عنوان

Studies in neural data science :

(Number (ISBN

3030000397

(Number (ISBN

9783030000394

Erroneous ISBN

9783030000387

Title Proper

Studies in neural data science :

General Material Designation

[Book]

Other Title Information

StartUp Research 2017, Siena, Italy, June 25-27 /

First Statement of Responsibility

Antonio Canale, Daniele Durante, Lucia Paci, Bruno Scarpa, editors.

Place of Publication, Distribution, etc.

Cham, Switzerland :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

[2018]

Specific Material Designation and Extent of Item

1 online resource

Series Title

Springer proceedings in mathematics and statistics ;

Volume Designation

volume 257

Text of Note

4 Clustering Subject-Specific Imaging Patterns

Text of Note

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

Text of Note

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

Text of Note

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

Text of Note

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

Text of Note

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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Text of Note

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.

Source for Acquisition/Subscription Address

Springer Nature

Stock Number

com.springer.onix.9783030000394

Title

Studies in Neural Data Science : StartUp Research 2017, Siena, Italy, June 25-27.

International Standard Book Number

9783030000387

Neurosciences-- Mathematical models, Congresses.

Mathematical statistics.

Neurosciences.

Statistics.

MAT029000

PBT

PBT

Number

Edition

Class number

Book number

Canale, Antonio

Durante, Daniele

Paci, Lucia

Scarpa, Bruno

StartUp Research (Meeting)(2017 :, Siena, Italy)

Date of Transaction

20200823071330.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y