Incorporating resting state dynamics in the analysis of encephalographic responses by means of the MahalanobisTaguchi strategy
The analysis of encephalographic responses has mostly been attempted via signal analytic techniques aiming at revealing the useful information from recordings which are considered as contaminated by the ubiquitous ongoing (or background) brain activity. There is continuously accumulating evidence for the existence of well-defined resting-state-networks (RSNs) in the brain, which play a crucial role in the generation of spontaneous activity and the associated neural responses. Hence, the signal plus noise is no longer a valid model and the ongoing fluctuations may influence the response. We introduce here the use of a multivariate statistical methodology, known as MahalanobisTaguchi (MT) strategy, which can be tailored to the spontaneous fluctuations so as to optimize the subsequent response detection. A subject-specific version of the MT strategy that combines the original methodology with a clustering algorithm for refining the training set is presented. The proposed methodology serves as an explorative tool for the detailed study of temporal patterning in brain responses. We demonstrate the potential of approach by applying it to experimental magneto-encephalographic (MEG) data. The results indicate vividly the effectiveness of the MT-strategy in analyzing and enhancing auditory responses.
mahalanobistaguchi strategy brain responses resting state activity magnetoencephalography