigate effects of lithium and other psychotropics using network-property metrics as biomarkers which can be used for treatment monitoring and drug development.