Model-free Functional MRI Analysis Using Cluster-Based Methods

Conventional model-based or statistical analysis methods for functional MRI (fMRI) are easy to implement, and are effective in analyzing data with simple paradigms. However, they are not applicable in situations in which patterns of neural response are complicated and when fMRI response is unknown. In this paper the "neural gas" network is adapted and rigorously studied for analyzing fMRI data. The algorithm supports spatial connectivity aiding in the identification of activation sites in functional brain imaging. A comparison of this new method with Kohonen's self-organizing map and with a minimal free energy vector quantizer is done in a systematic fMRI study showing comparative quantitative evaluations. The most important findings in this paper are: (1) the "neural gas" network outperforms the other two methods in terms of detecting small activation areas, and (2) computed reference function several that the "neural gas" network outperforms the other two methods. The applicability of the new algorithm is demonstrated on experimental data