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Post-processing for spectral coherence of magnetoencephalogram background activity: Application to Alzheimer's disease

Estimating the connectivity between magnetoencephalogram (MEG) signals provides an excellent opportunity to analyze whole brain functional integration across a spectrum of conditions from health to disease. For this purpose, spectral coherence has been used widely as an easy-to-interpret metric of signal coupling. However, a number of systematic effects may influence the estimations of spectral coherence and subsequent inferences about brain activity. In this pilot study, we focus on the potentially confounding effects of the field spread and the on-going dynamic temporal variability inherent in the signals. We propose two simple post-processing approaches to account for these: 1) a jack-knife procedure to account for the variance in the estimation of spectral coherence; and 2) a detrending technique to reduce its dependence on sensor proximity. We illustrate the effect of these techniques in the estimation of MEG spectral coherence in the α band for 36 patients with Alzheimer's disease and 26 control subjects.