Permutation Tests for Classification: Towards Statistical Significance in Image-Based Studies

By Polina Golland, Bruce Fischl. 2003

363 citations recorded by the source corpus.

Subfields: Biophysics, Cognitive Neuroscience, Computational Theory and Mathematics

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Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, neutral): “Work by Golland and Fischl (2003) Ojala and Garriga (2010) Hsing et al. (2003) Pesarin and Wiley (2001) is more relevant to addressing these problems.”
Classification Accuracy as a Proxy for Two Sample Testing (2016, background): “(2009) and Gretton et al. (2012), but none of them set out to solve our problem.”
Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. (2015, positive): “Here, we used MATLAB implementation for the LDA and NB and the libsvm library for multi-class SVM”
Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. (2012, background): “based classifier can label new samples with better-than-random accuracy, then the two populations are indeed likely to be different, and the classifier can capture the population differences (Golland and Fischl, 2003).”
Machine learning classifiers and fMRI: a tutorial overview (2008, methodology): “A good review of permutation tests in classification (with one functional neuroimaging application) is Golland and Fischl, (2003), and we provide additional details in Appendix A.”
Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or With Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis (2018, background): “Role functioning may be more strongly determined by concurrent environmental and clinical factors than social deficits, leading to a greater degree of temporal fluctuation, which in turn potentially mediates differential associationswithbaselineclinical andneuroanatomical predictors (eFigure4 in the”
Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control (2011, neutral): “The reasoning behind permutation tests on a single subject level is to assess chance distributions and chance accuracy maps (Chen et al., 2011; Golland and Fischl, 2003).”
PRoNTo: Pattern Recognition for Neuroimaging Toolbox (2013, background): “Table 2 provides the balanced accuracies, where F/TP and F/TN are the false/true positives and negatives, respectively (please note that and Fischl, 2003).”