1,721,027 research outputs found
A Sparse Tensor Decomposition with Multi-Dictionary Learning Applied to Diffusion Brain Imaging
Full text linkWe use a multidimensional signal representation that integrates diffusion Magnetic Resonance Imaging (dMRI) and tractography (brain connections) using sparse tensor decomposition. The representation encodes brain connections (fibers) into a very-large, but sparse, core tensor and allows to predict dMRI measurements based on a dictionary of diffusion signals. We propose an algorithm to learn the constituent parts of the model from a dataset. The algorithm assumes a tractography model (support of core tensor) and iteratively minimizes the Frobenius norm of the error as a function of the dictionary atoms, the values of nonzero entries in the sparse core tensor and the fiber weights. We use a nonparametric dictionary learning (DL) approach to estimate signal atoms. Moreover, the algorithm is able to learn multiple dictionaries associated to different brain locations (voxels) allowing for mapping distinctive tissue types. We illustrate the algorithm through results obtained on a large in-vivo high-resolution dataset.Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina. Indiana University; Estados UnidosFil: Cichocki, Andrzej. Labsp. Riken; JapónFil: Pestilli, Franco. Indiana University; Estados UnidosSignal Processing with Adaptive Sparse Structured Representations workshopLisboaPortugalUniversity of Lisbo
Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays
Full text linkRecently, linear formulations and convex optimization methods have been proposed to predict diffusion-weighted Magnetic Resonance Imaging (dMRI) data given estimates of brain connections generated using tractography algorithms. The size of the linear models comprising such methods grows with both dMRI data and connectome resolution, and can become very large when applied to modern data. In this paper, we introduce a method to encode dMRI signals and large connectomes, i.e., those that range from hundreds of thousands to millions of fascicles (bundles of neuronal axons), by using a sparse tensor decomposition. We show that this tensor decomposition accurately approximates the Linear Fascicle Evaluation (LiFE) model, one of the recently developed linear models. We provide a theoretical analysis of the accuracy of the sparse decomposed model, LiFE_SD, and demonstrate that it can reduce the size of the model significantly. Also, we develop algorithms to implement the optimization solver using the tensor representation in an efficient way.Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina. Indiana University; Estados UnidosFil: Sporns, Olaf. Indiana University; Estados UnidosFil: Saykin, Andy. Indiana University; Estados UnidosFil: Pestilli, Franco. Indiana University; Estados Unidos31st Conference on Neural Information Processing SystemsLong BeachEstados UnidosNational Science Foundatio
COMPUTATIONAL SEGMENTATION OF WHITE MATTER ANATOMY: METHODS, INSIGHTS, AND STANDARDS
Full text linkThesis (Ph.D.) - Indiana University, Department of Psychological and Brain Sciences and the Program in Neuroscience, 2021The brain is fundamentally an information processing and behavioral control system. The key to achieving this role is the ability to move information about the brain in a fast, reliable, and organized fashion. The axons of neurons stand as the primary means of achieving this in the brain. However, as brains became larger and more gyrified the routes between grey matter structures became correspondingly longer and more complicated. This has required axons to form a complex network of bundles in order to maintain connectivity between distal regions, giving rise to the tissue known as “white matter”. Although the resultant architecture has been studied for hundreds of years, much is still unknown. This has hindered efforts to associate characteristics of the white matter with human behavior, development, and disorders. Here, we seek to ameliorate this. In this thesis, we present a three component body of work designed to help shed light on the white matter. The first component clarifies several white matter tracts which may facilitate a more complex system of information processing between the human dorsal (“what”) and ventral (“where”) visual streams. The second is a comprehensive review of our contemporary understanding of gross white matter architecture, featuring considerations of mutual insights from human and non-human primate studies, as well as apparent discrepancies between accounts. This work responds to recent calls for the formation of a consensus regarding the ontology and taxonomy of white matter. The final component responds to calls for more transparent and well-documented digital white matter segmentation methods, and is an interactive, online resource. It serves as both an educational resource and a transparent documentation of methodology. Ultimately, it is hoped that this body of work will support research in the field of white matter anatomy, across a broad range of approaches and endeavors
Anatomically-Informed Multiple Linear Assignment Problems for White Matter Bundle Segmentation
No full textSegmenting white matter bundles from human tractograms is a task of interest for several applications. Current methods for bundle segmentation consider either only prior knowledge about the relative anatomical position of a bundle, or only its geometrical properties. Our aim is to improve the results of segmentation by proposing a method that takes into account information about both the underlying anatomy and the geometry of bundles at the same time. To achieve this goal, we extend a state-of-the-art example-based method based on the Linear Assignment Problem (LAP) by including prior anatomical information within the optimization process. The proposed method shows a significant improvement with respect to the original method, in particular on small bundles
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services
Full text linkWe describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-objectidentifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientificFil: Avesani, Paolo. Fondazione Bruno Kessler,; ItaliaFil: McPherson, Brent. Indiana University; Estados UnidosFil: Hayashi, Soichi. Indiana University; Estados UnidosFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Henschel, Robert. Indiana University Bloomington; Estados UnidosFil: Garyfallidis, Eleftherios. Indiana University Bloomington; Estados UnidosFil: Kitchell, Lindsey. Indiana University Bloomington; Estados UnidosFil: Bullock, Daniel. Indiana University Bloomington; Estados UnidosFil: Patterson, Andrew. Indiana University Bloomington; Estados UnidosFil: Olivetti, Emanuele. University of Trento; ItaliaFil: Sporns, Olaf. Indiana University Bloomington; Estados UnidosFil: Saykin, Andrew J.. Indiana University; Estados UnidosFil: Wang, Lei. Northwestern University Feinberg School of Medicine; Estados UnidosFil: Dinov, Ivo. Indiana University Bloomington; Estados UnidosFil: Hancock, David. Indiana University Bloomington; Estados UnidosFil: Caron, Bradley. Indiana University Bloomington; Estados UnidosFil: Qian, Yiming. Indiana University Bloomington; Estados UnidosFil: Pestilli, Franco. Indiana University; Estados Unido
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Predictive Modeling on Cardiovascular Health
Full text linkWith expenses escalating in American healthcare, leveraging data analytics can
help cut costs and improve patient satisfaction. Employing machine learning for
predictive modeling can pinpoint high-risk patients, enabling proactive care instead
of reactive. In my thesis, I replicated healthcare data analytics studies to showcase
the potency of machine learning. I compared methods, algorithms, and models
while highlighting ethical issues in big data analytics for healthcare. In the study
analysis, Dristas & Trigka (2022) exhibited the most replicable and comprehensive
results. Tazin et al. (2021) erred by including synthetic samples in the testing set
during the SMOTE process while Dev et al. (2022) used an under-sampling
technique, diminishing an already small dataset and risking accuracy issues.
Despite the immense potential of machine learning in healthcare, my results
revealed execution flaws that highlight the importance of additional research to
validate big data analytics in healthcare. Replicating studies is crucial for making
well-informed decisions based on reliable evidence. A collaborative effort between
data scientists, healthcare professionals, and policymakers is essential to
safeguard patient privacy and ensure responsible technology use in healthcare.Plan II Honors Progra
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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