Feature evaluation in fMRI data using random matrix theory

Quantitative descriptors of intrinsic properties of fMRI data can be obtained from the theory of random matrices. We study data reduction based on the comparison of empirical correlation matrices with a suitably chosen ensemble of random positive matrices. Accordingly, data dimensions can be discarded if the quality of fit of the data spectrum deviates locally from the theoretical result, which is derived here analytically. Further, more complex quantities such as the number variance are discussed and shown to be potentially useful in an analogous manner.