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Graph Based Data Mining In Neuroimaging Of Neurological Diseases
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Book Synopsis Graph-Based Data Mining in Neuroimaging of Neurological Diseases by : Chenhui Hu
Download or read book Graph-Based Data Mining in Neuroimaging of Neurological Diseases written by Chenhui Hu and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Finally, we present a network diffusion model with sources to localize the origins of AD. By imposing a sparsity constraint on the number of sources, we solve the inverse problem efficiently. In addition, we precisely predict the changes of brain atrophy patterns through this model.
Book Synopsis Neuroimaging Workflow Design and Data-Mining by : John Van Horn
Download or read book Neuroimaging Workflow Design and Data-Mining written by John Van Horn and published by Frontiers Media SA. This book was released on 2012-04-01 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the increasing number of neuroimaging studies appearing yearly in the literature, the need to consider the synthesis of the underlying data into new knowledge and research directions has never been more important. The development of large-scale databases and grid-enabled computing has laid the groundwork for mining these rich datasets beyond the scope of their initial collection. Additionally, meta-analyses of the summary results contained in published research articles have provided a powerful way to explore hidden trends in the neuroscience literature. In each case, the processing of data requires a careful consideration of the individual processing steps involved and how they can be assembled into reliable workflows. In results from published studies, the manner in which data were processed may influence meta-analytic results which can have implications on clinical interpretation. Several efforts now exist that provide tools for use in the construction of data processing workflows. However, careful thought must be given to ensuring appropriate, efficient, optimal, and replicable processing. The results obtained from data-mining and meta-analysis must tell a story about a collection of existing data. Also they must suggest novel and testable hypotheses for further investigation with implications for understanding of the brain in health and disease. Where they do, these new results and interpretations often provide fresh insights into the data that extend beyond the rationale for their original collection. In this volume, we have asked leaders in the field of neuroimaging data mining and meta-analysis to provide their thoughts on methods for efficient workflow design, interoperability with large-scale databases, and to discuss their work in exploring the richness of brain imaging data as well as the literature of published research results.
Book Synopsis Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics by : M. Jorge Cardoso
Download or read book Graphs in Biomedical Image Analysis, Computational Anatomy and Imaging Genetics written by M. Jorge Cardoso and published by Springer. This book was released on 2017-09-06 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed joint proceedings of the First International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2017, the 6th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017, and the Third International Workshop on Imaging Genetics, MICGen 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 7 full papers presented at GRAIL 2017, the 10 full papers presented at MFCA 2017, and the 5 full papers presented at MICGen 2017 were carefully reviewed and selected. The GRAIL papers cover a wide range of graph based medical image analysis methods and applications, including probabilistic graphical models, neuroimaging using graph representations, machine learning for diagnosis prediction, and shape modeling. The MFCA papers deal with theoretical developments in non-linear image and surface registration in the context of computational anatomy. The MICGen papers cover topics in the field of medical genetics, computational biology and medical imaging.
Book Synopsis Machine Learning and Interpretation in Neuroimaging by : Georg Langs
Download or read book Machine Learning and Interpretation in Neuroimaging written by Georg Langs and published by Springer. This book was released on 2012-11-11 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brain imaging brings together the technology, methodology, research questions and approaches of a wide range of scientific fields including physics, statistics, computer science, neuroscience, biology, and engineering. Thus, methodological and technological advances that enable us to obtain measurements, examine relationships across observations, and link these data to neuroscientific hypotheses happen in a highly interdisciplinary environment. The dynamic field of machine learning with its modern approach to data mining provides many relevant approaches for neuroscience and enables the exploration of open questions. This state-of-the-art survey offers a collection of papers from the Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2011, held at the 25th Annual Conference on Neural Information Processing, NIPS 2011, in the Sierra Nevada, Spain, in December 2011. Additionally, invited speakers agreed to contribute reviews on various aspects of the field, adding breadth and perspective to the volume. The 32 revised papers were carefully selected from 48 submissions. At the interface between machine learning and neuroimaging the papers aim at shedding some light on the state of the art in this interdisciplinary field. They are organized in topical sections on coding and decoding, neuroscience, dynamcis, connectivity, and probabilistic models and machine learning.
Book Synopsis Identifying Neuroimaging-Based Markers for Distinguishing Brain Disorders by : Yuhui Du
Download or read book Identifying Neuroimaging-Based Markers for Distinguishing Brain Disorders written by Yuhui Du and published by Frontiers Media SA. This book was released on 2020-07-10 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: There has been increasing interests in exploring biomarkers from brain images, aiming to have a better understanding and a more effective diagnosis of brain disorders such as schizophrenia, bipolar disorder, schizoaffective disorder, autism spectrum disorder, attention-deficit/hyperactivity disorder, Alzheimer’s disease and so on. Therefore, it is important to identify disease-specific changes for distinguishing healthy controls and patients with brain disorders as well as for differentiating patients with different disorders showing similar clinical symptoms. Biomarkers can be identified from different types of brain Imaging techniques including functional magnetic resonance imaging (fMRI), structural MRI, positron emission tomography (PET), electroencephalography (EEG), and magnetoencephalography (MEG) by using statistical analysis methods. Furthermore, based on measures from brain imaging techniques, machine learning techniques can help to classify or predict disease for individual subjects. In fact, fusion of features from multiple modalities may benefit the understanding of disease mechanism and improve the classification performance. This Research Topic further explores the functional or structural alterations in brain disorders.
Book Synopsis Data Science for Neuroimaging by : Ariel Rokem
Download or read book Data Science for Neuroimaging written by Ariel Rokem and published by Princeton University Press. This book was released on 2023-12-12 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science methods and tools—including programming, data management, visualization, and machine learning—and their application to neuroimaging research As neuroimaging turns toward data-intensive discovery, researchers in the field must learn to access, manage, and analyze datasets at unprecedented scales. Concerns about reproducibility and increased rigor in reporting of scientific results also demand higher standards of computational practice. This book offers neuroimaging researchers an introduction to data science, presenting methods, tools, and approaches that facilitate automated, reproducible, and scalable analysis and understanding of data. Through guided, hands-on explorations of openly available neuroimaging datasets, the book explains such elements of data science as programming, data management, visualization, and machine learning, and describes their application to neuroimaging. Readers will come away with broadly relevant data science skills that they can easily translate to their own questions. • Fills the need for an authoritative resource on data science for neuroimaging researchers • Strong emphasis on programming • Provides extensive code examples written in the Python programming language • Draws on openly available neuroimaging datasets for examples • Written entirely in the Jupyter notebook format, so the code examples can be executed, modified, and re-executed as part of the learning process
Book Synopsis Connectomics in NeuroImaging by : Markus D. Schirmer
Download or read book Connectomics in NeuroImaging written by Markus D. Schirmer and published by Springer Nature. This book was released on 2019-10-10 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Workshop on Connectomics in NeuroImaging, CNI 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 13 full papers presented were carefully reviewed and selected from 14 submissions. The papers deal with new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies as well as in various neuroimaging applications.
Book Synopsis Individual and Collective Graph Mining by : Danai Koutra
Download or read book Individual and Collective Graph Mining written by Danai Koutra and published by Springer Nature. This book was released on 2022-06-01 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graphs naturally represent information ranging from links between web pages, to communication in email networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect anomalies that indicate critical events, such as an attack on a computer system, disease formation in the human brain, or the fall of a company? This book presents scalable, principled discovery algorithms that combine globality with locality to make sense of one or more graphs. In addition to fast algorithmic methodologies, we also contribute graph-theoretical ideas and models, and real-world applications in two main areas: Individual Graph Mining: We show how to interpretably summarize a single graph by identifying its important graph structures. We complement summarization with inference, which leverages information about few entities (obtained via summarization or other methods) and the network structure to efficiently and effectively learn information about the unknown entities. Collective Graph Mining: We extend the idea of individual-graph summarization to time-evolving graphs, and show how to scalably discover temporal patterns. Apart from summarization, we claim that graph similarity is often the underlying problem in a host of applications where multiple graphs occur (e.g., temporal anomaly detection, discovery of behavioral patterns), and we present principled, scalable algorithms for aligning networks and measuring their similarity. The methods that we present in this book leverage techniques from diverse areas, such as matrix algebra, graph theory, optimization, information theory, machine learning, finance, and social science, to solve real-world problems. We present applications of our exploration algorithms to massive datasets, including a Web graph of 6.6 billion edges, a Twitter graph of 1.8 billion edges, brain graphs with up to 90 million edges, collaboration, peer-to-peer networks, browser logs, all spanning millions of users and interactions.
Book Synopsis Graph Learning for Brain Imaging by : Feng Liu
Download or read book Graph Learning for Brain Imaging written by Feng Liu and published by Frontiers Media SA. This book was released on 2022-09-30 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis 2018 2nd European Conference on Electrical Engineering and Computer Science (EECS) by : IEEE Staff
Download or read book 2018 2nd European Conference on Electrical Engineering and Computer Science (EECS) written by IEEE Staff and published by . This book was released on 2018-12-20 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: EECS The 2018 European Conference on Electrical Engineering and Computer Science (EECS) will be held in Bern, Switzerland during December 20 22, 2018 and will be composed by the following Symposia Control & Systems, Circuits & Systems, Power, Power Electronics, Signal Processing, Math&Comp Biology, Biomed Engineering, Computers and Computing, Communications, Neural Networks, Fuzzy Systems, Evolutionary Comput EECS aims to be the leading international conference for presenting novel and fundamental advancements in the fields of Electrical Engineering and Computer Science EECS features invited keynotes as well as peer reviewed paper presentations The conference is completely open (one needs to register first), you will not have to be an author or a discussant to attend Submissions will be peer reviewed and evaluated based on originality, relevance to conference, contributions, and presentation
Book Synopsis Computational Methods for Neuroscience Discovery with Neuroimaging Data by : Yikang Liu
Download or read book Computational Methods for Neuroscience Discovery with Neuroimaging Data written by Yikang Liu and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Resting-state functional magnetic resonance imaging (rsfMRI) is a non-invasive method to study brain function and organization. The last decades have seen a dramatic growth of human rsfMRI studies, public datasets, and data analysis software, which advanced our understanding of neuroscience and brain diseases. However, studies and resources of rodent rsfMRI are still lacking, despite its essential role in translational research. In this dissertation, we first present an open database of rsfMRI data collected from 90 awake rats with a well-established awake imaging paradigm that avoids anesthesia interference, together with a preprocessing pipeline optimized for rat data. Based on this dataset, we propose two methods termed SHERM and fastClean towards automated preprocessing of rodent rsfMRI data. First, SHERM targets rodent brain extraction, which is an essential step to aid with rsfMRI image registration. Current methods usually require manual adjustment of input parameters due to widely different image qualities and/or contrasts. SHERM, however, only requires a brain template mask as the input and is shown to automatically and reliably extract the brain tissue in both rat and mouse MRI images. fastClean is an unsupervised deep learning method that removes rsfMRI artifacts induced by the scanner, head motion, and non-neural physiological noise. Existing machine learning methods either perform unsatisfactorily in low-dimensional rodent data or suffer from long online training. With an efficient network architecture and meta-learning techniques, fastClean generates equivalently clean or cleaner data in minutes on both rodent and human datasets. Finally, we systematically investigated the spatiotemporal dynamics of spontaneous brain activity in the awake rat brain with a graph-based data mining method. We found that brain activity traverse among multiple resting-state functional connectivity patterns with nonrandom and reproducible sequential orders and time delays, revealed a network structure of these transition paths, and showed prominent brain regions involved and their temporal evolutions during the propagation of spontaneous brain activity. Taken together, this dissertation presents multiple computational methods for rsfMRI studies, demonstrates their contribution to automatic data preprocessing, data cleaning, and spatiotemporal neural pattern discovery, and advances our understanding of network organization and dynamics of the awake rat brain.
Book Synopsis Fundamentals of Brain Network Analysis by : Alex Fornito
Download or read book Fundamentals of Brain Network Analysis written by Alex Fornito and published by Academic Press. This book was released on 2016-03-04 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. Winner of the 2017 PROSE Award in Biomedicine & Neuroscience and the 2017 British Medical Association (BMA) Award in Neurology Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain
Book Synopsis Computational and Network Modeling of Neuroimaging Data by : Kendrick Kay
Download or read book Computational and Network Modeling of Neuroimaging Data written by Kendrick Kay and published by Elsevier. This book was released on 2024-06-17 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neuroimaging is witnessing a massive increase in the quality and quantity of data being acquired. It is widely recognized that effective interpretation and extraction of information from such data requires quantitative modeling. However, modeling comes in many diverse forms, with different research communities tackling different brain systems, different spatial and temporal scales, and different aspects of brain structure and function. Computational and Network Modeling of Neuroimaging Data provides an authoritative and comprehensive overview of the many diverse modeling approaches that have been fruitfully applied to neuroimaging data. This book gives an accessible foundation to the field of computational and network modeling of neuroimaging data and is suitable for graduate students, academic researchers, and industry practitioners who are interested in adopting or applying model-based approaches in neuroimaging. Provides an authoritative and comprehensive overview of major modeling approaches to neuroimaging data Written by experts, the book's chapters use a common structure to introduce, motivate, and describe a specific modeling approach used in neuroimaging Gives insights into the similarities and differences across different modeling approaches Analyses details of outstanding research challenges in the field
Book Synopsis Development and Evaluation of Optimization Based Data Mining Techniques Analysis of Brain Data by : Mahdi Zarei
Download or read book Development and Evaluation of Optimization Based Data Mining Techniques Analysis of Brain Data written by Mahdi Zarei and published by . This book was released on 2015 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Neuroscience is an interdisciplinary science which deals with the study of structure and function of the brain and nervous system. Neuroscience encompasses disciplines such as computer science, mathematics, engineering, and linguistics. The structure of the healthy brain and representation of information by neural activity are among most challenging problems in neuroscience. Neuroscience is experiencing exponentially growing volumes of data obtained by using different technologies. The investigation of such data has tremendous impact on developing new and improving existing models of both healthy and diseased brains. Various techniques have been used for collecting brain data sets for addressing neuroscience problems. These data sets can be categorized into two main groups: resting-state and state-dependent data sets. Resting-state data is based on recording the brain activity when a subject does not think about any specific concept while state-dependent data is based on recording brain activity related to specific tasks. In general, brain data sets contain a large number of features (e.g. tens of thousands) and significantly fewer samples (e.g. several hundred). Such data sets are sparse and noisy. In addition to these problems, brain data sets have a few number of subjects. Brains are very complex systems and data about any brain activity reflects very complex relationship between neurons as well as different parts of the brain. Such relationships are highly nonlinear and general purpose data mining algorithms are not always efficient for their study. The development of machine learning techniques for brain data sets is an emerging research area in neuroscience. Over the last decade, various machine learning techniques have been developed for application to brain data sets. In the meantime, some well-known algorithms such as feature selection and supervised classification have been modified for analysis of brain data sets. Support vector machines, logistic regression, and Gaussian Naive Bayes classifiers are widely used for application to brain data sets. However, Support vector machines and logistic regression algorithms are not efficient for sparse and noisy data sets and Gaussian Naive Bayes classifiers do not give high accuracy. The aim of this study is to develop new and modify the existing data mining algorithms for the analysis brain data sets. Our contribution in this thesis can be listed as follow: 1. Development of new algorithms: 1.1. Development of new voxel (feature) selection algorithms for Functional magnetic resonance imaging (fMRI) data sets, and evaluation of these algorithms on the Haxby and Science 2008 data sets. 1.2. Development of new feature selection algorithm based on the catastrophe model for regression analysis problems. 2. Development and evaluation of different versions of the adaptive neuro-fuzzy model for the analysis of the spike-discharge as a function of other neuronal parameters. 3. Development and evaluation of the modified global k-means clustering algorithm for investigation of the structure of the healthy brain. 4. Development and evaluation of region of interest (ROI) method for analysis of brain functional connectivity in healthy subjects and schizophrenia patients." - Abstract.
Book Synopsis Database Systems for Advanced Applications by : Xin Wang
Download or read book Database Systems for Advanced Applications written by Xin Wang and published by Springer Nature. This book was released on 2023-04-14 with total page 779 pages. Available in PDF, EPUB and Kindle. Book excerpt: The four-volume set LNCS 13943, 13944, 13945 and 13946 constitutes the proceedings of the 28th International Conference on Database Systems for Advanced Applications, DASFAA 2023, held in April 2023 in Tianjin, China. The total of 125 full papers, along with 66 short papers, are presented together in this four-volume set was carefully reviewed and selected from 652 submissions. Additionally, 15 industrial papers, 15 demo papers and 4 PhD consortium papers are included. The conference presents papers on subjects such as model, graph, learning, performance, knowledge, time, recommendation, representation, attention, prediction, and network.
Book Synopsis Big Data in Psychiatry and Neurology by : Ahmed Moustafa
Download or read book Big Data in Psychiatry and Neurology written by Ahmed Moustafa and published by Academic Press. This book was released on 2021-06-11 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data in Psychiatry and Neurology provides an up-to-date overview of achievements in the field of big data in Psychiatry and Medicine, including applications of big data methods to aging disorders (e.g., Alzheimer’s disease and Parkinson’s disease), mood disorders (e.g., major depressive disorder), and drug addiction. This book will help researchers, students and clinicians implement new methods for collecting big datasets from various patient populations. Further, it will demonstrate how to use several algorithms and machine learning methods to analyze big datasets, thus providing individualized treatment for psychiatric and neurological patients. As big data analytics is gaining traction in psychiatric research, it is an essential component in providing predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level. Discusses longitudinal big data and risk factors surrounding the development of psychiatric disorders Analyzes methods in using big data to treat psychiatric and neurological disorders Describes the role machine learning can play in the analysis of big data Demonstrates the various methods of gathering big data in medicine Reviews how to apply big data to genetics
Book Synopsis Advanced Data Mining and Applications by : Gao Cong
Download or read book Advanced Data Mining and Applications written by Gao Cong and published by Springer. This book was released on 2017-10-30 with total page 879 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, held in Singapore in November 2017. The 20 full and 38 short papers presented in this volume were carefully reviewed and selected from 118 submissions. The papers were organized in topical sections named: database and distributed machine learning; recommender system; social network and social media; machine learning; classification and clustering methods; behavior modeling and user profiling; bioinformatics and medical data analysis; spatio-temporal data; natural language processing and text mining; data mining applications; applications; and demos.
