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Mapping Prefrontal Circuits In Vivo with Manganese-Enhanced Magnetic Resonance Imaging in Monkeys

机译:用锰增强磁共振成像技术在猴子体内绘制前额叶电路

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摘要

Manganese-enhanced magnetic resonance imaging (MEMRI) provides a powerful tool to study multisynaptic circuits in vivo and thereby to link information about neural structure and function within individual subjects. Making the best use of MEMRI in monkeys requires minimizing manganese-associated neurotoxicity, maintaining sensitivity to manganese-dependent signal changes and mapping transport throughout the brain without a priori anatomical hypotheses. Here, we performed intracortical injections of isotonic MnCl2, comparisons of preinjection and postinjection scans, and voxelwise statistical mapping. Isotonic MnCl2 did not cause cell death at the injection site, damage to downstream targets of manganese transport, behavioral deficits, or changes in neuronal responsiveness. We detected and mapped manganese transport throughout cortical–subcortical circuits by using voxelwise statistical comparisons of at least 10 preinjection and two postinjection scans. We were able to differentiate between focal and diffuse projection fields and to distinguish between the topography of striatal projections from orbitofrontal and anterior cingulate cortex in a single animal. This MEMRI approach provides a basis for combining circuit-based anatomical analyses with simultaneous single-unit recordings and/or functional magnetic resonance imaging in individual monkeys. Such studies will enhance our interpretations of functional data and our understanding of how neuronal activity is transformed as it propagates through a circuit.
机译:锰增强磁共振成像(MEMRI)提供了一个强大的工具,可以在体内研究多突触回路,从而链接有关单个受试者内神经结构和功能的信息。在猴子中充分利用MEMRI要求最大限度地减少与锰相关的神经毒性,保持对锰依赖性信号变化的敏感性,并在没有先验解剖学假设的情况下绘制整个大脑的转运图。在这里,我们进行了等渗MnCl2的皮层内注射,注射前和注射后扫描的比较以及体素统计映射。等渗的MnCl2不会在注射部位引起细胞死亡,对锰转运的下游靶点造成损害,行为缺陷或神经元反应性改变。通过使用至少10次注射前和两次注射后扫描的体素统计比较,我们检测并绘制了整个皮层-皮层下回路中锰的转运情况。我们能够在单个动物中区分聚焦和弥散投影场,并区分来自眶额叶和前扣带回皮层的纹状体投影的形貌。这种MEMRI方法为将基于电路的解剖分析与单个猴子的同时单记录和/或功能磁共振成像相结合提供了基础。这样的研究将增强我们对功能数据的解释,以及我们对神经元活动在电路中传播时如何转化的理解。

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