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The performance of the method is demonstrated by applications to tomograms of different biological samples and by quantitative comparison with standard template matching procedure.
We determined the DMN using an unbiased template matching procedure [18].
This result was not replicated by applying tensor PICA, although an ulterior application of the template matching procedure on the same data set did render similar results [11].
Within the DMN, the combination of individual ICA and template matching procedure for group analysis [8] demonstrated local bilateral decreases of activity in the hippocampus.
To identify genes specifically altered in the late adolescent period, we applied a template matching procedure.
Furthermore, a template matching procedure was applied to minimize blink artifacts (cf. Lamm, Fischmeister, & Bauer, 2005).
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The method employs a rolling-window template-matching procedure that identifies statistically significant deviations between a response matrix characteristic of an unflawed sample and a response matrix obtained from a test sample.
The DMN was selected using a template-matching procedure.
To do so, we used a "linear template-matching procedure" that involves taking the average z-score of voxels falling within the template minus the average z-score of voxels outside the template and selecting the component in which this difference (the goodness-of-fit) was the greatest [10], [20], [21], [22].
The template-matching procedure made predictions essentially identical to those we report here, perhaps even marginally better, but only after much close monitoring.
Independent component analysis and a template-matching procedure were used to identify the DMN, ECN, working memory networks (WMN), SN, sensorimotor (SM), auditory and visual (VN) networks.
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