Determining the optimal level of smoothing in cortical thickness analysis: A hierarchical approach based on sequential statistical thresholding
The extent of smoothing applied to cortical thickness maps critically influences sensitivity, anatomical precision and resolution of statistical change detection. Theoretically, it could be optimized by increasing the trade-off between vertex-wise sensitivity and specificity across several levels of smoothing. But to date neither parametric nor nonparametric methods are able to control the error at the vertex level if the null hypothesis is rejected after smoothing of cortical thickness maps. To overcome these drawbacks, we applied sequential statistical thresholding based on a simple hierarchical model. This methodology aims at controlling erroneous detections; firstly at the level of clusters, over smoothed statistical maps; and secondly at the vertex level, over unsmoothed statistical maps, by applying an adaptive false discovery rate (FDR) procedure to clusters previously detected. The superior performance of the proposed methodology over other conventional procedures was demonstrated in simulation studies. As expected, only the hierarchical method yielded a predictable false discovery proportion near the predefined FDR q-value for any smoothing level at the same time as being as sensitive as the others at the optimal setting. It was therefore the only method able to approximate the optimal size of spatial smoothing when the true change was assumed unknown. The hierarchical method was further validated in a cross-sectional study comparing moderate Alzheimer's disease (AD) patients with healthy elderly subjects. Results suggest that the extent of cortical thinning reported in previous AD studies might be artificially inflated by the choice of inadequate smoothing. In these cases, interpretation should be based on the location of local maxima of suprathreshold regions rather than on the spatial extent of the detected signal in the statistical parametric map.