Biology subjects

Sinisalo, H.

Publications and source records attributed to Sinisalo, H..

6 recordsLinked to original sources

bioRxiv2025

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↗

bioRxiv2025

A High-Precision Timing Method and Digital Interface for Closed-Loop TMS

ObjectiveCurrent transcranial magnetic stimulation (TMS) protocols exhibit high inter-subject variability in treatment outcomes, highlighting the need for personalized, brain-state-dependent closed-loop stimulation protocols. To enable such protocols, we aim to provide robust, precisely timed external control of TMS, with stimulation timed relative to feedback signals such as the electroencephalogram (EEG). ApproachCommercial TMS devices typically rely on trigger signals for precise external pulse timing, while adjusting stimulation parameters, such as intensity, is better handled via serial digital communication, which supports robust error detection and feedback. However, combining these communication methods is inherently complex and prone to timing issues, such as race conditions. Furthermore, trigger signals lack capabilities essential for real-time systems, such as preventing late pulse delivery. We present a method for precise and accurate pulse timing, implemented through a digital interface that uses exclusively serial digital messaging, eliminating the need for trigger signals. This interface enables external control of pulse timing, intensity, and other parameters. The TMS device maintains its own internal clock and delivers pulses at pre-scheduled times, decoupling timing precision from the control device. Additionally, we propose a method for synchronizing such time-tracking TMS devices with commercial EEG systems, enabling precisely timed EEG-TMS. Main resultsUsing these methods, our custom TMS device delivered pulses precisely aligned to the EEG signal, with timing errors consistently below 0.3 ms. These errors were constrained by the experimental setup, including the sampling rate of our EEG device and the signal-to-noise ratio affecting pulse detection. SignificanceOur timing method achieves sub-millisecond precision in brain-state-dependent closed-loop EEG-TMS, providing a foundation for robust TMS timing that supports adaptive, personalized stimulation protocols. The digital control interface, co-designed with our TMS device, integrates pulse timing and parameters, setting a precedent for future advancements in computer-controlled TMS.

bioengineering↗

bioRxiv2024

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↗

bioRxiv2023

A multi-channel TMS system enabling accurate stimulus orientation control during concurrent ultra-high-field MRI for preclinical applications

Monitoring cortical responses to neuromodulation protocols on preclinical models can elucidate fundamental mechanisms of brain function. Concurrent brain stimulation and imaging is challenging, usually compromising spatiotemporal resolution, accuracy, and versatility. Here we report a non-invasive brain stimulation system with electronic control of neuromodulation parameters in a 9.4-T magnetic resonance imaging (MRI) environment. In the imaging scanner, transcranial magnetic stimulation is delivered with a set of two coils and the MRI signals are recorded with a radiofrequency coil. The coil set provides millisecond-scale electronic control of the stimulus orientation with 1{degrees} resolution. Without physically rotating the coils, we evoked orientation-specific muscle responses after cortical stimulation on an anesthetized rat. We show that the stimulation pulses do not affect the anatomical imaging quality, and imaging signals are disrupted only if recorded before 10 ms after pulse delivery. Concurrent electronically targeted brain stimulation and neuroimaging sets the stage for the causal investigation of whole-brain network functions, endorsing more efficient treatment protocols.

neuroscience↗

bioRxiv2021

Multi-locus transcranial magnetic stimulation system for electronically targeted brain stimulation

BackgroundTranscranial magnetic stimulation (TMS) allows non-invasive stimulation of the cortex. In multi-locus TMS (mTMS), the stimulating electric field (E-field) is controlled electronically without coil movement by adjusting currents in the coils of a transducer. ObjectiveTo develop an mTMS system that allows adjusting the location and orientation of the E-field maximum within a cortical region. MethodsWe designed and manufactured a planar 5-coil mTMS transducer to allow controlling the maximum of the induced E-field within a cortical region approximately 30 mm in diameter. We developed electronics with a design consisting of independently controlled H-bridge circuits to drive up to six TMS coils. To control the hardware, we programmed software that runs on a field-programmable gate array and a computer. To induce the desired E-field in the cortex, we developed an optimization method to calculate the currents needed in the coils. We characterized the mTMS system and conducted a proof-of-concept motor-mapping experiment on a healthy volunteer. In the motor mapping, we kept the transducer placement fixed while electronically shifting the E-field maximum on the precentral gyrus and measuring electromyography from the contralateral hand. ResultsThe transducer consists of an oval coil, two figure-of-eight coils, and two four-leaf-clover coils stacked on top of each other. The technical characterization indicated that the mTMS system performs as designed. The measured motor evoked potential amplitudes varied consistently as a function of the location of the E-field maximum. ConclusionThe developed mTMS system enables electronically targeted brain stimulation within a cortical region.

neuroscience↗

bioRxiv2021

Closed-loop optimization of transcranial magnetic stimulation with electroencephalography feedback

BackgroundTranscranial magnetic stimulation (TMS) is widely used in brain research and treatment of various brain dysfunctions. However, the optimal way to target stimulation and administer TMS therapies, for example, where and in which electric field direction the stimuli should be given, is yet to be determined. ObjectiveTo develop an automated closed-loop system for adjusting TMS parameters (in this work, the stimulus orientation) online based on TMS-evoked brain activity measured with electroencephalography (EEG). MethodsWe developed an automated closed-loop TMS-EEG set-up. In this set-up, the stimulus parameters are electronically adjusted with multi-locus TMS. As a proof of concept, we developed an algorithm that automatically optimizes the stimulation orientation based on single-trial EEG responses. We applied the algorithm to determine the electric field orientation that maximizes the amplitude of the TMS- EEG responses. The validation of the algorithm was performed with six healthy volunteers, repeating the search twenty times for each subject. ResultsThe validation demonstrated that the closed-loop control worked as desired despite the large variation in the single-trial EEG responses. We were often able to get close to the orientation that maximizes the EEG amplitude with only a few tens of pulses. ConclusionOptimizing stimulation with EEG feedback in a closed-loop manner is feasible and enables effective coupling to brain activity.

bioengineering↗