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An Introduction to Variational Autoencoders provides a quick summary for the of a topic that has become an important tool in modern-day deep learning techniques.
Spontaneous brain activity, measured under the absence of any overt task, has been investigated under the label of "resting state" for about 20 years with rising interest. While it was known since the beginnings of modern electrophysiology that the brain exhibits spontaneous fluctuations also during rest, the discovery, in 1995, that these fluctuations possess a robust spatio-temporal structure had a profound impact on how we understand and investigate brain activity. In this dissertation, we characterize the spatio-temporal dynamics of resting state on a macroscopic level using fMRI recordings from humans and combining novel data analysis tools with theoretical models on the level of the whole brain. We demonstrate the presence of common patterns of functional connectivity, known as resting state networks (RSNs), that evolve in time in both empirical and model data. We show that spontaneous fluctuations and their statistics are determined by the structure of the brain network and its dynamics.
This book is written for scientists and engineers who use HHT (Hilbert-Huang Transform) to analyze data from nonlinear and non-stationary processes. It can be treated as a HHT user manual and a source of reference for HHT applications. The book contains the basic principle and method of HHT and various application examples, ranging from the correction of satellite orbit drifting to detection of failure of highway bridges.The thirteen chapters of the first edition are based on the presentations made at a mini-symposium at the Society for Industrial and Applied Mathematics in 2003. Some outstanding mathematical research problems regarding HHT development are discussed in the first three chapters. The three new chapters of the second edition reflect the latest HHT development, including ensemble empirical mode decomposition (EEMD) and modified EMD.The book also provides a platform for researchers to develop the HHT method further and to identify more applications.
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Functional magnetic resonance imaging (fMRI) based on blood-oxygen level dependent (BOLD) contrast is a powerful technique for non-invasive measurement of brain activity. Recent fMRI studies have revealed that the spontaneous BOLD fluctuations of the human brain organize into distributed, temporally-coherent networks ("resting-state networks"; RSNs). Examination of RSNs has yielded valuable insight into neural organization and development, and demonstrates potential as a biomarker for conditions such as Alzheimer's disease and depression. However, the accuracy by which the spatio-temporal properties of RSNs can be delineated using fMRI is compromised by the presence of physiological (cardiac and respiratory) noise and vascular hemodynamic variability. Further, our present understanding of how RSNs may interact and support cognitive function has been limited by the fact that the vast majority of studies to-date analyze RSNs in a manner that assumes temporal stationarity. Here, we describe efforts to correct for non-neural physiological influences on the BOLD signal, as well as investigations into the dynamic character of resting-state network connectivity. It is found that low-frequency variations in cardiac and respiratory processes account for significant noise across widespread gray matter regions, and that a constrained deconvolution approach may prove effective for modeling and reducing their effects. Application of the proposed noise-reduction procedure is observed to yield negative correlations between the spontaneous fluctuations of two major RSNs. The relationship between respiratory volume changes and the BOLD signal is further examined by simultaneously monitoring and comparing chest expansion data, end-tidal gas concentrations, and spontaneous BOLD fluctuations. The use of a breath-holding task is proposed for quantifying regional differences in BOLD signal timing that arise from local vasomotor response delays; such non-neural timing delays are found to impact inferences of resting-state connectivity and causality. Finally, a preliminary analysis of non-stationary connectivity between RSNs is performed using wavelet and sliding-window approaches, and it is observed that interactions between networks may reconfigure on time-scales of seconds to minutes.
Magnetoencephalography (MEG) is an invaluable functional brain imaging technique that provides direct, real-time monitoring of neuronal activity necessary for gaining insight into dynamic cortical networks. Our intentions with this book are to cover the richness and transdisciplinary nature of the MEG field, make it more accessible to newcomers and experienced researchers and to stimulate growth in the MEG area. The book presents a comprehensive overview of MEG basics and the latest developments in methodological, empirical and clinical research, directed toward master and doctoral students, as well as researchers. There are three levels of contributions: 1) tutorials on instrumentation, measurements, modeling, and experimental design; 2) topical reviews providing extensive coverage of relevant research topics; and 3) short contributions on open, challenging issues, future developments and novel applications. The topics range from neuromagnetic measurements, signal processing and source localization techniques to dynamic functional networks underlying perception and cognition in both health and disease. Topical reviews cover, among others: development on SQUID-based and novel sensors, multi-modal integration (low field MRI and MEG; EEG and fMRI), Bayesian approaches to multi-modal integration, direct neuronal imaging, novel noise reduction methods, source-space functional analysis, decoding of brain states, dynamic brain connectivity, sensory-motor integration, MEG studies on perception and cognition, thalamocortical oscillations, fetal and neonatal MEG, pediatric MEG studies, cognitive development, clinical applications of MEG in epilepsy, pre-surgical mapping, stroke, schizophrenia, stuttering, traumatic brain injury, post-traumatic stress disorder, depression, autism, aging and neurodegeneration, MEG applications in cognitive neuropharmacology and an overview of the major open-source analysis tools.
This book constitutes the proceedings of the 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 68 papers presented in this volume were carefully reviewed and selected from 101 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc.
Sleep and anesthesia resemble in many ways at a first glance. The most prominent common feature of course is the loss of consciousness, i.e. the loss of awareness of external stimuli. However a closer look at the loss of consciousness reveals already a difference between sleep and anesthesia: anesthesia is induced by an anesthetic drug whereas we may fall asleep without external cause. Other questions may arise about the difference of the two effects: do we dream during surgery under anesthesia, do we feel pain during sleep? Essentially, we may ask: what is common and what are the differences between sleep and anesthesia? To answer these questions, we may take a look at the neural origin of both effects and the involved physiological pathways. In which way do they resemble? Moreover, we ask what are the detailed features of normal sleep and general anesthesia as applied during surgery and which features exist in both phenomena? If yes in which way? To receive answers to these questions, it is necessary to consider several experimental techniques that reveal underlying neural mechanisms of sleep and anesthesia. Moreover, theoretical models of neural activity may model both phenomena and comes up with predictions or even theories on the underlying mechanisms. Such models may attack several different description levels, from the microscopic level of single neurons to the macroscopic level of neural populations. Such models may give deeper insight into the phenomena if their assumptions are based on experimental findings and their predictions can be compared to experimental results. This comparison step is essential for valuable theoretical models. The book is motivated by two successful workshops on anesthesia and sleep organized during the Computational Neuroscience Conferences in Toronto in 2007 and in Berlin 2009. It aims to cover all the previous aspects with a focus on the link to experimental findings. It elucidates important issues in theoretical models that at the same time reflect some current major research interests. Moreover it considers some diverse issues which are very important to get an overview of the fields. For instance, the book discusses not only neural activity in the brain but also the effects of general anesthesia on the cardio-vascular system and the spinal cord in the context of analgesia. In addition, it considers different experimental techniques on various spatial scales, such as fMRI and EEG-experiments on the macroscopic scale and single neuron and LFP-measurements on the microscopic scale. In total all book chapters reveal aspects of the neural correlates of sleep and anesthesia motivated by experimental data. This focus on the neural mechanism in the light of experimental data is the common feature of the topics and the chapters. In addition, the book aims to clarify the shared physiological mechanisms of both phenomena, but also reveal their physiological differences.
For those new to the field of resting state fMRI, the large variety of approaches to functional connectivity analysis is highly confusing. This primer provides an introduction to the concepts and analysis decisions that need to be made at every step of the processing pipeline, starting from data acquisition through to interpretation of findings.