Abstract
Application of multivoxel pattern analysis (MVPA) to functional magnetic resonance imaging (fMRI) data enables reconstruction and classification of cognitive status from brain activity. However, previous studies using MVPA have extracted information about cognitive status that is experienced simultaneously with fMRI scanning, but not one that will be observed after the scanning. In this study, by focusing on activity in the medial temporal lobe (MTL), we demonstrate that MVPA on fMRI data is capable of predicting subsequent recognition performance. In this experiment, six runs of fMRI signals were acquired during encoding of phonogram stimuli. In the analysis, using data acquired in runs 1-3, we first conducted MVPA-based voxel-wise search for the clusters in the MTL whose signals contained the most information about subsequent recognition performance. Next, using the fMRI signals acquired in runs 1-3 from the selected clusters, we trained a classifier function in MVPA. Finally, the trained classifier function was applied to fMRI signals acquired in runs 4-6. Consequently, we succeeded in predicting the subsequent recognition performance for stimuli studied in runs 4-6 with significant accuracy. This accurate prediction suggests that MVPA can extract information that is associated not only with concurrent cognitive status, but also with behavior in the near future.
| Original language | English |
|---|---|
| Pages (from-to) | 3085-3092 |
| Number of pages | 8 |
| Journal | NeuroImage |
| Volume | 54 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2011 Feb 14 |
| Externally published | Yes |
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Keywords
- FMRI
- Human
- Machine learning
- Multivariate pattern analysis
- Subsequent memory effect
ASJC Scopus subject areas
- Cognitive Neuroscience
- Neurology
Cite this
Prediction of subsequent recognition performance using brain activity in the medial temporal lobe. / Watanabe, Takamitsu; Hirose, Satoshi; Wada, Hiroyuki; Katsura, Masaki; Chikazoe, Junichi; Jimura, Koji; Imai, Yoshio; Machida, Toru; Shirouzu, Ichiro; Miyashita, Yasushi; Konishi, Seiki.
In: NeuroImage, Vol. 54, No. 4, 14.02.2011, p. 3085-3092.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Prediction of subsequent recognition performance using brain activity in the medial temporal lobe
AU - Watanabe, Takamitsu
AU - Hirose, Satoshi
AU - Wada, Hiroyuki
AU - Katsura, Masaki
AU - Chikazoe, Junichi
AU - Jimura, Koji
AU - Imai, Yoshio
AU - Machida, Toru
AU - Shirouzu, Ichiro
AU - Miyashita, Yasushi
AU - Konishi, Seiki
PY - 2011/2/14
Y1 - 2011/2/14
N2 - Application of multivoxel pattern analysis (MVPA) to functional magnetic resonance imaging (fMRI) data enables reconstruction and classification of cognitive status from brain activity. However, previous studies using MVPA have extracted information about cognitive status that is experienced simultaneously with fMRI scanning, but not one that will be observed after the scanning. In this study, by focusing on activity in the medial temporal lobe (MTL), we demonstrate that MVPA on fMRI data is capable of predicting subsequent recognition performance. In this experiment, six runs of fMRI signals were acquired during encoding of phonogram stimuli. In the analysis, using data acquired in runs 1-3, we first conducted MVPA-based voxel-wise search for the clusters in the MTL whose signals contained the most information about subsequent recognition performance. Next, using the fMRI signals acquired in runs 1-3 from the selected clusters, we trained a classifier function in MVPA. Finally, the trained classifier function was applied to fMRI signals acquired in runs 4-6. Consequently, we succeeded in predicting the subsequent recognition performance for stimuli studied in runs 4-6 with significant accuracy. This accurate prediction suggests that MVPA can extract information that is associated not only with concurrent cognitive status, but also with behavior in the near future.
AB - Application of multivoxel pattern analysis (MVPA) to functional magnetic resonance imaging (fMRI) data enables reconstruction and classification of cognitive status from brain activity. However, previous studies using MVPA have extracted information about cognitive status that is experienced simultaneously with fMRI scanning, but not one that will be observed after the scanning. In this study, by focusing on activity in the medial temporal lobe (MTL), we demonstrate that MVPA on fMRI data is capable of predicting subsequent recognition performance. In this experiment, six runs of fMRI signals were acquired during encoding of phonogram stimuli. In the analysis, using data acquired in runs 1-3, we first conducted MVPA-based voxel-wise search for the clusters in the MTL whose signals contained the most information about subsequent recognition performance. Next, using the fMRI signals acquired in runs 1-3 from the selected clusters, we trained a classifier function in MVPA. Finally, the trained classifier function was applied to fMRI signals acquired in runs 4-6. Consequently, we succeeded in predicting the subsequent recognition performance for stimuli studied in runs 4-6 with significant accuracy. This accurate prediction suggests that MVPA can extract information that is associated not only with concurrent cognitive status, but also with behavior in the near future.
KW - FMRI
KW - Human
KW - Machine learning
KW - Multivariate pattern analysis
KW - Subsequent memory effect
UR - http://www.scopus.com/inward/record.url?scp=78650932712&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650932712&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2010.10.066
DO - 10.1016/j.neuroimage.2010.10.066
M3 - Article
C2 - 21035553
AN - SCOPUS:78650932712
VL - 54
SP - 3085
EP - 3092
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
IS - 4
ER -