Using simulated dynamics and graph metrics to compare brain networks of MCI patients and healthy control subjects

Abstract: In recent years, several different methods have been proposed to compare brain networks with the joint use of graph theory and graph metrics. Another relatively unexplored comparison method is comparing the brain’s response to various input signals by simulating the brain’s dynamics. The brain activity signals are dependent on the physical connectivity patterns of the brain, therefore brain activity signals can be studied in addition to the study of connectivity patterns of brain graphs. Thus, in this study we use both static graph metrics and the dynamics of the brain to compare the brain networks of healthy test subjects and of patients with mild cognitive impairment (MCI). This was done by measuring and comparing the characteristic path length, clustering coefficient, small worldness and average degree distribution of each subject, as well as the Fourier transform amplitude at the frequency of the induced input signal. The result showed that the static graph metrics small worldness and clustering coefficient showed a small difference between the two groups. Differences between the two groups could also be observed with regards to the metric measured on the dynamics, however only between individual brain regions of the brain networks and only at certain input frequencies. The lack of consistency in these results hinders us from drawing deductions about this metric’s ability to differentiate the two groups. However, the study showed compelling enough results to argue for further research in comparing brain network with the use of simulated dynamics.