Sentence examples for multivariate pattern classification from inspiring English sources

Multivariate pattern classification analysis.

Zarogianni, E., Moorhead, T. W. & Lawrie, S. M. Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level.

The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification.

This study was designed to replicate and extend psychophysiological research, which has used multivariate pattern classification analysis (PCA) in support of ANS specificity.

To investigate the potentially distributed effects of methylphenidate on the brain's intrinsic network architecture, we coupled resting state imaging with multivariate pattern classification.

Novel multivariate pattern classification analyses have enabled the prediction of decision outcomes from brain activity prior to decision-makers' reported awareness.

Using multivariate neural pattern classification, we show that the hippocampus and putamen integrate event attributes across all of these levels in conjunction with other regions representing concrete-feature-selective (primarily visual cortex), category-selective (posterior frontal cortex), and control demand-selective (insula, caudate, anterior cingulate, and parietal cortex) event information.

One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data.

In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies.

Concurrent with research on basic processes, my laboratory has also investigated the application of new analysis methods for fMRI data, including functional connectivity analyses, pattern classification analyses, and combinatoric multivariate approaches.

Taken together, these findings extend recent multivariate approaches to study emotion and indicate that pattern classification tools may improve upon univariate approaches to reveal the underlying structure of emotional experience and physiological expression.