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

<p>This dataset contains the processed data used in<a href="https://doi.org/10.1038/s41467-024-50176-5"> Presigny, Corsi and De Vico Fallani (2024)</a>. It is made available for replication purposes. The code to treat these data is available <a href="https://github.com/Presigny/multilayer_duality">here</a>.</p> <p>Multilayer brain networks are obtained from the experimental data published in <a>Guillon <em>et al. </em>(2017)</a>. 23 Alzheimer&rsquo;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 &minus; 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.</p>