Big thanks to @patriciazvarova.bsky.social and @bahnebahners.bsky.social for their wonderful contributions, and to @andreashorn.org for his mentorship on this project!

Read the full study here:

onlinelibrary.wiley.com/doi/10.1002/...

I would like to thank the #ThiemannFoundation, who support me as well as amazing young talents like @barbarahollunder.bsky.social , @lukasgoede.bsky.social and many more.

None the least, we would like to thank all patients that devoted their time and effort to make this study possible.
Special thanks to the best #mentor you could wish for and who led me through this project: the one and only @andreashorn.org

I would like to thank the large set of interdisciplinary #collaborators from Berlin, Düsseldorf and Boston, who contributed their clinical, neuroscientific and technical expertise and helped gather the unique dataset over the last 15(!) years.

Our results provide cross-modal insight into the DBS response of Parkinson’s disease, showing that effective stimulation sites are maximally engaged with a specific brain network in the #highbeta frequency band.

We show that this network, once identified, is capable of #predicting the #DBS response across DBS centers, #surgeons, and even #MEG devices.

We show that the identified network spatially resembles the one characterized by #fMRI and that it predominantly #communicates in the #highbeta range, which has already been identified as key for DBS response, before.

To answer this question, we analyzed the lion’s share of the globally available #MEG-LFP data (n = 100 hemispheres) to characterize the #DBS response network in Parkinson’s Disease in both, the #spatial and #temporal domain.

While the #DBS response network is well-defined in #Parkinson’sDisease based on #fMRI network mapping studies, a big shortcoming of fMRI is temporal resolution.
Hence, we know where the network lives, but we do not know in which #frequency band(s) it operates. doi.org/10.1002/ana....