Studying Brain Circuit Function with Dynamic Causal Modeling for Optogenetic fMRI.
Studying Brain Circuit Function with Dynamic Causal Modeling for Optogenetic fMRI.
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DOI：
10.1016/j.neuron.2016.12.035
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发表时间：
2017-02-08
期刊：
影响因子：
16.2
通讯作者：
Lee JH
中科院分区：
文献类型：
--
作者：
Bernal-Casas D;Lee HJ;Weitz AJ;Lee JH
用细胞类型特异性定义脑回路的大规模行为是神经科学的一个主要目标。然而，神经元电路图通常依赖于分离获得的解剖和电生理测量。因此，一个动态的和特定细胞类型的连接图从来没有通过同时测量整个大脑来构建。在这里，我们为光遗传fMRI实验引入动态因果模型(DCM) -它独特地允许细胞类型特异性，全脑功能测量-参数化具有细胞类型特异性的分布式脑网络区域之间的因果关系。引人注目的是，当应用于全脑基底神经节-丘脑皮质网络时，DCM准确地再现了经验观察到的时间序列，最强的连接是光刺激通路的关键连接。我们预测动态连接的定量和细胞类型特异性描述，如本文所示，将增强对神经元电路动力学的新颖系统级理解，并促进设计更有效的神经调节疗法。
Defining the large-scale behavior of brain circuits with cell type specificity is a major goal of neuroscience. However, neuronal circuit diagrams typically draw upon anatomical and electrophysiological measurements acquired in isolation. Consequently, a dynamic and cell type-specific connectivity map has never been constructed from simultaneous measurements across the brain. Here, we introduce dynamic causal modeling (DCM) for optogenetic fMRI experiments – which uniquely allow cell type-specific, brain-wide functional measurements – to parameterize the causal relationships among regions of a distributed brain network with cell type specificity. Strikingly, when applied to the brain-wide basal ganglia-thalamocortical network, DCM accurately reproduced the empirically observed time series, and the strongest connections were key connections of optogenetically stimulated pathways. We predict that quantitative and cell type-specific descriptions of dynamic connectivity, as illustrated here, will empower novel systems-level understanding of neuronal circuit dynamics and facilitate the design of more effective neuromodulation therapies.