Multivariate and predictive modelling of neural variability in mild cognitive impairment

Brain signal variability has been proposed as an index of the brain's cognitive capacity. In this work, we examined neural variability by calculating the standard deviation of single trial activation estimates during memory encoding in 30 patients with mild cognitive impairment (MCI) and 31 elderly controls. We deployed a random forest (RF) classifier, using variability maps as features to distinguish MCI patients from controls, and obtained classification accuracies of up to 86%. We then used partial least squares correlation to identify variability patterns associated with task performance and compared them to the weight maps obtained with the RF classifier.