Statistical Analysis of Graph-Theoretic Indices to Study EEG-TMS Connectivity in Patients With Depression - Olejarczyk, Elzbieta; Jozwik, Adam; Valiulis, Vladas; Dapsys, Kastytis; Gerulskis, Giedrius; Germanavicius, Arunas (Frontiers in Neuroinformatics, 2021)
Abstract Aim: The objective of this work was to demonstrate the usefulness of a novel statistical method to study the impact of transcranial magnetic stimulation (TMS) on brain connectivity in patients with depression using different stimulation protocols, i.e., 1 Hz repetitive TMS over the right dorsolateral prefrontal cortex (DLPFC) (protocol G1), 10 Hz repetitive TMS over the left DLPFC (G2), and intermittent theta burst stimulation (iTBS) consisting of three 50 Hz burst bundle repeated at 5 Hz frequency (G3). Methods: Electroencephalography (EEG) connectivity analysis was performed using Directed Transfer Function (DTF) and a set of 21 indices based on graph theory. The statistical analysis of graph-theoretic indices consisted of a combination of the k-NN rule, the leave-one-out method, and a statistical test using a 2 × 2 contingency table. Results: Our new statistical approach allowed for selection of the best set of graph-based indices derived from DTF, and for differentiation between conditions (i.e., before and after TMS) and between TMS protocols. The effects of TMS was found to differ based on frequency band. Conclusions: A set of four brain asymmetry measures were particularly useful to study protocol- and frequency-dependent effects of TMS on brain connectivity. Significance: The new approach would allow for better evaluation of the therapeutic effects of TMS and choice of the most appropriate stimulation protocol.