In the next stage of the analysis, two regressions were conducted: (1) the spatial regression extracted subject-specific time series for each group-averaged ICA component and (2) the temporal regression computed subject-specific weighted spatial maps for each group-averaged ICA component.
To address this, we categorized data points to one or other condition (either gaze shift or open/close) using a 6-s shift in the transitions between conditions to reflect the delay to peak BOLD response (after Stephan et al. 2003) and computed subject-wise regression coefficients (betas) for the responses between the FG and pSTS seeds and the respective STG and MFG target regions.
A content analysis was conducted for 236 scholarly journal articles, which were published between 2009 and 2014 in order to (i) identify the possible trends and changes in cloud computing over the six years, (ii) compare publishing productivity of journals about the cloud computing subject, and (iii) guide future research about cloud computing.
Specifically, we first classify subject replicates at the sample-level and then perform a majority vote across the subject replicates in order to compute subject classification.
The purpose of this study was to take a first step towards this goal and create and validate two-dimensional FE simulations, using the intact glenoid, for computing subject-specific adaptive glenoid remodeling.
Second, we provide a means for computing subject-level classification.
Subject-specific profiles of PSA and serum CLU levels during treatment were characterized using statistical modeling to compute subject-specific summary measures; these measures were analyzed for relationship to survival using proportional hazard regression.
Statistical modeling was used to compute subject-specific summary measures of PSA and serum clusterin levels at baseline and at Day 100 of treatment, followed by a regression analysis to evaluate relationship to overall survival.
Using these, we then computed individual subject exposures for the same exposure windows as described above for the monitor-based approaches.
However, the vessels are blurred and duplicated in the aggregated image computed without subject stabilization (Fig. 9(B)) preventing accurate mapping of lymph flow (Fig. 9(D)) and hence accurate quantification of lymphatic contractile function.
