Abstract
Significant correlation exists in the blood-oxygen-level-dependent (BOLD) signals of resting-state fMRI across different regions in the brain. These regions form the default mode network (DMN), salience network (SN), sensory networks, and others. Among these, the DMN is widely investigated in relation to various mental diseases. Several analytic methods are available for obtaining the DMN activity from individuals' fMRI time-series signals, but a fully effective method has not yet been established. In the present study, we examined a functional connectivity analysis and three algorithms of blind source separation including independent component analysis, second-order blind identification, and non-negative matrix factorization using a set of resting-state fMRI data measured for twelve young participants. Results showed that the second-order blind identification yielded superior performance for the DMN detection, indicating significant activation in all DMN regions based on statistical parametric maps.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1805-1808 |
| Number of pages | 4 |
| Volume | 2015-November |
| ISBN (Print) | 9781424492718 |
| DOIs | |
| Publication status | Published - 2015 Nov 4 |
| Event | 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy Duration: 2015 Aug 25 → 2015 Aug 29 |
Other
| Other | 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 |
|---|---|
| Country | Italy |
| City | Milan |
| Period | 15/8/25 → 15/8/29 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing
- Biomedical Engineering
- Health Informatics
Cite this
Evaluation of performance to detect default mode network among some algorithms applied to resting-state fMRI data. / Tachikawa, Kenta; Izawa, Shun; Ono, Yumie; Kuriki, Shinya; Ishiyama, Atsushi.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Vol. 2015-November Institute of Electrical and Electronics Engineers Inc., 2015. p. 1805-1808 7318730.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Evaluation of performance to detect default mode network among some algorithms applied to resting-state fMRI data
AU - Tachikawa, Kenta
AU - Izawa, Shun
AU - Ono, Yumie
AU - Kuriki, Shinya
AU - Ishiyama, Atsushi
PY - 2015/11/4
Y1 - 2015/11/4
N2 - Significant correlation exists in the blood-oxygen-level-dependent (BOLD) signals of resting-state fMRI across different regions in the brain. These regions form the default mode network (DMN), salience network (SN), sensory networks, and others. Among these, the DMN is widely investigated in relation to various mental diseases. Several analytic methods are available for obtaining the DMN activity from individuals' fMRI time-series signals, but a fully effective method has not yet been established. In the present study, we examined a functional connectivity analysis and three algorithms of blind source separation including independent component analysis, second-order blind identification, and non-negative matrix factorization using a set of resting-state fMRI data measured for twelve young participants. Results showed that the second-order blind identification yielded superior performance for the DMN detection, indicating significant activation in all DMN regions based on statistical parametric maps.
AB - Significant correlation exists in the blood-oxygen-level-dependent (BOLD) signals of resting-state fMRI across different regions in the brain. These regions form the default mode network (DMN), salience network (SN), sensory networks, and others. Among these, the DMN is widely investigated in relation to various mental diseases. Several analytic methods are available for obtaining the DMN activity from individuals' fMRI time-series signals, but a fully effective method has not yet been established. In the present study, we examined a functional connectivity analysis and three algorithms of blind source separation including independent component analysis, second-order blind identification, and non-negative matrix factorization using a set of resting-state fMRI data measured for twelve young participants. Results showed that the second-order blind identification yielded superior performance for the DMN detection, indicating significant activation in all DMN regions based on statistical parametric maps.
UR - http://www.scopus.com/inward/record.url?scp=84953294443&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84953294443&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2015.7318730
DO - 10.1109/EMBC.2015.7318730
M3 - Conference contribution
C2 - 26736630
AN - SCOPUS:84953294443
SN - 9781424492718
VL - 2015-November
SP - 1805
EP - 1808
BT - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
PB - Institute of Electrical and Electronics Engineers Inc.
ER -