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Description: A fully open-access book with a truly practical guide to analyzing large neuroimaging datasets

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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...

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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...

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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 ...

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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...

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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...

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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...

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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...

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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...

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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...

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3.3 Diffusion MRI Data Processing and Analysis: A Practical Guide with ExploreDTI

Connaughton, Leemans, O’Hanlon & 1 more
https://osf.io/mbyjh This chapter introduces neuroimaging researchers to the concepts and techniques of diffusion magnetic resonance imaging data proc...

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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...

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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...

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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...

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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...

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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...

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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 ...

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5.1 List of resources

A list of useful resources for analysis of large neuroimaging datasets

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