Methods for analyzing large neuroimaging datasets /
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 lar...
| その他の著者: | , |
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| フォーマット: | Licensed eBooks |
| 言語: | 英語 |
| 出版事項: |
New York, NY :
Humana Press,
[2025]
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| シリーズ: | Neuromethods ;
v. 218. |
| オンライン･アクセス: | https://link.springer.com/book/10.1007/978-1-0716-4260-3 |
| 要約: | 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. |
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| 物理的記述: | 1 online resource (xi, 432 pages) : illustrations. |
| 書誌: | Includes bibliographical references. |
| ISBN: | 9781071642603 107164260X 9781071642597 1071642596 |
| アクセス: | Open access |