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Tutorials
Welcome to the tutorial page of Brainhack Western 2019! A short description of the tutorials being taught are available below.
The tutorials are ordered based on when participants added them. Here's an alphabetical list if you're here browsing. Make sure to add your own when you update this wiki page.
- Intro to DTI
- Intro to EEG
- Intro to fMRI
- Intro to fNIRS
- Intro to Image Manipulation
- Intro to Machine Learning
Organizers: TBA
This tutorial will teach one to manipulate and transform neuroimaging data using Python with commonly used. First, an understanding of how MRI data is represented in Python will be discussed followed by hands-on tasks such as basic manipulation of neuroimaging MRI data. All content will be performed using Jupyter notebooks in the spirit of reproducible and open science!
Organizers: TBA
In this tutorial, minimally preprocessing of functional data using fmriprep will be discussed and the steps the tool performs. Following preprocessing, the data will be cleaned and made workable through motion cleaning and dimensionality reduction. The final component of the tutorials involves functional connectivity analysis. All content will be performed using Jupyter notebooks in the spirit of reproducible and open science! It is recommended (thhough not required) to attend the Intro to Image Manipulation tutorial.
Organizers: TBA
Coming soon...
Organizers: Jordan DeKraker and Haider Al-Tahan
This tutorial will be broken up into a theoretical (~1hr) and practical component (~2hr). The theoretical component will provide 1) a basic overview of neural networks (NNs) and backpropagation, 2) an overview of several key NN architectures including convolutional and recurrent networks, and 3) an open discussion of active areas of NN research. The practical component of this tutorial will focus on hands-on experience with Tensorflow. Topics will include 1) organization of code into classes, functions, and the Tensorgraph, 2) examples of dense and convolutional networks for real problems using high-level Tensorflow tools such as built-in Keras functions, and 3) an open session for exploring and adapting existing NNs or for helping attendees build their own networks.
Organizers: TBA
This tutorial will go over the basics to process data from MRI acquisitions to computing diffusion tensors and tracking the structural connectome. Minimal preprocessing steps will be explained. All content will be performed using Jupyter notebooks and dipy in the spirit of reproducible and open science! It is recommended (though not required) to attend the Intro to Image Manipulation tutorial.
Organizers: TBA
Coming soon...