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: | , |
|---|---|
| フォーマット: | Licensed eBooks |
| 言語: | 英語 |
| 出版事項: |
New York, NY :
Humana Press,
[2025]
|
| 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 |
目次:
- Introduction to methods for analyzing large neuroimaging datasets
- Getting started, getting data
- Neuroimaging workflows in the cloud
- Establishing a reproducible and sustainable analysis workflow
- Optimizing your reproducible neuroimaging workflow with Git
- End-to-end processing of M/EEG data with BIDS, HED, and EEGLAB
- Actionable event annotation and analysis in fMRI : a practical guide to event handling
- Standardized preprocessing in neuroimaging : enhancing reliability and reproducibility
- Structural MRI and computational anatomy
- Diffusion MRI data processing and analysis : a practical guide with ExploreDTI
- A pipeline for large-scale assessments of dementia EEG connectivity across multicentric settings
- Brain predictability toolbox
- NBS-Predict : an easy-to-use toolbox for connectome-based machine learning
- Normative modeling with the predictive clinical neuroscience toolkit (PCNtoolkit)
- Studying the connectome at a large scale
- Deep learning classification based on raw MRI images.