The brain is organized in a modular way, serving multiple functionalities. This multiplicity requires that both positive (e.g. excitatory, phase-coherent) and negative (e.g. inhibitory, phase-opposing) interactions take place across brain modules. Unfortunately, most methods to detect modules from time series either neglect or convert to positive any measured negative correlation. This may leave a significant part of the sign-dependent functional structure undetected. Here we present a novel method, based on random matrix theory, for the identification of sign-dependent modules in the brain. Our method filters out the joint effects of local (unit-specific) noise and global (system-wide) dependencies that empirically obfuscate such structure. The method is guaranteed to identify an optimally contrasted functional `signature', i.e. a partition into modules that are positively correlated internally and negatively correlated across. The method is purely data-driven, does not use any arbitrary threshold or network projection, and outputs only statistically significant structure. In measurements of neuronal gene expression in the biological clock of mice, the method systematically uncovers two otherwise undetectable, negatively correlated modules whose relative size and mutual interaction strength are found to depend on photoperiod. The neurons alternating between the two modules define a candidate region of functional plasticity for circadian modulation.
KOCOBrain: Kuramoto-Guided Graph Network for Uncovering Structure-Function Coupling in Adolescent Prenatal Drug Exposure
Department of Computer Science, Georgia State University（计算机科学系，佐治亚州立大学）；Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)（转化神经影像与数据科学联合研究中心）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
The Neurobiological Craving Signature (NCS) predicts social craving and responds to social isolation
Lyon Neuroscience Research Center (CRNL), CNRS, Inserm, Université Claude Bernard Lyon 1（里昂神经科学研究中心，法国国家科学研究中心，法国国家健康与医学研究院，里昂第一大学）；Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University（卡迪夫大学脑研究成像中心，卡迪夫大学）；Cardiff University Centre for Human Developmental Science (CUCHDS), Cardiff University（卡迪夫大学人类发展科学中心，卡迪夫大学）；Department of Psychology, University of Cambridge（剑桥大学心理学系）；Le Vinatier Psychiatrie Universitaire Lyon Métropole（里昂大都会利维纳蒂耶大学精神病学）
A Tensor Network Framework for Interpretable Graph Analysis of Brain Networks
Università degli Studi di Bari Aldo Moro（巴里阿尔多·莫罗大学）；Istituto Nazionale di Fisica Nucleare, Sezione di Bari（意大利国家核物理研究所巴里分部）；Dipartimento di Scienze della Formazione, Psicologia, Comunicazione (For.Psi.Com.), Università degli Studi di Bari Aldo Moro（巴里阿尔多·莫罗大学教育、心理学与传播学院）；Biocruces-Bizkaia Health Research Institute（比斯开生物克鲁塞斯健康研究所）；University of the Basque Country（巴斯克大学）；IKERBASQUE Basque Foundation for Science（伊克尔巴斯奎科学巴斯克基金会）
DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks
Georgia State University（佐治亚州立大学）；TReNDs Center（TReNDs中心）；Georgia Institute of Technology（佐治亚理工学院）；Tulane University（杜兰大学）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Tribhuvan University（特里布文大学）
Multifractal Signatures of Ageing and Dementia Development: A Multifractal Space-Filling Curve Analysis
Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University（雅盖隆大学物理、天文学与应用科学学院）；Doctoral School of Exact and Natural Sciences, Jagiellonian University（雅盖隆大学精确与自然科学博士学院）；Institute of Theoretical Physics, Jagiellonian University（雅盖隆大学理论物理研究所）；Mark Kac Center for Complex Systems Research, Jagiellonian University（雅盖隆大学马克·卡克复杂系统研究中心）；Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences（波兰科学院核物理研究所复杂系统理论系）
Scale-Free Exponents of Resting State provide a Biomarker for Typical and Atypical Brain Activity
无标度网络（SFN）源于简单的增长过程，它可以鼓励高效，集中和容错的通信（1）。最近的研究表明，稳定的网络枢纽结构是由指数（>2.0）的相变所控制的，导致网络结构的急剧变化，包括全局连通性的丧失，最小支配节点集的增加，以及与节点增长相比向连通性增长的转变。这种SFN的变化在非典型的大脑活动中是可识别的吗？中心度（D）上的Pareto分布（P（D）~D^- \b{eta}）是无标度网络的一个特征。在静息状态下，我们评估了大范围的神经典型和非典型受试者的度指数。我们使用图复杂性理论来提供大脑网络结构的预测理论。结果我们发现，神经典型的静息态功能磁共振成像大脑活动具有无标度帕累托指数（1.8 SE .01）在一个单一的个人扫描超过66天，以及在60个不同的个人（1.8 SE .02）。我们还表明，60名自闭症谱系障碍患者和60名精神分裂症患者的无标度指数显着更高（>2.0）（2.4 se .03，2.3 se .04），这表明大脑网络中的动态更加细分和更不可控在休息状态。最后，我们表明，指数值随非典型疾病严重程度的表型指标而变化，表明网络本身的全局拓扑结构可以为非典型脑活动提供特定的诊断生物标志物。
