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Alternative thresholding methods for fMRI data optimized for surgical planning. Neuroimage 2014 Jan 01;84:554-61

Date

09/12/2013

Pubmed ID

24021837

Pubmed Central ID

PMC3919165

DOI

10.1016/j.neuroimage.2013.08.066

Scopus ID

2-s2.0-84884960624   18 Citations

Abstract

Current methods for thresholding functional magnetic resonance imaging (fMRI) maps are based on the well-known hypothesis-test framework, optimal for addressing novel theoretical claims. However, these methods as typically practiced have a strong bias toward protecting the null hypothesis, and thus may not provide an optimal balance between specificity and sensitivity in forming activation maps for surgical planning. Maps based on hypothesis-test thresholds are also highly sensitive to sample size and signal-to-noise ratio, whereas many clinical applications require methods that are robust to these effects. We propose a new thresholding method, optimized for surgical planning, based on normalized amplitude thresholding. We show that this method produces activation maps that are more reproducible and more predictive of postoperative cognitive outcome than maps produced with current standard thresholding methods.

Author List

Gross WL, Binder JR

Authors

Jeffrey R. Binder MD Professor in the Neurology department at Medical College of Wisconsin
William Gross MD, PhD Assistant Professor in the Anesthesiology department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Adult
Algorithms
Brain Mapping
Epilepsy
Female
Humans
Image Enhancement
Image Interpretation, Computer-Assisted
Magnetic Resonance Imaging
Male
Patient Selection
Pattern Recognition, Automated
Preoperative Care
Reproducibility of Results
Sensitivity and Specificity
Surgery, Computer-Assisted
Treatment Outcome