Graduate Student Seminar Series – Mohammad Parsa Oveisi

Graduate Student Seminar Series
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Location: HS610 – 155 College St, Room 610
Presentation Title: Using Computational Modeling to Disentangle Physiological Underpinnings of Excitability in TMS
Abstract:
Transcranial Magnetic Stimulation (TMS) is a powerful neuromodulation technique, yet its efficacy is hindered by variability in individual responses. This variability is largely attributed to differences in brain excitability, which is influenced by both external factors (e.g., stimulation intensity) and internal, brain-dependent factors. To disentangle these contributions and elucidate their interactions, we employed a novel approach combining empirical TMS-EEG measurements with advanced computational modeling.
We collected TMS-EEG data from the dorsolateral prefrontal cortex (DLPFC) at various stimulation intensities. Analysis of TMS-Evoked Potentials (TEPs) revealed multiple intensity-sensitive components (N45, P60, N120, P200, and Cluster Amplitude Variability), each showing distinct scaling properties. Notably, brain-dependent metrics, such as Resting Motor Threshold (RMT), proved to be superior predictors of TEP amplitude compared to stimulator output intensity.
To investigate the underlying physiological mechanisms, we utilized a whole-brain computational model “WhoBPyT”. The model successfully captured empirical dynamics with high accuracy and reproduced observed correlation patterns between excitability metrics. Importantly, different model parameters correlated with distinct excitability peaks, suggesting that various TEP components may reflect different neural processes. Network analysis further indicated that some components (e.g., N120) result from global network reverberations, while others (e.g., N45) represent more localized responses.
Our findings demonstrate that TMS-induced cortical responses are shaped by multiple, partially independent excitability-related processes. This work provides novel insights into the physiological underpinnings of TMS effects and offers a promising approach for developing more targeted and individualized TMS protocols. Future research should focus on incorporating these insights into clinical applications to enhance the efficacy of TMS therapies.
Supervisor Name: Drs. John D. Griffiths & Christoph Zrenner
Year of Study: 3
Program of Study: MASc
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