Application of ICA to realistically simulated 1H-MRS data

Ravi Kalyanam, David Boutte, Kent E. Hutchison, Vince D. Calhoun

Research output: Contribution to journalArticlepeer-review


Introduction: 1H-MRS signals from brain tissues capture information on in vivo brain metabolism and neuronal biomarkers. This study aims to advance the use of independent component analysis (ICA) for spectroscopy data by objectively comparing the performance of ICA and LCModel in analyzing realistic data that mimics many of the known properties of in vivo data. Methods: This work identifies key features of in vivo 1H-MRS signals and presents methods to simulate realistic data, using a basis set of 12 metabolites typically found in the human brain. The realistic simulations provide a much needed ground truth to evaluate performances of various MRS analysis methods. ICA is applied to collectively analyze multiple realistic spectra and independent components identified with our generative model to obtain ICA estimates. These same data are also analyzed using LCModel and the comparisons between the ground-truth and the analysis estimates are presented. The study also investigates the potential impact of modeling inaccuracies by incorporating two sets of model resonances in simulations. Results: The simulated fid signals incorporating line broadening, noise, and residual water signal closely resemble the in vivo signals. Simulation analyses show that the resolution performances of both LCModel and ICA are not consistent across metabolites and that while ICA resolution can be improved for certain resonances, ICA is as effective as, or better than, LCModel in resolving most model resonances. Conclusion: The results show that ICA can be an effective tool in comparing multiple spectra and complements existing approaches for providing quantified estimates.

Original languageEnglish (US)
Article numbere00345
JournalBrain and Behavior
Issue number7
StatePublished - Jul 1 2015


  • LCModel analysis
  • Proton spectroscopy
  • Realistic simulations
  • Spectral ICA

ASJC Scopus subject areas

  • Behavioral Neuroscience


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