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灵长类皮层大尺寸层状网络中的前馈和反馈频率依赖性相互作用。

Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex.

机构信息

Center for Neural Science, New York University (NYU), New York, NY 10003, USA.

Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA.

出版信息

Sci Adv. 2016 Nov 16;2(11):e1601335. doi: 10.1126/sciadv.1601335. eCollection 2016 Nov.

DOI:10.1126/sciadv.1601335
PMID:28138530
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5262462/
Abstract

Interactions between top-down and bottom-up processes in the cerebral cortex hold the key to understanding attentional processes, predictive coding, executive control, and a gamut of other brain functions. However, the underlying circuit mechanism remains poorly understood and represents a major challenge in neuroscience. We approached this problem using a large-scale computational model of the primate cortex constrained by new directed and weighted connectivity data. In our model, the interplay between feedforward and feedback signaling depends on the cortical laminar structure and involves complex dynamics across multiple (intralaminar, interlaminar, interareal, and whole cortex) scales. The model was tested by reproducing, as well as providing insights into, a wide range of neurophysiological findings about frequency-dependent interactions between visual cortical areas, including the observation that feedforward pathways are associated with enhanced gamma (30 to 70 Hz) oscillations, whereas feedback projections selectively modulate alpha/low-beta (8 to 15 Hz) oscillations. Furthermore, the model reproduces a functional hierarchy based on frequency-dependent Granger causality analysis of interareal signaling, as reported in recent monkey and human experiments, and suggests a mechanism for the observed context-dependent hierarchy dynamics. Together, this work highlights the necessity of multiscale approaches and provides a modeling platform for studies of large-scale brain circuit dynamics and functions.

摘要

大脑皮层自上而下和自下而上过程的相互作用是理解注意过程、预测编码、执行控制和一系列其他大脑功能的关键。然而,其潜在的电路机制仍了解甚少,这是神经科学的一个主要挑战。我们使用受新的定向和加权连接数据约束的灵长类动物皮层的大规模计算模型来解决这个问题。在我们的模型中,前馈和反馈信号之间的相互作用取决于皮层的分层结构,并涉及多个(层内、层间、区域间和整个皮层)尺度的复杂动力学。该模型通过再现以及提供对关于视觉皮层区域之间的频率依赖性相互作用的广泛神经生理学发现的深入了解来进行测试,包括观察到前馈通路与增强的伽马(30 至 70 赫兹)振荡相关,而反馈投射选择性地调节阿尔法/低贝塔(8 至 15 赫兹)振荡。此外,该模型再现了基于最近的猴子和人类实验中报道的区域间信号的频率依赖性格兰杰因果关系分析的功能层次结构,并提出了观察到的上下文相关层次结构动力学的机制。总之,这项工作强调了多尺度方法的必要性,并为研究大规模大脑电路动力学和功能提供了建模平台。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d13e/5262462/8c1d6f8acd14/1601335-F7.jpg
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