Sinergia Consortium Brain Communication Pathways

Katharina Glomb is originally from Germany and did both her BSc in Biology and her MSc in Computational Neuroscience in Berlin, at the Humboldt University and the Technical Institute/Bernstein Center for Computational Neuroscience, respectively. She is interested in whole-brain functional connectivity across different temporal and spatial scales, both in terms of data analysis and computational modelling.

She earned her PhD in 2017 in the group of Gustavo Deco in Barcelona. In her thesis work, she applied tensor decomposition – a dimensionality reduction technique that takes into account spatial and temporal information simultaneously – to resting state fMRI, using both empirical data and data simulated with a dynamic mean field model. Before moving to Lausanne, she was briefly involved in a project on vibrotactile categorization, using a simple model of effective connectivity to assess changes caused by the vibrotactile stimulus.

Since moving to the Connectomics Lab in 2017, she has been working on establishing techniques to link structural and functional connectivity in source projected EEG data, both for resting state and task. These techniques include graph signal processing – which she is also applying to fMRI – and simple computational models like the Kuramoto model.