Pre-conference workshops
Directed connectvity analysis of EEG/MEG signals
Miroslaw Wyczesany, PhD
Jagiellonian University in Krakow
This workshop will guide you through the analysis of directed brain communication based on EEG/MEG signals, focusing on:
Automated, machine-learning cleaning procedures
Classification method for the separation of artifactual and brain components
Source reconstruction of EEG/MEG signals
Source-based connectivity analysis with Directed Transfer Function based on granger causality
This workshop will consist of a theoretical introduction and a practical hands-on programming session in MATLAB. Participants will analyze sample data, starting from the raw signal to the testing of research hypotheses. There will also be time for step-by-step recommendations on how to analyze your own data.
Advanced fMRI data analysis
Jakub Szewczyk, PhD
Donders Institute for Brain, Cognition, and Behaviour
This workshop will introduce you and deepen your understanding of advanced fMRI data analysis techniques, focusing on:
Voxelwise Encoding Modelswith Ridge Regression: a method to predict neural activity patterns. This session will cover the principles of voxelwise encoding models, including how to select and apply the optimal regularization parameter for your data.
Representational Similarity Analysis (RSA): a powerful method to compare patterns of neural activity across conditions. Understand how to construct and interpret similarity matrices, and how to use RSA to uncover the representational structure of cognitive processes in the brain.
This workshop will consist of a theoretical introduction and a practical hands-on programming session using Python/numpy/scikit-learn (so at least rudimentary knowledge of Python is recommended).
Inferring circuit mechanisms from neural recordings: analysis, simulation, and probabilistic machine learning
Richard Gao, PhD
University of Tübingen
Brain signals are measurements from a messy bio-electro-chemical dynamical system and are often useful as correlates of behavior and cognition. But to further take advantage of them, we can leverage other experimental data and computational tools to decode physiological mechanisms underlying their fluctuations. This requires three ingredients: data analysis methods, mechanistic models (simulation), and a way to link models to data (inference)—combined in a process known as inverse modeling.
This workshop will:
cover the basics of Fourier(-based) analysis,
discuss spectral parameterization as a data analysis tool to infer circuit properties from periodic and aperiodic components of neural power spectra, such as cellular and network timescales and excitation-inhibition balance,
discuss mechanistic circuit models with many more parameters, and
explore how to use simulation-based inference and probabilistic deep learning to perform model inference using real experimental data.
This workshop will consist of a theoretical part and a hands-on programming session using Python toolboxes (so at least rudimentary knowledge of Python is recommended). Central focus will be given to hans-on session to facilitate analysis of your own data using spectral parameterization, as well as fitting your own models with simulation-based inference.