Sparse PCA, a new method for unsupervised analyses of fMRI data

Karl Sjöstrand, Torben E. Lund, Kristoffer Hougaard Madsen, Rasmus Larsen

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    176 Downloads (Pure)

    Abstract

    Exploratory analysis of functional MRI data aims at revealing known as well as unknown properties in a data-driven manner devoid of hypotheses on the time course of the hemodynamic response. This uncommitted approach usually precedes confirmatory modeling and may point to unexpected results that otherwise would be lost. Common approaches include clustering methods, principal component analysis (PCA) and in particular independent component analysis (ICA). ICA assumes that the measured activity patterns consist of linear combinations of a set of statistically independent source signals. Under favorable circumstances, one of more of these signals describe activation patterns, while others model noise and other nuisance factors. This work introduces a competing method for fMRI analysis known as sparse principal component analysis (SPCA). We argue that SPCA is less committed than ICA and show that similar results, with better suppression of noise, are obtained.
    Original languageEnglish
    Title of host publicationProc. International Society of Magnetic Resonance In Medicine - ISMRM 2006, Seattle, Washington, USA
    PublisherISMRM
    Publication date2006
    Publication statusPublished - 2006
    Event14th Scientfic Meeting and Exhibition of International Society for Magnetic Resonance in Medicine - Seattle, WA, United States
    Duration: 6 May 200612 May 2006
    Conference number: 14
    http://www.ismrm.org/06/index.htm

    Conference

    Conference14th Scientfic Meeting and Exhibition of International Society for Magnetic Resonance in Medicine
    Number14
    CountryUnited States
    CitySeattle, WA
    Period06/05/200612/05/2006
    Internet address

    Cite this