This tutorial walks participants through the application of dynamic causal modelling (DCM) to fMRI data using MATLAB. Participants are also shown various forms of DCM, how to generate and specify different models, and how to fit them to simulated neural and BOLD data.
This lesson corresponds to slides 158-187 of the PDF below.
This lesson demonstrates how to use MATLAB to implement a multivariate dimension reduction method, PCA, on time series data.
In this tutorial on simulating whole-brain activity using Python, participants can follow along using corresponding code and repositories, learning the basics of neural oscillatory dynamics, evoked responses and EEG signals, ultimately leading to the design of a network model of whole-brain anatomical connectivity.
This lecture goes into detailed description of how to process workflows in the virtual research environment (VRE), including approaches for standardization, metadata, containerization, and constructing and maintaining scientific pipelines.
This lesson gives an introduction to the central concepts of machine learning, and how they can be applied in Python using the scikit-learn package.