Abstract
The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta and alpha rhythms that are sleep onset-related EEG signatures along with the subsequent neural circuitries from a sleep-deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEGfMRI acquisitions, especially when a reference paradigm is unavailable.
| Original language | English |
|---|---|
| Pages (from-to) | 1118-1136 |
| Number of pages | 19 |
| Journal | International Journal of Neuroscience |
| Volume | 119 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2009 Nov 4 |
| Externally published | Yes |
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Keywords
- Electroencephalogram (EEG)
- Epileptic discharge
- Functional MRI (fMRI)
- Independent component analysis (ICA)
- Simultaneous EEGfMRI
- Sleep pattern
ASJC Scopus subject areas
- Neuroscience(all)
Cite this
Application of independent component analysis for the data mining of simultaneous eegfMRI : Preliminary experience on sleep onset. / Lee, Jong-Hwan; Oh, Sungsuk; Jolesz, Ferenc A.; Park, Hyunwook; Yoo, Seung Schik.
In: International Journal of Neuroscience, Vol. 119, No. 8, 04.11.2009, p. 1118-1136.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Application of independent component analysis for the data mining of simultaneous eegfMRI
T2 - Preliminary experience on sleep onset
AU - Lee, Jong-Hwan
AU - Oh, Sungsuk
AU - Jolesz, Ferenc A.
AU - Park, Hyunwook
AU - Yoo, Seung Schik
PY - 2009/11/4
Y1 - 2009/11/4
N2 - The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta and alpha rhythms that are sleep onset-related EEG signatures along with the subsequent neural circuitries from a sleep-deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEGfMRI acquisitions, especially when a reference paradigm is unavailable.
AB - The simultaneous acquisition of electroencephalogram (EEG) and functional MRI (fMRI) signals is potentially advantageous because of the superior resolution that is achieved in both the temporal and spatial domains, respectively. However, ballistocardiographic artifacts along with ocular artifacts are a major obstacle for the detection of the EEG signatures of interest. Since the sources corresponding to these artifacts are independent from those producing the EEG signatures, we applied the Infomax-based independent component analysis (ICA) technique to separate the EEG signatures from the artifacts. The isolated EEG signatures were further utilized to model the canonical hemodynamic response functions (HRFs). Subsequently, the brain areas from which these EEG signatures originated were identified as locales of activation patterns from the analysis of fMRI data. Upon the identification and subsequent evaluation of brain areas generating interictal epileptic discharge (IED) spikes from an epileptic subject, the presented method was successfully applied to detect the theta and alpha rhythms that are sleep onset-related EEG signatures along with the subsequent neural circuitries from a sleep-deprived volunteer. These results suggest that the ICA technique may be useful for the preprocessing of simultaneous EEGfMRI acquisitions, especially when a reference paradigm is unavailable.
KW - Electroencephalogram (EEG)
KW - Epileptic discharge
KW - Functional MRI (fMRI)
KW - Independent component analysis (ICA)
KW - Simultaneous EEGfMRI
KW - Sleep pattern
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UR - http://www.scopus.com/inward/citedby.url?scp=70350475329&partnerID=8YFLogxK
U2 - 10.1080/00207450902854627
DO - 10.1080/00207450902854627
M3 - Article
C2 - 19922343
AN - SCOPUS:70350475329
VL - 119
SP - 1118
EP - 1136
JO - International Journal of Neuroscience
JF - International Journal of Neuroscience
SN - 0020-7454
IS - 8
ER -