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Matsuda, R. H.

Publications and source records attributed to Matsuda, R. H..

7 recordsLinked to original sources

One placement, many fields: robust voxel-wise mapping with multichannel TMS

Understanding the human brain at the individual level is central to understanding its function and a prerequisite for personalized therapy. Transcranial magnetic stimulation (TMS) can map structure-function relationships at high resolution by regressing trial-wise variation in the TMS-induced electric field (E-field) against a response such as motor evoked potentials (MEPs). This requires distinct E-fields across pulses at different cortical locations, which single-coil TMS (sTMS) achieves by physically repositioning the coil (typically supported by neuronavigation software such as InVesalius Navigator; Souza et al., 2018)--increasingly reliant on robotic guidance and capped by the coil geometry. Multichannel TMS (mTMS) instead steers the E-field electronically via independent currents in several coils, opening a design space of high variability without physical repositioning. We adapt determinant-based sequence optimization from discrete sTMS coil-placement sets to the continuous mTMS current space (D-optimal design), identifying pulse sequences whose induced cortical E-fields are as mutually dissimilar as biophysical constraints allow. We benchmarked optimized mTMS sequences against random baselines and optimized sTMS placements in virtual motor mapping across multiple head models, assessing three mTMS array geometries (5-channel planar, 6- and 12-channel spherical), two figure-of-eight sTMS coils, and varying pulse counts, response noise, and cortical depth. D-optimal mTMS currents substantially outperformed random currents and optimized sTMS at matched pulse counts. The gain was modest without noise but increased significantly with it: at the highest noise level, optimized sTMS failed to converge after 100 pulses (~20-22 mm error), whereas all mTMS arrays reached median errors at or below 6 mm. mTMS also recovered deep targets better than sTMS (up to 35 mm skin-cortex distance, versus 25 mm) and performed uniformly across the region of interest. A coil-space scheme based only on the magnetic vector potential, precomputable once per array without per-subject MRI, captured most of the benefit, matching subject-specific optimization at 100 pulses. mTMS thus makes voxel-wise mapping robust throughout a region of interest without coil repositioning.

neuroscience↗

Adaptive Bayesian localization of motor representation areas

Transcranial magnetic stimulation (TMS) enables non-invasive localization of cortical motor representations, with important clinical applications in presurgical planning. Existing methods either disregard spatial information about the TMS-induced electric field (E-field) or use acquisition schemes that do not leverage previously elicited motor responses to guide subsequent stimulation. We present an adaptive Bayesian localization method that combines real-time E-field optimization with per-trial probabilistic inference. The cortical origin of the motor responses is represented as a spatial probability distribution that is updated after each stimulus. Subsequent stimuli are then optimized to maximize the expected localization improvement given previous responses. We validated the method experimentally, using multi-locus TMS for adaptive localization and single-coil TMS as a non-adaptive reference with randomized coil placements. Across eight subjects, the adaptive protocol at least halved the number of stimuli required for stable localization compared to the randomized protocol, converging in 60 stimuli on average, with 95% highest-density regions often below 10 mm2 by 150 stimuli.

neuroscience↗

Automated robotic control system for EEG-BCI-guided closed-loop TMS

Brain-state-guided and closed-loop transcranial magnetic stimulation (TMS) protocols have emerged as methods for decreasing the variability and increasing the therapeutic effectiveness of stimulation protocols. However, most existing brain-state-dependent TMS systems only control the timing of stimulation, while the location is fixed and manually adjusted between blocks or sessions. This limits flexible targeting of distributed networks. We developed a system that jointly manages TMS pulse timing and location automatically controlled by an electroencephalography (EEG)-based brain- computer interface (BCI). A machine-learning algorithm infers the brain state in real time to guide the robotic coil placement and target. We present a proof-of-concept study in which a BCI controlled both the target site and the timing of TMS. A pre-trained convolutional neural network discriminated between resting state and movements performed with the right or left hand; the classifier output determined the hemisphere in which primary-motor-cortex hand area was stimulated and when. Preprocessing and decoding of 2-s EEG segments required 150 ms, and the robot took 7.5 s to move from the vertex home position to the predefined motor targets. The EEG-BCI-guided robotic TMS system expands the toolkit for brain-state-dependent and closed-loop neurostimulation by enabling control of stimulus location based on volitional brain activity. Thus, the system can benefit both neuroscience research and clinical neuromodulation applications. A prominent application of the system is automatically controlling spinal cord injury or motor disorder TMS rehabilitation with motor imagery, optimizing stimulation timing to the brain state producing optimal rehabilitation results.

neuroscience↗

Tractography-based transcranial magnetic stimulation prediction using machine learning

BackgroundIdentifying the stimulation brain target is fundamental for transcranial magnetic stimulation (TMS). Currently, this process is time-consuming and heavily dependent on the operators expertise. ObjectiveThis study evaluated a deep learning-based approach to structural brain connectivity for improving stimulation site prediction and real-time cortical excitability mapping during neuronavigation. ResultsTractography-derived connectivity features and distal myographic responses were used to train four neural network models across five subjects. Neural network inputs were incrementally varied using either tractography alone, coil coordinates alone, or hybrid combinations of both using different concatenation regimes. The best performance was observed in two subjects out of 5, where hybrid models integrating coil coordinates and fiber information achieved higher F1 scores and accuracy. ConclusionDespite these promising results, substantial inter-subject variability was observed. This approach may also be applied to other brain regions to investigate connectivity, and the use of machine learning could be extended to functional domains beyond the motor cortex, including sensory and cognitive areas.

neuroscience↗

Fast and standardized motor-hotspot determination with automated TMS mapping

Determining the optimal stimulation target for motor responses (motor hotspot) and the required intensity for reliably eliciting said responses (motor threshold) are common procedures in transcranial magnetic stimulation (TMS) research and treatments. However, the procedures for determining them are user-dependent, slow, and lack standardization, leading to long stimulation sessions with potentially inadequate outcomes. Partially automated algorithms for determining the motor threshold have been developed, but the motor hotspot is still largely mapped by hand. Automating the hotspot mapping will accelerate the process and improve standardization and accuracy. We developed a fully automated algorithm for finding the motor hotspot with multi-locus TMS and Bayesian optimization. Tested online in five healthy participants, the algorithm located motor hotspots with (mean {+/-} 95% CI) 2.1 {+/-} 0.7 mm and 6 {+/-} 2{degrees} difference from the global best target with only (mean) 47 stimuli. This is a significant improvement from previous motor-mapping algorithms, which do not optimize for stimulation location and orientation simultaneously. This accurate, fast, and user-independent procedure paves the way for faster experimental processes and more streamlined clinical applications.

neuroscience↗

Characterizing an electronic-robotic targeting platform for precise and fast brain stimulation with multi-locus transcranial magnetic stimulation

1.BackgroundMulti-locus TMS (mTMS) enables precise electronic control of brain stimulation targeting, eliminating the need for physical coil movement. However, with a small number of coils, the stimulation area is constrained, and manually handling the coil array is cumbersome. Combining electronic mTMS targeting with robotics will enable automated, user-independent, and precise brain stimulation protocols. ObjectiveCharacterizing an open-source electronic-robotic mTMS platform for rapid and accurate brain stimulation targeting. MethodsWe developed an automated robotic mTMS positioning platform. The accuracy of the system was quantified with a TMS characterizer that measures the TMS-induced electric field on a spherical cortex model. We used a 5-coil mTMS device equipped with a set of five coils coupled to a collaborative robot. The induced electric-field distortion generated by robot coupling was evaluated for each coil. We compared the accuracy of robotic-electronic targeting by repositioning the mTMS coil set with the robotic and the conventional manual positioning. ResultsOur collaborative robot-based system offers submillimeter precision and autonomy in positioning mTMS coil sets. The electronic-robotic mTMS platform was approximately 1.8 mm and 1.0{degrees} more accurate than the conventional manual positioning. Integrating robotics and mTMS automates brain stimulation procedures, resulting in minimal reliance on user expertise and subjective analysis. ConclusionOur open-source platform combining rapid mTMS targeting with robotic precision enhances the safety and reproducibility of brain stimulation techniques, enabling more efficient and reliable outcomes than previous techniques.

neuroscience↗

Real-time tractography-assisted neuronavigation for TMS

BackgroundState-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 subjects 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 brains structural connections during TMS. ObjectiveTo develop a real-time tractography-assisted TMS neuronavigation system and investigate its feasibility. MethodWe propose a modular framework that seamlessly integrates offline (preparatory) analysis of diffusion MRI data with online (real-time) tractography. For tractography and neuronavigation we combine our custom software Trekker and InVesalius, respectively. We evaluate the feasibility of our system by comparing online and offline tractography results in terms of streamline count and their overlap. ResultsA real-time tractography-assisted TMS neuronavigation system is developed. Key features include the application of state-of-the-art tractography practices, the ability to tune tractography parameters on the fly, and the display of thousands of new streamlines every few seconds using a novel uncertainty visualization technique. We demonstrate in a video the feasibility and quantitatively show the agreement with offline filtered streamlines. ConclusionReal-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 brains networks.

neuroscience↗