PMC:4620161 / 56220-58235
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":"Limitations of the ESM approach\nIt is noteworthy that the reliability information from the individual subject analysis is not considered at the group level with the modeling methods discussed here, unlike the mixed-effect multilevel analysis (Worsley et al., 2002; Woolrich et al., 2004; Chen et al., 2012). In addition, the number of basis functions monotonically increases among FSM, ASM, and ESM, therefore it is expected that the goodness of fit at the individual subject analysis level improves across the three methods. On the other hand, as each condition is characterized through multiple (e.g., ≥7) basis functions in ESM, a reliable estimation of the HDR curve at the individual level pays a price through the lower degrees of freedom and requires enough (e.g., 20 or more) trials per condition, and may encounter the risk of numerical instability due to high correlations or even multicollinearity among the regressors. These latter issues can be exacerbated by poor stimulus timing designs. In addition, the typical regression analysis at the individual level assumes the linearity of HDR across trials. Although available (e.g., 3dNLfim in AFNI), a non-linear approach is usually difficult to handle and still requires some extent of prior information about the HDR shape. Furthermore, the ESM approach is generally considered to be susceptible to noise or effects unrelated to the effects of interest (e.g., head motion, physiological confounds). In other words, the confounding effects may leak into the HDR estimation through over-fitting. However, the false positives from the potential over-fitting at the individual level is less a concern at the group level for the following reasons: a) the likelihood is reduced unless most subjects systematically have similar or same confounding effects; b) cluster-based inferences may reduce the risk of false positives; and most importantly c) examination of the estimated HDR profiles offer an extra safeguard to filter out the potential false positives.","divisions":[{"label":"title","span":{"begin":0,"end":31}}],"tracks":[{"project":"0_colil","denotations":[{"id":"26578853-11771969-358056","span":{"begin":259,"end":263},"obj":"11771969"},{"id":"26578853-15050594-358057","span":{"begin":282,"end":286},"obj":"15050594"},{"id":"26578853-22245637-358058","span":{"begin":301,"end":305},"obj":"22245637"}],"attributes":[{"subj":"26578853-11771969-358056","pred":"source","obj":"0_colil"},{"subj":"26578853-15050594-358057","pred":"source","obj":"0_colil"},{"subj":"26578853-22245637-358058","pred":"source","obj":"0_colil"}]},{"project":"TEST0","denotations":[{"id":"26578853-227-235-358056","span":{"begin":259,"end":263},"obj":"[\"11771969\"]"},{"id":"26578853-233-241-358057","span":{"begin":282,"end":286},"obj":"[\"15050594\"]"},{"id":"26578853-231-239-358058","span":{"begin":301,"end":305},"obj":"[\"22245637\"]"}],"attributes":[{"subj":"26578853-227-235-358056","pred":"source","obj":"TEST0"},{"subj":"26578853-233-241-358057","pred":"source","obj":"TEST0"},{"subj":"26578853-231-239-358058","pred":"source","obj":"TEST0"}]},{"project":"2_test","denotations":[{"id":"26578853-11771969-38285045","span":{"begin":259,"end":263},"obj":"11771969"},{"id":"26578853-15050594-38285046","span":{"begin":282,"end":286},"obj":"15050594"},{"id":"26578853-22245637-38285047","span":{"begin":301,"end":305},"obj":"22245637"}],"attributes":[{"subj":"26578853-11771969-38285045","pred":"source","obj":"2_test"},{"subj":"26578853-15050594-38285046","pred":"source","obj":"2_test"},{"subj":"26578853-22245637-38285047","pred":"source","obj":"2_test"}]}],"config":{"attribute types":[{"pred":"source","value type":"selection","values":[{"id":"0_colil","color":"#b593ec","default":true},{"id":"TEST0","color":"#93ec9b"},{"id":"2_test","color":"#ec93a4"}]}]}}