linear svm approach within a library for svms (libsvms) toolkit  (MathWorks Inc)

Journal: Social Cognitive and Affective Neuroscience

Article Title: Electroconvulsive therapy selectively enhanced feedforward connectivity from fusiform face area to amygdala in major depressive disorder

doi: 10.1093/scan/nsx100

Figure Lengend Snippet: Multivariate pattern analysis using LIBSVM was applied to provide provisional evidence to determine whether identified neural indices might serve as biomarkers for diagnosing MDD. The regional GMV of amygdala, FC between SF amygdala and FFA and effective connectivity from FFA to SF amygdala were used as the features for classification. We used a leave-one-out cross-validation strategy to estimate the generalization ability of our classifier. The classification accuracy, sensitivity and specificity were showed.

Article Snippet: To explore whether the identified neural indices might serve as biomarkers for diagnosing MDD, a linear SVM approach within a library for SVMs (LIBSVMs) toolkit running on MATLAB ( ) was performed.

Techniques: Biomarker Discovery