Rule Discovery from fMRI Brain Images by Logical Regression Analysis

Tsukimoto, Hiroshi; Kakimoto, Mitsuru; Morita, Chie; Kikuchi, Yoshihiro

doi:10.1007/3-540-45884-0_15

Cited by 5 publications

(1 citation statement)

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“…The (coefficient) image obtained by nonparametric regression analysis tells intuitively which areas are roughly related to the task of the experiment, but it does not tell which areas are exactly related to the task. The authors are developing a technique that automatically extracts relevant areas (logical regression analysis [7]), which will be reported in another paper.…”

Section: (2) Automatic Extraction Of Related Areas From Two-dimensionmentioning

confidence: 99%

Nonparametric regression analysis of functional brain images

Tsukimoto

Kakimoto

Morita

et al. 2003

Systems & Computers in Japan

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SUMMARYThere has recently been remarkable progress in noninvasive measurement of the brain (such as f-MRI, MEG, and PET), providing detailed functional brain images. Using this technique, the correspondence between the areas and functions of the brain can be precisely analyzed. Researchers in this field are now examining correspondences between areas and functions of the brain on the basis of functional brain images. The authors have been attempting to apply nonparametric regression analysis to f-MRI images, in an effort to develop an algorithm that identifies the areas of the brain automatically and objectively. Since the brain has a three-dimensional structure, it is desirable to apply three-dimensional nonparametric regression analysis, but that requires a tremendous amount of computation. The idea is applied to two-dimensional images as a first step. Satisfactory results are obtained compared to the results obtained by z-score, a typical technique; the results are reported in this paper.

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Section: (2) Automatic Extraction Of Related Areas From Two-dimensionmentioning

confidence: 99%