Fig. 1: The PCA-Regression data reduction approach used to summarize the relationship between symptoms and connectivity in the current model. | Neuropsychopharmacology

Fig. 1: The PCA-Regression data reduction approach used to summarize the relationship between symptoms and connectivity in the current model.

From: Proof of concept study to develop a novel connectivity-based electric-field modelling approach for individualized targeting of transcranial magnetic stimulation treatment

Fig. 1

A Resting state functional connectivity (rsFC) is calculated using Pearson’s correlation across all subjects for all regions in the Gordon atlas [49]. B A principal component analysis (PCA) is used to identify orthogonal components in the rsFC data, and a geometric approach is used to identify a minimal number of components that explain a maximal proportion of the variability. C Component scores for the selected components are extracted and entered into a multiple linear regression to predict symptoms (D). The PCA loadings from the selected components (E) are combined with the coefficient vector from the regression (F) using matrix multiplication to create the output vector (G), which is used to represent multiple regression coefficients projected into the rsFC feature space. Network Color key: DMN = Default Mode Network; CP = CinguloParietal; VIS = Visual; FPN = FrontoParietal Network; DAN = Dorsal Attention Network; VAN = Ventral Attention Network, SN = Salience Network, CO = CinguloOpercular, SMh = SomatoMotor (hand), SMm = SomatoMotor (mouth), AUD = Auditory, RST = RetrosplenialTemporal, UN = Unassigned nodes.

Back to article page