Statistics and Its Interface

Volume 3 (2010)

Number 1

Adaptive statistical parametric mapping for fMRI

Pages: 33 – 43

DOI: http://dx.doi.org/10.4310/SII.2010.v3.n1.a3

Authors

Ping Bai (PayPal Inc., San Jose, Calif., U.S.A.)

Jianhua Z. Huang (Department of Statistics, Texas A&M University, College Station, Texas, U.S.A.)

Haipeng Shen (Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, N.C., U.S.A.)

Young K. Truong (Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, N.C., U.S.A.)

Abstract

Brain activity is accompanied by changes in cerebral blood flow (CBF) and the differential blood oxygenation that are detectable using functional magnetic resonance imaging (fMRI). The process of identifying brain activation regions can be facilitated by estimating the hemodynamic response function (HRF). There have been some remarkable new developments in statistics to handle this problem. In this paper, we introduce a novel procedure which is capable of adapting itself to any of the existing methods by improving its performance through the application of a penalized smoothing technique. Using a computer experiment and a real fMRI data set, the proposed procedure is assessed by comparing its performance very favorably to the popular SPM based method.

2010 Mathematics Subject Classification

Primary 62G08, 62H35. Secondary 62H12.

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