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

Area of Science:

• Neuroscience
• Psychiatry
• Computational Statistics

Background:

• Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique used to treat depression.
• Understanding TMS effects on brain connectivity is crucial for optimizing treatment protocols.
• Current methods for analyzing TMS-induced changes in brain connectivity may lack specificity.

Purpose of the Study:

• To introduce and validate a novel statistical method for analyzing brain connectivity changes after TMS in depression.
• To assess the impact of different TMS protocols (1 Hz, 10 Hz, iTBS) on brain connectivity.
• To demonstrate the method's ability to differentiate between stimulation conditions and protocols.

Main Methods:

• Electroencephalography (EEG) data was analyzed using Directed Transfer Function (DTF).
• Graph theory indices were derived from DTF, and a novel statistical approach combining k-NN, leave-one-out, and contingency table tests was applied.
• The statistical analysis differentiated between pre- and post-TMS conditions and among different TMS protocols.

Main Results:

• The new statistical method successfully identified key graph-based indices from DTF.
• The approach effectively distinguished between conditions (pre- vs. post-TMS) and among different TMS protocols (G1, G2, G3).
• The impact of TMS on brain connectivity was found to be frequency-band dependent.

Conclusions:

• Four specific brain asymmetry measures proved highly effective in discerning protocol- and frequency-dependent TMS effects on connectivity.
• The developed statistical framework offers a more robust evaluation of TMS therapeutic efficacy.
• This method facilitates the selection of the most suitable TMS protocol for individual patients.