Download or read book Connectivity driven parcellation methods for the human cerebral cortex written by Salim Arslan and published by Salim Arslan. This book was released on 2017-11-01 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: The macro connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform cognitive functions. It embodies the notion of representing, analysing, and understanding all connections within the brain as a network, while the subdivision of the brain into interacting cortical units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Parcellations derived from anatomical brain atlases or random parcellations are traditionally used for node identification, however these approaches do not always fully reflect the functional/structural organisation of the brain. Connectivity-driven methods have arisen only recently, aiming to delineate parcellations that are more faithful to the underlying connectivity. Such parcellation methods face several challenges, including but not limited to poor signal-to-noise ratio, the curse of dimensionality, and functional/structural variations inherent in individual brains, which are only limitedly addressed by the current state of the art. In this thesis, we present robust and fully-automated methods for the subdivision of the entire human cerebral cortex based on connectivity information. Our contributions are four-fold: First, we propose a clustering approach to delineate a cortical parcellation that provides a reliable abstraction of the brain's functional organisation. Second, we cast the parcellation problem as a feature reduction problem and make use of manifold learning and image segmentation techniques to identify cortical regions with distinct structural connectivity patterns. Third, we present a multi-layer graphical model that combines within- and between-subject connectivity, which is then decomposed into a cortical parcellation that can represent the whole population, while accounting for the variability across subjects. Finally, we conduct a large-scale, systematic comparison of existing parcellation methods, with a focus on providing some insight into the reliability of brain parcellations in terms of reflecting the underlying connectivity, as well as, revealing their impact on network analysis. We evaluate the proposed parcellation methods on publicly available data from the Human Connectome Project and a plethora of quantitative and qualitative evaluation techniques investigated in the literature. Experiments across multiple resolutions demonstrate the accuracy of the presented methods at both subject and group levels with regards to reproducibility and fidelity to the data. The neuro-biological interpretation of the proposed parcellations is also investigated by comparing parcel boundaries with well-structured properties of the cerebral cortex. Results show the advantage of connectivity-driven parcellations over traditional approaches in terms of better fitting the underlying connectivity. However, the benefit of using connectivity to parcellate the brain is not always as clear regarding the agreement with other modalities and simple network analysis tasks carried out across healthy subjects. Nonetheless, we believe the proposed methods, along with the systematic evaluation of existing techniques, offer an important contribution to the field of brain parcellation, advancing our understanding of how the human cerebral cortex is organised at the macroscale.

Download or read book Connectivity driven Parcellation Methods for the Human Cerebral Cortex written by Salim Arslan and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Micro Meso and Macro Connectomics of the Brain written by Henry Kennedy and published by Springer. This book was released on 2016-03-10 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book has brought together leading investigators who work in the new arena of brain connectomics. This includes ‘macro-connectome’ efforts to comprehensively chart long-distance pathways and functional networks; ‘micro-connectome’ efforts to identify every neuron, axon, dendrite, synapse, and glial process within restricted brain regions; and ‘meso-connectome’ efforts to systematically map both local and long-distance connections using anatomical tracers. This book highlights cutting-edge methods that can accelerate progress in elucidating static ‘hard-wired’ circuits of the brain as well as dynamic interactions that are vital for brain function. The power of connectomic approaches in characterizing abnormal circuits in the many brain disorders that afflict humankind is considered. Experts in computational neuroscience and network theory provide perspectives needed for synthesizing across different scales in space and time. Altogether, this book provides an integrated view of the challenges and opportunities in deciphering brain circuits in health and disease.

Download or read book Perspectives on Cognitive Neuroscience written by Richard G. Lister and published by Oxford University Press, USA. This book was released on 1991 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains a series of original essays by researchers in the fields of cognitive psychology, the neurosciences and neuropsychology, whose goal is to integrate the diverse and growing body of research emerging in these diverse areas.

Download or read book Microstructural Parcellation of the Human Cerebral Cortex written by Stefan Geyer and published by Springer Science & Business Media. This book was released on 2013-07-04 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unraveling the functional properties of structural elements in the brain is one of the fundamental goals of neuroscientific research. In the cerebral cortex this is no mean feat, since cortical areas are defined microstructurally in post-mortem brains but functionally in living brains with electrophysiological or neuroimaging techniques – and cortical areas vary in their topographical properties across individual brains. Being able to map both microstructure and function in the same brains noninvasively in vivo would represent a huge leap forward. In recent years, high-field magnetic resonance imaging (MRI) technologies with spatial resolution below 0.5 mm have set the stage for this by detecting structural differences within the human cerebral cortex, beyond the Stria of Gennari. This provides the basis for an in vivo microanatomical brain map, with the enormous potential to make direct correlations between microstructure and function in living human brains. This book starts with Brodmann’s post-mortem map published in the early 20th century, moves on to the almost forgotten microstructural maps of von Economo and Koskinas and the Vogt-Vogt school, sheds some light on more recent approaches that aim at mapping cortical areas noninvasively in living human brains, and culminates with the concept of “in vivo Brodmann mapping” using high-field MRI, which was introduced in the early 21st century.

Download or read book Discovering the Human Connectome written by Olaf Sporns and published by MIT Press. This book was released on 2012-09-07 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: A pioneer in the field outlines new empirical and computational approaches to mapping the neural connections of the human brain. Crucial to understanding how the brain works is connectivity, and the centerpiece of brain connectivity is the connectome, a comprehensive description of how neurons and brain regions are connected. In this book, Olaf Sporns surveys current efforts to chart these connections—to map the human connectome. He argues that the nascent field of connectomics has already begun to influence the way many neuroscientists collect, analyze, and think about their data. Moreover, the idea of mapping the connections of the human brain in their entirety has captured the imaginations of researchers across several disciplines including human cognition, brain and mental disorders, and complex systems and networks. Discovering the Human Connectome offers the first comprehensive overview of current empirical and computational approaches in this rapidly developing field.

Download or read book Handbook of Neuroengineering written by Nitish V. Thakor and published by Springer Nature. This book was released on 2023-02-02 with total page 3686 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Handbook serves as an authoritative reference book in the field of Neuroengineering. Neuroengineering is a very exciting field that is rapidly getting established as core subject matter for research and education. The Neuroengineering field has also produced an impressive array of industry products and clinical applications. It also serves as a reference book for graduate students, research scholars and teachers. Selected sections or a compendium of chapters may be used as “reference book” for a one or two semester graduate course in Biomedical Engineering. Some academicians will construct a “textbook” out of selected sections or chapters. The Handbook is also meant as a state-of-the-art volume for researchers. Due to its comprehensive coverage, researchers in one field covered by a certain section of the Handbook would find other sections valuable sources of cross-reference for information and fertilization of interdisciplinary ideas. Industry researchers as well as clinicians using neurotechnologies will find the Handbook a single source for foundation and state-of-the-art applications in the field of Neuroengineering. Regulatory agencies, entrepreneurs, investors and legal experts can use the Handbook as a reference for their professional work as well.​

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2015 written by Nassir Navab and published by Springer. This book was released on 2015-09-28 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 9349, 9350, and 9351 constitutes the refereed proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, held in Munich, Germany, in October 2015. Based on rigorous peer reviews, the program committee carefully selected 263 revised papers from 810 submissions for presentation in three volumes. The papers have been organized in the following topical sections: quantitative image analysis I: segmentation and measurement; computer-aided diagnosis: machine learning; computer-aided diagnosis: automation; quantitative image analysis II: classification, detection, features, and morphology; advanced MRI: diffusion, fMRI, DCE; quantitative image analysis III: motion, deformation, development and degeneration; quantitative image analysis IV: microscopy, fluorescence and histological imagery; registration: method and advanced applications; reconstruction, image formation, advanced acquisition - computational imaging; modelling and simulation for diagnosis and interventional planning; computer-assisted and image-guided interventions.

Download or read book Biomedical Texture Analysis written by Adrien Depeursinge and published by Academic Press. This book was released on 2017-08-25 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biomedical Texture Analysis: Fundamentals, Applications, Tools and Challenges describes the fundamentals and applications of biomedical texture analysis (BTA) for precision medicine. It defines what biomedical textures (BTs) are and why they require specific image analysis design approaches when compared to more classical computer vision applications. The fundamental properties of BTs are given to highlight key aspects of texture operator design, providing a foundation for biomedical engineers to build the next generation of biomedical texture operators. Examples of novel texture operators are described and their ability to characterize BTs are demonstrated in a variety of applications in radiology and digital histopathology. Recent open-source software frameworks which enable the extraction, exploration and analysis of 2D and 3D texture-based imaging biomarkers are also presented. This book provides a thorough background on texture analysis for graduate students and biomedical engineers from both industry and academia who have basic image processing knowledge. Medical doctors and biologists with no background in image processing will also find available methods and software tools for analyzing textures in medical images. Defines biomedical texture precisely and describe how it is different from general texture information considered in computer vision Defines the general problem to translate 2D and 3D texture patterns from biomedical images to visually and biologically relevant measurements Describes, using intuitive concepts, how the most popular biomedical texture analysis approaches (e.g., gray-level matrices, fractals, wavelets, deep convolutional neural networks) work, what they have in common, and how they are different Identifies the strengths, weaknesses, and current challenges of existing methods including both handcrafted and learned representations, as well as deep learning. The goal is to establish foundations for building the next generation of biomedical texture operators Showcases applications where biomedical texture analysis has succeeded and failed Provides details on existing, freely available texture analysis software, helping experts in medicine or biology develop and test precise research hypothesis

Download or read book Novel Frontiers of Advanced Neuroimaging written by Kostas Fountas and published by BoD – Books on Demand. This book was released on 2013-01-09 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Medical Image Computing and Computer Assisted Intervention MICCAI 2016 written by Sebastien Ourselin and published by Springer. This book was released on 2016-10-17 with total page 723 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three-volume set LNCS 9900, 9901, and 9902 constitutes the refereed proceedings of the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, held in Athens, Greece, in October 2016. Based on rigorous peer reviews, the program committee carefully selected 228 revised regular papers from 756 submissions for presentation in three volumes. The papers have been organized in the following topical sections: Part I: brain analysis; brain analysis - connectivity; brain analysis - cortical morphology; Alzheimer disease; surgical guidance and tracking; computer aided interventions; ultrasound image analysis; cancer image analysis; Part II: machine learning and feature selection; deep learning in medical imaging; applications of machine learning; segmentation; cell image analysis; Part III: registration and deformation estimation; shape modeling; cardiac and vascular image analysis; image reconstruction; and MR image analysis.

Download or read book Applied Multidimensional Scaling and Unfolding written by Ingwer Borg and published by Springer. This book was released on 2018-05-16 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces multidimensional scaling (MDS) and unfolding as data analysis techniques for applied researchers. MDS is used for the analysis of proximity data on a set of objects, representing the data as distances between points in a geometric space (usually of two dimensions). Unfolding is a related method that maps preference data (typically evaluative ratings of different persons on a set of objects) as distances between two sets of points (representing the persons and the objects, resp.). This second edition has been completely revised to reflect new developments and the coverage of unfolding has also been substantially expanded. Intended for applied researchers whose main interests are in using these methods as tools for building substantive theories, it discusses numerous applications (classical and recent), highlights practical issues (such as evaluating model fit), presents ways to enforce theoretical expectations for the scaling solutions, and addresses the typical mistakes that MDS/unfolding users tend to make. Further, it shows how MDS and unfolding can be used in practical research work, primarily by using the smacof package in the R environment but also Proxscal in SPSS. It is a valuable resource for psychologists, social scientists, and market researchers, with a basic understanding of multivariate statistics (such as multiple regression and factor analysis).

Download or read book Computational Diffusion MRI written by Andrea Fuster and published by Springer. This book was released on 2017-05-11 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume offers a valuable starting point for anyone interested in learning computational diffusion MRI and mathematical methods for brain connectivity, while also sharing new perspectives and insights on the latest research challenges for those currently working in the field. Over the last decade, interest in diffusion MRI has virtually exploded. The technique provides unique insights into the microstructure of living tissue and enables in-vivo connectivity mapping of the brain. Computational techniques are key to the continued success and development of diffusion MRI and to its widespread transfer into the clinic, while new processing methods are essential to addressing issues at each stage of the diffusion MRI pipeline: acquisition, reconstruction, modeling and model fitting, image processing, fiber tracking, connectivity mapping, visualization, group studies and inference. These papers from the 2016 MICCAI Workshop “Computational Diffusion MRI” – which was intended to provide a snapshot of the latest developments within the highly active and growing field of diffusion MR – cover a wide range of topics, from fundamental theoretical work on mathematical modeling, to the development and evaluation of robust algorithms and applications in neuroscientific studies and clinical practice. The contributions include rigorous mathematical derivations, a wealth of rich, full-color visualizations, and biologically or clinically relevant results. As such, they will be of interest to researchers and practitioners in the fields of computer science, MR physics, and applied mathematics.

Download or read book Magnetic Resonance Imaging of Healthy and Diseased Brain Networks written by Yong He and published by Frontiers Media SA. This book was released on 2015-03-05 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Brain Inspired Computing written by Katrin Amunts and published by Springer. This book was released on 2016-12-10 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the Second International Workshop on Brain-Inspired Computing, BrainComp 2015, held in Cetraro, Italy, in July 2015. The 14 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with brain structure and function; computational models and brain-inspired computing methods with practical applications; high performance computing; and visualization for brain simulations.

Download or read book Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN written by Alfonso Nieto-Castanon and published by Hilbert Press. This book was released on 2020-01-31 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Deep Learning Via Stacked Sparse Autoencoders for Automated Voxel wise Brain Parcellation Based on Functional Connectivity written by Céline Gravelines and published by . This book was released on 2014 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Functional brain parcellation - the delineation of brain regions based on functional connectivity - is an active research area lacking an ideal subject-specific solution independent of anatomical composition, manual feature engineering, or heavily labelled examples. Deep learning is a cutting-edge area of machine learning on the forefront of current artificial intelligence developments. Specifically, autoencoders are artificial neural networks which can be stacked to form hierarchical sparse deep models from which high-level features are compressed, organized, and extracted, without labelled training data, allowing for unsupervised learning. This thesis presents a novel application of stacked sparse autoencoders to the problem of parcellating the brain based on its components' (voxels') functional connectivity, focusing on the medial parietal cortex. Various depths of autoencoders are investigated, yielding results of up to (68 ± 3)% accuracy compared with ground truth parcellations using Dice's coefficient. This data-driven functional parcellation technique offers promising growth to both the neuroimaging and machine learning communities.