ROC Analysis of Statistical Methods Used in Functional MRI: Individual Subjects

Abstract

The complicated structure of fMRI signals and associated noise sources make it difficult to assess the validity of various steps involved in the statistical analysis of brain activation. Most methods used for fMRI analysis assume that observations are independent and that the noise can be treated as white gaussian noise. These assumptions are usually not true but it is difficult to assess how severely these assumptions are violated and what are their practical consequences. In this study a direct comparison is made between the power of various analytical methods used to detect activations, without reference to estimates of statistical significance. The statistics used in fMRI are treated as metrics designed to detect activations and are not interpreted probabilistically. The receiver operator characteristic (ROC) method is used to compare the efficacy of various steps in calculating an activation map in the study of a single subject based on optimizing the ratio of the number of detected activations to the number of false-positive findings. The main findings are as follows: Preprocessing. The removal of intensity drifts and high-pass filtering applied on the voxel time-course level is beneficial to the efficacy of analysis. Temporal normalization of the global image intensity, smoothing in the temporal domain, and low-pass filtering do not improve power of analysis. Choices of statistics. the cross-correlation coefficient and t-statistic, as well as nonparametric Mann-Whitney statistics, prove to be the most effective and are similar in performance, by our criterion. Task design. the proper design of task protocols is shown to be crucial. In an alternating block design the optimal block length is be approximately 18 s. Spatial clustering. an initial spatial smoothing of images is more efficient than cluster filtering of the statistical parametric activation maps.