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Methods for analyzing large neuroimaging datasets
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Description: A fully open-access book with a truly practical guide to analyzing large neuroimaging datasets. Get the book: https://link.springer.com/book/10.1007/978-1-0716-4260-3
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1.0 Introduction chapter
https://osf.io/vme3d
In this chapter, we provide an overview of this book on methods for analyzing large neuroimaging datasets. There is a recognition...
1.1 Getting Started, Getting Data
This chapter explores the availability and accessibility of open-access neuroimaging datasets. It describes how to download datasets using command-lin...
1.2 Neuroimaging workflows in the cloud
https://osf.io/7synk
Analysis of large neuroimaging datasets requires scalable computing power and storage, plus methods for secure collaboration and ...
2.1 Establishing a reproducible and sustainable analysis workflow
https://osf.io/rcxg8/
Getting started on any project is often the hardest thing - and when it comes to starting your career in research, just figuring...
2.2 Optimising your reproducible neuroimaging workflow with Git
https://osf.io/jqwpv/
As a neuroimager working with open-source software and tools, you will quickly become familiar with the website GitHub, which is...
2.3 End-to-end processing of M/EEG data with BIDS, HED, and EEGLAB
https://osf.io/h7puk/
Reliable and reproducible machine-learning enabled neuroscience research requires large-scale data sharing and analysis. Essenti...
2.4 Actionable event annotation and analysis in fMRI: A practical guide to event handling
https://osf.io/xdbrv/
Many common analysis methods for task-based functional MRI rely on detailed information about experiment design and events. Even...
3.1Standardized Preprocessing in Neuroimaging: Enhancing Reliability and Reproducibility
https://osf.io/42bsu
This chapter critically examines the standardization of preprocessing in neuroimaging, exploring the field's evolution, the nece...
3.2 Structural MRI and computational anatomy
https://osf.io/cvpeq
Structural magnetic resonance imaging can yield highly detailed images of the human brain. In order to quantify the variability i...
3.3 Diffusion MRI Data Processing and Analysis: A Practical Guide with ExploreDTI
https://osf.io/mbyjh
This chapter introduces neuroimaging researchers to the concepts and techniques of diffusion magnetic resonance imaging data proc...
3.4 Methods for large-scale EEG analyses (ConnEEGtome)
https://osf.io/h2wgv/
Multicentric initiatives based on high-density electroencephalography (hd-EEG) are urgently needed for classification and charac...
4.1 Brain Predictability toolbox
https://osf.io/8zyg9/
The Brain Predictability toolbox (BPt) is a Python-based library with a unified framework of machine learning (ML) tools designe...
4.2 NBS-Predict: An easy-to-use toolbox for connectome-based machine learning
https://osf.io/cfm7j/
NBS-Predict is a prediction-based extension of the Network-based Statistic (NBS, Zalesky et al., 2010) approach, which aims to a...
4.3 Normative Modeling with the Predictive Clinical Neuroscience Toolkit (PCNtoolkit)
https://osf.io/2c8s9
In this chapter we introduce normative modeling as a tool for mapping variation across large neuroimaging datasets. We provide pr...
4.4 Studying the connectome at a large scale
https://osf.io/ay95f
This chapter outlines a flexible connectome-based predictive modeling method that is optimised for large neuroimaging datasets vi...
4.5 Deep Learning classification based on raw MRI images
https://osf.io/f4zhn/
In this chapter, we describe a step-by-step implementation of an automated anatomical MRI feature extractor based on artificial ...
5.1 List of resources
A list of useful resources for analysis of large neuroimaging datasets
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