Abstract
It has become common for neurological studies to gather data from multiple modalities, since the modalities examine complementary aspects of neural activity. Functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) data, in particular, enable the study of functional changes within the brain at different temporal and spatial scales; hence their fusion has received much attention. Joint independent component analysis (jICA) enables symmetric and fully multivariate fusion of these modalities and is thus one of the most widely used methods. In its application to jICA, Infomax has been the widely used, however the relative performance of Infomax is rarely shown on real neurological data, since the ground truth is not known. We propose the use of number of voxels in physically meaningful masks and statistical significance to assess algorithm performance of ICA for jICA on real data and show that entropy bound minimization (EBM) provides a more attractive solution for jICA of EEG and fMRI.
Y. Levin-Schwartz—This work was supported in part by NSF grant NSF-IIS 1017718 and NIH grant R01 EB 005846.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abney, S.: Bootstrapping. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 360–367 (2002)
Adalı, T., Levin-Schwartz, Y., Calhoun, V.D.: Multi-modal data fusion using source separation: Application to medical imaging. In: Proceedings of the IEEE (2015, in review)
Bell, A., Sejnowski, T.: An information maximization approach to blind separation and blind deconvolution. Neural Comput. 7, 1129–1159 (1995)
Calhoun, V.D., Adalı, T., Pearlson, G., Kiehl, K.: Neuronal chronometry of target detection: Fusion of hemodynamic and event related potential data. NeuroImage 30, 544–553 (2006)
Calhoun, V.D., Adalı, T.: Feature-based fusion of medical imaging data. IEEE Trans. Inf. Technol. Biomed. 13(5), 711–720 (2009)
Calhoun, V.D., Allen, E.: Extracting intrinsic functional networks with feature-based group independent component analysis. Psychometrika 78(2), 243–259 (2013)
Cardoso, J.F.: Infomax and maximum likelihood for blind source separation. IEEE Signal Process. Lett. 4(4), 112–114 (1997)
Correa, N., Adalı, T., Li, Y.O., Calhoun, V.D.: Canonical correlation analysis for data fusion and group inferences. IEEE Signal Process. Mag. 27(4), 39–50 (2010)
Ernst, M., Nelson, E.E., McClure, E.B., Monk, C.S., Munson, S., Eshel, N., Zarahn, E., Leibenluft, E., Zametkin, A., Towbin, K., Blair, J., Charney, D., Pine, D.S.: Choice selection and reward anticipation: an fMRI study. Neuropsychologia 42(12), 1585–1597 (2004)
Friston, K.J.: Modalities, modes, and models in functional neuroimaging. Science 326(5951), 399–403 (2009)
Gu, X., Han, S.: Neural substrates underlying evaluation of pain in actions depicted in words. Behav. Brain Res. 181(2), 218–223 (2007)
James, A.P., Dasarathy, B.V.: Medical image fusion: A survey of the state of the art. Inf. Fusion 19, 4–19 (2014)
Li, X.L., Adalı, T.: Independent component analysis by entropy bound minimization. IEEE Trans. Signal Process. 58(10), 5151–5164 (2010)
Maldjian, J.A., Laurienti, P.J., Burdette, J.H.: Precentral gyrus discrepancy in electronic versions of the talairach atlas. NeuroImage 21(1), 450–455 (2004)
Maldjian, J.A., Laurienti, P.J., Kraft, R.A., Burdette, J.H.: An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. NeuroImage 19(3), 1233–1239 (2003)
Mangalathu-Arumana, J., Beardsley, S., Liebenthal, E.: Within-subject joint independent component analysis of simultaneous fMRI/ERP in an auditory oddball paradigm. NeuroImage 60(4), 2247–2257 (2012)
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)
Smith, S.M., Fox, P.T., Miller, K.L., Glahn, D.C., Fox, P.M., Mackay, C.E., Filippini, N., Watkins, K.E., Toro, R., Laird, A.R.: Correspondence of the brain’s functional architecture during activation and rest. Proc. Natl. Acad. Sci. U.S.A. 106(31), 13040–13045 (2009)
Sui, J., Adalı, T., Yu, Q., Chen, J., Calhoun, V.D.: A review of multivariate methods for multimodal fusion of brain imaging data. J. Neurosci. Methods 204(1), 68–81 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Levin-Schwartz, Y., Calhoun, V.D., Adalı, T. (2015). Multivariate Fusion of EEG and Functional MRI Data Using ICA: Algorithm Choice and Performance Analysis. In: Vincent, E., Yeredor, A., Koldovský, Z., Tichavský, P. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2015. Lecture Notes in Computer Science(), vol 9237. Springer, Cham. https://doi.org/10.1007/978-3-319-22482-4_57
Download citation
DOI: https://doi.org/10.1007/978-3-319-22482-4_57
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22481-7
Online ISBN: 978-3-319-22482-4
eBook Packages: Computer ScienceComputer Science (R0)