Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints.

ABSTRACT: The human brain can be parcellated into diverse anatomical areas. We investigated whether rhythmic brain activity in these areas is characteristic and can be used for automatic classification. To this end, resting-state MEG data of 22 healthy adults was analysed. Power spectra of 1-s long data segments for atlas-defined brain areas were clustered into spectral profiles ("fingerprints"), using k-means and Gaussian mixture (GM) modelling. We demonstrate that individual areas can be identified from these spectral profiles with high accuracy. Our results suggest that each brain area engages in different spectral modes that are characteristic for individual areas. Clustering of brain areas according to similarity of spectral profiles reveals well-known brain networks. Furthermore, we demonstrat

SUBMITTER: Keitel A 

PROVIDER: S-EPMC4927181 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature