Abstract
It has been suggested that resting-state brain activity reflects task-induced brain activity patterns. In this study, we examined whether neural representations of specific movements can be observed in the resting-state brain activity patterns of motor areas. First, we defined two regions of interest (ROIs) to examine brain activity associated with two different behavioral tasks. Using multi-voxel pattern analysis with regularized logistic regression, we designed a decoder to detect voxel-level neural representations corresponding to the tasks in each ROI. Next, we applied the decoder to resting-state brain activity. We found that the decoder discriminated resting-state neural activity with accuracy comparable to that associated with task-induced neural activity. The distribution of learned weighted parameters for each ROI was similar for resting-state and task-induced activities. Large weighted parameters were mainly located on conjunctive areas. Moreover, the accuracy of detection was higher than that for a decoder whose weights were randomly shuffled, indicating that the resting-state brain activity includes multi-voxel patterns similar to the neural representation for the tasks. Therefore, these results suggest that the neural representation of resting-state brain activity is more finely organized and more complex than conventionally considered.
| Original language | English |
|---|---|
| Title of host publication | 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 4290-4293 |
| Number of pages | 4 |
| Volume | 2015-November |
| ISBN (Electronic) | 9781424492718 |
| DOIs | |
| Publication status | Published - 2015 Nov 4 |
| Externally published | Yes |
| 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
Resting-state brain activity in the motor cortex reflects task-induced activity : A multi-voxel pattern analysis. / Kusano, Toshiki; Kurashige, Hiroki; Nambu, Isao; Moriguchi, Yoshiya; Hanakawa, Takashi; Wada, Yasuhiro; Osu, Rieko.
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Vol. 2015-November Institute of Electrical and Electronics Engineers Inc., 2015. p. 4290-4293 7319343.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Resting-state brain activity in the motor cortex reflects task-induced activity
T2 - A multi-voxel pattern analysis
AU - Kusano, Toshiki
AU - Kurashige, Hiroki
AU - Nambu, Isao
AU - Moriguchi, Yoshiya
AU - Hanakawa, Takashi
AU - Wada, Yasuhiro
AU - Osu, Rieko
PY - 2015/11/4
Y1 - 2015/11/4
N2 - It has been suggested that resting-state brain activity reflects task-induced brain activity patterns. In this study, we examined whether neural representations of specific movements can be observed in the resting-state brain activity patterns of motor areas. First, we defined two regions of interest (ROIs) to examine brain activity associated with two different behavioral tasks. Using multi-voxel pattern analysis with regularized logistic regression, we designed a decoder to detect voxel-level neural representations corresponding to the tasks in each ROI. Next, we applied the decoder to resting-state brain activity. We found that the decoder discriminated resting-state neural activity with accuracy comparable to that associated with task-induced neural activity. The distribution of learned weighted parameters for each ROI was similar for resting-state and task-induced activities. Large weighted parameters were mainly located on conjunctive areas. Moreover, the accuracy of detection was higher than that for a decoder whose weights were randomly shuffled, indicating that the resting-state brain activity includes multi-voxel patterns similar to the neural representation for the tasks. Therefore, these results suggest that the neural representation of resting-state brain activity is more finely organized and more complex than conventionally considered.
AB - It has been suggested that resting-state brain activity reflects task-induced brain activity patterns. In this study, we examined whether neural representations of specific movements can be observed in the resting-state brain activity patterns of motor areas. First, we defined two regions of interest (ROIs) to examine brain activity associated with two different behavioral tasks. Using multi-voxel pattern analysis with regularized logistic regression, we designed a decoder to detect voxel-level neural representations corresponding to the tasks in each ROI. Next, we applied the decoder to resting-state brain activity. We found that the decoder discriminated resting-state neural activity with accuracy comparable to that associated with task-induced neural activity. The distribution of learned weighted parameters for each ROI was similar for resting-state and task-induced activities. Large weighted parameters were mainly located on conjunctive areas. Moreover, the accuracy of detection was higher than that for a decoder whose weights were randomly shuffled, indicating that the resting-state brain activity includes multi-voxel patterns similar to the neural representation for the tasks. Therefore, these results suggest that the neural representation of resting-state brain activity is more finely organized and more complex than conventionally considered.
UR - http://www.scopus.com/inward/record.url?scp=84953271319&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84953271319&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2015.7319343
DO - 10.1109/EMBC.2015.7319343
M3 - Conference contribution
C2 - 26737243
AN - SCOPUS:84953271319
VL - 2015-November
SP - 4290
EP - 4293
BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -