The Same Analysis Approach: Practical protection against the pitfalls of novel neuroimaging analysis methods

Abstract

Standard neuroimaging data analysis based on traditional principles of experimental design, modelling, and statistical inference is increasingly complemented by novel analysis methods, driven e.g. by machine learning methods. While these novel approaches provide new insights into neuroimaging data, they often have unexpected properties, generating a growing literature on possible pitfalls. We propose to meet this challenge by adopting a habit of systematic testing of experimental design, analysis procedures, and statistical inference. Specifically, we suggest to apply the analysis method used for experimental data also to aspects of the experimental design, simulated confounds, simulated null data, and control data. We stress the importance of keeping the analysis method the same in main and test analyses, because only this way possible confounds and unexpected properties can be reliably detected and avoided. We describe and discuss this Same Analysis Approach in detail, and demonstrate it in two worked examples using multivariate decoding. With these examples, we reveal two sources of error: A mismatch between counterbalancing (crossover designs) and cross-validation which leads to systematic below-chance accuracies, and linear decoding of a nonlinear effect, a difference in variance. Highlights: 1. Traditional design principles can be unsuitable when combined with cross-validation; 2. This can explain both inflated accuracies and below-chance accuracies; 3. We propose the novel "same analysis approach" (SAA) for checking analysis pipelines; 4. The principle of SAA is to perform additional analyses using the same analysis; 5. SAA analysis should be performed on design variables, control data, and simulations