Applying FSL to the FIAC data: Model-based and model-free analysis of voice and sentence repetition priming

被引：56

作者：

Beckmann, CF ^{[1
]}

Jenkinson, M ^{[1
]}

Woolrich, MW ^{[1
]}

Behrens, TEJ ^{[1
]}

Flitney, DE ^{[1
]}

Devlin, JT ^{[1
]}

Smith, SM ^{[1
]}

机构：

[1] Univ Oxford, John Radcliffe Hosp, FMRIB, Oxford OX3 9DU, England

来源：

HUMAN BRAIN MAPPING | 2006年 / 27卷 / 05期

基金：

英国惠康基金; 英国医学研究理事会; 英国工程与自然科学研究理事会;

关键词：

functional magnetic resonance imaging (FMRI); independent component analysis (ICA); linear modeling; Functional Image Analysis Contest (FIAC);

D O I：

10.1002/hbm.20246

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

This article presents results obtained from applying various tools from FSL (FMRIB Software Library) to data from the repetition priming experiment used for the HBM'05 Functional Image Analysis Contest. We present analyses from the model-based General Linear Model (GLM) tool (FEAT) and from the model-free independent component analysis tool (MELODIC). We also discuss the application of tools for the correction of image distortions prior to the statistical analysis and the utility of recent advances in functional magnetic resonance imaging (FMRI) time series modeling and inference such as the use of optimal constrained HRF basis function modeling and mixture modeling inference. The combination of hemodynamic response function (HRF) and mixture modeling, in particular, revealed that both sentence content and speaker voice priming effects occurred bilaterally along the length of the superior temporal sulcus (STS). These results suggest that both are processed in a single underlying system without any significant asymmetries for content vs. voice processing.

引用

页码：380 / 391

页数：12

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