PMC:4620161 / 66351-67944 JSONTXT

{"target":"http://pubannotation.org/docs/sourcedb/PMC/sourceid/4620161","sourcedb":"PMC","sourceid":"4620161","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4620161","text":"Here we demonstrate with simulations and experimental data that the fixed-shape (FSM) or adjusted-shape (ASM) method may fail to detect most of the shape subtleties (e.g., the speed of rise or recovery, undershoot) in hemodynamic response (HDR). In contrast, the estimated-shape method (ESM) through multiple basis functions would more accurately characterize the cerebral blood flow regulation, and significantly improve the detection power at both individual and group levels. In addition, we propose an analysis scheme for ESM that still fits within the conventional two-tier analysis pipeline and achieves higher statistical power than the alternatives: one performs regression time series analysis separately for each individual subject, and then conducts group analysis with the individual effect estimates. For one group of subjects, a linear mixed-effects (LME) model is preferred if no other explanatory variables are present. In all other scenarios, statistical inferences on the HDR shape can be achieved through a hybrid combination of multivariate testing (MVT) and dimensional reduction approaches with a multivariate model (MVM). Simulations are shown in terms of controllability for false positive rate (FPR) and power achievement among various testing methods. The strategy was applied to a dataset from a real experiment to compare among different testing strategies in terms of power assessment. In addition, we showcase that the MVM flexibility allows any number of explanatory variables including between- and within-subject factors as well as between-subjects covariates.","tracks":[]}