Scientific publications

Exploring Functional Brain Networks in Alzheimer’s Disease Using Resting State EEG Signals

Oikonomou, V. P., Georgiadis, K., Lazarou, I., Nikolopoulos, S., Kompatsiaris, I., & PREDICTOM Consortium. (2025). Exploring functional brain networks in Alzheimer’s disease using resting state EEG signals. J. Dement. Alzheimer's Dis., 2(2), 12. https://doi.org/10.3390/jdad2020012

The study found progressive changes in functional brain connectivity across stages of Alzheimer’s disease, marked by increased theta-band betweenness centrality and decreased centrality in the alpha and beta bands. Theta-band betweenness centrality showed the highest discriminative power in identifying disease stages. Connectivity metrics derived from EEG performed on par with advanced deep learning models, highlighting their promise as noninvasive and interpretable biomarkers for early detection and monitoring of Alzheimer’s disease.

INR Meets Multi-contrast MRI Reconstruction

Natascha Niessen, Carolin M. Pirkl, Ana Beatriz Solana, Hannah Eichhorn, Veronika Spieker, Wenqi Huang, Tim Sprenger, Marion I. Menzel & Julia A. Schnabel on behalf of the PREDICTOM consortium https://link.springer.com/chapter/10.1007/978-3-032-06103-4_3

Multi-contrast MRI sequences allow for the acquisition of images with varying tissue contrast within a single scan. The resulting multi-contrast images can be used to extract quantitative information on tissue microstructure. To make such multi-contrast sequences feasible for clinical routine, the usually very long scan times need to be shortened e.g. through undersampling in k-space. However, this comes with challenges for the reconstruction. In this work, we leverage redundant anatomical information of multi-contrast sequences to achieve even higher acceleration rates.