Biology subjects

Tervo, A. E.

Publications and source records attributed to Tervo, A. E..

4 recordsLinked to original sources

bioRxiv2021

Local brain-state dependency of effective connectivity: evidence from TMS-EEG

BackgroundSpontaneous cortical oscillations have been shown to modulate cortical responses to transcranial magnetic stimulation (TMS). If not controlled for, they might increase variability in responses and mask meaningful changes in the signals of interest when studying the brain with TMS combined with electroencephalography (TMS-EEG). To address this challenge in future closed-loop stimulation paradigms, we need to understand how spontaneous oscillations affect TMS-evoked responses. ObjectiveTo describe the effect of the pre-stimulus phase of cortical mu (8-13 Hz) and beta (13-30 Hz) oscillations on TMS-induced effective connectivity patterns. MethodsWe applied TMS to the left primary motor cortex and right pre-supplementary motor area of three subjects while recording EEG. We classified trials off-line into positive- and negative-phase classes according to the mu and beta rhythms. We calculated differences in the global mean-field amplitude (GMFA) and compared the cortical spreading of the TMS-evoked activity between the two classes. ResultsPhase had significant effects on the GMFA in 11 out of 12 datasets (3 subjects x 2 stimulation sites x 2 frequency bands). Seven of the datasets showed significant differences in the time range 15-50 ms, nine in 50-150 ms, and eight after 150 ms post-stimulus. Source estimates showed complex spatial differences between the classes in the cortical spreading of the TMS-evoked activity. ConclusionsTMS-evoked effective connectivity appears to depend on the phase of local cortical oscillations at the stimulated site. This may be crucial for efficient design of future brain-state-dependent and closed-loop stimulation paradigms.

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↗

bioRxiv2020

Automated search of stimulation targets with closed-loop transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) protocols often include a manual search of an optimal location and orientation of the coil or peak stimulating electric field to elicit motor responses in a target muscle. This target search is laborious, and the result is user-dependent. Here, we present a closed-loop search method that utilizes automatic electronic adjustment of the stimulation based on the previous responses. The electronic adjustment is achieved by multi-locus TMS, and the adaptive guiding of the stimulation is based on the principles of Bayesian optimization to minimize the number of stimuli (and time) needed in the search. We compared our target-search method with other methods, such as systematic sampling in a predefined cortical grid. Validation experiments on five healthy volunteers and further offline simulations showed that our adaptively guided search method needs only a relatively small number of stimuli to provide outcomes with good accuracy and precision. The automated method enables fast and user-independent optimization of stimulation parameters in research and clinical applications of TMS.Competing Interest StatementRJI is an advisor and a minority shareholder of Nexstim Plc. The other authors declare no competing interests.View Full Text

bioengineering↗