Outcome of TMS-based motor mapping depends on TMS current direction

Jj, Vink; Pi, Petrov; Mandija, Stefano; Dijkhuizen, Rick M.; Sf, Neggers

doi:10.1101/371997

Cited by 5 publications

(7 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Figures F1/2, it is appropriate to the include coil orientation to attain (slightly) better prediction accuracy. This is consistent with earlier findings about the relevance of TMS orientation when eliciting MEPs (Opitz et al, 2013; Vink et al (2018).…”

Section: Discussionsupporting

confidence: 93%

“…Apart from position, however, coil orientation and stimulation intensity also determine whether an MEP will be elicited (Laakso et al, 2013;Opitz et al, 2013;Weise et al, 2020). Vink et al (2018) showed that MEP amplitudes altered with changing coil orientation even when the stimulation occurred in the same position. Here we asked to what extent one can predict whether a TMS stimulation with a certain intensity and at a certain position and orientation does (or does not) elicit an MEP in a muscle.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning approaches to predicting whether muscles can be elicited via TMS

Jin,

Bruijn,

Daffertshofer

2023

Preprint

View full text Add to dashboard Cite

BackgroundTranscranial magnetic stimulation (TMS) is a valuable technique for assessing the function of the motor cortex and cortico-muscular pathways. TMS activates the motoneurons in the cortex, and this activation is transmitted through the cortico-muscular pathway, after which it can be measured as a motor evoked potential (MEP) in the muscles. The position and orientation of the TMS coil and the intensity used to deliver a TMS pulse are considered central TMS setup parameters influencing the presence/absence of MEPs.New MethodWe sought to predict the presence of MEPs from TMS setup parameters using machine learning. We trained different machine learners using either within-subject or between-subject designs.ResultsWe obtained prediction accuracies of on average 77% and 65% with maxima up to up to 90% and 72% within and between subjects, respectively. Across the board, a bagging ensemble appeared to be the most suitable approach to predict the presence of MEPs, although a comparably simple logistic regression model also performed well.ConclusionsWhile the prediction between subjects clearly leaves room for improvement, the within-subject performance encourages to supplement TMS by machine learning to improve its diagnostic capacity with respect to motor impairment.

show abstract

Section: Discussionsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Machine learning approaches to predicting whether muscles can be elicited via TMS

Jin,

Bruijn,

Daffertshofer

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Our study was limited by the superior power decay curve of the small coil, which may have caused the applied stimulation intensities (at 100% MT for each rat) to be insufficient for activation of the front limb region in its entirety to elicit reliable MEPs. TMS MEP assessment in humans (41) and animals (20) is often performed at 120-150% of the MT. Thus, stimulation with the small coil at these levels might generate more reliable MEPs.…”

Section: Discussionmentioning

confidence: 99%

Design and Evaluation of a Rodent-Specific Transcranial Magnetic Stimulation Coil: An In Silico and In Vivo Validation Study

Boonzaier

Petrov

Otte

et al. 2020

Neuromodulation: Technology at the Neural Interface

View full text Add to dashboard Cite

Background: Rodent models are fundamental in unraveling cellular and molecular mechanisms of transcranial magnetic stimulation (TMS)-induced effects on the brain. However, proper translation of human TMS protocols to animal models have been restricted by the lack of rodent-specific focal TMS coils. Objective: We aimed to improve TMS focalization in rodent brain with a novel small, cooled, and rodent-specific TMS coil. Methods: A rodent-specific 25-mm figure-of-eight TMS coil was developed. Stimulation focalization was simulated in silico for the rodent coil and a commercial human 50-mm figure-of-eight TMS coil. Both coils were also compared in vivo by electromyography measurements of brachialis motor evoked potential (MEP) responses to TMS at different brain sites in anesthetized rats (n = 6). Focalization was determined from the coils' level of stimulation laterality. Differences in MEPs were statistically analyzed with repeated-measures, within-subjects, ANOVA. Results: In silico simulation results deemed the human coil insufficient for unilateral stimulation of the rat motor cortex, whereas lateralized electrical field induction was projected attainable with the rodent coil. Cortical, in vivo MEP amplitude measurements from multiple points in each hemisphere, revealed unilateral activation of the contralateral brachialis muscle, in absence of ipsilateral brachialis activation, with both coils. Conclusion: Computer simulations motivated the design of a smaller rodent-specific TMS coil, but came short in explaining the capability of a larger commercial human coil to induce unilateral MEPs in vivo. Lateralized TMS, as demonstrated for both TMS coils, corroborates their use in translational rodent studies, to elucidate mechanisms of action of therapeutic TMS protocols.

show abstract

“…This has implications for the current practice of aiming the coil iso-center, which is assumed to be the focal point of stimulation for TMS, to a target of interest. We recently published a thorough study with a 5×5 grid mapping approach, observing the same phenomenon [38]. Another observation worth mentioning is the demonstrated capability of outward injected currents, that is TMS coil pointing away from the target of stimulation, to produce strong responses systematically.…”

Section: Discussionmentioning

confidence: 77%

A validation approach for computational models of TMS induced brain currents using motor evoked potentials

Petrov

Vink

Mandija

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

The adoption of transcranial magnetic stimulation (TMS) has steadily increased in research as a tool capable to safely and non-invasively stimulate both the central and peripheral nervous systems. Initial clinical applications were limited to diagnostic use of TMS and readout signals such as electromyograms (EMG). Subsequently, repetitive TMS (rTMS) was appreciated for its therapeutics benefits as well. However, even after a decade of use of rTMS as an alternative treatment of major depression disorder in psychiatry, the mechanism of action is still not well understood. Computer models predicting the induced electric field distribution in the brain have been suggested before in the hope to resolve at least some of the uncertainty and resulting variable treatment response associated with the clinical use of TMS. We constructed a finite element model (FEM) of the head using individual volumetric tissue meshes obtained from an MRI scan and a detailed model of a TMS coil that together can predict the current induced in the head of a patient at any given location with any given coil position and orientation. We further designed several potential metrics of how a TMS induced current induced neuronal activation in the motor cortex, and added this to the model. We validated this model with motor evoked potentials (MEPs), EMG responses of the hand muscles after TMS on the motor cortex, in an experiment on 9 healthy subjects. We adopted a tailored MEP mapping protocol for model validation, which unlike traditional grid mappings, varies the TMS machine output intensity between stimulation locations. We further varied coil orientation on each point stimulated to allow exploration of the angular dependency of the model MEPs. Taken together, this approach covers a wide domain and scope of the modeled and measured responses, which are optimally suited for model validation. For each subject the motor hotspot was carefully identified using individual cortical anatomy and BOLD fMRI measurements. Modeled activation in the motor cortex did not show a good correlation to the observed magnitude of the observed MEPs, for none of the neuronal activation metrics adopted. For an activation metric that was asymmetric, taking into account induced current direction with respect to the motor cortex sulcal wall, was marginally better than other metrics. Generally all activation metrics based on induced currents performed better than a control metric agnostic of induced electric field magnitude. Our results suggest that one should take into account components of the injected currents and their relationship to the morphology of the underlying motor cortex, but the coarse metrics we used to model the relationship between induced current and neuronal activation probably did not do justice to the complex neuronal circuitry of the cortical sheet. Furthermore, it seemed MEP magnitudes in our experiment are too variable over subsequent stimulations, which could be mitigated by more repetitions per stimulation location and orientation. Further efforts to construct validated models predicting TMS effects in individual patients brains should incorporate microcircuits interactions in the cortical sheet, in addition to induced electrical field models, and take into account inherent trial to trial variability of MEPs.

show abstract

Outcome of TMS-based motor mapping depends on TMS current direction

Cited by 5 publications

References 37 publications

Machine learning approaches to predicting whether muscles can be elicited via TMS

Machine learning approaches to predicting whether muscles can be elicited via TMS

Design and Evaluation of a Rodent-Specific Transcranial Magnetic Stimulation Coil: An In Silico and In Vivo Validation Study

A validation approach for computational models of TMS induced brain currents using motor evoked potentials

Contact Info

Product

Resources

About