Introduction To Resting State Fmri Functional Connectivity

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.

This accessible primer gives an introduction to the wide array of MRI-based neuroimaging methods that are used in research. It provides an overview of the fundamentals of what different MRI modalities measure, what artifacts commonly occur, the essentials of the analysis, and common 'pipelines'

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.

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Emerging imaging modalities continuously increase the diagnostic sensitivity and accuracy of neuroimaging, and have transformed diagnostic radiology into a powerful research and clinical tool. Various novel neuroimaging modalities have become of paramount importance, not only in establishing diagnosis but also in guiding surgical intervention, and in evaluating the treatment effect. Advanced MR based techniques such as Fractional Anisotropy, Diffusion Tensor Imaging, Proton Spectroscopy, and task-generated as well as resting-state functional MRI have tremendously increased the power of the modern neuroscientist’s armamentarium. The employment of advanced neuroimaging techniques have been expanded in the scientific fields of neuropsychology, consumer’s psychology, and forensic medicine. Our current textbook presents exactly a collection of such innovative work, and explores new frontiers, and future applications of neuroimaging

Spontaneous 'resting-state' fluctuations in neuronal activity offer insights into the inherent organisation of the human brain, and may provide markers for diagnosis and treatment of mental disorders. Resting state functional magnetic resonance imaging (fMRI) can be used to investigate intrinsic functional connectivity networks, which are identified based on similarities in the signal measured from different regions. From data acquisition to results interpretation, An Introduction to Resting State fMRI Functional Connectivity discusses a wide range of approaches without expecting previous knowledge of the reader, making it truly accessible to readers from a broad range of backgrounds. Supplemented with online examples to enable the reader to obtain hands-on experience working with data, the text also provides details to enhance learning for those already experienced in the field. The Oxford Neuroimaging Primers are written for new researchers or advanced undergraduates in neuroimaging to provide a thorough understanding of the ways in which neuroimaging data can be analysed and interpreted. Aimed at students without a background in mathematics or physics, this book is also important reading for those familiar with task fMRI but new to the field of resting state fMRI.

Edited and authored by a wealth of international experts in neuroscience and related disciplines, this key new resource aims to offer medical students and graduate researchers around the world a comprehensive introduction and overview of modern neuroscience. Neuroscience research is certain to prove a vital element in combating mental illness in its various incarnations, a strategic battleground in the future of medicine, as the prevalence of mental disorders is becoming better understood each year. Hundreds of millions of people worldwide are affected by mental, behavioral, neurological and substance use disorders. The World Health Organization estimated in 2002 that 154 million people globally suffer from depression and 25 million people from schizophrenia; 91 million people are affected by alcohol use disorders and 15 million by drug use disorders. A more recent WHO report shows that 50 million people suffer from epilepsy and 24 million from Alzheimer’s and other dementias. Because neuroscience takes the etiology of disease—the complex interplay between biological, psychological, and sociocultural factors—as its object of inquiry, it is increasingly valuable in understanding an array of medical conditions. A recent report by the United States’ Surgeon General cites several such diseases: schizophrenia, bipolar disorder, early-onset depression, autism, attention deficit/ hyperactivity disorder, anorexia nervosa, and panic disorder, among many others. Not only is this volume a boon to those wishing to understand the future of neuroscience, it also aims to encourage the initiation of neuroscience programs in developing countries, featuring as it does an appendix full of advice on how to develop such programs. With broad coverage of both basic science and clinical issues, comprising around 150 chapters from a diversity of international authors and including complementary video components, Neuroscience in the 21st Century in its second edition serves as a comprehensive resource to students and researchers alike.