Abstract
A multivariate non-parametric approach for the processing of fMRI group data is important to address variability of hemodynamic responses across subjects, sessions, and brain regions. Independent component analysis (ICA) has a limitation during the inference of group effects due to a permutation problem of independent components. In order to address this limitation, we present an independent vector analysis (IVA) for the processing of fMRI group data. Compared to the ICA, the IVA offers an extra dimension for the dependent parameters, which can be assigned for the automated grouping of dependent activation patterns across subjects. The IVA was applied to the fMRI data obtained from 12 subjects performing a left-hand motor task. In comparison with conventional univariate methods, IVA successfully characterized the group-representative activation time courses (as component vectors) without extra data processing schemes to circumvent the permutation problem, while effectively detecting the areas with hemodynamic responses deviating from canonical, model-driven ones.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Worsley, K.J., Friston, K.J.: Analysis of fMRI time-series revisited—again. Neuroimage 2, 173–181 (1995)
Aguirre, G.K., Zarahn, E., D’Esposito, M.: The variability of human, BOLD hemodynamic responses. NeuroImage 8, 360–369 (1998)
McKeown, M.J., Makeig, S., Brown, G.G., Jung, T.P., Kindermann, S.S., Bell, A.J., Sejnowski, T.J.: Analysis of fMRI data by blind separation into independent spatial components. Hum. Brain Mapp. 6, 160–188 (1998)
Quigley, M.A., Haughton, V.M., Carew, J., Cordes, D., Moritz, C.H., Meyerand, M.E.: Comparison of independent component analysis and conventional hypothesis-driven analysis for clinical functional MR image processing. AJNR Am. J. Neuroradiol. 23, 49–58 (2002)
Calhoun, V.D., Adali, T., McGinty, V.B., Pekar, J.J., Watson, T.D., Pearlson, G.D.: fMRI activation in a visual-perception task: network of areas detected using the generalized linear model and independent component analysis. Neuroimage 14, 1080–1088 (2001)
Kim, T.S., Attias, H.T., Lee, S.Y., Lee, T.W.: Blind source separation exploiting higher-order frequency dependencies. IEEE Trans. Audio, Speech and Language Process 15, 70–79 (2007)
Duann, J.R., Jung, T.P., Kuo, W.J., Yeh, T.C., Makeig, S., Hsieh, J.C., Sejnowski, T.J.: Single-trial variability in event-related BOLD signals. Neuroimage 15, 823–835 (2002)
Friston, K.J., Holmes, A.P., Worsley, K.J.: How many subjects constitute a study? Neuroimage 10, 1–5 (1999)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, JH., Lee, TW., Jolesz, F.A., Yoo, SS. (2007). Multivariate Analysis of fMRI Group Data Using Independent Vector Analysis. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_79
Download citation
DOI: https://doi.org/10.1007/978-3-540-74494-8_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74493-1
Online ISBN: 978-3-540-74494-8
eBook Packages: Computer ScienceComputer Science (R0)