Exploring BOLD Data with Blind Source Separation
Minnesota Workshops on High-Field MR Imaging and Spectroscopy University of Minnesota
October 5-10, 2001Scott Makeig,
Institute Institute for Neural Computation,
University of California San Diego
La Jolla CA 92037-0523
Most current methods for analyzing functional magnetic resonance imaging (fMRI) data assume a priori knowledge of the shape of the hemodynamic response (HR) to experimental stimuli or events in brain areas of interest. In addition, they typically assume homogeneity of both the HR and the non-HR "noise" signals across brain regions and across similar experimental events. Such approaches are inadequate when HRs vary unpredictably from area to area or from trial to trial, or when the expected hemodynamic time course is unknown.
I will demonstrate the use of a data-driven method, infomax Independent Component Analysis (ICA), to detect and visualize single-trial HRs in event-related fMRI data. ICA assumes that the recorded time course at each brain or non-brain voxel is the sum of a number of activities that are expressed with different strengths (and, possibly, polarities) across different (but possibly overlapping) voxel subsets. Different "independent component" processes have separable time courses and stable spatial structure (cf. McKeown et al., Human Brain Mapping, 1998). ICA further assumes that the component processes are maximally spatially independent. Each ICA component can be represented by a spatially-fixed three-dimensional component map and an associated activity time course, and represents a source of signal variance affecting a compact or multifocal brain area of linearly dependent influence.
I will show results of a visual stimulation experiment that demonstrate that the hemodynamic time course even in primary sensory areas may exhibit unassumed variance, incompatible with a "noise" model, that may include important effects of top-down cognitive processes. Combining ICA with adequate single-trial visualization methods allows combined exploratory and hypothesis-driven analysis of functional imaging data.
References
- Makeig, S., Bell, A.J., Jung, T-P, and Sejnowski, T.J., "Independent component analysis of electroencephalographic data," In: D. Touretzky, M. Mozer and M. Hasselmo (Eds). Advances in Neural Information Processing Systems 8:145-151 MIT Press, Cambridge, MA, 1996.
- Makeig, S., T-P. Jung, D. Ghahremani, A.J. Bell & T.J. Sejnowski, "Blind separation of auditory event-related brain responses into independent components." Proc Natl Acad Sci USA 94:10979-10984, 1997.
- McKeown, M., Makeig, S., Brown, G., Jung, T-P., Kindermann, S., Bell, Iragui, V. and Sejnowski, T.J., "Blind separation of functional magnetic resonance imaging (fMRI) data," Human Brain Mapping 6:160-188, 1998.
- McKeown, M., Tzyy-Ping Jung, Makeig, S., Greg Brown, Sandra S. Kindermann, Te-Won Lee and Terrence J. Sejnowski, "Spatially independent activity patterns in functional magnetic resonance imaging data during the Stroop color-naming task." Proc. Natl. Acad. Sci USA 95:803-810, 1998.
- Makeig, S., Marissa Westerfield, Tzyy-Ping Jung, James Covington, Jeanne Townsend, Terrence J. Sejnowski and Eric Courchesne. "Functionally independent components of the late positive event-related potential during visual spatial attention." J. Neurosci. 19:2665-2680, 1999.
- Jung, T-P., Makeig, S., McKeown, Martin J., Bell, Anthony J., Lee, Te-Won, Sejnowski, Terrence J., "Imaging brain dynamics using independent component analysis." Proceedings of the IEEE, 89(7):1107-22, 2001.
- Duann, J-R., T-P. Jung, W-J. Kuo, T-C.. Yeh, S. Makeig, J-C. Hsieh, J Sejnowski, TJ. Measuring the variability of event-related BOLD signals. Submitted.