Neuronal avalanches in the neocortex

1.神经元雪崩对皮层功能的重要性日益被认识到。我所在的部门与约翰·霍普金斯大学的恩斯特·尼布尔合作，牵头组织了第一届神经系统临界性会议。2012年4月，为期两天的会议在NIH位于贝塞斯达的纳彻会议中心园区举行，约有100人参加，有19名国际和国内演讲者和海报。从那时起，一本由大约22个章节和国际作者组成的书被汇编在一起，并于2014年春季出版(Plenz，D.&Niebur，E.Critical in神经系统，Wiley-VCH，柏林(2014)。这本书涵盖了大脑中关键现象的所有主要方面，并有望成为大脑中快速增长的关键现象领域的标准教科书。除了作为主编，我的部门还贡献了4个章节，涵盖了我们的主要成就，展示了从体外准备到清醒的动物和正常的人类受试者在大脑中的关键作用。 2.神经元雪崩识别关键的大脑动力学，信息处理的几个方面在这里得到优化，正如我们之前的工作所证明的那样。基于控制系统在临界状态下的性能的精确临界指数，已经识别出几类关键系统。然而，主要问题之一是在关键的大脑皮层动力学中正确识别幂定律。我们澄清了文献中关于标度不变临界动力学中截止点的正确识别的几个误解。正确识别幂定律标度的有效范围是仔细评估临界脑动力学的先决条件(Yu等人。2014 PLoS One)。 摘要： 尺度不变的神经元雪崩动力学和大小分布的截止点 同时记录尽可能多的神经元，对大脑皮层动力学的识别大有裨益。然而，目前的技术只提供了对哺乳动物皮质的不完全访问，需要从那里得出关于动力学的充分结论。在这里，我们确定了通过使用有限数量的电极，即空间窗口，对特征良好的关键动力学神经元雪崩进行二次采样所引入的限制。用长期植入的96微电极阵列记录了两只清醒猕猴安静时运动前和前额叶皮质的局部场电位(LFP)。在阵列的完整和紧凑子区域上识别出LFP(NLFP)中的负偏转，该阵列由电极数量N(10，95)，即窗口大小来量化。被组织为神经元雪崩的时空nLFP簇，即在簇大小中的概率p(S)总是服从指数1.5到N的幂定律，超过该指数，p(S)下降得更急剧，产生一个随N和LFP滤波器参数变化的截止值。大小为S的星团主要由来自唯一的、非重复的大脑皮层部位的nLFP组成，来自附近部位之间的局部繁殖，并携带关于集群组织的空间信息。相反，S和N大小的星团以重复站点激活为主，携带的空间信息很少，反映了严重扭曲的采样条件。我们的发现在神经元-电极网络模型中得到了证实。因此，雪崩分析需要受限于观察窗口的大小，以揭示由局部展开的、主要是前馈神经元级联所产生的潜在的尺度不变组织。 3.为了促进神经元雪崩指标的使用，我们发布了一个用Python语言定制设计的软件包，允许对获取的数据进行相对容易的幂定律拟合(Alstott等人，2014，PLoS One)。 摘要： PowerLaw：用于分析重尾分布的Python包 幂定律在理论上是有趣的概率分布，也经常被用来描述经验数据。近些年来，有选择性的统计方法被用来拟合幂定律，但这些技术的适当使用需要大量的编程和统计洞察力。为了大大降低使用良好的统计方法来拟合幂定律分布的障碍，我们开发了PowerLaw Python包。该软件包为分布的基本拟合和统计分析提供了简单的命令。值得注意的是，它还试图通过穷尽用户可用的选项来支持各种用户需求。源代码是公开提供的，并且很容易扩展。 4.我们申请了一项专利，允许使用基于神经元雪崩的指标来量化睡眠剥夺的行为有害影响(Plenz等人。2013)。在这个项目中，我们的团队没有进行初级数据收集，而是对美国威斯康星大学的Giulio Tononi博士提供的临床数据进行了二级分析。

1. Neuronal avalanches are increasingly recognized to be important for cortex function. My Section took the lead in organizing the first conference on Criticality in Neural Systems in collaboration with Ernst Niebur, Johns Hopkins University. In April 2012, the 2-day conference took place on the NIH campus in Bethesda at the Natcher Conference center with about 100 attendees and featured 19 international and national speakers and posters. Since then, a book with about 22 chapters and international authors, most of who presented at the conference, has been assembled and was published in Spring 2014 (Plenz, D. & Niebur, E. Criticality in Neural Systems, Wiley-VCH, Berlin (2014). The book covers all major aspects of criticality in the brain and is on track to become a standard text book for a rapidly increasing field of critical phenomena in the brain. Besides being the main editor, my Section has contributed 4 chapters covering our major accomplishments demonstrating criticality in the brain from in vitro preparations to the awake animals and normal human subjects. 2. Neuronal avalanches identify critical brain dynamics at which several aspects of information processing are optimized as demonstrated in our previous work. Several classes of critical systems have been identified based on the precise critical exponents that control a systems performance at criticality. One of the main issues though was the proper identification of power laws in critical cortical dynamics. We clarified several misconceptions in the literature regarding the proper identification of cut-offs in scale-invariant critical dynamics. The proper identification of the valid range of power law scaling is a prerequisite for careful evalution of critical brain dynamics (Yu et al. 2014 PLoS One). Abstract: Scale-Invariant Neuronal Avalanche Dynamics and the Cut-off in Size Distributions Identification of cortical dynamics strongly benefits from the simultaneous recording of as many neurons as possible. Yet current technologies provide only incomplete access to the mammalian cortex from which adequate conclusions about dynamics need to be derived. Here, we identify constraints introduced by sub-sampling with a limited number of electrodes, i.e. spatial windowing, for well-characterized critical dynamics ― neuronal avalanches. The local field potential (LFP) was recorded from premotor and prefrontal cortices in two awake macaque monkeys during rest using chronically implanted 96-microelectrode arrays. Negative deflections in the LFP (nLFP) were identified on the full as well as compact sub-regions of the array quantified by the number of electrodes N (10 95), i.e., the window size. Spatiotemporal nLFP clusters organized as neuronal avalanches, i.e., the probability in cluster size, p(s), invariably followed a power law with exponent 1.5 up to N, beyond which p(s) declined more steeply producing a cut-off that varied with N and the LFP filter parameters. Clusters of size s ≤ N consisted mainly of nLFPs from unique, non-repeated cortical sites, emerged from local propagation between nearby sites, and carried spatial information about cluster organization. In contrast, clusters of size s > N were dominated by repeated site activations and carried little spatial information reflecting greatly distorted sampling conditions. Our findings were confirmed in a neuron-electrode network model. Thus, avalanche analysis needs to be constrained to the size of the observation window to reveal the underlying scale-invariant organization produced by locally unfolding, predominantly feed-forward neuronal cascades. 3. To facilitate the use of neuronal avalanche metrics, we published a software package custom-designed in python that allows for relatively easy power law fits to acquired data (Alstott et al., 2014, PLoS One). Abstract: powerlaw: a Python package for analysis of heavy-tailed distributions Power laws are theoretically interesting probability distributions that are also frequently used to describe empirical data. In recent years selective statistical methods for fitting power laws have been developed, but appropriate use of these techniques requires significant programming and statistical insight. In order to greatly decrease the barriers to using good statistical methods for fitting power law distributions, we developed the powerlaw Python package. This software package provides easy commands for basic fitting and statistical analysis of distributions. Notably, it also seeks to support a variety of user needs by being exhaustive in the options available to the user. The source code is publicly available and easily extensible. 4. We filed for a patent that allows quantification of the behaviorally detrimental effects of sleep deprivation using neuronal avalanche based metrics (Plenz et al. 2013). For this project, our group did not perform primary data collection, but performed secondary analysis of clinical data provided by Dr. Giulio Tononi at the University of Wisconsin, USA.