The multivariate classification models chosen for this study were K-nearest neighbors (KNN) and support vector machine (SVM).
Lastly, to our knowledge, no study in ADHD has used multivariate classification methods to task-related functional imaging data.
This segmentation approach has been used for multivariate classification of AD and HC [ 17], MCI volumetric studies [ 9, 10], neuropsychological-image analysis [ 18, 19], imaging-genetic analysis [ 20, 21], and biomarker discovery [ 22].
Confounds, systemic or otherwise, introduced in a study design cannot be recorded or tested using multivariate classification methods in as flexible a fashion as in (e.g.,) general linear models [Todd et al., 2013].
The application of multivariate classification techniques on fMRI data has been shown effective in multiple studies, e.g. (LaConte et al. 2005, 2007; Sitaram et al. 2011).
Furthermore, the fact that differences between PCA and tAD patients were sufficient to enable a multivariate classification approach to achieve statistically significant group separation may encourage further studies that assess longitudinal structural changes in larger subject groups.
We used the multivariate classification tree approach to investigate which of the fitted model parameters differed significantly between the study communities and which may, therefore, be considered to underlie any between-community variation in the model outputs [ 47].
Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) [ 18, 20] is a multivariate classification technique used for finding patterns in large multivariate data sets that describe differences between the groups under study.
Time-resolved multivariate classification of target type.
Time-resolved multivariate classification of target location.
