Poster K3 - Systematic Analysis of Transcranial Magnetic Stimulation Motor Response and EEG Functional Connectivity Relationship in Mild to Moderate TBI Patients

Friday, October 25, 2024

3:30 PM - 4:30 PM EST

Location: Exhibit Hall E, F & G

Presenting Author(s)

ES

Erin R. Sprouse (she/her/hers)

Department of Biomedical Engineering
Virginia Commonwealth University
Richmond, Virginia, United States

Co-Author(s)

CH

Connor M. Harris, BS

Department of Life Sciences
Virginia Commonwealth University, United States
AJ

Asif Jamil

Massachusetts General Hospital
Harvard Medical School, United States
LM

Laura Manning Franke

Department of Physical Medicine and Rehabilitation
Virginia Commonwealth University, United States

Primary Investigator(s)

RH

Ravi Hadimani

Department of Mechanical and Nuclear Engineering
Virginia Commonwealth University, United States

Introduction: Transcranial Magnetic Stimulation (TMS) is a neuromodulation procedure used to treat psychiatric and neurological disorders. When electroencephalography (EEG), a neuroimaging technique, is applied in conjunction with TMS, the analysis of resting-state EEG activity can be used to quantify functional connectivity (FC) in the brain. These modulations can then be related to a subject’s resting motor threshold (RMT), a baseline parameter in TMS therapy that determines the treatment intensity (dose) of subjects undergoing TMS. Due to the highly variable nature of RMT, previous work has attempted to predict individual values by examining specific EEG nodes suspected to relate to motor activity, and their calculated average FC values to all other nodes [3]. We continue this with an investigation of the relationship of the FC values of a larger set of EEG scalp electrodes to RMT values collected from experimental participants.

Materials and

Methods: We performed a post-hoc, time-frequency analysis of EEG data gathered in a previous experiment [2] via MATLAB[1]. A broad analysis of the global FC values of right-hemisphere EEG electrodes guided our pairwise FC analysis. We then fit linear regressions between pairs of EEG electrodes surrounding FC4 in the delta and alpha bands, as those are shown in previous work to be significant when predicting RMT [3].

Results, Conclusions, and Discussions: Results
Figure 1 shows a matrix of pairwise FC values and a map of the ROI pairs explored. When compared to the average FC of the FC4 node in the delta band, it is clear that the average FC value of the FC4 delta band alone is the strongest predictor of RMT. Figure 1 supports this finding by showing the negative correlation that the FC4 delta band has with RMT.

Conclusion
Understanding the relationship between neural synchronization and RMT could have clinical implications for TMS, by providing an easy way to determine the optimal dosing parameters for treatment.

Acknowledgements (Optional): Acknowledgments
Authors acknowledge the NSF Grant #2349694 and VCU Breakthrough Grant #AP00001868.

References
[1] A. M. Bastos and J.-M. Schoffelen, "A tutorial review of functional connectivity analysis
methods and their interpretational pitfalls", Frontiers Syst. Neurosci., vol. 9, pp. 175, Jan.2016.
[2]Franke, Laura M., et al. “Randomized Trial of RTMS in Traumatic Brain Injury: Improved Subjective Neurobehavioral Symptoms and Increases in EEG Delta Activity.” Brain Injury, vol. 36, no. 5, Apr. 2022, pp. 683–92, https://doi.org/10.1080/02699052.2022.2033845.
[3]Hussain, Mishal Z., et al. “Investigation of EEG Neurophysiological Relationship to TMS Response in Mild Traumatic Brain Injury Patients.” IEEE Transactions on Magnetics, vol. 59, no. 11, Nov. 2023, pp. 1–5, https://doi.org/10.1109/TMAG.2023.3282614.