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" Studies in neural data science : "
Antonio Canale, Daniele Durante, Lucia Paci, Bruno Scarpa, editors.
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BL
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| Record Number
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859070
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| Main Entry
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StartUp Research (Meeting)(2017 :, Siena, Italy)
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| Title & Author
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Studies in neural data science : : StartUp Research 2017, Siena, Italy, June 25-27 /\ Antonio Canale, Daniele Durante, Lucia Paci, Bruno Scarpa, editors.
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| Publication Statement
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Cham, Switzerland :: Springer,, [2018]
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| Series Statement
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Springer proceedings in mathematics and statistics ;; volume 257
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1 online resource
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| ISBN
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3030000397
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: 9783030000394
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9783030000387
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| Notes
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4 Clustering Subject-Specific Imaging Patterns
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| Contents
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Intro; Preface; Contents; About the Editors; Understanding Dependency Patterns in Structural and Functional Brain Connectivity Through fMRI and DTI Data; 1 Motivating Real World Dataset; 2 Descriptive Analysis; 3 Latent Space Model for DTI Dataset; 3.1 Results on the DTI Dataset; 4 Time-Varying Dynamic Bayesian Networks for the fMRI Dataset; 4.1 Results on the fMRI Dataset; 5 Discussion; 6 A. Desikan Atlas Codes; 7 B. MCMC Diagnostics of Intercept Parameters of the Latent Space Model; References; Hierarchical Graphical Model for Learning Functional Network Determinants; 1 Introduction
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1 Introduction2 Data Description; 2.1 Data Selection; 3 Methodology; 3.1 k-Means Clustering; 3.2 Smoothing Procedure; 3.3 Functional Boxplot; 4 Results; 4.1 Smoothing Procedure; 4.2 Functional Boxplot; 4.3 k-Means Clustering; 5 Discussion and Future Directions; References; Robust Methods for Detecting Spontaneous Activations in fMRI Data; 1 Introduction; 1.1 Dataset Description; 2 Modelling fMRI Data; 2.1 The BOLD Signal; 2.2 HRF Estimation; 3 Illustrative Examples; 4 Concluding Remarks and Further Developments; References; Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data
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1 Introduction2 The rs-fMRI Dataset; 3 Modeling and Theory; 3.1 Low-Rank Multivariate Processes; 3.2 A Time-Dependent Latent Factor Model; 3.3 Identifiability; 3.4 Prior Specification; 4 Posterior Inference; 4.1 Computational Difficulties; 5 Data Analysis; 5.1 Model Checking; 5.2 Network Analysis; 6 Discussion; 7 Computational Details; References; Challenges in the Analysis of Neuroscience Data; 1 Introduction; 2 Statistical Analysis of Brain Imaging Data; 2.1 Structural Imaging; 2.2 Functional Imaging; 3 Describing the Heterogeneity of Brain Mechanisms
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2 Hierarchical Model3 Modular Estimation Using Connectome Data; 3.1 Denoising; 3.2 Estimation of the Graphical Model; 3.3 Regression with Covariates; 3.4 Multiscale Analysis; 4 Discussion; References; Three Testing Perspectives on Connectome Data; 1 Introduction; 2 Testing Functional Correlations in Connectomic Maps; 2.1 Background and Motivation; 2.2 Methodology and Application; 3 A Bayesian Framework for Fiber Count Estimation; 3.1 Introduction; 3.2 Model Formulation; 3.3 Application to DTI Data; 4 Object-Oriented Nonparametric Exploration and Hypothesis Testing for Network Data
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4.1 Introduction4.2 Metrics for Network Data; 4.3 Hypothesis Testing; 4.4 Results; 4.5 Discussion; References; An Object Oriented Approach to Multimodal Imaging Data in Neuroscience; 1 Introduction; 2 Curves and Correlation Matrices as Data Objects; 3 Clustering of Functional Networks; 4 Low Dimensional Representation; 5 Hypothesis Testing for Correlation Structures; 6 Eingenstructure of the Mean Correlation Matrices; 7 Spatial Dependence for Functional Networks; 8 Conclusions and Future Research Directions; References; Curve Clustering for Brain Functional Activity and Synchronization
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| Abstract
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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.
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| Subject
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Neurosciences-- Mathematical models, Congresses.
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Mathematical statistics.
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Neurosciences.
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Statistics.
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| Dewey Classification
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612.8/233
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| LC Classification
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QP351.S83 2017
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| Added Entry
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Canale, Antonio
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Durante, Daniele
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Paci, Lucia
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Scarpa, Bruno
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