Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints.
PLoS Biol
; 14(6): e1002498, 2016 06.
Article
en En
| MEDLINE
| ID: mdl-27355236
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 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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Encéfalo
/
Mapeo Encefálico
/
Magnetoencefalografía
/
Electroencefalografía
/
Red Nerviosa
Tipo de estudio:
Prognostic_studies
Límite:
Adult
/
Female
/
Humans
/
Male
Idioma:
En
Revista:
PLoS Biol
Asunto de la revista:
BIOLOGIA
Año:
2016
Tipo del documento:
Article
País de afiliación:
Reino Unido