A Spectral Clustering Approach to fMRI Activation Detection

Conventional clustering methods for fMRI activation detection implicitly assume that data scatter in clusters with certain shapes. But this assumption is inconsistent with the general reality in fMRI data, and will consequently achieve detection results with higher false alarm rate. To solve this problem, we propose an alternative clustering method, namely spectral cluster analysis (SCA), which uses eigenvectors of a matrix derived from the dataset to cluster the wavelet coefficients extracted from the fMRI time series. Experimental results demonstrate reliability and flexibility of this new fMRI clustering approach.