Dr. Daniele Marinazzo is a full professor in the department of data analysis at the University of Ghent, in Belgium. For over a decade he has been showing us what further information and insight we may extract from brain imaging data - from EEG and MEG to fMRI. He is technically a statistical physicist, but in reality, he is a network neuroscientist and data modeler who is constantly pushing the envelope.

In this podcast he discusses some recent papers that go into how we might be able to improve the impact and relevance of new findings and models through careful benchmarking and well considered experimental design.

He talks about his desire to move from correlation to causation in functional connectivity studies, he discusses granger causality, as well as moving from pairwise correlation to multivariate correlation.

Furthermore, he delves into the limits of hemodynamics - limits that may be pushed back to a degree, as suggested by his compelling work showing that hemodynamic response function, which varies over space, may be estimated on a voxel-wise basis using resting state data alone.

His work in estimating and mapping the Excitation/Inhibition ratio in the brain by using gamma frequency coherence as a signature was also discussed. This has potentially profound clinical and research applications.

Lastly, his collaborative work with the European Human Brain Project towards the creation of the useful website, called ebrains (https://www.ebrains.eu), was discussed, which serves as a repository and tool for exploring shared data and code, as well as providing a user-friendly encapsulation of the project's collective effort.

It is an all-around fun, eye-opening discussion featuring an outstanding scientist who is not only deep in the trenches of network modelling, but also a strong proponent of open science and constant engagement across disciplines.

Episode producers:

Omer Faruk Gulban

Alfie Wearn

Stephania Assimopoulos

Referenced Papers:

Mika Rubinov. Circular and unified analysis in network neuroscience. eLife. 2023; 12:e79559. Doi: 10.7554/eLife.79559

Reid AT, et al. Advancing functional connectivity research from association to causation. Nat Neurosci. 2019 Nov;22(11):1751-1760. Doi: 10.1038/s41593-019-0510-4.

Valdes-Sosa PA et al. Effective connectivity: Influence, causality and biophysical modelling. Neuroimage. 2009; 58(2): 339-361. Doi: 10.1016/j.neuroimage.2011.03.058.

Wu GR, et al. A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data. Medical Image Analysis. 2013; 17(3):365-374. Doi:

10.1016/j.media.2013.01.003.

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