Adaptive behavior, cognition and emotion are the result of a bewildering variety of brain spatiotemporal activity patterns. An important problem in neuroscience is to understand the mechanism by which the human brain's 100 billion neurons and 100 trillion synapses manage to produce this large repertoire of cortical configurations in a flexible manner. In addition, it is recognized that temporal correlations across such configurations cannot be arbitrary, but they need to meet two conflicting demands: while diverse cortical areas should remain functionally segregated from each other, they must still perform as a collective, i.e., they are functionally integrated. Here, we investigate these large-scale dynamical properties by inspecting the character of the spatiotemporal correlations of brain resting-state activity. In physical systems, these correlations in space and time are captured by measuring the correlation coefficient between a signal recorded at two different points in space at two different times. We show that this two-point correlation function extracted from resting-state fMRI data exhibits self-similarity in space and time. In space, self-similarity is revealed by considering three successive spatial coarse-graining steps while in time it is revealed by the 1/f frequency behavior of the power spectrum. The uncovered dynamical self-similarity implies that the brain is spontaneously at a continuously changing (in space and time) intermediate state between two extremes, one of excessive cortical integration and the other of complete segregation. This dynamical property may be seen as an important marker of brain well-being both in health and disease.
A defense of using resting state fMRI as null data for estimating false positive rates
Slotnick（2017）最近的一篇社论重新考虑了我们关于fMRI中聚类推断的假阳性率控制准确性的论文的研究结果（Eklund et al，2016），特别是批评我们使用静息状态fMRI数据作为评估任务fMRI方法的空数据来源。我们为这种使用静息功能磁共振成像数据进行辩护，因为虽然这些数据中有很多结构，但我们认为它代表了任务数据噪声，因此分析软件应该能够适应这种噪声。我们还讨论了一个潜在的问题与Slotnick自己的方法。
Towards precise resting-state fMRI biomarkers in psychiatry: synthesizing developments in transdiagnostic research, dimensional models of psychopathology, and normative neurodevelopment
Deep Learning Inductive Biases for fMRI Time Series Classification during Resting-state and Movie-watching
School of Electrical and Computer Engineering, University of Tehran（埃特瓦尔大学电气与计算机工程学院）；School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM)（基础科学研究所（IPM）认知科学学院）
A Framework for Exploring Non-Linear Functional Connectivity and Causality in the Human Brain: Mutual Connectivity Analysis (MCA) of Resting-State Functional MRI with Convergent Cross-Mapping and Non-Metric Clustering
VarCoNet: A variability-aware self-supervised framework for functional connectome extraction from resting-state fMRI
Department of Biomedical Engineering and Biotechnology, and the Health Engineering Innovation Group (HEIG), Khalifa University（生物医学工程与生物技术系，以及健康工程创新小组（HEIG），卡利法大学）；Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki（电气与计算机工程系，阿尔基洛斯大学）
Multi-Scale Graph Theoretical Analysis of Resting-State fMRI for Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Healthy Controls
University of Bojnord（博季努尔德大学）；Munster Technological University（芒斯特理工大学）
Point process analysis of large-scale brain fMRI dynamics
功能性磁共振成像（fMRI）技术为我们了解大脑功能做出了重大贡献。目前的方法是基于脑血氧水平依赖性（BOLD）信号中的\emph{gradual and continuous}变化的分析。与该方法不同，最近的工作表明，通过仅检查相对大幅度的BOLD信号峰值可以获得等效的结果，这表明相关信息可以浓缩在\emph{discrete}事件中。这里进一步探讨了这个想法，以证明如何通过这些事件的时间和位置来捕获静息状态下的大脑动力学，即，在时空点过程中。作为原理证明，我们表明可以从这样的点过程中提取静息状态网络（RSN）映射。此外，分析揭示了雪崩的活动，这是由相同的动力学和统计特性之前所描述的神经元事件在较小的尺度。鉴于静息状态脑动力学的功能相关性，其表示为离散过程可能有助于大规模分析健康和疾病中的脑功能。
Functional Brain Network Identification in Opioid Use Disorder Using Machine Learning Analysis of Resting-State fMRI BOLD Signals
Old Dominion University（奥多明尼昂大学）；Institute of Drug and Alcohol Studies（药物与酒精研究所）；Virginia Commonwealth University（弗吉尼亚联邦大学）；C. Kenneth and Dianne Wright Center for Clinical and Translational Research（C.肯尼斯和黛安·赖特临床与转化研究中心）；Data Science Institute（数据科学研究所）
Evaluation of augmentation methods in classifying autism spectrum disorders from fMRI data with 3D convolutional neural networks
a Division of Medical Informatics, Department of Biomedical Engineering b Division of Statistics \& Machine Learning, Department of Computer；Information Science c Center for Medical Image Science；Visualization (CMIV) \ \" o ping University, Sweden
BrainWorld: A Structural-Prior-Conditioned Generative Model for Whole-Brain 4D fMRI Dynamics
Department of Biomedical Engineering, Southern University of Science and Technology（南方科技大学生物医学工程系）；School of Biomedical Engineering, Shenzhen University（深圳大学生物医学工程学院）
Deep learning and whole-brain networks for biomarker discovery: modeling the dynamics of brain fluctuations in resting-state and cognitive tasks
Electric and Computer Engineering Department, Universidad Nacional del Sur（国家南方大学电子与计算机工程系）；Institute of Computer Science and Engineering, National Scientific and Technological Research Council of Argentina (CONICET)（阿根廷国家科学与技术研究理事会计算机科学与工程研究所）；Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra（庞培法布拉大学脑认知中心、信息与通信技术系计算神经科学组）；Institució Catalana de la Recerca i Estudis Avançats (ICREA)（加泰罗尼亚高级研究与教育研究所）；Department of Information and Communication Technologies, Universitat Pompeu Fabra（庞培法布拉大学信息与通信技术系）；ViRVIG, University of Girona（吉罗纳大学ViRVIG）
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。
