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Magn Reson Imaging. 2013 Oct;31(8):1331-7. doi: 10.1016/j.mri.2013.03.009. Epub 2013 May 10.

False positive control of activated voxels in single fMRI analysis using bootstrap resampling in comparison to spatial smoothing.

Author information

1
Neuro Imaging and Analysis group, Cell and Molecular Imaging Research Center, Tehran University of Medical Science, Tehran, Iran.

Abstract

Functional magnetic resonance imaging (fMRI) is an effective tool for the measurement of brain neuronal activities. To date, several statistical methods have been proposed for analyzing fMRI datasets to select true active voxels among all the voxels appear to be positively activated. Finding a reliable and valid activation map is very important and becomes more crucial in clinical and neurosurgical investigations of single fMRI data, especially when pre-surgical planning requires accurate lateralization index as well as a precise localization of activation map. Defining a proper threshold to determine true activated regions, using common statistical processes, is a challenging task. This is due to a number of variation sources such as noise, artifacts, and physiological fluctuations in time series of fMRI data which affect spatial distribution of noise in an expected uniform activated region. Spatial smoothing methods are frequently used as a preprocessing step to reduce the effect of noise and artifacts. The smoothing may lead to a shift and enlargement of activation regions, and in some extend, unification of distinct regions. In this article, we propose a bootstrap resampling technique for analyzing single fMRI dataset with the aim of finding more accurate and reliable activated regions. This method can remove false positive voxels and present high localization accuracy in activation map without any spatial smoothing and statistical threshold setting.

KEYWORDS:

Anatomical accuracy; Bootstrap resampling; GLM analysis; Spatial smoothing; fMRI preprocessing

PMID:
23664823
DOI:
10.1016/j.mri.2013.03.009
[Indexed for MEDLINE]

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