Dataset Information

Real-Time Computation of Brain E-Field for Enhanced Transcranial Magnetic Stimulation Neuronavigation and Optimization.

ABSTRACT: Transcranial Magnetic Stimulation (TMS) coil placement and pulse waveform current are often chosen to achieve a specified E-field dose on targeted brain regions. TMS neuronavigation could be improved by including real-time accurate distributions of the E-field dose on the cortex. We introduce a method and develop software for computing brain E-field distributions in real-time enabling easy integration into neuronavigation and with the same accuracy as 1^st -order finite element method (FEM) solvers. Initially, a spanning basis set (< 400) of E-fields generated by white noise magnetic currents on a surface separating the head and permissible coil placements are orthogonalized to generate the modes. Subsequently, Reciprocity and Huygens' principles are utilized to compute fields induced by the modes on a surface separating the head and coil by FEM, which are used in conjunction with online (real-time) computed primary fields on the separating surface to evaluate the mode expansion. We conducted a comparative analysis of E-fields computed by FEM and in real-time for eight subjects, utilizing two head model types (SimNIBS's 'headreco' and 'mri2mesh' pipeline), three coil types (circular, double-cone, and Figure-8), and 1000 coil placements (48,000 simulations). The real-time computation for any coil placement is within 4 milliseconds (ms), for 400 modes, and requires less than 4 GB of memory on a GPU. Our solver is capable of computing E-fields within 4 ms, making it a practical approach for integrating E-field information into the neuronavigation systems without imposing a significant overhead on frame generation (20 and 50 frames per second within 50 and 20 ms, respectively).

SUBMITTER: Hasan NI

PROVIDER: S-EPMC10635016 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Real-Time Computation of Brain E-Field for Enhanced Transcranial Magnetic Stimulation Neuronavigation and Optimization.

Hasan Nahian I NI Dannhauer Moritz M Wang Dezhi D Deng Zhi-De ZD Gomez Luis J LJ

bioRxiv : the preprint server for biology 20231030

Transcranial Magnetic Stimulation (TMS) coil placement and pulse waveform current are often chosen to achieve a specified E-field dose on targeted brain regions. TMS neuronavigation could be improved by including real-time accurate distributions of the E-field dose on the cortex. We introduce a method and develop software for computing brain E-field distributions in real-time enabling easy integration into neuronavigation and with the same accuracy as 1<sup>st</sup> -order finite element method ...[more]

PMID: 37961454

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Project description:State-of-the-art navigated transcranial magnetic stimulation (nTMS) systems can display the TMS coil position relative to the structural magnetic resonance image (MRI) of the subject's brain and calculate the induced electric field. However, the local effect of TMS propagates via the white-matter network to different areas of the brain, and currently there is no commercial or research neuronavigation system that can highlight in real time the brain's structural connections during TMS. This lack of real-time visualization may overlook critical inter-individual differences in brain connectivity and does not provide the opportunity to target brain networks. In contrast, real-time tractography enables on-the-fly parameter tuning and detailed exploration of connections, which is computationally inefficient and limited with offline methods. To target structural brain connections, particularly in network-based treatments like major depressive disorder, a real-time tractography-based neuronavigation solution is needed to account for each individual's unique brain connectivity. The objective of this work is to develop a real-time tractography-assisted TMS neuronavigation system and investigate its feasibility. We propose a modular framework that seamlessly integrates offline (preparatory) analysis of diffusion MRI data with online (real-time) probabilistic tractography using the parallel transport approach. For tractography and neuronavigation, we combine our open source software Trekker and InVesalius, respectively. We evaluate our system using synthetic data and MRI scans of four healthy volunteers obtained using a multi-shell high-angular resolution diffusion imaging protocol. The feasibility of our online approach is assessed by studying four major TMS targets via comparing streamline count and overlap against offline tractography results based on filtering of one hundred million streamlines. Our development of a real-time tractography-assisted TMS neuronavigation system showcases advanced tractography techniques, with interactive parameter tuning and real-time visualization of thousands of streamlines via an innovative uncertainty visualization method. Our analysis reveals considerable variability among subjects and TMS targets in the streamline count, for example, while 15,000 streamlines were observed for the TMS target on the visual cortex (V1) of subject #4, in the case of subject #3's V1, no streamlines were obtained. Overlap analysis against offline tractograms demonstrated that real-time tractography can quickly cover a substantial part of the target areas' connectivity, often surpassing the coverage of offline approaches within seconds. For instance, significant portions of Broca's area and the primary motor cortex were effectively visualized after generating tens of thousands of streamlines, highlighting the system's efficiency and feasibility in capturing brain connectivity in real-time. Overall, our work shows that real-time tractography-assisted TMS neuronavigation is feasible. With our system, it is possible to target specific brain regions based on their structural connectivity, and to aim for the fiber tracts that make up the brain's networks. Real-time tractography provides new opportunities for TMS targeting through novel visualization techniques without compromising structural connectivity estimates when compared to the offline approach.

Project description:Introduction: Conventional transcranial direct current stimulation (tDCS) and high-definition tDCS (HD-tDCS) may improve motor learning in children. Mechanisms are not understood. Neuronavigated robotic transcranial magnetic stimulation (TMS) can produce individualised maps of primary motor cortex (M1) topography. We aimed to determine the effects of tDCS- and HD-tDCS-enhanced motor learning on motor maps. Methods: Typically developing children aged 12-18 years were randomised to right M1 anodal tDCS, HD-tDCS, or Sham during training of their left-hand on the Purdue Pegboard Task (PPT) over 5 days. Bilateral motor mapping was performed at baseline (pre), day 5 (post), and 6-weeks retention time (RT). Primary muscle was the first dorsal interosseous (FDI) with secondary muscles of abductor pollicis brevis (APB) and adductor digiti minimi (ADM). Primary mapping outcomes were volume (mm2/mV) and area (mm2). Secondary outcomes were centre of gravity (COG, mm) and hotspot magnitude (mV). Linear mixed-effects modelling was employed to investigate effects of time and stimulation type (tDCS, HD-tDCS, Sham) on motor map characteristics. Results: Twenty-four right-handed participants (median age 15.5 years, 52% female) completed the study with no serious adverse events or dropouts. Quality maps could not be obtained in two participants. No effect of time or group were observed on map area or volume. LFDI COG (mm) differed in the medial-lateral plane (x-axis) between tDCS and Sham (p = 0.038) from pre-to-post mapping sessions. Shifts in map COG were also observed for secondary left-hand muscles. Map metrics did not correlate with behavioural changes. Conclusion: Robotic TMS mapping can safely assess motor cortex neurophysiology in children undergoing motor learning and neuromodulation interventions. Large effects on map area and volume were not observed while changes in COG may occur. Larger controlled studies are required to understand the role of motor maps in interventional neuroplasticity in children.

Dataset Information

Real-Time Computation of Brain E-Field for Enhanced Transcranial Magnetic Stimulation Neuronavigation and Optimization.

Publications

Real-Time Computation of Brain E-Field for Enhanced Transcranial Magnetic Stimulation Neuronavigation and Optimization.

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