Download or read book High dimensional Statistical Methods for Inter subject Studies in Neuroimaging written by Virgile Fritsch and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: La variabilité inter-individuelle est un obstacle majeur à l'analyse d'images médicales, en particulier en neuroimagerie. Il convient de distinguer la variabilité naturelle ou statistique, source de potentiels effets d'intérêt pour du diagnostique, de la variabilité artefactuelle, constituée d'effets de nuisance liés à des problèmes expérimentaux ou techniques, survenant lors de l'acquisition ou le traitement des données. La dernière peut s'avérer bien plus importante que la première : en neuroimagerie, les problèmes d'acquisition peuvent ainsi masquer la variabilité fonctionnelle qui est par ailleurs associée à une maladie, un trouble psychologique, ou à l'expression d'un code génétique spécifique. La qualité des procédures statistiques utilisées pour les études de groupe est alors diminuée car lesdites procédures reposent sur l'hypothèse d'une population homogène, hypothèse difficile à vérifier manuellement sur des données de neuroimagerie dont la dimension est élevée. Des méthodes automatiques ont été mises en oeuvre pour tenter d'éliminer les sujets trop déviants et ainsi rendre les groupes étudiés plus homogènes. Cette pratique n'a pas entièrement fait ses preuves pour autant, attendu qu'aucune étude ne l'a clairement validée, et que le niveau de tolérance à choisir reste arbitraire. Une autre approche consiste alors à utiliser des procédures d'analyse et de traitement des données intrinsèquement insensibles à l'hypothèse d'homogénéité. Elles sont en outre mieux adaptées aux données réelles en ce qu'elles tolèrent dans une certaine mesure d'autres violations d'hypothèse plus subtiles telle que la normalité des données. Un autre problème, partiellement lié, est le manque de stabilité et de sensibilité des méthodes d'analyse au niveau voxel, sources de résultats qui ne sont pas reproductibles.Nous commençons cette thèse par le développement d'une méthode de détection d'individus atypiques adaptée aux données de neuroimagerie, qui fournit un contrôle statistique sur l'inclusion de sujets : nous proposons une version regularisée d'un estimateur de covariance robuste pour le rendre utilisable en grande dimension. Nous comparons plusieurs types de régularisation et concluons que les projections aléatoires offrent le meilleur compromis. Nous présentons également des procédures non-paramétriques dont nous montrons la qualité de performance, bien qu'elles n'offrent aucun contrôle statistique. La seconde contribution de cette thèse est une nouvelle approche, nommée RPBI (Randomized Parcellation Based Inference), répondant au manque de reproductibilité des méthodes classiques. Nous stabilisons l'approche d'analyse à l'échelle de la parcelle en agrégeant plusieurs analyses indépendantes, pour lesquelles le partitionnement du cerveau en parcelles varie d'une analyse à l'autre. La méthode permet d'atteindre un niveau de sensibilité supérieur à celui des méthodes de l'état de l'art, ce que nous démontrons par des expériences sur des données synthétiques et réelles. Notre troisième contribution est une application de la régression robuste aux études de neuroimagerie. Poursuivant un travail déjà existant, nous nous concentrons sur les études à grande échelle effectuées sur plus de cent sujets. Considérant à la fois des données simulées et des données réelles, nous montrons que l'utilisation de la régression robuste améliore la sensibilité des analyses. Nous démontrons qu'il est important d'assurer une résistance face aux violations d'hypothèse, même dans les cas où une inspection minutieuse du jeu de données a été conduite au préalable. Enfin, nous associons la régression robuste à notre méthode d'analyse RPBI afin d'obtenir des tests statistiques encore plus sensibles.

Download or read book Probabilistic Methods for Learning Variations of High dimensional Neuroimaging Data written by Ke Zeng and published by . This book was released on 2018 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building quantitative models to summarize the structural variability of the human brain is an essential task in brain image analysis. Such quantitative models can be used to measure the normative variation of healthy brains, to capture their change over time, and to find imaging patterns of a diseased group. These model can be further applied to individual brain scans for tissue segmentation, lesion delineation, abnormality detection and image registration. A common approach to derive a representation of a population is through the use of atlases (i.e., characteristic brains) that are either manually determined or automatically inferred. However, atlases are first-order statistical measures that do not convey information about the amount and direction of variability within a population and are therefore inadequate for many applications. Most previous works on statistical modeling of imaging data have resorted to voxel-based constructions in which image values at different voxels are assumed to be statistically independent. Although voxel-based methods can identify structural variations that are well localized, they are myopic to correlations between different regions and cannot capture any global patterns of the underlying data. Contrarily, classical multivariate statistical methods can be useful for finding the most dominant trends of variability. However, they are incapable of providing a statistically consistent estimate of the full covariance structure or the joint probabilistic density function of high-dimensional image data with a limited amount of samples. In this thesis, we introduce a multivariate framework for learning probability distributions over high-dimensional image data to capture the inter-subject structural variability of the brain. Specifically, we adopt the divide and conquer strategy by breaking the challenging task of learning high-dimensional image data into a collection of smaller, more tractable problems. In Chapter 2, we present a generative model built upon the aforementioned strategy to capture normative variations of image appearance. The model is incorporated within a novel framework for locating imaging abnormalities. In particular, a 3-Dimensional image volume is modeled as an ensemble of overlapping local regions. A sparse probabilistic model is used to approximate the marginal distribution of local intensity patterns, while pairwise potentials are incorporated to account for correlations across local regions. To tackle the difficulties associated with registering an image of a healthy brain to a scan of a diseased brain, we develop an iterative procedure that interleaves abnormality detection with registration. The method was evaluated using simulated data and tested using images with real lesions. Experimental results demonstrate that the framework can achieve accurate registration and abnormality detection simultaneously.In Chapter 3, we introduce a generative probabilistic model of high-dimensional spatial transformations. To make use of linear statistical methods while preserving diffeomorphisms, we adopt the Log-Euclidean framework and parametrize diffeomorphisms as exponentials of stationary velocity fields. Following the divide and conquer principle, we treat a velocity field as a collection of local velocities that reside in much lower-dimensional sub-spaces. Differing from the model for image appearances, principal component analysis is used to estimate the covariance structure for each local velocity and canonical correlation analysis is used to learn the dependencies between pairs of local velocities. The learned model is used as the foundation of a statistically constrained diffeomorphic registration algorithm. The method was tested using both simulated and real data. The results indicate that the proposed model is able to capture the normative variations of deformations with sub-millimeter accuracy and that the learned statistical constraints lead to substantially more robust registration results in the presence of abnormalities. Lastly, in Chapter 4, we shift our attention to the segmentation of specific pathological structures in a supervised setting. In particular, we demonstrate how a generative model similar to the one described in Chapter 2 can be combined with discriminative learning techniques to form a hybrid segmentation framework. The hybrid method was validated using 132 scans of patients with high-grade gliomas. Quantitative evaluation of the segmentation shows that the hybrid approach outperforms both the baseline generative method and the baseline discriminative model.

Download or read book The Statistical Analysis of Functional MRI Data written by Nicole Lazar and published by Springer Science & Business Media. This book was released on 2008-06-10 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study of brain function is one of the most fascinating pursuits of m- ern science. Functional neuroimaging is an important component of much of the current research in cognitive, clinical, and social psychology. The exci- ment of studying the brain is recognized in both the popular press and the scienti?c community. In the pages of mainstream publications, including The New York Times and Wired, readers can learn about cutting-edge research into topics such as understanding how customers react to products and - vertisements (“If your brain has a ‘buy button,’ what pushes it?”, The New York Times,October19,2004),howviewersrespondtocampaignads(“Using M. R. I. ’s to see politics on the brain,” The New York Times, April 20, 2004; “This is your brain on Hillary: Political neuroscience hits new low,” Wired, November 12,2007),howmen and womenreactto sexualstimulation (“Brain scans arouse researchers,”Wired, April 19, 2004), distinguishing lies from the truth (“Duped,” The New Yorker, July 2, 2007; “Woman convicted of child abuse hopes fMRI can prove her innocence,” Wired, November 5, 2007), and even what separates “cool” people from “nerds” (“If you secretly like Michael Bolton, we’ll know,” Wired, October 2004). Reports on pathologies such as autism, in which neuroimaging plays a large role, are also common (for - stance, a Time magazine cover story from May 6, 2002, entitled “Inside the world of autism”).

Download or read book Handbook of Neuroimaging Data Analysis written by Hernando Ombao and published by CRC Press. This book was released on 2016-11-18 with total page 702 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores various state-of-the-art aspects behind the statistical analysis of neuroimaging data. It examines the development of novel statistical approaches to model brain data. Designed for researchers in statistics, biostatistics, computer science, cognitive science, computer engineering, biomedical engineering, applied mathematics, physics, and radiology, the book can also be used as a textbook for graduate-level courses in statistics and biostatistics or as a self-study reference for Ph.D. students in statistics, biostatistics, psychology, neuroscience, and computer science.

Download or read book Issues in Medical Lasers Imaging and Devices Research and Application 2013 Edition written by and published by ScholarlyEditions. This book was released on 2013-05-01 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: Issues in Medical Lasers, Imaging, and Devices Research and Application: 2013 Edition is a ScholarlyEditions™ book that delivers timely, authoritative, and comprehensive information about Medical Ultrasonography. The editors have built Issues in Medical Lasers, Imaging, and Devices Research and Application: 2013 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Medical Ultrasonography in this book to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Medical Lasers, Imaging, and Devices Research and Application: 2013 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

Download or read book Statistical and Computational Methods in Brain Image Analysis written by Moo K. Chung and published by CRC Press. This book was released on 2013-07-23 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: The massive amount of nonstandard high-dimensional brain imaging data being generated is often difficult to analyze using current techniques. This challenge in brain image analysis requires new computational approaches and solutions. But none of the research papers or books in the field describe the quantitative techniques with detailed illustrations of actual imaging data and computer codes. Using MATLAB® and case study data sets, Statistical and Computational Methods in Brain Image Analysis is the first book to explicitly explain how to perform statistical analysis on brain imaging data. The book focuses on methodological issues in analyzing structural brain imaging modalities such as MRI and DTI. Real imaging applications and examples elucidate the concepts and methods. In addition, most of the brain imaging data sets and MATLAB codes are available on the author’s website. By supplying the data and codes, this book enables researchers to start their statistical analyses immediately. Also suitable for graduate students, it provides an understanding of the various statistical and computational methodologies used in the field as well as important and technically challenging topics.

Download or read book Recent Advances and the Future Generation of Neuroinformatics Infrastructure written by Xi Cheng and published by Frontiers Media SA. This book was released on 2015-12-11 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: The huge volume of multi-modal neuroimaging data across different neuroscience communities has posed a daunting challenge to traditional methods of data sharing, data archiving, data processing and data analysis. Neuroinformatics plays a crucial role in creating advanced methodologies and tools for the handling of varied and heterogeneous datasets in order to better understand the structure and function of the brain. These tools and methodologies not only enhance data collection, analysis, integration, interpretation, modeling, and dissemination of data, but also promote data sharing and collaboration. This Neuroinformatics Research Topic aims to summarize the state-of-art of the current achievements and explores the directions for the future generation of neuroinformatics infrastructure. The publications present solutions for data archiving, data processing and workflow, data mining, and system integration methodologies. Some of the systems presented are large in scale, geographically distributed, and already have a well-established user community. Some discuss opportunities and methodologies that facilitate large-scale parallel data processing tasks under a heterogeneous computational environment. We wish to stimulate on-going discussions at the level of the neuroinformatics infrastructure including the common challenges, new technologies of maximum benefit, key features of next generation infrastructure, etc. We have asked leading research groups from different research areas of neuroscience/neuroimaging to provide their thoughts on the development of a state of the art and highly-efficient neuroinformatics infrastructure. Such discussions will inspire and help guide the development of a state of the art, highly-efficient neuroinformatics infrastructure.

Download or read book Multivariate Analysis for Neuroimaging Data written by Atsushi Kawaguchi and published by CRC Press. This book was released on 2021-07-01 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes methods for statistical brain imaging data analysis from both the perspective of methodology and from the standpoint of application for software implementation in neuroscience research. These include those both commonly used (traditional established) and state of the art methods. The former is easier to do due to the availability of appropriate software. To understand the methods it is necessary to have some mathematical knowledge which is explained in the book with the help of figures and descriptions of the theory behind the software. In addition, the book includes numerical examples to guide readers on the working of existing popular software. The use of mathematics is reduced and simplified for non-experts using established methods, which also helps in avoiding mistakes in application and interpretation. Finally, the book enables the reader to understand and conceptualize the overall flow of brain imaging data analysis, particularly for statisticians and data-scientists unfamiliar with this area. The state of the art method described in the book has a multivariate approach developed by the authors’ team. Since brain imaging data, generally, has a highly correlated and complex structure with large amounts of data, categorized into big data, the multivariate approach can be used as dimension reduction by following the application of statistical methods. The R package for most of the methods described is provided in the book. Understanding the background theory is helpful in implementing the software for original and creative applications and for an unbiased interpretation of the output. The book also explains new methods in a conceptual manner. These methodologies and packages are commonly applied in life science data analysis. Advanced methods to obtain novel insights are introduced, thereby encouraging the development of new methods and applications for research into medicine as a neuroscience.

Download or read book Statistical Parametric Mapping The Analysis of Functional Brain Images written by William D. Penny and published by Elsevier. This book was released on 2011-04-28 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt: In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically, Statistical Parametric Mapping provides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis. - An essential reference and companion for users of the SPM software - Provides a complete description of the concepts and procedures entailed by the analysis of brain images - Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data - Stands as a compendium of all the advances in neuroimaging data analysis over the past decade - Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes - Structured treatment of data analysis issues that links different modalities and models - Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible

Download or read book Exploratory Analysis and Data Modeling in Functional Neuroimaging written by Friedrich T. Sommer and published by MIT Press. This book was released on 2003 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of theoretical and computational approaches to neuroimaging.

Download or read book Statistical Methods for High dimensional Data with Complex Correlation Structure Applied to the Brain Dynamic Functional Connectivity Study written by Maria Aleksandra Kudela and published by . This book was released on 2017 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: A popular non-invasive brain activity measurement method is based on the functional magnetic resonance imaging (fMRI). Such data are frequently used to study functional connectivity (FC) defined as statistical association among two or more anatomically distinct fMRI signals (Friston, 1994). FC has emerged in recent years as a valuable tool for providing a deeper understanding of neurodegenerative diseases and neuropsychiatric disorders, such as Alzheimer's disease and autism. Information about complex association structure in high-dimensional fMRI data is often discarded by a calculating an average across complex spatiotemporal processes without providing an uncertainty measure around it. First, we propose a non-parametric approach to estimate the uncertainty of dynamic FC (dFC) estimates. Our method is based on three components: an extension of a boot strapping method for multivariate time series, recently introduced by Jentsch and Politis (2015); sliding window correlation estimation; and kernel smoothing. Second, we propose a two-step approach to analyze and summarize dFC estimates from a task-based fMRI study of social-to-heavy alcohol drinkers during stimulation with avors. In the first step, we apply our method from the first paper to estimate dFC for each region subject combination. In the second step, we use semiparametric additive mixed models to account for complex correlation structure and model dFC on a population level following the study's experimental design. Third, we propose to utilize the estimated dFC to study the system's modularity defined as the mutually exclusive division of brain regions into blocks with intra-connectivity greater than the one obtained by chance. As a result, we obtain brain partition suggesting the existence of common functionally-based brain organization. The main contribution of our work stems from the combination of the methods from the fields of statistics, machine learning and network theory to provide statistical tools for studying brain connectivity from a holistic, multi-disciplinary perspective.

Download or read book Statistical Methods in Epilepsy written by Sharon Chiang and published by CRC Press. This book was released on 2024-03-25 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: Epilepsy research promises new treatments and insights into brain function, but statistics and machine learning are paramount for extracting meaning from data and enabling discovery. Statistical Methods in Epilepsy provides a comprehensive introduction to statistical methods used in epilepsy research. Written in a clear, accessible style by leading authorities, this textbook demystifies introductory and advanced statistical methods, providing a practical roadmap that will be invaluable for learners and experts alike. Topics include a primer on version control and coding, pre-processing of imaging and electrophysiological data, hypothesis testing, generalized linear models, survival analysis, network analysis, time-series analysis, spectral analysis, spatial statistics, unsupervised and supervised learning, natural language processing, prospective trial design, pharmacokinetic and pharmacodynamic modeling, and randomized clinical trials. Features: Provides a comprehensive introduction to statistical methods employed in epilepsy research Divided into four parts: Basic Processing Methods for Data Analysis; Statistical Models for Epilepsy Data Types; Machine Learning Methods; and Clinical Studies Covers methodological and practical aspects, as well as worked-out examples with R and Python code provided in the online supplement Includes contributions by experts in the field https://github.com/sharon-chiang/Statistics-Epilepsy-Book/ The handbook targets clinicians, graduate students, medical students, and researchers who seek to conduct quantitative epilepsy research. The topics covered extend broadly to quantitative research in other neurological specialties and provide a valuable reference for the field of neurology.

Download or read book Optimizing Statistical Methods for Connectivity Mapping in MR Neuroimaging written by Anita Meghan Sinha and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Magnetic resonance imaging (MRI) plays an integral role in the study, diagnosis and treatment of neurological diseases. Neuroimaging analyses involve high-dimensional, large-scale data that contain rich spatial and temporal information about the dynamic and integrated systems in the brain. Therefore, it has become imperative to develop and optimize analytical approaches drawn from engineering and mathematics to more precisely model these complex patterns and interactions, which will advance our understanding of functional brain organization in health and disease. Chapter 1 provides an overview and background of MRI, with a particular focus on the use of resting-state functional magnetic resonance imaging (rs-fMRI) to capture and characterize brain connectivity. Previous work of statistical methods developed for fMRI analysis are reviewed. Chapter 2 presents an analysis of changes in functional connectivity and behavioral outcomes in patients of stroke who undergo brain-computer interface (BCI) interventional therapy. This work employs a widely used network-based inference method for fMRI analysis that serves as motivation for subsequent work to overcome statistical challenges associated with its use to more effectively model and characterize brain network dynamics and organization in a robust manner. Chapter 3 presents a novel application of differential covariance trajectory analysis as promising framework for brain network modeling using rs-fMRI data. The proposed algorithm models functional connectivity as trajectories on the manifold and employs a localization procedure to search over and identify subsets of first- and second-order differences in brain connectivity features between patients with Temporal Lobe Epilepsy (TLE) and healthy control subjects. Chapter 4 extends the work presented in the previous chapter to apply the combined differential covariance trajectory and scan statistics framework to characterize the Alzheimer's Disease connectome. We demonstrate the utility and robustness of this method to study altered brain network organization in large-scale functional networks in a different and older clinical population, which is notably of smaller sample size, where the statistical signal may be weak. Chapter 5 discusses conclusions and key takeaways of the work, along with potential future avenues of research.

Download or read book Handbook of Neuroimaging Data Analysis written by Hernando Ombao and published by CRC Press. This book was released on 2016-11-18 with total page 907 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores various state-of-the-art aspects behind the statistical analysis of neuroimaging data. It examines the development of novel statistical approaches to model brain data. Designed for researchers in statistics, biostatistics, computer science, cognitive science, computer engineering, biomedical engineering, applied mathematics, physics, and radiology, the book can also be used as a textbook for graduate-level courses in statistics and biostatistics or as a self-study reference for Ph.D. students in statistics, biostatistics, psychology, neuroscience, and computer science.

Download or read book Third Generation Neuroimaging Translating Research into Clinical Utility written by André Schmidt and published by Frontiers Media SA. This book was released on 2016-11-02 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Psychiatric imaging needs to move away from simple investigations of the neurobiology underling the early phases of psychiatric diseases to translate imaging findings in the clinical field targeting clinical outcomes including transition, remission and response to preventative interventions. This research topic aims to bring psychiatric neuroimaging studies towards translational impacts in clinical practice, suggesting that brain abnormalities may be of potential use for detecting clinical outcomes as treatment response. First-generation psychiatric neuroimaging focused on simple structural brain alterations associated with the neurobiology of the illness. These early studies adopted imaging methods mainly including computerized tomography (CT) to investigate brain size. Second-generation psychiatric neuroimaging studies benefited from more sophisticated techniques which included structural methods (sMRI) coupled with whole-brain automated methods (voxel based morphometry, VBM), white-matter methods (diffusion tensor imaging, DTI and tractography), functional methods (functional magnetic resonance imaging, fMRI) and advanced neurochemical imaging (PET techniques addressing receptor bindings and pre/post synaptic functions, magnetic resonance spectroscopy, MRS) and sophisticated meta-analytical imaging methods. However, no consistent or reliable anatomical or functional brain alterations have been univocally associated with any psychiatric disorder and no clinical applications have been developed in psychiatric neuroimaging. There is thus urgent need of psychiatric imaging to move towards third-generation paradigms. In this research topic, these novel neuroimaging studies here requested to move away from simple investigations of the neurobiology to translate imaging findings in the clinical field targeting longitudinal outcomes including transition, remission and response to preventative interventions. With respect to methods, the most recent neuroimaging approaches (e.g. structural and functional MRI, EEG, DTI, spectroscopy, PET) are welcome. Third generation psychiatric imaging studies including multimodal approaches, multi-center analyses, mega-analyses, effective connectivity, dynamic causal modelling, support vector machines, structural equation modelling, or graph theory analysis are highly appreciated. Furthermore, these third-generation imaging studies may benefit from the incorporation of new sources of neurobiological information such as whole genome sequencing, proteomic, lipidomic and expression profiles and cellular models derived from recent induced pluripotent stem cells research. We collect Original Research, Reviews, Mini-Reviews, Book Review, Clinical Case Study, Clinical Trial, Editorial, General Commentary, Hypothesis & Theory, Methods, Mini Opinion, Perspective, and Technology Report from international researcher and clinicians in this field. The purpose of this research topic is intended to provide the field with current third-generation neuroimaging approaches in translational psychiatry that is hoped to improve and create therapeutic options for psychiatric diseases.

Download or read book Studies in Neural Data Science written by Antonio Canale and published by Springer. This book was released on 2018-12-28 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents a collection of peer-reviewed contributions arising from StartUp Research: a stimulating research experience in which twenty-eight early-career researchers collaborated with seven senior international professors in order to develop novel statistical methods for complex brain imaging data. During this meeting, which was held on June 25–27, 2017 in Siena (Italy), the research groups focused on recent multimodality imaging datasets measuring brain function and structure, and proposed a wide variety of methods for network analysis, spatial inference, graphical modeling, multiple testing, dynamic inference, data fusion, tensor factorization, object-oriented analysis and others. The results of their studies are gathered here, along with a final contribution by Michele Guindani and Marina Vannucci that opens new research directions in this field. The book offers a valuable resource for all researchers in Data Science and Neuroscience who are interested in the promising intersections of these two fundamental disciplines.

Download or read book Information based methods for neuroimaging analyzing structure function and dynamics written by Jesus M. Cortés and published by Frontiers Media SA. This book was released on 2015-05-07 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this Research Topic is to discuss the state of the art on the use of Information-based methods in the analysis of neuroimaging data. Information-based methods, typically built as extensions of the Shannon Entropy, are at the basis of model-free approaches which, being based on probability distributions rather than on specific expectations, can account for all possible non-linearities present in the data in a model-independent fashion. Mutual Information-like methods can also be applied on interacting dynamical variables described by time-series, thus addressing the uncertainty reduction (or information) in one variable by conditioning on another set of variables. In the last years, different Information-based methods have been shown to be flexible and powerful tools to analyze neuroimaging data, with a wide range of different methodologies, including formulations-based on bivariate vs multivariate representations, frequency vs time domains, etc. Apart from methodological issues, the information bit as a common unit represents a convenient way to open the road for comparison and integration between different measurements of neuroimaging data in three complementary contexts: Structural Connectivity, Dynamical (Functional and Effective) Connectivity, and Modelling of brain activity. Applications are ubiquitous, starting from resting state in healthy subjects to modulations of consciousness and other aspects of pathophysiology. Mutual Information-based methods have provided new insights about common-principles in brain organization, showing the existence of an active default network when the brain is at rest. It is not clear, however, how this default network is generated, the different modules are intra-interacting, or disappearing in the presence of stimulation. Some of these open-questions at the functional level might find their mechanisms on their structural correlates. A key question is the link between structure and function and the use of structural priors for the understanding of the functional connectivity measures. As effective connectivity is concerned, recently a common framework has been proposed for Transfer Entropy and Granger Causality, a well-established methodology originally based on autoregressive models. This framework can open the way to new theories and applications. This Research Topic brings together contributions from researchers from different backgrounds which are either developing new approaches, or applying existing methodologies to new data, and we hope it will set the basis for discussing the development and validation of new Information-based methodologies for the understanding of brain structure, function, and dynamics.