Pre-conference workshops
Please note that all the workshops last around 5 hours and will take place in parallel, so you may register for a single event only.
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.
Diffusion MRI of the brain
Tomasz Pieciak, PhD; Antonio Tristán-Vega, PhD
Universiad de Valladolid
Diffusion magnetic resonance imaging (MRI) has gained much interest from the neuroimaging community over the last two decades due to its ability to analyze in vivo structures within the white matter of the brain. The current trend in diffusion MRI analysis is the calculation of increasingly advanced quantitative metrics focused on subtle aspects of the diffusion and brain microstructure.
This workshop will:
provide a theoretical introduction to diffusion MRI and reviews the state-of-the-art diffusion MRI methods of the brain, including Diffusion Tensor Imaging and Free-Water Imaging.
discuss recent advances in diffusion MRI techniques beyond the standard Diffusion Tensor Imaging and capable of handling more sophisticated data acquisition schemes, including a higher number of shells (b-values) and higher number of samples per shell, such as the AMURA, MiSFIT or HYDI-DSI-QP.
This workshop will consist of a theoretical introduction and a practical hands-on programming session in MATLAB (so at least basic knowledge of MATLAB is recommended). Hands-on session will be focused on selecting appropriate diffusion MRI technique for your data, handling the data and estimating quantitative metrics from in vivo brain scans. The attendees should bring their own laptops with preinstalled MATLAB
Directed connectvity analysis of EEG/MEG signals
[this workshop is full. write to us if you want to be on the waiting list]
Miroslaw Wyczesany, PhD
Jagiellonian University in Krakow
This workshop presents the theoretical and practical introduction to source reconstruction and the analysis of directed connectivity from EEG and MEG data. It will guide you through the whole processing pipeline, providing step-by-step recommendations on how to analyze your own data. We will focus 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 that implements Granger causality
For a practical hands-on session, we will use the Atlantis Source Connectivity Toolbox (ASCT; https://atlantis.psychologia.uj.edu.pl). that works in the MATLAB environment. Participants will analyze sample data, starting from the raw signal to the testing of research hypotheses.
Advanced fMRI data analysis
Jakub Szewczyk, PhD
Jagiellonian University in Krakow
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). The attendees should bring their own laptops.
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