Evaluating the Effect of Neuroanatomy on Forecasting Transcranial Magnetic Stimulation Motor Effects
Abstract/Description/Artist Statement
Transcranial magnetic stimulation (TMS) is an FDA approved, noninvasive therapy for major depressive disorder (MDD) that uses magnetic pulses to induce neuromodulation. A significant barrier to its broader application for other neurological disorders is the high individual variability in treatment outcomes. This fluctuation may be influenced by subject-specific neuroanatomy, which modulates the induced electric field, but this relationship is poorly understood. Current MDD treatments including antidepressants and mood stabilizers often have adverse effects, highlighting the need for optimized, personalized neuromodulation therapies like TMS. This study aims to determine the influence of individual neuroanatomy, specifically cortical thickness and grey-white matter contrast, on TMS-induced corticomotor excitability.. The hypothesis is as follows: it is anticipated that interindividual variability in cortical thickness and grey-white matter contrast modulates TMS-induced electric field distributions, thereby affecting corticomotor excitability and overall treatment efficacy. As part of the proposed research, 30 healthy adults (18-65 years) were recruited for the study. Each participant underwent multi-modal MRI scanning, including: structural MRI (T1-weighted MPRAGE), diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (fMRI) in order to characterize their neuroanatomy and functional connectivity. Single-pulse TMS was applied to the primary motor cortex hotspot to elicit motor-evoked potentials (MEPs) in the first dorsal interosseous muscle using a Magstim BiStim2 stimulator and a 70mm figure-of-eight coil. Corticomotor excitability was quantified by measuring the peak-to-peak amplitude of motor-evoked potentials (MEPs) recorded via electromyography (EMG) to measure corticomotor excitability. The relationship between MRI derived neuroanatomical features , functional connectivity, and MEP amplitude was modeled using deep neural networks (DNNs) implemented in MATLAB. Preliminary data analysis is currently underway. Initial explorations are focused on evaluating the hypothesis that combined structural and functional connectivity data will yield a model with superior predictive power for MEP amplitude compared to models using either data type in isolation. It is expected for the proposed DNN model to accurately predict MEP amplitudes based on individual neuroanatomical data.
This research will establish a foundational model for predicting TMS efficacy based on individual brain structure. The development of a predictive model represents a significant step towards personalized neuromodulation therapy. This approach holds the power to optimize treatment planning, reduce costs, and improve accessibility for a wide range of neurological and psychiatric disorders.
Faculty Advisor/Mentor
Carrie Peterson
Faculty Advisor/Mentor Email
clpeterson@vcu.edu
Faculty Advisor/Mentor Department
Biomedical engineering
College/School Affiliation
Other
Student Level Group
Undergraduate
Presentation Type
Poster
Evaluating the Effect of Neuroanatomy on Forecasting Transcranial Magnetic Stimulation Motor Effects
Transcranial magnetic stimulation (TMS) is an FDA approved, noninvasive therapy for major depressive disorder (MDD) that uses magnetic pulses to induce neuromodulation. A significant barrier to its broader application for other neurological disorders is the high individual variability in treatment outcomes. This fluctuation may be influenced by subject-specific neuroanatomy, which modulates the induced electric field, but this relationship is poorly understood. Current MDD treatments including antidepressants and mood stabilizers often have adverse effects, highlighting the need for optimized, personalized neuromodulation therapies like TMS. This study aims to determine the influence of individual neuroanatomy, specifically cortical thickness and grey-white matter contrast, on TMS-induced corticomotor excitability.. The hypothesis is as follows: it is anticipated that interindividual variability in cortical thickness and grey-white matter contrast modulates TMS-induced electric field distributions, thereby affecting corticomotor excitability and overall treatment efficacy. As part of the proposed research, 30 healthy adults (18-65 years) were recruited for the study. Each participant underwent multi-modal MRI scanning, including: structural MRI (T1-weighted MPRAGE), diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (fMRI) in order to characterize their neuroanatomy and functional connectivity. Single-pulse TMS was applied to the primary motor cortex hotspot to elicit motor-evoked potentials (MEPs) in the first dorsal interosseous muscle using a Magstim BiStim2 stimulator and a 70mm figure-of-eight coil. Corticomotor excitability was quantified by measuring the peak-to-peak amplitude of motor-evoked potentials (MEPs) recorded via electromyography (EMG) to measure corticomotor excitability. The relationship between MRI derived neuroanatomical features , functional connectivity, and MEP amplitude was modeled using deep neural networks (DNNs) implemented in MATLAB. Preliminary data analysis is currently underway. Initial explorations are focused on evaluating the hypothesis that combined structural and functional connectivity data will yield a model with superior predictive power for MEP amplitude compared to models using either data type in isolation. It is expected for the proposed DNN model to accurately predict MEP amplitudes based on individual neuroanatomical data.
This research will establish a foundational model for predicting TMS efficacy based on individual brain structure. The development of a predictive model represents a significant step towards personalized neuromodulation therapy. This approach holds the power to optimize treatment planning, reduce costs, and improve accessibility for a wide range of neurological and psychiatric disorders.