MEG optimal fingerprint functional connectome sub-networks

These files represent optimal fingerprinting functional connectome sub-networks from 90 regions of the Automated Anatomical Labeling atlas. Each sub-network contains 10/90 regions optimized on the fingerprint metrics of multi-session data of 43 subjects. The data is usable in the NumPy library with a recent version of Python.The search space is 5.72 trillion, and our data is 46,071 sub-networks in optimalSubnetworksAll.npy in form (46071, 10). In optimalSubnetworksIndependent.npy 24,197 sub-networks in form (24197, 10) are provided, independently sampled from unique optimization runs. brainRegionsByIndex.npy` in form (90,) stores a list of the names of the 90 AAL regions, where their list index corresponds to the values in optimalSubnetworksAll.npy and optimalSubnetworksIndependent.npy.This data is generated using the code in the following Github link:https://github.com/VasilesBalabanis/MEGSubnetworksThe bioRxiv Preprint link for the research study:https://www.biorxiv.org/content/10.1101/2024.06.23.599587v1Citation:Please cite this data as: V.Balabanis, J.Zhang, X.Xie, S.Yang. (2024). Robust sub-network fingerprints of brief signals in the MEG functional connectome for single-patient classification. Figshare. DOI: 10.6084/m9.figshare.26130376