Methods for Analyzing Large Neuroimaging Datasets

Robert Whelan

ISBN 10: 1071642596 / ISBN 13: 9781071642597

Published by Springer, 2024

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This Open Access volume explores the latest advancements and challenges in standardized methodologies, efficient code management, and scalable data processing of neuroimaging datasets. The chapters in this book are organized in four parts. Part One shows the researcher how to access and download large datasets, and how to compute at scale. Part Two covers best practices for working with large data, including how to build reproducible pipelines and how to use Git. Part Three looks at how to do structural and functional preprocessing data at scale, and Part Four describes various toolboxes for interrogating large neuroimaging datasets, including machine learning and deep learning approaches. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory.Authoritative and comprehensive, Methods for Analyzing Large Neuroimaging Datasets is a valuable resource that will help researchers obtain the practical knowledge necessary for conducting robust and reproducible analyses of large neuroimaging datasets.

Seller Inventory # 9781071642597

Bibliographic Details

• Title: Methods for Analyzing Large Neuroimaging Datasets
• Publisher: Springer
• Publication Date: 2024
• Language: English
• ISBN 10: 1071642596
• ISBN 13: 9781071642597
• Binding: Buch
• Condition: Neu
• Dimensions: 260W x 183H x 30D millimeters 260 millimeters width by 183 millimeters height by 30 millimeters depth
• Weight: 1025 grams

About this title

Synopsis

This Open Access volume explores the latest advancements and challenges in standardized methodologies, efficient code management, and scalable data processing of neuroimaging datasets. The chapters in this book are organized in four parts. Part One shows the researcher how to access and download large datasets, and how to compute at scale. Part Two covers best practices for working with large data, including how to build reproducible pipelines and how to use Git. Part Three looks at how to do structural and functional preprocessing data at scale, and Part Four describes various toolboxes for interrogating large neuroimaging datasets, including machine learning and deep learning approaches. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory.

Authoritative and comprehensive, Methods for Analyzing Large Neuroimaging Datasets is a valuable resource that will help researchers obtain the practical knowledge necessary for conducting robust and reproducible analyses of large neuroimaging datasets.