The following list outlines the general topics that participants of the brainhack school may learn, along with representative tutorials. The exact content of the course will be defined by the material required by each project.
Existing resources:
- Omega: the open magneto-encephalography archive.
- INDI: the international data sharing initiative.
- CORR: Consortium for Reliability and Reproducibility.
- ABIDE: Autism Brain Imaging Data Exchange.
- ADHD200: Imaging data sharing in Attention Deficit and Hyperactivity Disorder.
- NKI enhanced: community-based, lifespan imaging sample.
- OpenNeuro: various open imaging datasets.
- Neurovault: open brain maps (mostly activation maps).
- Neurosynth: published activation coordinates and meta-analysis.
Tools for data sharing:
Building software:
- Github: version control and social coding.
- Docsify: Building software documentation.
- Nose: Building automated tests for software.
- Circle CI: Continuous integration testing
Sharing software:
- Pandas: data analysis in python.
- Regression, cross validation and generalization error.
- Support vector machine, naive Bayes, random forest.
- Cluster analysis, component analysis.
- Deep neural networks, convolutional networks and gradient descent.
- Scikit-learn: machine learning with python.
- Pytorch: deep learing library.
- MNE: MEG and EEG analysis and visualization in python.
- qMRLab: open-source Matlab software for quantitative MR image analysis.
- nilearn machine learning for neuroimaging, in python.
- fMRI preprocessing and feature extraction.
- M/EEG preprocessing and feature extraction.
- structural MRI preprocessing and feature extraction.
