Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints.
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 segme...
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Public Library of Science (PLoS)
2016-06-01
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| Series: | PLoS Biology |
| Online Access: | https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.1002498&type=printable |
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| author | Anne Keitel Joachim Gross |
| author_facet | Anne Keitel Joachim Gross |
| author_sort | Anne Keitel |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e62259d9634b4b2e995e4f473dd9813a |
| institution | Kabale University |
| issn | 1544-9173 1545-7885 |
| language | English |
| publishDate | 2016-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Biology |
| spelling | doaj-art-e62259d9634b4b2e995e4f473dd9813a2025-08-20T03:26:14ZengPublic Library of Science (PLoS)PLoS Biology1544-91731545-78852016-06-01146e100249810.1371/journal.pbio.1002498Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints.Anne KeitelJoachim GrossThe 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.https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.1002498&type=printable |
| spellingShingle | Anne Keitel Joachim Gross Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints. PLoS Biology |
| title | Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints. |
| title_full | Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints. |
| title_fullStr | Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints. |
| title_full_unstemmed | Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints. |
| title_short | Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints. |
| title_sort | individual human brain areas can be identified from their characteristic spectral activation fingerprints |
| url | https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.1002498&type=printable |
| work_keys_str_mv | AT annekeitel individualhumanbrainareascanbeidentifiedfromtheircharacteristicspectralactivationfingerprints AT joachimgross individualhumanbrainareascanbeidentifiedfromtheircharacteristicspectralactivationfingerprints |