These statistics may facilitate clinical trial design using voxel-based brain mapping methods such as TBM.
In doing so, much less memory is needed to represent the robot's roaming environment, as compared to traditional mapping methods, such as occupancy grids.
Finally, due to acceptable spatial accuracy, this study suggests expert-based mapping methods such as MCE and statistical algorithms such as LR can be used as ground truth layers for a sampling of presence/absence data.
However functional units cannot always be distinguished from their surrounding areas by conventional activation mapping methods such as correlation or hypothesis tests, which only consider temporal variation within each individual voxel.
Recently, automated brain mapping methods, such as tensor-based morphometry (TBM) of structural MRI, have outperformed cognitive measures in their precision and power to track disease progression, greatly reducing sample size estimates for drug trials.
Such many-to-one mapping is difficult to achieve by conventional linear mapping methods such as fMLLR or piecewise linear mapping methods such as SPLICE due to their limited flexibility.
In this study, we have used two robust QTL mapping methods, such as IM and ICIM, of which former is a commonly used method for mapping QTLs from biparental crosses (Li et al. 2007).
The use of ecological niche models for quantitative prediction of geographical distributions of agent, reservoir, and vector species has been advocated to augment traditional mapping methods such as splining and kriging [9].
Demonstrating the genetic basis for saltation through genome mapping methods, such as quantitative trait loci (e.g.[31]) and DNA microarray analysis may be a pathway to understanding how many genes are involved in each saltational trait.
In preliminary analyses, we found related univariate mapping methods such as EM interval mapping [ 21] and ANOVA to have performance almost indistinguishable from HK (data not shown).
