Narula Vaibhav, Zippo Antonio Giuliano, Muscoloni Alessandro, Biella Gabriele Eliseo M, Cannistraci Carlo Vittorio
1Biomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Center for Systems Biology Dresden (CSBD), Department of Physics, Technische Universität Dresden, Dresden, Germany.
3Institute of Molecular Bioimaging and Physiology, Consiglio Nazionale delle Ricerche, Segrate, Milan, Italy.
Appl Netw Sci. 2017;2(1):28. doi: 10.1007/s41109-017-0048-x. Epub 2017 Aug 30.
The mystery behind the origin of the pain and the difficulty to propose methodologies for its quantitative characterization fascinated philosophers (and then scientists) from the dawn of our modern society. Nowadays, studying patterns of information flow in mesoscale activity of brain networks is a valuable strategy to offer answers in computational neuroscience. In this paper, complex network analysis was performed on the time-varying brain functional connectomes of a rat model of persistent peripheral neuropathic pain, obtained by means of local field potential and spike train analysis. A wide range of topological network measures (14 in total, the code is publicly released at: https://github.com/biomedical-cybernetics/topological_measures_wide_analysis) was employed to quantitatively investigate the rewiring mechanisms of the brain regions responsible for development and upkeep of pain along time, from three hours to 16 days after nerve injury. The time trend (across the days) of each network measure was correlated with a behavioural test for rat pain, and surprisingly we found that the rewiring mechanisms associated with two local topological measure, the local-community-paradigm and the power-lawness, showed very high statistical correlations (higher than 0.9, being the maximum value 1) with the behavioural test. We also disclosed clear functional connectivity patterns that emerged in association with chronic pain in the primary somatosensory cortex (S1) and ventral posterolateral (VPL) nuclei of thalamus. This study represents a pioneering attempt to exploit network science models in order to elucidate the mechanisms of brain region re-wiring and engram formations that are associated with chronic pain in mammalians. We conclude that the is a model of complex network organization that triggers a which seems associated to processing, learning and memorization of chronic pain in the brain functional connectivity. This rule is based exclusively on the network topology, hence was named .
疼痛起源背后的奥秘以及难以提出对其进行定量表征的方法,从现代社会伊始便吸引着哲学家(随后是科学家)。如今,研究大脑网络中尺度活动的信息流模式是计算神经科学中提供答案的一项有价值的策略。在本文中,对通过局部场电位和尖峰序列分析获得的持续性外周神经病理性疼痛大鼠模型的时变脑功能连接组进行了复杂网络分析。采用了广泛的拓扑网络测量方法(总共14种,代码已在https://github.com/biomedical-cybernetics/topological_measures_wide_analysis上公开发布),从神经损伤后三小时到16天,定量研究负责疼痛发展和维持的脑区随时间的重新布线机制。每个网络测量的时间趋势(跨天)与大鼠疼痛行为测试相关,令人惊讶的是,我们发现与两种局部拓扑测量相关的重新布线机制,即局部社区范式和幂律度,与行为测试显示出非常高的统计相关性(高于0.9,最大值为1)。我们还揭示了与慢性疼痛相关的、出现在初级体感皮层(S1)和丘脑腹后外侧(VPL)核中的清晰功能连接模式。本研究代表了利用网络科学模型来阐明与哺乳动物慢性疼痛相关的脑区重新布线和记忆痕迹形成机制的开创性尝试。我们得出结论,是一种复杂网络组织模型,它触发了一种似乎与大脑功能连接中慢性疼痛的处理、学习和记忆相关的机制。这条规则完全基于网络拓扑,因此被命名为。