Fast alternatives to Perlin's bias and gain functions
Graphics gems IV
Rank-score tests in factorial designs with repeated measures
Journal of Multivariate Analysis
Method Matters: An Empirical Study of Impact in Cognitive Neuroscience
Journal of Cognitive Neuroscience
A studentized permutation test for the non-parametric Behrens-Fisher problem
Computational Statistics & Data Analysis
Power in Voxel-based Lesion-Symptom Mapping
Journal of Cognitive Neuroscience
Patient registries in cognitive neuroscience research: Advantages, challenges, and practical advice
Journal of Cognitive Neuroscience
Journal of Cognitive Neuroscience
Spatial attention evokes similar activation patterns for visual and auditory stimuli
Journal of Cognitive Neuroscience
Decrements in hippocampal activity with item repetition during continuous recognition: An fmri study
Journal of Cognitive Neuroscience
A novel tumor grading technique using functional magnetic resonance imaging
Proceedings of the 2011 workshop on Data mining for medicine and healthcare
Journal of Cognitive Neuroscience
Journal of Cognitive Neuroscience
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Measures of brain activation (e.g., changes in scalp electrical potentials) have become the most popular method for inferring brain function. However, examining brain disruption (e.g., examining behavior after brain injury) can complement activation studies. Activation techniques identify regions involved with a task, whereas disruption techniques are able to discover which regions are crucial for a task. Voxel-based lesion mapping can be used to determine relationships between behavioral measures and the location of brain injury, revealing the function of brain regions. Lesion mapping can also correlate the effectiveness of neurosurgery with the location of brain resection, identifying optimal surgical targets. Traditionally, voxel-based lesion mapping has employed the chi-square test when the clinical measure is binomial and the Student's t test when measures are continuous. Here we suggest that the Liebermeister approach for binomial data is more sensitive than the chi-square test. We also suggest that a test described by Brunner and Munzel is more appropriate than the t test for nonbinomial data because clinical and neuropsychological data often violate the assumptions of the t test. We test our hypotheses comparing statistical tests using both simulated data and data obtained from a sample of stroke patients with disturbed spatial perception. We also developed software to implement these tests (MRIcron), made freely available to the scientific community.