Evoked Potentials and Effective Connectivity: Exploring Cortical Communication with TMS-EEG

• Grant number: 25/06042-4
• Support Opportunities: Scholarships in Brazil - Master
• Start date: May 01, 2025
• End date: April 30, 2027
• Field of knowledge: Physical Sciences and Mathematics - Physics
• Principal Investigator: Oswaldo Baffa Filho
• Grantee: Márcio Adriano de Campos Júnior
• Host Institution: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• Associated research grant: 13/07699-0 - Research, Innovation and Dissemination Center for Neuromathematics - NeuroMat, AP.CEPID

Abstract

Understanding the mechanisms of causal communication between cortical regions, essential for motor and cognitive functions, requires interdisciplinary approaches that unify principles of physics and neuroscience. To this end, this study uses Transcranial Magnetic Stimulation (TMS) - a technique that generates pulsed magnetic fields to depolarize neurons noninvasively - together with the electroencephalogram (EEG), which will record postsynaptic potentials with millisecond temporal resolution, allowing the investigation of effective connectivity through TMS-evoked potentials (TEPs). These TEPs, characterized by well-defined temporal components, will be correlated with causality metrics derived from multivariate autoregressive models (MVAR), such as partial directed coherence (PDC) and directed transfer function (DTF), in frequency bands associated with motor control. To overcome methodological challenges-such as electromagnetic artifacts, peripheral sensory responses, and brain state fluctuations-the protocol incorporates robotic neuronavigation, adaptive signal processing techniques, and sensory interference control, ensuring robust TEP extraction. Ten healthy volunteers will perform sequential motor tasks with single TMS pulses in the primary motor cortex (M1), and the data will be analyzed by Pearson correlation and Bhattacharyya distance, validating the relationship between TEP spatiotemporal patterns and neural network dynamics. The method has potential for translational applications, such as exploring early biomarkers for neurodegenerative diseases, where changes in effective connectivity precede clinical symptoms, and mapping compensatory networks to guide personalized neuromodulation therapies. By harmonizing concepts from physics (magnetic fields, modeling), neuroscience (cortical plasticity) and statistics (multivariate analysis), the project will advance the standardization of TMS-EEG protocols and help consolidate the technique as a tool for exploring neural mechanisms, promoting accurate diagnoses and therapeutic interventions targeted at clinical reality.