Frontiers in Neuroinformatics (Apr 2021)

Statistical Analysis of Graph-Theoretic Indices to Study EEG-TMS Connectivity in Patients With Depression

  • Elzbieta Olejarczyk,
  • Adam Jozwik,
  • Vladas Valiulis,
  • Vladas Valiulis,
  • Kastytis Dapsys,
  • Kastytis Dapsys,
  • Giedrius Gerulskis,
  • Giedrius Gerulskis,
  • Arunas Germanavicius,
  • Arunas Germanavicius

DOI
https://doi.org/10.3389/fninf.2021.651082
Journal volume & issue
Vol. 15

Abstract

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AimThe 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).MethodsElectroencephalography (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.ResultsOur 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.ConclusionA set of four brain asymmetry measures were particularly useful to study protocol- and frequency-dependent effects of TMS on brain connectivity.SignificanceThe new approach would allow for better evaluation of the therapeutic effects of TMS and choice of the most appropriate stimulation protocol.

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