Prediction of subsequent recognition performance using brain activity in the medial temporal lobe

Takamitsu Watanabe, Satoshi Hirose, Hiroyuki Wada, Masaki Katsura, Junichi Chikazoe, Koji Jimura, Yoshio Imai, Toru Machida, Ichiro Shirouzu, Yasushi Miyashita, Seiki Konishi

Research output: Contribution to journalArticle

18 Citations (Scopus)

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 languageEnglish
Pages (from-to)3085-3092
Number of pages8
JournalNeuroImage
Volume54
Issue number4
DOIs
Publication statusPublished - 2011 Feb 14
Externally publishedYes

Keywords

  • FMRI
  • Human
  • Machine learning
  • Multivariate pattern analysis
  • Subsequent memory effect

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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