Potential for false positive results from multi-voxel pattern analysis on functional imaging data

Abstract

Background: Multi-voxel pattern analysis (MVPA) provides a powerful tool to investigate neural mechanisms for various cognitive processes under functional brain imaging. However, the high sensitivity of the MVPA method could bring about false positive results, which has been overlooked by previous research. Objective: We investigated the potential for obtaining false positives from the MVPA method. Methods: We conducted MVPA on a public functional MRI dataset on the neural encoding of various object categories. Different scenarios for pattern classification were involved by varying the number of voxels for each region of interest (ROI) and the number of object categories. Results: The classification accuracy became higher with more voxels involved, and false positive results emerged for the primary auditory cortex and even a white matter ROI, where object-related neural processing was not supposed to occur. Conclusions: Our results imply that the classification accuracy obtained from MVPA may be inflated due to the high sensitivity of the method. Therefore, we suggest involving control ROIs in future MVPA studies and comparing the classification accuracy for a target ROI with that for a control ROI, instead of comparing the obtained accuracy with the chance-level accuracy.