paperKB
coga / coga-kb
Help
Sign in

Chunk #40 — 2. Material and Methods — 2.4. Current Source Density (CSD) and Principal Components Analysis (PCA)

Source
Stimulus- and response-locked neuronal generator patterns of auditory and visual word recognition memory in schizophrenia.
Embedded
yes

Text

Averaged ERP waveforms were transformed into current source density (CSD) estimates (μV/cm2 units) using a spherical spline surface Laplacian (Perrin et al., 1989) as detailed elsewhere (e.g., Kayser and Tenke, 2006a; Kayser et al., 2007). To determine common sources of variance in these reference-free transformations of the original ERP data, CSD waveforms were submitted to temporal principal components analysis (PCA) derived from the covariance matrix, followed by unrestricted Varimax rotation of the covariance loadings. However, only a limited number of meaningful, high-variance CSD factors are retained for further statistical analysis (for complete rationale, see Kayser and Tenke, 2003, 2005 for complete rationale, see Kayser and Tenke, 2006a, 2006c). By virtue of the reference-independent Laplacian transform, CSD factors have an unambiguous component polarity and topography.