PMC:4620161 / 7913-9698
Annnotations
{"target":"http://pubannotation.org/docs/sourcedb/PMC/sourceid/4620161","sourcedb":"PMC","sourceid":"4620161","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4620161","text":"With one parameter per condition, FSM is the most efficient1 and statistically powerful among the three, if the presumed shape is reasonably close to the ground truth, and the group analysis strategies have been developed to reasonable maturity: The β values at the individual level are typically brought to the group level using the Student's t-test, permutation tests (Nichols and Holmes, 2002; Dehaene-Lambertz et al., 2006; Mériaux et al., 2006; Winkler et al., 2014), AN(C)OVA, general linear model (GLM) (Poline and Brett, 2012), multivariate modeling (MVM) (Chen et al., 2014), linear mixed-effects (LME) method (Bernal-Rusiel et al., 2013; Chen et al., 2013), or mixed-effect multilevel analysis (Worsley et al., 2002; Woolrich et al., 2004; Chen et al., 2012), with the assumption that each effect estimate is equally reliable across all subjects. However, deviations of the HDR from the presumed shape would result in biased estimates of the amplitude, in addition to failing to capture differences in shape such as during the undershoot or recovery phase. ESM is the most flexible among the three methods in terms of providing a more accurate characterization of the BOLD response and can achieve higher activation detection power in individuals. In addition, the estimated HDR curve with a unique signature shape offers much stronger support for the existence of activation than a single scaling factor or β value with FSM or ASM. Compared with FSM, ASM also results in a less biased response amplitude for the principal kernel, and can account for more variance compared to FSM; however, the common practice of using only the principal kernel's coefficient at the group level will not allow the detection of shape changes between conditions and or groups when those 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