Data Analysis in Functional MRI
Benali H.1, Descombes X.2, Kruggel F.3, Rajapakse J.C.4, Svensen M.5
1 Faculte de Medecine Pitie-Salpetriere, 2 INRIA, Sophia Antipolis, 3 University of California, Irvine, 4 Nanyang Technological University, Singapore, 5 Max-Planck-Institute of Cognitive Neuroscience, Leipzig
Most functional MRI (fMRI) studies apply the blood-oxygen-level-dependent (BOLD) effect, where an indirect correlate of brain activation, the so-called hemodynamic response (HR) of the vascular system, is measured. Activations are found as stimulus-linked and time-dependent local intensity changes in the images. Data analysis is complicated by the following circumstances: the effect is rather small, data are noisy and overlaid by artifacts, the HR due to a brief stimulus is typically delayed by 3-5 s and dispersed by 1.5-2.5 s, and perhaps also displaced with respect to the activation site. So the question remained open, how much could be inferred from the HR shape characteristics about the underlying neuronal activation. Strategies for fMRI data analysis are best cast in a classical framework for signal analysis:
- Signal preprocessing: artifact and noise filtering, image restoration,
- Signal detection: using test statistics and/or techniques for exploratory data analysis to detect stimulus-related activation, and
- Signal description: modeling the response shape by parameters.
Read more...
Descombes X., Kruggel F., von Cramon D.Y. (1999) A Markov-Pixon-Information Approach for Low-Level Image Description. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 482-494.
Kruggel F., Pelegrini-Issac N., Benali H. (2002) Estimating the Effective Degrees of Freedom in Univariate Multiple Regression Analysis. Medical Image Analysis 6, 63-75.
Kruggel F., Zysset, S., von Cramon D.Y. (2000) Nonlinear Regression of Functional MRI Data: An Item Recognition Task Study. NeuroImage 12, 173-183.
Kruggel F., von Cramon, D.Y. (1999) Comparison of Filtering Methods for fMRI Datasets. NeuroImage 10, 530-543.
Kruggel F., von Cramon D.Y. (1999) Modeling the Hemodynamic Response in Single Trial fMRI Experiments. Magnetic Resonance in Medicine 42, 787-797.
Kruggel F., von Cramon D.Y. (1999) Temporal Properties of the Hemodynamic Response in Functional MRI. Human Brain Mapping 8, 259-271.
Rajapakse J.C., Kruggel F., Maisog J., von Cramon D.Y. (1998) Modeling the Hemodynamic Response for Analysis of Functional MRI Time-Series. Human Brain Mapping 6, 283-300.
Svensen M., Kruggel F., Benali H. (2002) ICA of fMRI Group Study Data. NeuroImage 15, 551-563.