In many biological systems, the network of interactions between the elements can only be inferred from experimental measurements. In neuroscience, non-invasive imaging tools are extensively used to derive either structural or functional brain networks in-vivo. As a result of the inference process, we obtain a matrix of values corresponding to an unrealistic fully connected and weighted network. To turn this into a useful sparse network, thresholding is typically adopted to cancel a percentage of the weakest connections. The structural properties of the resulting network depend on how much of the inferred connectivity is eventually retained. However, how to fix this threshold is still an open issue. We introduce a criterion, the efficiency cost optimization (ECO), to select a threshold based on the optimization of the trade-off between the efficiency of a network and its wiring cost. We prove analytically and we confirm through numerical simulations that the connection density maximizing this trade-off emphasizes the intrinsic properties of a given network, while preserving its sparsity. Moreover, this density threshold can be determined a-priori, since the number of connections to filter only depends on the network size according to a power-law. We validate this result on several brain networks, from micro- to macro-scales, obtained with different imaging modalities. Finally, we test the potential of ECO in discriminating brain states with respect to alternative filtering methods. ECO advances our ability to analyze and compare biological networks, inferred from experimental data, in a fast and principled way.
Supervised Tractogram Filtering using Geometric Deep Learning
Fondazione Bruno Kessler（布鲁诺·凯塞勒基金会）；Istituto Italiano di Tecnologia（意大利技术研究院）；University of Trento（特伦托大学）；CNRS（法国国家科学研究中心）；CEA（法国原子能和替代能源委员会）；Université de Bordeaux（波尔多大学）；Azienda Provinciale per i Servizi Sanitari（省卫生服务局）；NNAISENSE
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程学系）；S-Lab, Nanyang Technological University（南洋理工大学S-Lab）
Exponential Random Graph Modeling for Complex Brain Networks
指数随机图模型（Exponential random graph models，ERGM），也被称为p* 模型，在社会科学文献中被广泛用于研究复杂网络及其全局结构如何依赖于底层结构组件。然而，关于它们在生物网络（特别是大脑网络）中的应用的文献仍然很少。基于图的特定特征（聚类系数、度分布等）的描述性模型已经主导了神经科学中的连通性研究。相应的生成模型已被开发，以重现这些功能之一。然而，全脑网络数据固有的复杂性需要开发和使用工具，以便同时系统地探索几个功能以及它们如何相互作用以形成全球网络架构。ERGM提供了一种统计原则方法来评估一组相互作用的局部大脑网络特征如何产生全局结构。我们说明了实用的ERGMs的建模，分析和模拟复杂的全脑网络与网络数据正常受试者。我们还提供了一个基础，通过实施和评估三种选择方法：传统的基于p值的向后选择方法，信息准则方法（AIC），和图形拟合优度（GOF）的方法选择重要的本地功能。图形GOF方法是最好的方法，因为科学家们对能够捕获和再现拟合的大脑网络的结构感兴趣。
Optimized EEG based mood detection with signal processing and deep neural networks for brain-computer interface
Spiraldevs Automation Industries Pvt. Ltd.（Spiraldevs自动化工业私人有限公司）；Vardhman Mahaveer Medical College（瓦德曼·马哈特维尔医学院）；Safdarjung Hospital（萨夫达尔琼医院）；Dr. B.C. Roy Engineering College（B.C.罗伊博士工程学院）；International Institute of Information Technology Naya Raipur（新赖布尔国际信息技术学院）
