A Study Of Dynamic Functional Brain Connectivity Using Functional Magnetic Resonance Imaging Fmri
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Study Of Dynamic Functional Br
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Author : Zening Fu
language : en
Publisher: Open Dissertation Press
Release Date : 2017-01-26
Study Of Dynamic Functional Br written by Zening Fu and has been published by Open Dissertation Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-26 with Technology & Engineering categories.
This dissertation, "A Study of Dynamic Functional Brain Connectivity Using Functional Magnetic Resonance Imaging (fMRI): Method and Applications" by Zening, Fu, 傅泽宁, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Identifying the statistical interdependence (functional connectivity, FC) between brain regions using functional magnetic resonance imaging (fMRI)is an important approach towards understanding how brain system is organized. Most fMRI studies assumed temporal stationarity of FC, so that the dynamic fluctuations of FC were overlooked. Emerging evidence has shown that FC fluctuates significantly across time and such fluctuations are physiologically relevant. The objectives of this work were (1) to develop novel methods for estimating dynamic FC from non-stationary fMRI signals, and (2) to apply new methods on real-life fMRI datasets for exploring dynamic patterns of FC in tasks and at rest. In particular, new methods were introduced to tackle two key issues in dynamic FC estimation: how to adaptively select window size to estimate dynamic FC and how to estimate dynamic FC networks with sparse architecture and sparse evolution. Firstly, a local polynomial regression (LPR) method was introduced to estimate time-varying covariance (TVCOV) for the inference of dynamic FC. The asymptotic analysis of this covariance estimator was performed and then a data-driven method, intersection of confidence intervals (ICI), was adopted to adaptively determine the window size. Simulation results showed that the LPR-ICI method could achieve robust and reliable performance in estimating TVCOV, making it a powerful tool for studying the dynamic FC from fMRI signals. Secondly, the LPR-ICI method was applied to a visual task fMRI dataset for studying the changes of FC in a block-designed visual checkerboard experiment. Reliable task-related FC changes were identified among activated visual regions during the task block. The results suggested that characterizing the task-related FC dynamics might provide greater insight into condition shifts and coordination between brain regions. Thirdly, the LPR-ICI method was applied to a resting-state fMRI dataset for exploring FC dynamics across the whole brain and investigating their relationships with dynamics of local brain activities. Converging results demonstrated that resting-state FC exhibited remarkable different dynamic patterns across the brain and these dynamic patterns were significantly correlated with the dynamic patterns of brain activities. These findings suggested that the brain might bean adaptive network, in which brain activities and their FC coevolve across time. Lastly, a novel dual l0-penalized (DLP) time-varying in verse covariance estimation method was introduced for estimating sparse dynamic FC networks. This DLP method was able to estimate dynamic networks with sparse architecture and sparse evolution by minimizing a log-likelihood function regularized by two l0-penalties (to enforce sparse architecture and sparse evolution, respectively).A coordinate descent algorithm was developed for searching the local minimizers of the objective function. Extensive simulation results showed that the DLP method could achieve better performance than conventionall1-penalized methods. In summary, two newly-developed methods (LPR-ICI and DLP) could be effective tools for studying dynamic brain FC and our results have advanced the knowledge of how brain regions dynamically coordinate. This study was also clinically relevant, as the quantification of altered FC dynamics in clinical populations of neuropsyc
A Study Of Dynamic Functional Brain Connectivity Using Functional Magnetic Resonance Imaging Fmri
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Author : Zening Fu
language : en
Publisher:
Release Date : 2017-01-26
A Study Of Dynamic Functional Brain Connectivity Using Functional Magnetic Resonance Imaging Fmri written by Zening Fu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-26 with Brain categories.
Pattern Analysis Of The Human Connectome
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Author : Dewen Hu
language : en
Publisher: Springer Nature
Release Date : 2019-11-12
Pattern Analysis Of The Human Connectome written by Dewen Hu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-12 with Medical categories.
This book presents recent advances in pattern analysis of the human connectome. The human connectome, measured by magnetic resonance imaging at the macroscale, provides a comprehensive description of how brain regions are connected. Based on machine learning methods, multiviarate pattern analysis can directly decode psychological or cognitive states from brain connectivity patterns. Although there are a number of works with chapters on conventional human connectome encoding (brain-mapping), there are few resources on human connectome decoding (brain-reading). Focusing mainly on advances made over the past decade in the field of manifold learning, sparse coding, multi-task learning, and deep learning of the human connectome and applications, this book helps students and researchers gain an overall picture of pattern analysis of the human connectome. It also offers valuable insights for clinicians involved in the clinical diagnosis and treatment evaluation of neuropsychiatric disorders.
Toward Improved Characterization Of Brain Network Temporal Properties With Functional Magnetic Resonance Imaging
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Author : Catherine Elizabeth Chang
language : en
Publisher: Stanford University
Release Date : 2011
Toward Improved Characterization Of Brain Network Temporal Properties With Functional Magnetic Resonance Imaging written by Catherine Elizabeth Chang and has been published by Stanford University this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
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.
Handbook Of Functional Connectivity Magnetic Resonance Imaging Methods In Conn
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Author : Alfonso Nieto-Castanon
language : en
Publisher: Hilbert Press
Release Date : 2020-01-31
Handbook Of Functional Connectivity Magnetic Resonance Imaging Methods In Conn written by Alfonso Nieto-Castanon and has been published by Hilbert Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-31 with Science categories.
This handbook describes methods for processing and analyzing functional connectivity Magnetic Resonance Imaging (fcMRI) data using the CONN toolbox, a popular freely-available functional connectivity analysis software. Content description [excerpt from introduction] The first section (fMRI minimal preprocessing pipeline) describes standard and advanced preprocessing steps in fcMRI. These steps are aimed at correcting or minimizing the influence of well-known factors affecting the quality of functional and anatomical MRI data, including effects arising from subject motion within the scanner, temporal and spatial image distortions due to the sequential nature of the scanning acquisition protocol, and inhomogeneities in the scanner magnetic field, as well as anatomical differences among subjects. Even after these conventional preprocessing steps, the measured blood-oxygen-level-dependent (BOLD) signal often still contains a considerable amount of noise from a combination of physiological effects, outliers, and residual subject-motion factors. If unaccounted for, these factors would introduce very strong and noticeable biases in all functional connectivity measures. The second section (fMRI denoising pipeline) describes standard and advanced denoising procedures in CONN that are used to characterize and remove the effect of these residual non-neural noise sources. Functional connectivity Magnetic Resonance Imaging studies attempt to quantify the level of functional integration across different brain areas. The third section (functional connectivity measures) describes a representative set of functional connectivity measures available in CONN, each focusing on different indicators of functional integration, including seed-based connectivity measures, ROI-to-ROI measures, graph theoretical approaches, network-based measures, and dynamic connectivity measures. Second-level analyses allow researchers to make inferences about properties of groups or populations, by generalizing from the observations of only a subset of subjects in a study. The fourth section (General Linear Model) describes the mathematics behind the General Linear Model (GLM), the approach used in CONN for all second-level analyses of functional connectivity measures. The description includes GLM model definition, parameter estimation, and hypothesis testing framework, as well as several practical examples and general guidelines aimed at helping researchers use this method to answer their specific research questions. The last section (cluster-level inferences) details several approaches implemented in CONN that allow researchers to make meaningful inferences from their second-level analysis results while providing appropriate family-wise error control (FWEC), whether in the context of voxel-based measures, such as when studying properties of seed-based maps across multiple subjects, or in the context of ROI-to-ROI measures, such as when studying properties of ROI-to-ROI connectivity matrices across multiple subjects.
Brain Connectivity Analysis Investigating Brain Disorders
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Author : Barry Horwitz
language : en
Publisher: Frontiers E-books
Release Date :
Brain Connectivity Analysis Investigating Brain Disorders written by Barry Horwitz and has been published by Frontiers E-books this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
In the last few years, advances in human structural and functional neuroimaging (fMRI, PET, EEG/MEG) have resulted in an explosion of studies investigating the anatomical and functional connectivity between different regions of the brain. More and more studies have employed resting and task-related connectivity analyses to assess functional interactions, and diffusion-weighted tractography to study white matter organization. Many of these studies have addressed normal human function, but recently, a number of investigators have turned their attention to examining brain disorders. The study of brain disorders is a complex endeavor; not only does it require understanding the normal brain, and the regions involved in a particular function, but also it needs a deeper understanding of brain networks and their dynamics. This Research Topic will provide the scientific community with an overview of how to apply connectivity methods to study brain disease, and with perspectives on what are the strength and limitations of each modality. For this Research Topic, we solicit both reviews and original research articles on the use of brain connectivity analysis, with non-human or human models, to explore neurological, psychiatric, developmental and neurodegenerative disorders from a system perspective. Connectivity studies that have focused on one or more of the following will be of particular interest: (1) detection of abnormal functional/structural connectivity; (2) neural plasticity, assessed by changes in connectivity, in patients with brain disorders; (3) assessment of therapy using connectivity measures; (4) relation of connectivity changes to behavioral changes.
Mapping Psychopathology With Fmri And Effective Connectivity Analysis
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Author : Baojuan Li
language : en
Publisher: Frontiers Media SA
Release Date : 2017-06-22
Mapping Psychopathology With Fmri And Effective Connectivity Analysis written by Baojuan Li and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-22 with Electronic book categories.
There is a growing appreciation that many psychiatric (and neurological) conditions can be understood as functional disconnection syndromes – as reflected in aberrant functional integration and synaptic connectivity. This Research Topic considers recent advances in understanding psychopathology in terms of aberrant effective connectivity – as measured noninvasively using functional magnetic resonance imaging (fMRI). Recently, there has been increasing interest in inferring directed connectivity (effective connectivity) from fMRI data. Effective connectivity refers to the influence that one neural system exerts over another and quantifies the directed coupling among brain regions – and how they change with pathophysiology. Compared to functional connectivity, effective connectivity allows one to understand how brain regions interact with each other in terms of context sensitive changes and directed coupling – and therefore may provide mechanistic insights into the neural basis of psychopathology. Established models of effective connectivity include psychophysiological interaction (PPI), structural equation modeling (SEM) and dynamic causal modelling (DCM). DCM is unique because it explicitly models the interaction among brain regions in terms of latent neuronal activity. Moreover, recent advances in DCM such as stochastic and spectral DCM, make it possible to characterize the interaction between different brain regions both at rest and during a cognitive task.
Magnetic Resonance Imaging Of Healthy And Diseased Brain Networks
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Author : Yong He
language : en
Publisher: Frontiers Media SA
Release Date : 2015-03-05
Magnetic Resonance Imaging Of Healthy And Diseased Brain Networks written by Yong He and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-03-05 with Brain categories.
An important aspect of neuroscience is to characterize the underlying connectivity patterns of the human brain (i.e., human connectomics). Over the past few years, researchers have demonstrated that by combining a variety of different neuroimaging technologies (e.g., structural MRI, diffusion MRI and functional MRI) with sophisticated analytic strategies such as graph theory, it is possible to noninvasively map the patterns of structural and functional connectivity of human whole-brain networks. With these novel approaches, many studies have shown that human brain networks have nonrandom properties such as modularity, small-worldness and highly connected hubs. Importantly, these quantifiable network properties change with age, learning and disease. Moreover, there is growing evidence for behavioral and genetic correlates. Network analysis of neuroimaging data is opening up a new avenue of research into the understanding of the organizational principles of the brain that will be of interest for all basic scientists and clinical researchers. Such approaches are powerful but there are a number of challenging issues when extracting reliable brain networks from various imaging modalities and analyzing the topological properties, e.g., definitions of network nodes and edges and reproducibility of network analysis. We assembled contributions related to the state-of-the-art methodologies of brain connectivity and the applications involving development, aging and neuropsychiatric disorders such as Alzheimer’s disease, schizophrenia, attention deficit hyperactivity disorder and mood and anxiety disorders. It is anticipated that the articles in this Research Topic will provide a greater range and depth of provision for the field of imaging connectomics.
Handbook Of Brain Connectivity
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Author : Viktor K. Jirsa
language : en
Publisher: Springer
Release Date : 2007-08-16
Handbook Of Brain Connectivity written by Viktor K. Jirsa and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-16 with Technology & Engineering categories.
Our contemporary understanding of brain function is deeply rooted in the ideas of the nonlinear dynamics of distributed networks. Cognition and motor coordination seem to arise from the interactions of local neuronal networks, which themselves are connected in large scales across the entire brain. The spatial architectures between various scales inevitably influence the dynamics of the brain and thereby its function. But how can we integrate brain connectivity amongst these structural and functional domains? Our Handbook provides an account of the current knowledge on the measurement, analysis and theory of the anatomical and functional connectivity of the brain. All contributors are leading experts in various fields concerning structural and functional brain connectivity. In the first part of the Handbook, the chapters focus on an introduction and discussion of the principles underlying connected neural systems. The second part introduces the currently available non-invasive technologies for measuring structural and functional connectivity in the brain. Part three provides an overview of the analysis techniques currently available and highlights new developments. Part four introduces the application and translation of the concepts of brain connectivity to behavior, cognition and the clinical domain.
Dynamic Functional Connectivity In Neuropsychiatric Disorders Methods And Applications
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Author : Wenbin Guo
language : en
Publisher: Frontiers Media SA
Release Date : 2020-12-03
Dynamic Functional Connectivity In Neuropsychiatric Disorders Methods And Applications written by Wenbin Guo and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-03 with Science categories.
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.