Biased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions

• Main Authors: Quentin Noirhomme, Damien Lesenfants, Francisco Gomez, Andrea Soddu, Jessica Schrouff, Gaëtan Garraux, André Luxen, Christophe Phillips, Steven Laureys
• Format: Article
• Language: English
• Published: Elsevier 2014-01-01
• Series: NeuroImage: Clinical
• Subjects: classification; cross-validation; binomial; permutation test
• Online Access: http://www.sciencedirect.com/science/article/pii/S2213158214000485

Multivariate classification is used in neuroimaging studies to infer brain activation or in medical applications to infer diagnosis. Their results are often assessed through either a binomial or a permutation test. Here, we simulated classification results of generated random data to assess the influence of the cross-validation scheme on the significance of results. Distributions built from classification of random data with cross-validation did not follow the binomial distribution. The binomial test is therefore not adapted. On the contrary, the permutation test was unaffected by the cross-validation scheme. The influence of the cross-validation was further illustrated on real-data from a brain–computer interface experiment in patients with disorders of consciousness and from an fMRI study on patients with Parkinson disease. Three out of 16 patients with disorders of consciousness had significant accuracy on binomial testing, but only one showed significant accuracy using permutation testing. In the fMRI experiment, the mental imagery of gait could discriminate significantly between idiopathic Parkinson's disease patients and healthy subjects according to the permutation test but not according to the binomial test. Hence, binomial testing could lead to biased estimation of significance and false positive or negative results. In our view, permutation testing is thus recommended for clinical application of classification with cross-validation.