Predicting the Impact of Connectomes on Cortical Function using Statistical Inference

我们如何从现代连接组学方法产生的大量数据中了解皮层回路组织的原理？这些结构原理如何帮助我们理解大脑功能？该项目的目标是解决哺乳动物新皮层的这两个主要挑战。具体来说，我们建议开发一个强大的统计框架，允许发现和测试的突触组织的“法律”原则上可以作为网络连接和功能测量的基础。这个框架将使神经科学家通过基于网络的工具（1）制定突触形成策略的数学假设，（2）发现这种“布线规则”对网络架构的影响，（3）通过用规则预测的连接模式约束模拟来揭示规则与功能的相关性，以及（4）根据经验连接和活动测量来测试模拟预测。为了实现这一目标，我们将联合收割机在数据科学（Baum），贝叶斯统计和机器学习（Macke）与神经解剖学和神经生理学（Oberlaender）的体内测量相结合。因此，我们的合作旨在确定哪些结构和/或功能参数是（或不是）预测突触的形成，量化布线规则如何受到经验数据的约束，揭示哪些额外的数据将是最有用的约束，并进行定量模型比较，以确定一组规则中的哪一个与所有可用的经验数据最一致。我们将应用和验证我们的方法，通过比较计算机模拟预测与新的基于体内的连接和活动测量的主要输出细胞类型的新皮层-锥体束神经元在第5层。我们的初步数据提供了第一个证据，表明我们设想的方法有可能揭示新皮层回路全球组织的局部规则。因此，拟议的项目将提供基础，探索神经元网络的结构特性之间的基本关系，其基本原则/规则的突触组织和皮层功能。这种见解将最终允许调查导致这些关系的发育机制，以及在疾病和病理条件下皮质功能障碍的结构起源和/或相关因素。根据SPP计算连接组学的目标，我们希望开发通用的强大计算方法来发现，比较和统计测试不同的突触组织理论与经验观察。

How can we learn about principles of cortical circuit organization from the wealth of data generated by modern connectomics approaches? How could these structural principles help us to understand brain function? The goal of this project is to address these two major challenges for the mammalian neocortex. Specifically, we propose the development of a powerful statistical framework that allows discovering and testing which ‘laws’ of synaptic organization could in principle underlie measurements of both network connectivity and function. This framework will enable neuroscientists via web-based tools to (1) formulate mathematically hypotheses of synapse formation strategies, (2) discover the impact of such ‘wiring rules’ on network architecture, (3) reveal the rules’ relevance for function by constraining simulations with rule-predicted connectivity patterns, and (4) test simulation predictions against empirical connectivity and activity measurements. To achieve this goal, we will combine complementary expertise in data science (Baum), Bayesian statistics and machine-learning (Macke) with in vivo measurements of neuroanatomy and neurophysiology (Oberlaender). Our collaboration thereby seeks to identify which structural and/or functional parameters are (or are not) predictive for synapse formation, quantify how well a wiring rule is constrained by empirical data, reveal which additional data would be most useful in constraining it, and perform quantitative model comparison for determining which of a set of rules is most consistent with all of the available empirical data. We will apply and validate our approaches by comparing the in silico predictions against novel in vivo-based connectivity and activity measurements for the major output cell type of the neocortex – pyramidal tract neurons in layer 5. Our preliminary data provides first evidence that our envisioned approaches have the potential to be revealing of the local rules that underlie the global organization of neocortical circuits. The proposed project will thus provide the foundation to explore fundamental relationships between structural properties of neuronal networks, their underlying principles/rules of synaptic organization and cortical functions. This insight will ultimately allow investigating developmental mechanisms leading to these relationships, and the structural origin and/or correlates of cortical malfunctions during diseased and pathological conditions. In line with the goals of the SPP Computational Connectomics, we want to develop general powerful computational methods to discover, compare and statistically test different synapse organization theories against empirical observations.