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Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study
- Source :
- Frontiers in Human Neuroscience, Frontiers in Human Neuroscience, Vol 10 (2016)
- Publication Year :
- 2016
- Publisher :
- Frontiers Media SA, 2016.
-
Abstract
- The topological architecture of brain connectivity has been well characterized by graph theory based analysis. However, previous studies have primarily built brain graphs based on a single modality of brain imaging data. Here we develop a framework to construct multi-modal brain graphs using concurrent EEG-fMRI data which are simultaneously collected during eyes open (EO) and eyes closed (EC) resting states. FMRI data are decomposed into independent components with associated time courses by group independent component analysis (ICA). EEG time series are segmented, and then spectral power time courses are computed and averaged within 5 frequency bands (delta; theta; alpha; beta; low gamma). EEG-fMRI brain graphs, with EEG electrodes and fMRI brain components serving as nodes, are built by computing correlations within and between fMRI ICA time courses and EEG spectral power time courses. Dynamic EEG-fMRI graphs are built using a sliding window method, versus static ones treating the entire time course as stationary. In global level, static graph measures and properties of dynamic graph measures are different across frequency bands and are mainly showing higher values in eyes closed than eyes open. Nodal level graph measures of a few brain components are also showing higher values during eyes closed in specific frequency bands. Overall, these findings incorporate fMRI spatial localization and EEG frequency information which could not be obtained by examining only one modality. This work provides a new approach to examine EEG-fMRI associations within a graph theoretic framework with potential application to many topics.
- Subjects :
- genetic structures
Computer science
multi-modal
Electroencephalography
EEG-fMRI
050105 experimental psychology
lcsh:RC321-571
03 medical and health sciences
Behavioral Neuroscience
0302 clinical medicine
brain graph
Neuroimaging
Sliding window protocol
medicine
0501 psychology and cognitive sciences
Spatial localization
ICA
lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry
Biological Psychiatry
Original Research
dynamic
medicine.diagnostic_test
business.industry
05 social sciences
Pattern recognition
Graph theory
Graph
Psychiatry and Mental health
Modal
Neuropsychology and Physiological Psychology
Neurology
Artificial intelligence
business
030217 neurology & neurosurgery
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 16625161
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- Frontiers in Human Neuroscience
- Accession number :
- edsair.doi.dedup.....9b2829bcf90077afb92105e3d3c82948
- Full Text :
- https://doi.org/10.3389/fnhum.2016.00476