Bernal-Casas David, Lee Hyun Joo, Weitz Andrew J, Lee Jin Hyung
Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA.
Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Neuron. 2017 Feb 8;93(3):522-532.e5. doi: 10.1016/j.neuron.2016.12.035. Epub 2017 Jan 26.
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.
以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,神经元回路图通常借鉴孤立获得的解剖学和电生理测量结果。因此,从未通过全脑同步测量构建出动态的、细胞类型特异性的连接图谱。在此,我们为光遗传学功能磁共振成像实验引入动态因果模型(DCM)——它独特地允许进行细胞类型特异性的全脑功能测量——以用细胞类型特异性参数化分布式脑网络各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准确地重现了经验观察到的时间序列,并且最强的连接是光遗传学刺激通路的关键连接。我们预测,如此处所示的对动态连接性的定量和细胞类型特异性描述,将使对神经元回路动力学有新的系统层面的理解,并有助于设计更有效的神经调节疗法。