عنوان

Studies in neural data science :

شابک

3030000397

شابک

9783030000394

شابک اشتباه

9783030000387

عنوان اصلي

Studies in neural data science :

نام عام مواد

[Book]

ساير اطلاعات عنواني

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

نام نخستين پديدآور

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

محل نشرو پخش و غیره

Cham, Switzerland :

نام ناشر، پخش کننده و غيره

Springer,

تاریخ نشرو بخش و غیره

[2018]

نام خاص و کميت اثر

1 online resource

عنوان فروست

Springer proceedings in mathematics and statistics ;

مشخصه جلد

volume 257

متن يادداشت

4 Clustering Subject-Specific Imaging Patterns

متن يادداشت

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

متن يادداشت

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

متن يادداشت

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

متن يادداشت

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

متن يادداشت

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

بدون عنوان

0

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

بدون عنوان

8

متن يادداشت

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.

منبع سفارش / آدرس اشتراک

Springer Nature

شماره انبار

com.springer.onix.9783030000394

عنوان

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

شماره استاندارد بين المللي کتاب و موسيقي

9783030000387

موضوع مستند نشده

Neurosciences-- Mathematical models, Congresses.

موضوع مستند نشده

Mathematical statistics.

موضوع مستند نشده

Neurosciences.

موضوع مستند نشده

Statistics.

موضوع مستند نشده

MAT029000

موضوع مستند نشده

PBT

موضوع مستند نشده

PBT

شماره

ويراست

شماره رده

نشانه اثر

مستند نام اشخاص تاييد نشده

Canale, Antonio

مستند نام اشخاص تاييد نشده

Durante, Daniele

مستند نام اشخاص تاييد نشده

Paci, Lucia

مستند نام اشخاص تاييد نشده

Scarpa, Bruno

مستند نام تنالگان تاييد نشده

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

تاريخ عمليات

20200823071330.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y