An event based topic learning pipeline for neuroimaging literature mining

Neuroimaging text mining extracts knowledge from neuroimaging texts and has received widespread attention. Topic learning is an important research focus of neuroimaging text mining. However, current neuroimaging topic learning researches mainly used traditional probability topic models to extract topics from literature and cannot obtain high-quality neuroimaging topics. The existing topic learning methods also cannot meet the requirements of topic learning oriented to full-text neuroimaging literature. In this paper, three types of neuroimaging research topic events are defined to describe the process and result of neuroimaging researches. An event based topic learning pipeline, called neuroimaging Event-BTM, is proposed to realize topic learning from full-text neuroimaging literature. The experimental results on the PLoS One data set show that the accuracy and completeness of the proposed method are significantly better than the existing main topic learning methods

Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data

Abstract Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be ‘team science’.http://deepblue.lib.umich.edu/bitstream/2027.42/134522/1/13742_2016_Article_117.pd

Theory and Practice of Data Citation

Citations are the cornerstone of knowledge propagation and the primary means of assessing the quality of research, as well as directing investments in science. Science is increasingly becoming "data-intensive", where large volumes of data are collected and analyzed to discover complex patterns through simulations and experiments, and most scientific reference works have been replaced by online curated datasets. Yet, given a dataset, there is no quantitative, consistent and established way of knowing how it has been used over time, who contributed to its curation, what results have been yielded or what value it has. The development of a theory and practice of data citation is fundamental for considering data as first-class research objects with the same relevance and centrality of traditional scientific products. Many works in recent years have discussed data citation from different viewpoints: illustrating why data citation is needed, defining the principles and outlining recommendations for data citation systems, and providing computational methods for addressing specific issues of data citation. The current panorama is many-faceted and an overall view that brings together diverse aspects of this topic is still missing. Therefore, this paper aims to describe the lay of the land for data citation, both from the theoretical (the why and what) and the practical (the how) angle.Comment: 24 pages, 2 tables, pre-print accepted in Journal of the Association for Information Science and Technology (JASIST), 201

Measuring Human-Robot Interaction Through Motor Resonance

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Trajectories from Mild Cognitive Impairment to Alzheimer’s Disease: A machine learning approach in the context of Precision Medicine

Mild Kognitv Svikt (MKS) er en diagnostisk kategori som beskriver en heterogen gruppe pasienter. For noen representerer MKS et tidlig tegn på en nevrodegenerativ sykdom, mens andre forbli stabile eller forbedrer seg over tid. Tidlig identifisering av nevrodegenerasjon er svært viktig for å kunne påbegynne behandling før sykdommen allerede har forårsaket store skader i hjernen. Dette motiverte den aktuelle studien, der longitudinelle data fra Alzheimer’s Disease Neuroimaging Initiative (ADNI) benyttes for å undersøke to grupper av pasienter som ved baseline viste MKS av den amnestiske typen (aMKS): en gruppe som forble stabile over tid (sMKS) og en gruppe som etterhvert fikk diagnosen Alzheimer’s sykdom (cMKS). Det ble valgt ut variabler som gjerne inngår i en klinisk undersøkelse av pasienter med aMKS. Disse omfatter mål på hukommelses- og eksekutiv funksjon, depresive symptomer, intellektuell funksjon, hippocampusvolum og genotype (ApoE). Resultatene viste bedre resultater på tester av hukommelse og eksekutiv funksjon, større hippocampusvolum, og færre individer med ApoE-ε4 i sMKS enn cMKS gruppen. Vi undersøkte deretter hvor godt et utviklingsforløp mot AD kunne predikeres basert på de utvalgte variablene ved å benytte en Random Forest (RF) modell. Evaluering av modellens nøyaktighet i et testset viste en nøyaktighet på 68.3%. Beregninger av de ulike variablenes betydning for klassifikasjonen viste at den var sterkest for mål på hukommelse, hippocampusvolum og eksekutiv funksjon. Partial dependency plots viste terskelverdier som øker sannsynligheten for å klassifiseres i cMKS gruppen. Resultatene diskuteres fra et klinisk, teoretisk og analytisk perspektiv, med vekt på studiens relevans for en fremtidsrettet presisjonsmedisin.Masteroppgave i psykologiMAPSYK360MAPS-PSYKINTL-HFINTL-MEDINTL-JUSINTL-SVINTL-MNINTL-KMDINTL-PSY

Social and Affective Neuroscience of Everyday Human Interaction

This Open Access book presents the current state of the art knowledge on social and affective neuroscience based on empirical findings. This volume is divided into several sections first guiding the reader through important theoretical topics within affective neuroscience, social neuroscience and moral emotions, and clinical neuroscience. Each chapter addresses everyday social interactions and various aspects of social interactions from a different angle taking the reader on a diverse journey. The last section of the book is of methodological nature. Basic information is presented for the reader to learn about common methodologies used in neuroscience alongside advanced input to deepen the understanding and usability of these methods in social and affective neuroscience for more experienced readers

Conceptualization of Computational Modeling Approaches and Interpretation of the Role of Neuroimaging Indices in Pathomechanisms for Pre-Clinical Detection of Alzheimer Disease

With swift advancements in next-generation sequencing technologies alongside the voluminous growth of biological data, a diversity of various data resources such as databases and web services have been created to facilitate data management, accessibility, and analysis. However, the burden of interoperability between dynamically growing data resources is an increasingly rate-limiting step in biomedicine, specifically concerning neurodegeneration. Over the years, massive investments and technological advancements for dementia research have resulted in large proportions of unmined data. Accordingly, there is an essential need for intelligent as well as integrative approaches to mine available data and substantiate novel research outcomes. Semantic frameworks provide a unique possibility to integrate multiple heterogeneous, high-resolution data resources with semantic integrity using standardized ontologies and vocabularies for context- specific domains. In this current work, (i) the functionality of a semantically structured terminology for mining pathway relevant knowledge from the literature, called Pathway Terminology System, is demonstrated and (ii) a context-specific high granularity semantic framework for neurodegenerative diseases, known as NeuroRDF, is presented. Neurodegenerative disorders are especially complex as they are characterized by widespread manifestations and the potential for dramatic alterations in disease progression over time. Early detection and prediction strategies through clinical pointers can provide promising solutions for effective treatment of AD. In the current work, we have presented the importance of bridging the gap between clinical and molecular biomarkers to effectively contribute to dementia research. Moreover, we address the need for a formalized framework called NIFT to automatically mine relevant clinical knowledge from the literature for substantiating high-resolution cause-and-effect models

NEArBy : normalização lexical na pesquisa de imagens cerebrais com atlas

Mestrado em Engenharia de Computadores e TelemáticaBrain atlases have been used as spatial references to classify and tag either structural or functional topological information from brain images. Semantic information obtained from the existing image data is thus spatially mapped according the atlas descriptors. However the process of classifying and tagging brain images using an atlas is often tedious and mostly dependent on human observation and validation. At the same time, even when available, it is often difficult to use, particularly when using standard query and retrieve services in modern imaging repositories (e.g. DICOM based PACS). In this work we propose NEArBy, a cloud based solution that provides query and retrieve services based on brain atlas semantics that can be easily integrated in existing DICOM based imaging repositories. Using a web interface, NEArBy supports not only typical DICOM query retrieve searches but also query tokens matching the brain atlas dictionary. To automate the semantic tagging of the brain images we rely on external methods to identify relevant spatial features that are later labelled using standard brain atlas. Being DICOM a tag based standard, atlas related tags are then privately embedded into DICOM files as NEArBy JSON descriptors using lexicon as proposed in NeuroLex. These descriptors encode the mapping between feature type, spatial location in the atlas and the respective atlas tag. JSON encoded tags are also suitable for indexing by a medical imaging Q/R tool such as Dicoogle allowing queries based both on standard DICOM tags and specifically on atlas related tokens included by NEArBy middleware. NEArBy provides a new way to perform non- patient centric queries over neuro-imaging repositories using technical and atlas based topological information. During this dissertation, the NEArBy potential usage is illustrated over a set of functional magnetic resonance imaging (fMRI) datasets using the web user interface to formulate the queries with atlas related criteria and access the retrieved results.Brain atlases have been used as spatial references to classify and tag either structural or functional topological information from brain images. Semantic information obtained from the existing image data is thus spatially mapped according the atlas descriptors. However the process of classifying and tagging brain images using an atlas is often tedious and mostly dependent on human observation and validation. At the same time, even when available, it is often difficult to use, particularly when using standard query and retrieve services in modern imaging repositories (e.g. DICOM based PACS). In this work we propose NEArBy, a cloud based solution that provides query and retrieve services based on brain atlas semantics that can be easily integrated in existing DICOM based imaging repositories. Using a web interface, NEArBy supports not only typical DICOM query retrieve searches but also query tokens matching the brain atlas dictionary. To automate the semantic tagging of the brain images we rely on external methods to identify relevant spatial features that are later labelled using standard brain atlas. Being DICOM a tag based standard, atlas related tags are then privately embedded into DICOM files as NEArBy JSON descriptors using lexicon as proposed in NeuroLex. These descriptors encode the mapping between feature type, spatial location in the atlas and the respective atlas tag. JSON encoded tags are also suitable for indexing by a medical imaging Q/R tool such as Dicoogle allowing queries based both on standard DICOM tags and specifically on atlas related tokens included by NEArBy middleware. NEArBy provides a new way to perform non- patient centric queries over neuro-imaging repositories using technical and atlas based topological information. During this dissertation, the NEArBy potential usage is illustrated over a set of functional magnetic resonance imaging (fMRI) datasets using the web user interface to formulate the queries with atlas related criteria and access the retrieved results