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Activity Number:
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63
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Type:
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Contributed
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Date/Time:
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Sunday, August 6, 2006 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #306991 |
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Title:
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A Semiparametric Approach To Estimate the Family-Wise Error Rate in fMRI Using Resting-State Data
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Author(s):
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Rajesh Nandy*+
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Companies:
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University of California, Los Angeles
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Address:
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1285 Franz Hall, Los Angeles, CA, 90095,
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Keywords:
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fMRI ; multiple comparison ; semi-parametric ; resampling ; normalized spacings ; p-value
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Abstract:
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An important consideration in any hypothesis based fMRI data analysis is to choose the appropriate threshold to construct the activation maps, which is usually based on p-values. However, there are three factors which necessitate severe corrections in the process of estimating the p-values. First, the fMRI time series at an individual voxel has strong temporal autocorrelation. The second factor is the multiple comparisons problem arising from simultaneously testing tens of thousands of voxels for activation. The third problem is the effect of inherent low frequency processes in the brain that may introduce a large number of false positives without proper adjustment. A novel semi-parametric method, using resampling of normalized spacings of order statistics, is introduced to address all the three problems mentioned above. Results using the proposed method are compared with SPM2.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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