Mathematics in Independent Component Analysis

258 Chapter 19. LNCS 3195:977-984, 2004
3D Spatial Analysis of fMRI Data on a Word Perception Task 983
Comparison of the Regression Analysis Versus ICA. To compare the
results of the fixed-effect analysis with the results of the ICA the correlation
coefficients between the expected time-trends of the fixed-effect analysis and the
time-trends of the ICs were calculated. No substantial correlation was found:
87 % of all these coefficients were in the range of −0.1 to0.1, the highest coefficient
found being 0.36 for an IC within the auditory cortex (figure 2). The
correlation coefficients for the proposed word detection network (figure 3) were
0.14, 0.08, 0.19 and 0.18 for FB1–FB4. Therefor it is quite obvious that this
network of areas in the inferior frontal gyrus cannot be detected with a classic
fixed-effect regression analysis.
While the reasons for the differences between the activation-trends of the
ICs and the assumed time-trends are still subject to on-going research, it can be
expected that the results of this ICA will help to gain further information about
the work flow of the brain concerning the task of word detection.
Fig. 5. The activation of the ICs shown in figure 3 (dotted) and 4 (solid), plotted
for scan no. 25–75. While these time-trends obviously appear to be correlated, their
correlation coefficient remains very low due to temporary baseline- and time-shifts in
the trends.
3 Conclusions
We have shown that ICA can be a valuable tool to detect hidden or suspected
links and activity in the brain that cannot be found using the classical approach
of a model-based analysis like the general linear model. While clearly ICA cannot
be used to validate a model (being in itself model-free), it can give useful hints
to understand the internal organization of the brain and help to develop new
models and study designs which then can be validated using a classic regression
analysis.