A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Brains and phantoms: An ICA study of fMRI
Tekijät: Ylipaavalniemi J, Mattila S, Tarkiainen A, Vigario P
Julkaisuvuosi: 2006
Journal: Lecture Notes in Computer Science
Tietokannassa oleva lehden nimi: INDEPENDENT COMPONENT ANALYSIS AND BLIND SIGNAL SEPARATION, PROCEEDINGS
Lehden akronyymi: LECT NOTES COMPUT SC
Vuosikerta: 3889
Aloitussivu: 503
Lopetussivu: 510
Sivujen määrä: 8
ISBN: 3-540-32630-8
ISSN: 0302-9743
Tiivistelmä
Biomedical signal processing is arguably the most successful application of independent component analysis (ICA) to real world data. For almost a decade, its use in connection with functional magnetic resonance imaging (fMRI) has allowed for data-driven analysis, partly removing the constraints for stringent experimental setups, which are often required by traditional methods based on the use of temporal references. Recent studies on the consistency of independent components have resulted in a series of tools enabling a more reliable use of ICA. In particular, it is now rather easy to detect algorithmic overfitting and isolate subspaces of related activation. Yet, often the nature of the components may not be determined unambiguously. Focal fMRI signals, seemingly originating from within a subject's brain and showing physiologically plausible temporal behavior, are typically considered relevant. This paper presents a study, which makes use of a standard homogeneous spherical phantom and shows evidence for artifacts caused by the measuring device or environment, with characteristics that could easily be misinterpreted as physiological. Our results suggest that reliable analysis of fMRI data using ICA may be far more difficult than previously thought. At least, artificial behavior revealed by phantom analysis should be considered when conclusions are drawn from real subject measurements.
Biomedical signal processing is arguably the most successful application of independent component analysis (ICA) to real world data. For almost a decade, its use in connection with functional magnetic resonance imaging (fMRI) has allowed for data-driven analysis, partly removing the constraints for stringent experimental setups, which are often required by traditional methods based on the use of temporal references. Recent studies on the consistency of independent components have resulted in a series of tools enabling a more reliable use of ICA. In particular, it is now rather easy to detect algorithmic overfitting and isolate subspaces of related activation. Yet, often the nature of the components may not be determined unambiguously. Focal fMRI signals, seemingly originating from within a subject's brain and showing physiologically plausible temporal behavior, are typically considered relevant. This paper presents a study, which makes use of a standard homogeneous spherical phantom and shows evidence for artifacts caused by the measuring device or environment, with characteristics that could easily be misinterpreted as physiological. Our results suggest that reliable analysis of fMRI data using ICA may be far more difficult than previously thought. At least, artificial behavior revealed by phantom analysis should be considered when conclusions are drawn from real subject measurements.
Turun yliopiston Tutkimustietojärjestelmän portaali