Brain response in the processing of Chinese characters: An event-related fMRI study

H. L. Liu, Y. Pu, C. M. Feng, L. H. Tan, J. A. Spinks, C. A. Perfetti, J. Xiong, P. T. Fox, J. H. Gao

Producción científica: Articlerevisión exhaustiva


Due to the entirely different surface form of the writing system and the large population of native speakers, Chinese (Mandarin) processing has evoked broad interests in the field of language study. Recently, fMRI with blocked design has been used in the mapping of Chinese language processing. Comparing to blocked task design, event-related task design takes the advantages of the high temporal resolution of fMRI and provides greater experimental flexibility in task design. In this study, we examine event-related fMRI (ER-fMRI) response in the process of Chinese characters. Experiments were performed on a 1.9 T GE/Elscint Prestige MRI scanner, with six subjects. A gradient-echo EPI sequence was used. Ten axial slices were acquired. A single Chinese character was used for each stimulus. The subjects were asked to perform a word generation task. In all the subjects, strong activation was detected in left middle to inferior prefrontal gyri. Highly left hemisphere dominance of the activation in prefrontal cortex was observed in all the subjects, with the locations in well agreement with the previous studies using blocked design. Percent maximum BOLD signal change was 1.45 ± 0.14%, averaged across the six subjects. Time-to-peak was 7.72 ± 0.31 sec, and FWHM was 5.99 ± 0.43 sec. In this study, we demonstrated that ER-fMRI signal change can be reliably detected in single subject, with response to repeated single trials of Chinese character processing. Based on this result, more sophisticated paradigm design can be utilized in the future study of Chinese language.

Idioma originalEnglish (US)
Páginas (desde-hasta)2020-2024
Número de páginas5
PublicaciónAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
EstadoPublished - 2000

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics


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