Statistical limitations in functional neuroimaging II. Signal detection and statistical inference

K. M. Petersson, T. E. Nichols, J.-B. Poline, A. P. Holmes
1999 Philosophical Transactions of the Royal Society of London. Biological Sciences  
Low-pass ¢ltering in relation to functional^anatomical variability and some e¡ects of ¢ltering on signal detection of interest to FNI are discussed. Also, some general aspects of hypothesis testing and statistical inference are discussed. This includes the need for characterizing the signal in data when the null hypothesis is rejected, the problem of multiple comparisons that is central to FNI data analysis, omnibus tests and some issues related to statistical power in the context of FNI. In
more » ... n, random ¢eld, scale space, non-parametric and Monte Carlo approaches are reviewed, representing the most common approaches to statistical inference used in FNI. Complementary to these issues an overview and discussion of noninferential descriptive methods, common statistical models and the problem of model selection is given in a companion paper. In general, model selection is an important prelude to subsequent statistical inference. The emphasis in both papers is on the assumptions and inherent limitations of the methods presented. Most of the methods described here generally serve their purposes well when the inherent assumptions and limitations are taken into account. Signi¢cant di¡erences in results between di¡erent methods are most apparent in extreme parameter ranges, for example at low e¡ective degrees of freedom or at small spatial autocorrelation. In such situations or in situations when assumptions and approximations are seriously violated it is of central importance to choose the most suitable method in order to obtain valid results.
doi:10.1098/rstb.1999.0478 pmid:10466150 pmcid:PMC1692643 fatcat:lapdgfwdmvdj7brs22f5oqe3y4