WORKSHOPS

In the afternoon of 27 June 2022 each participant has the possibility to attend a workshop. Please choose from the workshops listed below your first and second preference. There is a limited number of participants for each workshop. Places will be allocated on a first come, first serve principle. However, for each participant a spot in a workshop is guaranteed.

Workshop Number

Format

Instructor(s)

Title with Abstract

1	Hybrid	Prof. Dr Thomas Knoesche	Modeling the building blocks of cognition with neural masses TBA
2	Hybrid	Dr Misun Kim	Model-based fMRI: a non-invasive way to probe the grid code in the human brain Model-based analyses can be a powerful approach to link neural coding mechanism to the BOLD-response we measure using fMRI. For example, a model derived from the characteristic hexagonal firing patterns of grid cells in the rodent entorhinal cortex led to the discovery of hexadirectional signal in the human brain (Doeller et al. Nature 2010). Such grid-like codes are now thought to be central for human cognition beyond spatial navigation (Bellmund et al. Science 2018). In this workshop, we will discuss how hexadirectional signals and other complex coding mechanisms can be studied using fMRI. We will introduce advanced analysis concepts and tools (MATLAB or R).
3	Hybrid	Dr Nico Scherf, Dr Sebastian Niehaus, Charlotte Beylier, & Ferney Beltran	A gentle introduction to Deep Learning TBA
4	Hybrid	Dr Vadim Nikulin & Dr Lars Meyer	Promises, Practices, and Pitfalls of Current M/EEG Analysis Approaches The toolkit of cognitive-neuroscientific M/EEG analysis has expanded in recent years. If handled appropriately, these new tools streamline workflows, improve statistical power, and establish close correspondence between dependent measures and neuronal activity / computations. But if handled inappropriately, the tools may lead to biased conclusions. We summarize current (automated) preprocessing approaches, (single-trial) timeâ€“ and frequency-domain analysis (evoked activity; oscillatory phase, power, and connectivity), parametric / non-factorial designs (LMEs, multiple regression, and mTRF) in combination with (feature-based and computational) models, and source-level analysis. We will emphasize volume conduction, signal-to-noise ratio, and long-range dependencies in electrophysiological and behavioral data as factors that strongly affect the interpretation of M/EEG recorded during neurocognitive experiments.
5	On-Site	Dr Sabrina Turker, Dr Philipp Kuhnke, Sandra Martin & Ole Numsen	Hands-on TMS workshop TBA
6	Hybrid	Dr Katja Seeliger & Dr Martin Hebart	Relating brains and artificial intelligence TBA
7	Hybrid	Dr Sofie Valk	Surface-based modelling of brain organisation Presentation and explaination of: brainspace.readthedocs.io BrainSpace is a lightweight cross-platform toolbox primarily intended for macroscale gradient mapping and analysis of neuroimaging and connectome level data. The current version of BrainSpace is available in Python and MATLAB, programming languages widely used by the neuroimaging and network neuroscience communities. The toolbox also contains several maps that allow for exploratory analysis of gradient correspondence with other brain-derived features, together with tools to generate spatial null models.
8	Hybrid	Dr Yulia Revina & Dr Falk Eippert	Perception - a predictive processing perspective TBA
9	Hybrid	Prof. Dr Hellmuth Obrig	Aspects of clinical research using optical imaging (fNIRS) and structural MRI TBA
10	Hybrid	Dr Robert Trampel & Prof. Dr Nik Weiskopf	From protons to segmentation of neuroanatomical structures- Hands on 7T TBA