Materials and Methods ICA is a linear time-invariant

method that decomposes a set of observations into a linear combination of basis signals. It may be seen as a higher order generalization (Comon 1994) of PCA, often employed for dimension reduction prior to ICA. Unlike PCA, which imposes independence up to second order and defines orthogonal directions, ICA minimizes statistical dependence between its components, and is uniquely defined when at most one component is Gaussian (Bell and Sejnowski 1995). As MR spectra are made of contributions from individual metabolite spectra that can vary independently, estimated ICs are expected to characterize well any Inhibitors,research,lifescience,medical independently varying signals from metabolites. The linear construct in equation (2) expresses a composite spectrum or Inhibitors,research,lifescience,medical observation xn, as a linear combination of a set of k components or sources si, weighted by mixing coefficients ai. (1) ICA estimates

the matrix W that demixes multivariate data X to extract estimates Y of sources S, such that Y = WX are mutually independent. If the sources are mutually independent, then Y is close to S and W is the pseudoinverse of A. A variety of algorithms Inhibitors,research,lifescience,medical implementing the iterative learning and estimations of W exist. They construct unmixing matrix W such that negentropy, or distance from normality, of Y is maximized. As negentropy Inhibitors,research,lifescience,medical is difficult to compute, many algorithms rely on kurtosis as its estimate. In our implementation, we use the infomax algorithm (Bell and Sejnowski 1995) on the real part of input Selisistat spectral data from our simulation experiment or in vivo and demonstrate ICA’s ability to resolve spectra and extract resonances having different statistical properties. Data simulation The design objective of our simulation experiment was to assess how well ICA extracts underlying components and ground truth-mixing Inhibitors,research,lifescience,medical coefficients from simulated data resembling in vivo human brain MR spectra; and to explore how ICA results compare to LCModel results from the same data. Data simulated

with two different sets of modeled resonances, with no added noise or artifacts, provided a means to compare ICA approach with LCModel analysis, as well as to establish upper bounds of ICA’s ability in MR spectral applications. Resminostat The composition of our basis set of metabolites was based on a list of metabolites typically included in a LCModel basis set with analysis window of 1.8–4.2 ppm, the analysis window used in a prior report on these data (Yeo et al. 2013). The basis set was composed of 12 metabolites: aspartate (Asp), creatine (Cr), γ-amino butyric acid (GABA), glucose (Glc), glutamine (Gln), glutamate (Glu), N-acetyl aspartate (NAA), the N-acetyl peak of N-acetylaspartylglutamate (NAAG), the trimethyl amine group of phosphocholine (PCh), taurine (Tau), and myo-inositol (m-Ins) and its isomer scyllo-inositol (s-Ins).