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26.6 MB
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48826A4A6A649854D6580C23D7C9EC31AD130E4C
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April 21, 2026, 5:02 p.m.
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(Last updated: April 21, 2026, 5:03 p.m.)
| File | Size |
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| Whelan R. Methods for Analyzing Large Neuroimaging Datasets 2025.pdf | 26.6 MB |
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| Uploaded by nomdeguerre | Size 976.3 MB | Health [ 1 /26 ] | Added 2024-05-22 |
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SOURCE: Whelan R. Methods for Analyzing Large Neuroimaging Datasets 2025
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COVER
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MEDIAINFO
Textbook in PDF format 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. Front Matter 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 Back Matter
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