Figure 7 Results from 193 spectra in vivo data: Select ICs and matching LCModel spectra shown; all spectra zero-mean, unit-norm. See resonance peaks of the ICs substantially overlapping matched basis spectra;

also notice minor covarying resonances along the baseline. … Figure 8 illustrates the capability of ICA to extract certain resonances of interest in the presence of confounds, and toward this, we present three sets of plots in columns. The Inhibitors,research,lifescience,medical first plot in each set (top row) is the 193 subjects spectral data input to ICA, the composite spectra reconstructed from principal components. The second plot (middle row) is the variability in the data explained by an individual independent Inhibitors,research,lifescience,medical component or group of ICs. The final plot (bottom row) is the residue or the variability unexplained by the respective component(s). For the purposes of this illustration, we selected two individual ICs (Cho, NAA), and the whole set of six

ICs (Cr, m-Ins, NAA, NAAG, PCh, and s-Ins) shown in Figure 7. Figure 8 Cut-out plots from in vivo experiment: Top row shows the input to ICA, real part of in Inhibitors,research,lifescience,medical vivo spectra from 193 subjects. Mid row captures the variability explained by select component(s): Cho, NAA, and “all select spectra”. Bottom row captures … Discussion Our simulation results show that ICA unmixes noise-free, multivariate data and extracts components closely resembling underlying spectra and that the ICA estimates closely track the ground truth-mixing coefficients. We also demonstrate that ICA offers superior consistency of results with simulated data compared to LCModel; while both results are nearly identical in the ideal case for LCModel, ICA is much more robust than LCModel in the Inhibitors,research,lifescience,medical nonideal case where the actual ground truth deviates from the assumed basis set, illustrating the effects of modeling inaccuracies. A close look at the effects of spectral

correlations of the two sets of basis spectra reveals that the varying degrees of correlations Inhibitors,research,lifescience,medical of LCModel estimates in the nonideal case are due to the extent of the differences of spectral patterns Rolziracetam between the models. A wealth of information can be gleaned from the ICA results alone, by closely examining ICA’s performance in selleck inhibitor extracting modeled resonances having different statistical properties. The illustration in Figure 4, where the modeled resonances of m-Ins and Gly are compared with their matching ICs, helps bring out the limitations and advantages of the ICA approach. The modeled spectra are correlated to each other, due to their common peak at ~3.56 ppm. However, as the variability associated with that peak does not covary with other peaks in the modeled m-Ins resonance, ICA resolves the peak at 3.56 ppm separately and thus provides a clean estimate of Gly. As ICA minimizes mutual information among the components, the 3.56-ppm peak does not appear in the m-Ins like component, even though modeled spectrum has a 3.