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

Journal: Nature and Science of Sleep

Article Title: Increased Gray Matter Density and Functional Connectivity of the Pons in Restless Legs Syndrome

doi: 10.2147/NSS.S239852

Figure Lengend Snippet: Multivariate pattern analysis using support vector machine (SVM) was applied to provide provisional evidence to determine whether identified neural indices might serve to distinguish RLS patients from NC. ( A ) We used a leave-one-out cross-validation strategy to estimate the generalization ability of our classifier. Features of gray matter density in pons_2, and functional connectivity between pons_2 and SMA were used. The classification accuracy, specificity, and precision were showed. ( B ) The receiver operating characteristic (ROC) curve. AUC, area under the curve.

Article Snippet: To clarify whether the identified abnormal features might have potential power for diagnosing RLS, we performed a linear SVM approach within LIBSVM in MATLAB.

Techniques: Plasmid Preparation, Biomarker Discovery, Functional Assay

Journal: Human brain mapping

Article Title: A multivariate lesion symptom mapping toolbox and examination of lesion-volume biases and correction methods in lesion-symptom mapping

doi: 10.1002/hbm.24289

Figure Lengend Snippet: Screenshot of the MATLAB-based graphical user interface for SVR-LSM. The single window contains a pane for configuring an analysis, a pane for configuring covariates, a pane for permutation testing, and a feedback pane that reports the progress of an analysis.

Article Snippet: Inclusion of MATLAB SVM The original implementation of SVR-LSM runs within the MATLAB environment, but relies on a popular open-source third-party machine learning library for SVR functionality, LibSVM ( Chang & Lin, 2011 ).

Techniques:

Journal: Human brain mapping

Article Title: A multivariate lesion symptom mapping toolbox and examination of lesion-volume biases and correction methods in lesion-symptom mapping

doi: 10.1002/hbm.24289

Figure Lengend Snippet: Comparison of uncorrected SVR-β value output for original SVR-LSM (left; Zhang et al., 2014), for the new implementation using LibSVM (middle), and MATLAB’s SVM functionality (right). For each output, the value is shown at the same sample voxel coordinate of MNI = −42, 2, 9 also indicated by crosshairs. This analysis was conducted on individuals with chronic left-hemisphere stroke (N = 48) modeling the score for WAB repetition and SVR parameters of cost = 30 and gamma = 5, minimum lesion threshold of 10.

Article Snippet: Inclusion of MATLAB SVM The original implementation of SVR-LSM runs within the MATLAB environment, but relies on a popular open-source third-party machine learning library for SVR functionality, LibSVM ( Chang & Lin, 2011 ).

Techniques: