Examining the Effects of Suppression of Slow Rhythms on Resting State fMRI

项目摘要/摘要 血氧水平依赖(BOLD)静息状态功能磁共振成像(RsfMRI)已成为 探索脑功能和病理学。然而，rsfmri信号的神经生理学基础并不完全。 理解这些研究，阻碍了对这些研究的全面解释。最近，大脑的慢节奏一直是 提出rsfMRI信号的可能来源。RsfMRI信号的特征是存在瞬时 准周期模式(QPP)，通常不特定于任何规范的大脑功能网络(例如，那些 这有助于认知)。QPP混淆了对脑功能连接性(FC)的准确估计 规范的大脑功能网络。这些瞬变信号与大脑皮层慢速有许多共同的特征 节奏。它们在没有刺激的情况下存在，在大脑皮层中传播，并受到 警觉，类似于慢波。可能只有这些组件QPP，而不是rsfMRI信号特定的 在规范的大脑功能网络中，Fc是由慢节奏驱动的。一种表达和 大脑中皮层慢节奏的维持是通过丘脑皮质耦合振荡器网络实现的 由低阈值T-型钙(Ca~(2+))通道诱发的爆发性放电驱动。系统地管理 选择性T型钙通道阻滞剂(TTCCB)TTA-P2抑制缓慢的大脑节律(即，降低 在给定的时间窗口内观察到的慢波的数量)在大鼠中高达60%。在这项研究中，我们将检查 慢波抑制对rsfmri信号的影响。我们假设抑制慢节奏 将降低QPP的强度。而这种QPPs表达的降低将增强Fc在肺癌中的特异性 规范的大脑功能网络。 我们将从一组25只大鼠的90分钟前和90分钟后同时获取rsfMRI和EEG数据 皮下注射药物TTA-P2的最佳剂量将在20人的单独队列中确定 老鼠。另一组15只大鼠将接受这种交通工具作为对照。表达的强度和频率 QPP将在TTA-P2(或车辆)前(基线)和后(或车辆)条件下估计。我们将在年估计本币 基于种子与先验区域互相关分析的不同典型脑功能网络 特定于所检查的每个网络的兴趣。脑功能网络中QPP指标和Fc的差异 将通过适当的假设检验来检验不同条件之间的关系。我们预计QPP指标将为 TTA-P2术后慢波抑制后较基线显著降低。Fc在规范脑功能中的作用 网络将增加，未连接的大脑区域之间的非特定相关性将从 TTA-P2后的基线，从而提高了Fc在规范脑功能网络中的特异性。不是 在基线和车辆条件下，rsfMRI信号度量将会有显著差异。 该项目的成功完成将大大有助于解决有关 RsfMRI信号的神经基础。

PROJECT SUMMARY/ABSTRACT Blood oxygenation level dependent (BOLD) resting state fMRI (rsfMRI) has become the preeminent tool for exploring brain function and pathology. However, the neurophysiological basis of rsfMRI signals is not fully understood, impeding comprehensive interpretations of these studies. Recently, brain slow rhythms have been put forth as possible sources of rsfMRI signal. RsfMRI signals are characterized by presence of transient quasiperiodic patterns (QPPs), which are often not specific any canonical brain function networks (e.g., those that subserve cognition). QPPs confound the estimation of accurate brain functional connectivity (FC) in canonical brain function networks. These transient signals share a number of properties with cortical slow rhythms. They exist in the absence of stimulation, propagate across the cortex, and are strongly modulated by vigilance, similar to slow waves. It is possible that only these components QPPs, and not rsfMRI signal specific to FC in canonical brain function networks are driven by slow rhythms. One mechanism for expression and maintenance of cortical slow rhythms in the brain is through a thalamocortical network of coupled oscillators driven by burst firing induced by low-threshold T-type calcium (Ca2+) channels. Systemic administration of the selective T-type Ca2+ channel blocker (TTCCB) TTA-P2 suppresses slow brain rhythms (i.e., decrease the number of slow waves observed in a given time window) by up to 60% in rats. In this study, we will examine the effects of suppression of slow waves on rsfMRI signals. We hypothesize that suppression of slow rhythms will reduce the strength of QPPs. And this reduction in expression of QPPs will enhance the specificity of FC in canonical brain function network. We will acquire simultaneous rsfMRI and EEG data from a group of 25 rats 90 min before and 90 minutes after subcutaneous injection of the drug TTA-P2 at an optimal dose to be determined on a separate cohort of 20 rats. Another group of 15 rats will receive the vehicle as control. The strength and frequency of expression of QPPs will be estimated under pre- (Baseline) and post-TTA-P2 (or Vehicle) conditions. We will estimate FC in different canonical brain function networks through seed-based cross-correlation analysis with a priori regions of interest specific to each network examined. Differences in QPP metrics, and FC in brain function networks between different conditions will be examined with appropriate hypotheses tests. We expect QPP metrics to be significantly reduced from Baseline after slow wave suppression post-TTA-P2. FC in canonical brain function networks will increase, and non-specific correlations between unconnected brain regions will decrease from baseline post-TTA-P2, thereby increasing the specificity of FC in canonical brain function networks. No significant differences in rsfMRI signal metrics will be observed between Baseline and Vehicle conditions. Successful completion of this project will go a long way towards resolving profound questions regarding the neural basis of rsfMRI signals.