Abstract
Functional magnetic resonance imaging (fMRI) modality has been widely employed to measure neuronal activations of the human brain using such as a model-based general linear model (GLM) and data-driven independent component analysis (ICA) approaches. In this study, we were motivated to investigate the performance of two popular methods with a hypothesis that these methods would have advantages and disadvantages depending on the variability of the fMRI data across subjects in both temporal and spatial domain. To quantitatively evaluate two methods, the pseudo-real fMRI data were generated by combining the decomposed non-neuronal components estimated from real resting-state fMRI data and artificially generated neuronal components with varying degree of temporal and spatial pattern variability of task related activation patterns in an individual level. Using the pseudo-real fMRI data, the assessment of each method was conducted by comparing the estimated activations to reference neuronal activations. Our results indicated that the degree of spatial overlap size across subjects and degree of temporal pattern variability would be important factor to choose a proper analytical method.
| Original language | English |
|---|---|
| Title of host publication | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics |
| Pages | 1095-1099 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - 2012 Dec 1 |
| Event | 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul, Korea, Republic of Duration: 2012 Oct 14 → 2012 Oct 17 |
Other
| Other | 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 |
|---|---|
| Country | Korea, Republic of |
| City | Seoul |
| Period | 12/10/14 → 12/10/17 |
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Keywords
- Functional magnetic resonance imaging
- general linear model
- group inference
- independent component analysis
- semi-artificial fMRI
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Human-Computer Interaction
Cite this
Pseudo-real fMRI data generation and its utility toward quantitative evaluation of analytical methods. / Kim, Dong Youl; Lee, Jong-Hwan.
Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2012. p. 1095-1099 6377876.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Pseudo-real fMRI data generation and its utility toward quantitative evaluation of analytical methods
AU - Kim, Dong Youl
AU - Lee, Jong-Hwan
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Functional magnetic resonance imaging (fMRI) modality has been widely employed to measure neuronal activations of the human brain using such as a model-based general linear model (GLM) and data-driven independent component analysis (ICA) approaches. In this study, we were motivated to investigate the performance of two popular methods with a hypothesis that these methods would have advantages and disadvantages depending on the variability of the fMRI data across subjects in both temporal and spatial domain. To quantitatively evaluate two methods, the pseudo-real fMRI data were generated by combining the decomposed non-neuronal components estimated from real resting-state fMRI data and artificially generated neuronal components with varying degree of temporal and spatial pattern variability of task related activation patterns in an individual level. Using the pseudo-real fMRI data, the assessment of each method was conducted by comparing the estimated activations to reference neuronal activations. Our results indicated that the degree of spatial overlap size across subjects and degree of temporal pattern variability would be important factor to choose a proper analytical method.
AB - Functional magnetic resonance imaging (fMRI) modality has been widely employed to measure neuronal activations of the human brain using such as a model-based general linear model (GLM) and data-driven independent component analysis (ICA) approaches. In this study, we were motivated to investigate the performance of two popular methods with a hypothesis that these methods would have advantages and disadvantages depending on the variability of the fMRI data across subjects in both temporal and spatial domain. To quantitatively evaluate two methods, the pseudo-real fMRI data were generated by combining the decomposed non-neuronal components estimated from real resting-state fMRI data and artificially generated neuronal components with varying degree of temporal and spatial pattern variability of task related activation patterns in an individual level. Using the pseudo-real fMRI data, the assessment of each method was conducted by comparing the estimated activations to reference neuronal activations. Our results indicated that the degree of spatial overlap size across subjects and degree of temporal pattern variability would be important factor to choose a proper analytical method.
KW - Functional magnetic resonance imaging
KW - general linear model
KW - group inference
KW - independent component analysis
KW - semi-artificial fMRI
UR - http://www.scopus.com/inward/record.url?scp=84872369396&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872369396&partnerID=8YFLogxK
U2 - 10.1109/ICSMC.2012.6377876
DO - 10.1109/ICSMC.2012.6377876
M3 - Conference contribution
AN - SCOPUS:84872369396
SN - 9781467317146
SP - 1095
EP - 1099
BT - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ER -