Pre-conference workshops

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 hands-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
Universidad 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 review 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 standard DTI, including multi-shell acquisition schemes and methods like AMURA, MiSFIT and 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 focus on selecting appropriate diffusion MRI technique, handling the data, and estimating quantitative metrics from in vivo brain scans. Participants should bring their own laptops with MATLAB preinstalled.

Directed connectivity analysis of EEG/MEG signals

Miroslaw Wyczesany, PhD
Jagiellonian University in Krakow

This workshop presents a theoretical and practical introduction to source reconstruction and directed connectivity analysis from EEG/MEG data. It will guide participants through the full analysis pipeline with step-by-step recommendations:

• Automated, machine-learning cleaning procedures
• Classification methods for separating artifacts from brain components
• Source reconstruction of EEG/MEG signals
• Directed Transfer Function (Granger causality) for connectivity analysis

The hands-on session will use the Atlantis Source Connectivity Toolbox (ASCT): https://atlantis.psychologia.uj.edu.pl (MATLAB-based). Participants will analyze sample data—from raw signal to hypothesis testing.

Advanced fMRI data analysis

Jakub Szewczyk, PhD
Jagiellonian University in Krakow

This workshop will introduce and deepen your understanding of advanced fMRI data analysis techniques, focusing on:

• Voxelwise Encoding Models with Ridge Regression: a method to predict neural activity patterns, including selection of optimal regularization parameters,
• Representational Similarity Analysis (RSA): a method to compare patterns of neural activity across conditions, including construction of similarity matrices and interpretation of representational structures.

The workshop includes a theoretical introduction and a practical programming session using Python/numpy/scikit-learn (basic Python knowledge recommended). Participants should bring their own laptops.