Table 1.
Demographic and clinical characteristics of the 49 participants.
Fig 1.
Schematic illustration of the multi-feature combination and classification.
ALFF, ReHo, RFCS and GM measures are used to map resting-state brain function and brain structure, respectively. A SVM classifier is then designed using a multi-kernel combination strategy to classify MWoA and HC.
Table 2.
Classification performance using different types of feature.
Table 3.
Top 10 frequently selected features for proposed classification.
Fig 2.
Classification performance of the proposed framework.
ROC curve of the classifier, showing the trade-off between sensitivity (y-axis) and specificity (x-axis, 1-specificity). The area under the ROC curve is 0.83 for the proposed approach.
Fig 3.
Top ten most discriminative features (regional ALFF, ReHo, RFCS and GM).
To visually represent the relative contribution of brain regions for classification, the ROIs were projected onto the cortical surface (top) and shown in 2D slice images (down). Different colors in the figure indicate different brain regions. The surface maps were visualized using BrainNet Viewer (http://www.nitrc.org/projects/bnv/) and the 2D slice map was generated using MRIcron (http://www.mccauslandcenter.sc.edu/mricro/mricron/). L: left, R: right.