Search bioRxiv⌕ Search

Biology subjects

Roine, T.

Publications and source records attributed to Roine, T..

5 recordsLinked to original sources

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↗

NeuroSimo: an open-source software for closed-loop EEG- or EMG-guided TMS

ObjectiveOur goal was to create open-source software for closed-loop EEG-TMS that allows researchers to rapidly prototype and develop novel stimulation paradigms in a high-level programming language. This addresses the limitations of current solutions, which often rely on proprietary hardware and software, limiting their accessibility and customizability, or comprise ad-hoc pipelines tailored to specific use cases. ApproachWe developed NeuroSimo, a software platform that enables arbitrary EEG-TMS stimulation protocols written in Python, leveraging Pythons ecosystem of scientific, neuroimaging, and machine learning libraries. The core software is written in C++ with an embedded Python interpreter and employs the Robot Operating System (ROS 2) for inter-process communication. NeuroSimo runs on real-time-enabled Linux Ubuntu, using LabJack T4 for pulse triggering, and supports two EEG device models (Bittium NeurOne, BrainProducts actiCHamp) and TMS devices that deliver pulses via trigger signals. The software includes a graphical user interface for configuration and performance monitoring, and supports GPU processing for neural network computations. Main resultsIn brain-state-dependent stimulation using the Phastimate algorithm, which targets TMS pulses to the trough of sensorimotor -rhythm, NeuroSimo achieved a median timing error of 0.2 ms (95% CI: 0.2-0.2 ms), a 99th-percentile of 0.6 ms (0.6-0.6 ms), and a maximum of 1.4 ms. SignificanceAs an open-source platform combining Pythons flexibility with real-time closed-loop EEG-TMS, NeuroSimo enables researchers to develop and implement novel therapeutic approaches, marking a significant advance in personalized brain stimulation.

bioengineering↗

Optimization of TMS target engagement: novel evidence based on combined TMS-EEG and dMRI tractography of brain circuitry

Neuromodulation is based on the principle that brain stimulation produces plastic changes in cerebral circuitry. Given the intersubject structural and functional variability, neuromodulation has a personalized effect in the brain. Moreover, because of cerebral dominance and interhemispheric functional and structural differences in the same individual, the characterization of specific brain circuitries involved is currently not feasible. This notion is extremely important for neuromodulation treatments applied in neuropsychiatry. Specifically, the efficacy of the neuromodulation treatments is critically dependent on the anatomical precision of the brain target and the circuitry which has been affected. However, a complete understanding of how the brain behaves under stimulation needs a combined characterization of its neurophysiological response. This can be achieved by TMS-EEG guided by current multimodal neuroimaging techniques in real time. Herein, we present novel data based on dMRI tractography-guided TMS-EEG on one healthy young adult volunteer.

neuroscience↗

Forecasting EEG time series with WaveNet

Forecasting electroencephalography (EEG) signals, i.e., estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain-computer interfaces and closed-loop brain stimulation. As these applications are becoming more and more common, the importance of a good prediction model has increased. Previously, the autoregressive model (AR) has been employed for this task -- however, its prediction accuracy tends to fade quickly as multiple steps are predicted. We aim to improve on this by applying probabilistic deep learning to make robust longer-range forecasts. For this, we applied the probabilistic deep neural network model WaveNet to forecast resting-state EEG in theta- (4-7.5 Hz) and alpha-frequency (8-13 Hz) bands and compared it to the AR model. WaveNet reliably predicted EEG signals in both theta and alpha frequencies over 100 ms ahead, with mean errors of 0.8{+/-}0.6 {micro}V (theta) and 0.7{+/-}0.5 {micro}V (alpha), and outperformed the AR model in estimating the signal amplitude and phase. Furthermore, we found that the probabilistic approach offers a way of forecasting even more accurately while effectively discarding uncertain predictions. We demonstrate for the first time that probabilistic deep learning can be utilised to forecast resting-state EEG time series. In the future, the developed model can enhance the real-time estimation of brain states in brain-computer interfaces and brain stimulation protocols. It may also be useful for answering neuroscientific questions and for diagnostic purposes.

neuroscience↗

Test-retest reliability of MEG functional brain connectivity related to language processing

The number of studies examining changes in functional connectivity of the human brain is increasing rapidly. In this magnetoencephalography (MEG) study, we examined the reliability of dynamic connectivity related to language processing in a picture naming test-retest paradigm, using data collected from the same participants on two separate days. We determined the connections that were reliable across both days and also examined the behavioral, functional, and structural properties underlying this reliability. A particularly salient finding among a rich set of results was a reliable pattern of beta connectivity increase in the left motor and frontal regions (0-400 ms and 400-800 ms after stimulus onset) and gamma connectivity decrease in the bilateral motor regions (800-1200 ms) which we suggest to represent the motor preparation of speech production. Furthermore, the reliable connections tended to be more frequently associated with the behavioral performance than the non-reliable ones. Finally, the reliable connections were also linked to stronger functional connectivity, as well as to stronger structural connectivity and shorter structural path length, as determined through diffusion MRI (magnetic resonance imaging). Overall, this study defines reliable language-related functional connectivity and introduces practices that may increase reliability. AUTHOR SUMMARYResearch applying connectivity metrics in neuroimaging has increased rapidly during recent years. Hence, the focus has also been to define the best methods for increasing the reliability of connectivity estimation. This study determined reliable functional connectivity from MEG data related to language processing. Moreover, we defined what makes a connection reliable by studying the behavioral, functional, and structural properties underlying the reliable connections.

neuroscience↗