In addition, we performed multivariate pattern analysis [38] to distinguish between the two levels of task-specific information for each task in face selective areas.
Here, SVM has been used with traditional neuropsychological data for the first time, and we aim to test whether multivariate pattern information could also be useful for distinguishing between two groups.
In contrast, multivariate pattern analyses (MVPA) for imaging data take into account interactions between regions (i.e., brain structure or function patterns) and can make predictions (e.g., of class membership) for individual subjects as opposed to group-level inferences.
Differences in FA values of white matter between OCD and healthy controls were examined using a multivariate pattern classification technique known as support vector machine (SVM).
The optimal hyperplane was computed based on the whole multivariate pattern of ReHo map across each image and could most accurately capture the relationship between each example and its respective label.
Multivariate pattern analysis (MVPA) is a relatively recent innovation in functional magnetic resonance imaging (fMRI) methods.
The second technique, multivariate pattern analysis (MVPA), makes use of multivariate statistics to recover small biases in individual voxels in their responses to different stimuli.
Multivariate pattern classification, as described in Klöppel et al. [10], was performed with a linear kernel by identifying a separating hyperplane that maximizes the distance between different clinical groups based on whole-brain or ROIs information.
Multivariate pattern classification techniques have been applied to BCI systems across many modalities [1], [7].
In order to address these issues, we used multivariate pattern analysis of fMRI data to test whether response patterns in human right STS distinguish between leftward and rightward dynamic head turns.
