Can a context tree model predict the primary motor cortex excitability? An electromyography and electroencephalography robotized navigated transcranial magnetic stimulation study.

• Grant number: 22/14526-3
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: March 01, 2023
• End date: July 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Physics
• Principal Investigator: Oswaldo Baffa Filho
• Grantee: Renan Hiroshi Matsuda
• 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

Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation technique. A strong pulse can excite the neurons by an induced electric field in the cortex. The time interval between TMS pulses and the TMS intensity are crucial parameters for brain interventions. Depending on the TMS intensity and the time interval of TMS pulses, applied at the same location, we can evoke distinct brain excitability responses. Studies have been done to optimize the parameters to enhance the reliability of cortical excitability. However, the cortical excitabilities have a high variability across subjects and sessions, and the causes are not fully understood. Reducing the TMS variables, such as coil positioning reproducibility, is a critical component to having a reliable foundation. To increase the TMS reproducibility, we highlight the navigated robotic TMS coil positioning, providing an accurate and reliable placement of the stimulator. Furthermore, new approaches to investigating the brain could help the understating of the cortical excitabilities' mechanisms. Recent studies from the NeuroMat group have shown that the brain responses to auditory stimuli, given by a stochastic chain, can model the auditory sequence stimuli with the electroencephalography (EEG) signal (DUARTE et al., 2019; HERNÁNDEZ et al., 2021). A stochastic chain with a memory of variable length, also known as context tree models, is a structure of the chains generating the stimuli sequence, in summary, the past stimuli govern the choice of the next stimulus, i.e., we can retrieve the structured stimuli sequence from the physiological data. We hypothesize that the primary motor cortex (M1) can hold a memory from the sequence of TMS pulses driven by a context tree model. Thus, the goal of this project is to develop neurostimulation protocols based on the time interval and TMS intensity, to apply a sequence of TMS pulses driven by a context tree in the primary motor cortex and retrieve the physiological signatures, such as the motor evoked potential (MEP) and EEG response of the TMS protocol. Answering the following question: Is there a TMS protocol able to retrieve from the EEG and the MEP response the structure of the sequence of pulses driven by a context tree model? The execution of this project requires a very advanced equipment infrastructure which few places in the world have available. The Biomag lab at FFCLRP-USP, headed by Professor Oswaldo Baffa, is the only laboratory in Brazil with a robotized neuronavigation TMS system and devices to record physiological data induced by the TMS. Also, the group has easy and fast access to a magnetic resonance imaging (MRI) center at Hospital das Clínicas de Ribeirão Preto. The MRI is mandatory to perform an accurate TMS positioning.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

MORAES, VICTOR HUGO; VARGAS, CLAUDIA D.; RAMALHO, BIA L.; MATSUDA, RENAN H.; SOUZA, VICTOR H.; IMBIRIBA, LUIS AURELIANO; GARCIA, MARCO ANTONIO C.. Effect of muscle length in a handgrip task on corticomotor excitability of extrinsic and intrinsic hand muscles under resting and submaximal contraction conditions. SCANDINAVIAN JOURNAL OF MEDICINE & SCIENCE IN SPORTS, v. N/A, p. 10-pg., 2023-08-29. (22/00582-9, 13/07699-0, 22/14526-3)