Application of EEG Time-Varying Networks in the Evaluation of Dynamic Functional Brain Networks

Abstract

The variability that occurs in spontaneous network communication has brought about increased attention in the area of study that is centered on analytical approaches and models aimed at addressing the shorter timescales conceivable with dynamic functional networks. As the shifts in functional connectivity have been immense in the quantification of task performance in the cognitive domain so has the usefulness in the clinical setting been predicted. More so, the analysis of dynamic functional connections can be of considerable clinical relevance as had been observed in the studies of pathologies such as schizophrenia, Alzheimer's disease mild cognitive impairment. The evaluation of dynamic functional connectivity is however far from being perfect. Though functional magnetic resonance, imaging which has been vastly employed in evaluating neural communication in the human brain, does not appear to be efficient in measuring neuronal dynamics, and this could be down to the variability in sampling, physiological, noise, and head motion that usually accompany fMRI. This is where EEG, despite its limited spatial resolution, has found significance owing to the delivery of temporal resolution which is higher in measuring the time-varying relationships feasible in the rhythmic patterns of neural activity.

In this paper, we shall aim at reviewing the strides that have been made in the efforts to develop an effective technique for quantifying the transitions in functional connectivity that take place over specific timescales.