Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Florence, Italy.
CSDC, University of Florence, Sesto Fiorentino, Florence, Italy.
J Comput Neurosci. 2021 May;49(2):159-174. doi: 10.1007/s10827-020-00774-1. Epub 2021 Apr 7.
An inverse procedure is developed and tested to recover functional and structural information from global signals of brains activity. The method assumes a leaky-integrate and fire model with excitatory and inhibitory neurons, coupled via a directed network. Neurons are endowed with a heterogenous current value, which sets their associated dynamical regime. By making use of a heterogenous mean-field approximation, the method seeks to reconstructing from global activity patterns the distribution of in-coming degrees, for both excitatory and inhibitory neurons, as well as the distribution of the assigned currents. The proposed inverse scheme is first validated against synthetic data. Then, time-lapse acquisitions of a zebrafish larva recorded with a two-photon light sheet microscope are used as an input to the reconstruction algorithm. A power law distribution of the in-coming connectivity of the excitatory neurons is found. Local degree distributions are also computed by segmenting the whole brain in sub-regions traced from annotated atlas.
从大脑活动的全局信号中恢复功能和结构信息，为此开发并测试了一种逆过程。该方法假设具有兴奋性和抑制性神经元的漏积分和点火模型，通过有向网络耦合。神经元具有异质电流值，从而确定其相关的动力学状态。通过使用异质平均场近似，该方法试图从全局活动模式中重建传入度的分布，包括兴奋性和抑制性神经元，以及分配电流的分布。所提出的逆方案首先针对合成数据进行验证。然后，将使用双光子光片显微镜记录的斑马鱼幼虫的时变采集用作重建算法的输入。发现兴奋性神经元传入连接的幂律分布。通过从注释图谱中跟踪的子区域分割整个大脑，还计算了局部度分布。
