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Adaptive thresholding for fMRI data

Joke Durnez UGent and Beatrijs Moerkerke UGent (2011) Berlin workshop on statistics and neuroimaging 2011, Abstracts.
abstract
In the analysis of functional MRI-data, several thresholding procedures are available to account for the huge number of volume units or features that are tested simultaneously. The main focus of these methods is to prevent an inflation of false positives. However, this comes with a serious decrease in power and therefore leads to a problematic imbalance between type I and type II errors (Lieberman & Cunningham, 2009). In this research, we present a method to estimate the number of activated features. The goal is twofold: • Given the expected number of active units, widely used methods to control the false discovery rate (FDR) can be made adaptive and more powerful. • The type I and type II error rate following such a thresholding technique can be estimated enabling a direct trade-off between sensitivity and specificity. Chen, Wang, Eberly, Caffo, & Schwartz (2009) argue that activation foci in fMRI data are often small and local leading to a large proportion of null voxels. However, task-related activation is expected to occur in clusters of voxels rather than in isolated single voxels. We consider peaks of activation instead of voxels and provide a procedure to estimate the number of active peaks. Concentrating on peaks leads to an enormous data reduction, and the proportion of non-null hypotheses can be expected to be much larger among peaks than among voxels. Given an estimate of the number of active and non-active peaks, we demonstrate how an adaptive FDR controlling procedure on peaks can be obtained and how false positive and negative rates associated with this procedure can be estimated. This allows researchers to reconsider the balance between sensitivity and specificity in function of study goals. The method is evaluated and illustrated using simulation studies and a real data example. References Chen, S., Wang, C., Eberly, L., Caffo, B., & Schwartz, B. (2009). Addaptive control of the false discovery rate in voxel-based morphometry. Human Brain Mapping , 30 , 2304-2311. Lieberman, M. D., & Cunningham, W. A. (2009). Type i and type ii error concerns in fmri research; re-balancing the scale. Social cognitive and affective neuroscience, 4 , 423-428.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
in
Berlin workshop on statistics and neuroimaging 2011, Abstracts
conference name
Berlin Workshop on Statistics and Neuroimaging 2011
conference location
Berlin, Germany
conference start
2011-11-23
conference end
2011-11-25
language
English
UGent publication?
yes
classification
C3
copyright statement
I have retained and own the full copyright for this publication
id
2019814
handle
http://hdl.handle.net/1854/LU-2019814
date created
2012-02-06 09:13:06
date last changed
2012-02-08 09:21:07
@inproceedings{2019814,
  abstract     = {In the analysis of functional MRI-data, several thresholding procedures are available to account for the huge number of volume units or features that are tested simultaneously. The main focus of these methods is to prevent an inflation of false positives. However, this comes with a serious decrease in power and therefore leads to a problematic imbalance between type I and type II errors (Lieberman \& Cunningham, 2009). In this research, we present a method to estimate the number of activated features. The goal is twofold: {\textbullet} Given the expected number of active units, widely used methods to control the false discovery rate (FDR) can be made adaptive and more powerful. {\textbullet} The type I and type II error rate following such a thresholding technique can be estimated enabling a direct trade-off between sensitivity and specificity. Chen, Wang, Eberly, Caffo, \& Schwartz (2009) argue that activation foci in fMRI data are often small and local leading to a large proportion of null voxels. However, task-related activation is expected to occur in clusters of voxels rather than in isolated single voxels. We consider peaks of activation instead of voxels and provide a procedure to estimate the number of active peaks. Concentrating on peaks leads to an enormous data reduction, and the proportion of non-null hypotheses can be expected to be much larger among peaks than among voxels. Given an estimate of the number of active and non-active peaks, we demonstrate how an adaptive FDR controlling procedure on peaks can be obtained and how false positive and negative rates associated with this procedure can be estimated. This allows researchers to reconsider the balance between sensitivity and specificity in function of study goals. The method is evaluated and illustrated using simulation studies and a real data example. References Chen, S., Wang, C., Eberly, L., Caffo, B., \& Schwartz, B. (2009). Addaptive control of the false discovery rate in voxel-based morphometry. Human Brain Mapping , 30 , 2304-2311. Lieberman, M. D., \& Cunningham, W. A. (2009). Type i and type ii error concerns in fmri research; re-balancing the scale. Social cognitive and affective neuroscience, 4 , 423-428.},
  author       = {Durnez, Joke and Moerkerke, Beatrijs},
  booktitle    = {Berlin workshop on statistics and neuroimaging 2011, Abstracts},
  language     = {eng},
  location     = {Berlin, Germany},
  title        = {Adaptive thresholding for fMRI data},
  year         = {2011},
}

Chicago
Durnez, Joke, and Beatrijs Moerkerke. 2011. “Adaptive Thresholding for fMRI Data.” In Berlin Workshop on Statistics and Neuroimaging 2011, Abstracts.
APA
Durnez, J., & Moerkerke, B. (2011). Adaptive thresholding for fMRI data. Berlin workshop on statistics and neuroimaging 2011, Abstracts. Presented at the Berlin Workshop on Statistics and Neuroimaging 2011.
Vancouver
1.
Durnez J, Moerkerke B. Adaptive thresholding for fMRI data. Berlin workshop on statistics and neuroimaging 2011, Abstracts. 2011.
MLA
Durnez, Joke, and Beatrijs Moerkerke. “Adaptive Thresholding for fMRI Data.” Berlin Workshop on Statistics and Neuroimaging 2011, Abstracts. 2011. Print.