Node-layer duality in networked systems: multilayer brain networks dataset

This dataset contains the processed data used in Presigny, Corsi and De Vico Fallani (2024). It is made available for replication purposes. The code to treat these data is available here. Multilayer brain networks are obtained from the experimental data published in Guillon et al. (2017). 23 Alzheimer’s diseased (AD) patients and 27 healthy age-matched control (HC) subjects, participated in the study. For each subject, 6 minutes resting-state eyes-closed brain activity was recorded noninvasively using a whole-head MEG system with 102 magnetometers and 204 planar gradiometers (Elekta Neuromag TRIUX MEG system) at a sampling rate of 1000Hz. Signal artefacts were removed using different techniques including removed signal space separation, principal component analysis, and visual inspection. Finally, source-imaging was used to project the signals from the sensor to the source space consisting of N = 70 regions of interest (ROI) defined by the Lausanne cortical atlas parcellation (see file name_of_ROIs.txt for the order). Here, we used spectral bicoherence to estimate functional connectivity between ROIs and between frequencies of brain activity. Specifically, we considered M = 77 layers corresponding to frequencies in the 2 − 40 Hz range with a resolution of 0.5 Hz. Other parameters were non overlapping windows of 2s averaged according to the Welch method. The resulting networks are full-multilayer consisting of both intralayer and interlayer connections, including weighted links between replica nodes.