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

Anne Keitel, Joachim Gross

Research output: Contribution to journalArticlepeer-review

103 Citations (Scopus)
185 Downloads (Pure)


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 demonstrate task-specific modulations of auditory spectral profiles during auditory processing. These findings have important implications for the classification of regional spectral activity and allow for novel approaches in neuroimaging and neurostimulation in health and disease.

Original languageEnglish
Pages (from-to)e1002498
Number of pages22
JournalPLoS Biology
Issue number6
Publication statusPublished - 29 Jun 2016


  • functional brain anatomy
  • spectral fingerprints
  • Brain/anatomy & histology
  • Brain Mapping/methods
  • Humans
  • Magnetoencephalography/methods


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