Biophysical Basis of Functional Connectivity by MRI

总结/摘要 这是我们项目“MRI静息状态连接的生物物理基础”的第一次竞争性更新。 我们的目标是确定MRI静息状态波动（rsfMRI）的区域间相关性是否 来自大脑的信号可靠地测量大脑区域之间的功能连接（rsFC），并建立 MRI数据如何与其他连通性指标相关。这些目标与验证直接相关 以及rsfMRI在人类中的应用。迄今为止进行的研究集中在介观 在初级躯体感觉皮层（S1）的明确功能区域内的尺度网络（100µm -10 mm） 在非人类灵长类动物中，我们可以以高分辨率测量静息状态相关性的空间模式， 并通过电生理信号和解剖示踪剂验证其解释。在下一阶段，我们 旨在扩展这些研究，以进一步确定rsFC测量的起源和意义。 大脑皮层呈现层状结构，但rsFC的层状分布知之甚少。在 此外，rsfMRI相关性直接代表和链接功能连接的强推论是否 延伸到S1中的子区域的细粒度水平之外， 未开发的此外，最近的研究表明，rsfMRI相关性的明显缓慢变化表明，静息 状态本身表现出可能具有功能重要性的动态变化。因此，我们建议三个 具体目标：[1]通过测量rsfMRI信号的连接模式来确定rsFC的起源。 S_1、S_2、丘脑及相应对侧皮质层间也有分布。我们 将使用振动触觉刺激在9.4T下采集fMRI数据，以识别功能不同的候选区域， 激活双侧S1，S2和丘脑，并测量静息状态之间的相关性体素内， [2]为了测量选择性剥夺脊髓、丘脑、皮质和海马神经元的影响， 和大脑半球间的输入，并证明它们与行为的关系：[3]以验证 在正常和输入剥夺条件下通过与定量的直接比较测量rsFC信号 颅内电生理学和组织学。我们将获取rsfMRI和侵入性多电极测量 在相同的动物中，定量比较神经活动和解剖连接的不同指标。 我们将在9.4T下从猴脑中获取fMRI数据，以研究SI，SII和 丘脑我们将使用创新的数学分析来量化静息状态相关性的变化 以及这些模式是否与电生理相关性的缓慢变化一致。我们将 在相同的动物中进行侵入性多通道微电极阵列测量， 定量比较神经活动和解剖连接的不同度量。我们认为 所提出的研究对于验证静息状态功能的神经基础具有相当重要的意义。 连接的措施，并对人类功能磁共振成像研究及其应用的直接影响。

SUMMARY / ABSTRACT This is a 1st competitive renewal of our project “Biophysical Basis of Resting State Connectivity by MRI”. Our goals are to determine whether inter-regional correlations in resting state fluctuations of MRI (rsfMRI) signals from the brain reliably measure functional connectivity (rsFC) between brain regions, and to establish how MRI data correlate with other metrics of connectivity. These goals are directly relevant for the validation and interpretation of human applications of rsfMRI. Studies performed to date have focused on mesoscopic scale networks (100µm - 10mm) within a well defined functional region of primary somatosensory cortex (S1) in non-human primates, where we can measure spatial patterns of resting state correlations at high resolution and validate their interpretation with electrophysiological signals and anatomic tracers. In the next phase, we aim to expand these studies to further establish the origins and significance of rsFC measurements. Cerebral cortex exhibits a laminar structure, but the laminar distribution of rsFC is poorly understood. In addition, whether the strong inference that rsfMRI correlations directly represent and link functional connectivity extends beyond the fine-grained level of sub-regions in S1 to more macroscopic dimensions remains unexplored. Moreover, recent studies of apparent slow variations of rsfMRI correlations suggest that the resting state itself exhibits dynamic variations that may be of functional importance. We therefore propose three specific aims: [1] to identify the origins of rsFC by measuring the connectivity patterns of rsfMRI signals across and between cortical layers in sub-regions of S1, S2, thalamus and corresponding contralateral regions. We will acquire fMRI data at 9.4T using vibrotactile stimuli to identify functionally distinct candidate areas of activation in bilateral S1, S2, and thalamus, and measure resting state correlations between voxels within and across layers in these regions: [2] to measure the effects of selective deprivation of spinal, thalamic, cortical and inter-hemispheric inputs on rsfMRI and demonstrate their relationships to behavior: [3] to validate measurements of rsFC signals in normal and input-deprived conditions by direct comparisons with quantitative intracranial electrophysiology and histology. We will acquire rsfMRI and invasive multi-electrode measurements in the same animals to quantitatively compare different metrics of neural activity and anatomical connections. We will acquire fMRI data at 9.4T from monkey brain to study functionally distinct areas in SI, SII, and thalamus. We will use innovative mathematical analyses to quantify variations in resting state correlations across time and whether these patterns agree with slow variations in electrophysiological correlations. We will perform invasive multichannel microelectrode array measurements in the same animals so that we can quantitatively compare different metrics of neural activity and anatomical connections. We believe that the proposed studies have considerable importance for validating the neural basis of resting state functional connectivity measures, and have direct implications for human fMRI studies and their applications.