Exploring Motor Network Connectivity in State-Dependent Transcranial Magnetic Stimulation: A Proof-of-Concept Study

State-dependent non-invasive brain stimulation (NIBS) informed by electroencephalography (EEG) has contributed to the understanding of NIBS inter-subject and inter-session variability. While these approaches focus on local EEG characteristics, it is acknowledged that the brain exhibits an intrinsic long-range dynamic organization in networks. This proof-of-concept study explores whether EEG connectivity of the primary motor cortex (M1) in the pre-stimulation period aligns with the Motor Network (MN) and how the MN state affects responses to the transcranial magnetic stimulation (TMS) of M1. One thousand suprathreshold TMS pulses were delivered to the left M1 in eight subjects at rest, with simultaneous EEG. Motor-evoked potentials (MEPs) were measured from the right hand. The source space functional connectivity of the left M1 to the whole brain was assessed using the imaginary part of the phase locking value at the frequency of the sensorimotor μ-rhythm in a 1 s window before the pulse. Group-level connectivity revealed functional links between the left M1, left supplementary motor area, and right M1. Also, pulses delivered at high MN connectivity states result in a greater MEP amplitude compared to low connectivity states. At the single-subject level, this relation is more highly expressed in subjects that feature an overall high cortico-spinal excitability. In conclusion, this study paves the way for MN connectivity-based NIBS.

Quasistatic approximation in neuromodulation

We define and explain the quasistatic approximation (QSA) as applied to field modeling for electrical and magnetic stimulation. Neuromodulation analysis pipelines include discrete stages, and QSA is applied specifically when calculating the electric and magnetic fields generated in tissues by a given stimulation dose. QSA simplifies the modeling equations to support tractable analysis, enhanced understanding, and computational efficiency. The application of QSA in neuro-modulation is based on four underlying assumptions: (A1) no wave propagation or self-induction in tissue, (A2) linear tissue properties, (A3) purely resistive tissue, and (A4) non-dispersive tissue. As a consequence of these assumptions, each tissue is assigned a fixed conductivity, and the simplified equations (e.g., Laplace's equation) are solved for the spatial distribution of the field, which is separated from the field's temporal waveform. Recognizing that electrical tissue properties may be more complex, we explain how QSA can be embedded in parallel or iterative pipelines to model frequency dependence or nonlinearity of conductivity. We survey the history and validity of QSA across specific applications, such as microstimulation, deep brain stimulation, spinal cord stimulation, transcranial electrical stimulation, and transcranial magnetic stimulation. The precise definition and explanation of QSA in neuromodulation are essential for rigor when using QSA models or testing their limits.

A bicoherence approach to analyze multi-dimensional cross-frequency coupling in EEG/MEG data

We introduce a blockwise generalisation of the Antisymmetric Cross-Bicoherence (ACB), a statistical method based on bispectral analysis. The Multi-dimensional ACB (MACB) is an approach that aims at detecting quadratic lagged phase-interactions between vector time series in the frequency domain. Such a coupling can be empirically observed in functional neuroimaging data, e.g., in electro/magnetoencephalographic signals. MACB is invariant under orthogonal trasformations of the data, which makes it independent, e.g., on the choice of the physical coordinate system in the neuro-electromagnetic inverse procedure. In extensive synthetic experiments, we prove that MACB performance is significantly better than that obtained by ACB. Specifically, the shorter the data length, or the higher the dimension of the single data space, the larger the difference between the two methods.

Acoustic noise generated by TMS in typical environment and inside an MRI scanner

BACKGROUND

The operation of a transcranial magnetic stimulation (TMS) coil produces high-intensity impulse sounds. In TMS, a magnetic field is generated by a short-duration pulse in the range of thousands of amperes in the TMS coil. When placed in a strong magnetic field, such as inside a magnetic resonance imaging (MRI) bore, the interaction of the magnetic field and the current in the TMS coil can cause strong forces on the coil casing. The strengths of these forces depend on the coil orientation in the main magnetic field (B0). Part of the energy in this process is dissipated in the form of acoustic noise.

OBJECTIVE

Our objective was to measure the sound pressure levels (SPL) of TMS "click" sounds created by commercial TMS stimulators and coils in a typical environment and inside a 3-T MRI scanner and advance the knowledge of the acoustic behaviour of TMS to safely conduct TMS alone as well as concurrently with functional MRI (fMRI).

METHODS

We report SPL measurements of two commercial MRI-compatible TMS systems in the 3-T B0 field of an MRI scanner and in the earth's magnetic field. Also, we present the acoustic noise measurements of four commercial TMS stimulators and three different TMS coils in a typical operational environment without the B0 field.

RESULTS

The maximum peak SPL measured was 158 dB(C) inside the 3-T MRI scanner. Outside the scanner, the maximum peak SPL was 117 dB(C). Inside the scanner, the peak SPL increased by 21-45 dB(C) depending on the stimulator and the orientation of the electric field relative to the B field.

CONCLUSIONS

Hearing protection is obligatory during concurrent TMS-fMRI experiments and highly recommended during any TMS experiment. The manufacturing of quieter TMS systems is encouraged to reduce the risk of hearing damage and other unwanted effects.

Measuring the accuracy of ICA-based artifact removal from TMS-evoked potentials

BACKGROUND

The analysis and interpretation of transcranial magnetic stimulation (TMS)-evoked potentials (TEPs) relies on successful cleaning of the artifacts, which typically mask the early (0-30 ms) TEPs. Independent component analysis (ICA) is possibly the single most utilized methodology to clean these signals.

OBJECTIVE

ICA-based cleaning is reliable provided that the input data are composed of independent components. Differently, in case the underlying components are to some extent dependent, ICA algorithms may yield erroneous estimates of the components, resulting in incorrectly cleaned data. We aim to ascertain whether TEP signals are suited for ICA.

METHODS

We present a systematic analysis of how the properties of simulated artifacts imposed on measured artifact-free TEPs affect the ICA results. The variability of the artifact waveform over the recorded trials is varied from deterministic to stochastic. We measure the accuracy of ICA-based cleaning for each level of variability.

RESULTS

Our findings indicate that, when the trial-to-trial variability of an artifact component is small, which can result in dependencies between underlying components, ICA-based cleaning biases towards eliminating also non-artifactual TEP data. We also show that the variability can be measured using the ICA-derived components, which in turn allows us to estimate the cleaning accuracy.

CONCLUSION

As TEP artifacts tend to have small trial-to-trial variability, one should be aware of the possibility of eliminating brain-derived EEG when applying ICA-based cleaning strategies. In practice, after ICA, the artifact component variability can be measured, and it predicts to some extent the cleaning reliability, even when not knowing the clean ground-truth data.

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies

The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the characterization of thousands of MEPs. Given the difficulty of developing reliable and accurate algorithms, currently the assessment of MEPs is performed with visual inspection and manual annotation by a medical expert; making it a time-consuming, inaccurate, and error-prone process. In this study, we developed DELMEP, a deep learning-based algorithm to automate the estimation of MEP latency. Our algorithm resulted in a mean absolute error of about 0.5 ms and an accuracy that was practically independent of the MEP amplitude. The low computational cost of the DELMEP algorithm allows employing it in on-the-fly characterization of MEPs for brain-state-dependent and closed-loop brain stimulation protocols. Moreover, its learning ability makes it a particularly promising option for artificial-intelligence-based personalized clinical applications.

MarLe: Markerless estimation of head pose for navigated transcranial magnetic stimulation

Navigated transcranial magnetic stimulation (nTMS) is a valuable tool for non-invasive brain stimulation. Currently, nTMS requires fixing of markers on the patient's head. Head marker displacements lead to changes in coil placement and brain stimulation inaccuracy. A markerless neuronavigation method is needed to increase the reliability of nTMS and simplify the nTMS protocol. In this study, we introduce and release MarLe, a Python markerless head tracker neuronavigation software for TMS. This novel software uses computer-vision techniques combined with low-cost cameras to estimate the head pose for neuronavigation. A coregistration algorithm, based on a closed-form solution, was designed to track the patient's head and the TMS coil referenced to the individual's brain image. We show that MarLe can estimate head pose based on real-time video processing. An intuitive pipeline was developed to connect the MarLe and nTMS neuronavigation software. MarLe achieved acceptable accuracy and stability in a mockup nTMS experiment. MarLe allows real-time tracking of the patient's head without any markers. The combination of face detection and a coregistration algorithm can overcome nTMS head marker displacement concerns. MarLe can improve reliability, simplify, and reduce the protocol time of brain intervention techniques such as nTMS.

TMS-Induced Modulation of EEG Functional Connectivity Is Affected by the E-Field Orientation

Coregistration of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) allows non-invasive probing of brain circuits: TMS induces brain activation due to the generation of a properly oriented focused electric field (E-field) using a coil placed on a selected position over the scalp, while EEG captures the effects of the stimulation on brain electrical activity. Moreover, the combination of these techniques allows the investigation of several brain properties, including brain functional connectivity. The choice of E-field parameters, such as intensity, orientation, and position, is crucial for eliciting cortex-specific effects. Here, we evaluated whether and how the spatial pattern, i.e., topography and strength of functional connectivity, is modulated by the stimulus orientation. We systematically altered the E-field orientation when stimulating the left pre-supplementary motor area and showed an increase of functional connectivity in areas associated with the primary motor cortex and an E-field orientation-specific modulation of functional connectivity intensity.

TMS combined with EEG: Recommendations and open issues for data collection and analysis

Transcranial magnetic stimulation (TMS) evokes neuronal activity in the targeted cortex and connected brain regions. The evoked brain response can be measured with electroencephalography (EEG). TMS combined with simultaneous EEG (TMS-EEG) is widely used for studying cortical reactivity and connectivity at high spatiotemporal resolution. Methodologically, the combination of TMS with EEG is challenging, and there are many open questions in the field. Different TMS-EEG equipment and approaches for data collection and analysis are used. The lack of standardization may affect reproducibility and limit the comparability of results produced in different research laboratories. In addition, there is controversy about the extent to which auditory and somatosensory inputs contribute to transcranially evoked EEG. This review provides a guide for researchers who wish to use TMS-EEG to study the reactivity of the human cortex. A worldwide panel of experts working on TMS-EEG covered all aspects that should be considered in TMS-EEG experiments, providing methodological recommendations (when possible) for effective TMS-EEG recordings and analysis. The panel identified and discussed the challenges of the technique, particularly regarding recording procedures, artifact correction, analysis, and interpretation of the transcranial evoked potentials (TEPs). Therefore, this work offers an extensive overview of TMS-EEG methodology and thus may promote standardization of experimental and computational procedures across groups.

Effective Intracerebral Connectivity in Acute Stroke: A TMS-EEG Study

Stroke is a major cause of disability because of its motor and cognitive sequelae even when the acute phase of stabilization of vital parameters is overcome. The most important improvements occur in the first 8-12 weeks after stroke, indicating that it is crucial to improve our understanding of the dynamics of phenomena occurring in this time window to prospectively target rehabilitation procedures from the earliest stages after the event. Here, we studied the intracortical excitability properties of delivering transcranial magnetic stimulation (TMS) to the primary motor cortex (M1) of left and right hemispheres in 17 stroke patients who suffered a mono-lateral left hemispheric stroke, excluding pure cortical damage. All patients were studied within 10 days of symptom onset. TMS-evoked potentials (TEPs) were collected via a TMS-compatible electroencephalogram system (TMS-EEG) concurrently with motor-evoked responses (MEPs) induced in the contralateral first dorsal interosseous muscle. Comparison with age-matched healthy volunteers was made by collecting the same bilateral-stimulation data in nine healthy volunteers as controls. Excitability in the acute phase revealed relevant changes in the relationship between left lesioned and contralesionally right hemispheric homologous areas both for TEPs and MEPs. While the paretic hand displayed reduced MEPs compared to the non-paretic hand and to healthy volunteers, TEPs revealed an overexcitable lesioned hemisphere with respect to both healthy volunteers and the contra-lesion side. Our quantitative results advance the understanding of the impairment of intracortical inhibitory networks. The neuronal dysfunction most probably changes the excitatory/inhibitory on-center off-surround organization that supports already acquired learning and reorganization phenomena that support recovery from stroke sequelae.

Advanced Pipeline for Designing Multi-Locus TMS Coils with Current Density Constraints

OBJECTIVE

This work aims for a method to design manufacturable windings for transcranial magnetic stimulation (TMS) coils with fine control over the induced electric field (E-field) distributions. Such TMS coils are required for multi-locus TMS (mTMS).

METHODS

We introduce a new mTMS coil design workflow with increased flexibility in target E-field definition and faster computations compared to our previous method. We also incorporate custom current density and E-field fidelity constraints to ensure that the target E-fields are accurately reproduced with feasible winding densities in the resulting coil designs. We validated the method by designing, manufacturing, and characterizing a 2-coil mTMS transducer for focal rat brain stimulation.

RESULTS

Applying the constraints reduced the computed maximum surface current densities from 15.4 and 6.6 kA/mm to the target value 4.7 kA/mm, yielding winding paths suitable for a 1.5-mm-diameter wire with 7-kA maximum currents while still replicating the target E-fields with the predefined 2.8% maximum error in the FOV. The optimization time was reduced by two thirds compared to our previous method.

CONCLUSION

The developed method allowed us to design a manufacturable, focal 2-coil mTMS transducer for rat TMS impossible to attain with our previous design workflow.

SIGNIFICANCE

The presented workflow enables considerably faster design and manufacturing of previously unattainable mTMS transducers with increased control over the induced E-field distribution and winding density, opening new possibilities for brain research and clinical TMS.

Accuracy and precision of navigated transcranial magnetic stimulation

Objective.Transcranial magnetic stimulation (TMS) induces an electric field (E-field) in the cortex. To facilitate stimulation targeting, image-guided neuronavigation systems have been introduced. Such systems track the placement of the coil with respect to the head and visualize the estimated cortical stimulation location on an anatomical brain image in real time. The accuracy and precision of the neuronavigation is affected by multiple factors. Our aim was to analyze how different factors in TMS neuronavigation affect the accuracy and precision of the coil-head coregistration and the estimated E-field.Approach.By performing simulations, we estimated navigation errors due to distortions in magnetic resonance images (MRIs), head-to-MRI registration (landmark- and surface-based registrations), localization and movement of the head tracker, and localization of the coil tracker. We analyzed the effect of these errors on coil and head coregistration and on the induced E-field as determined with simplistic and realistic head models.Main results.Average total coregistration accuracies were in the range of 2.2-3.6 mm and 1°; precision values were about half of the accuracy values. The coregistration errors were mainly due to head-to-MRI registration with average accuracies 1.5-1.9 mm/0.2-0.4° and precisions 0.5-0.8 mm/0.1-0.2° better with surface-based registration. The other major source of error was the movement of the head tracker with average accuracy of 1.5 mm and precision of 1.1 mm. When assessed within an E-field method, the average accuracies of the peak E-field location, orientation, and magnitude ranged between 1.5 and 5.0 mm, 0.9 and 4.8°, and 4.4 and 8.5% across the E-field models studied. The largest errors were obtained with the landmark-based registration. When computing another accuracy measure with the most realistic E-field model as a reference, the accuracies tended to improve from about 10 mm/15°/25% to about 2 mm/2°/5% when increasing realism of the E-field model.Significance.The results of this comprehensive analysis help TMS operators to recognize the main sources of error in TMS navigation and that the coregistration errors and their effect in the E-field estimation depend on the methods applied. To ensure reliable TMS navigation, we recommend surface-based head-to-MRI registration and realistic models for E-field computations.

Local brain-state dependency of effective connectivity: a pilot TMS-EEG study

Background: Spontaneous cortical oscillations have been shown to modulate cortical responses to transcranial magnetic stimulation (TMS). However, whether these oscillations influence cortical effective connectivity is largely unknown. We conducted a pilot study to set the basis for addressing how spontaneous oscillations affect cortical effective connectivity measured through TMS-evoked potentials (TEPs). Methods: We 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. Results: Phase affected the GMFA in four out of 12 datasets (3 subjects × 2 stimulation sites × 2 frequency bands). Two of the observed significant intervals were before 50 ms, two between 50 and 100 ms, and one after 100 ms post-stimulus. Source estimates showed complex spatial differences between the classes in the cortical spreading of the TMS-evoked activity. Conclusions: TMS-evoked effective connectivity seems to depend on the phase of local cortical oscillations at the stimulated site. This work paves the way to design future closed-loop stimulation paradigms.

Minimum-norm Estimation of TMS-activated Motor Cortical Sites in Realistic Head and Brain Geometry

Navigated transcranial magnetic stimulation (nTMS) is a widely used tool for motor cortex mapping. However, the full details of the activated cortical area during the mapping remain unknown due to the spread of the stimulating electric field (E-field). Computational tools, which combine the E-field with physiological responses, have potential for revealing the activated source area. We applied the minimum-norm estimate (MNE) method in a realistic head geometry to estimate the activated cortical area in nTMS motor mappings of the leg and hand muscles. We calculated the MNE also in a spherical head geometry to assess the effect of the head model on the MNE maps. Finally, we determined optimized coil placements based on the MNE map maxima and compared these placements with the initial hotspot placement. The MNE maps generally agreed well with the original motor maps: in the realistic head geometry, the distance from the MNE map maximum to the motor map center of gravity (CoG) was 8.8 ± 4.6 mm in the leg motor area and 6.6 ± 2.5 mm in the hand motor area. The head model did not have a significant effect on these distances; however, it had a significant effect on the distance between the MNE CoG and the motor map (p < 0.05). The optimized coil locations were < 1 cm from the initial hotspot in 7/10 subjects. Further research is required to determine the level of anatomical detail and the optimal mapping parameters required for robust and accurate localization.

Closed-loop optimization of transcranial magnetic stimulation with electroencephalography feedback

Background

Transcranial 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.

Objective

To 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).

Methods

We 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.

Results

The 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.

Conclusion

Optimizing stimulation with EEG feedback in a closed-loop manner is feasible and enables effective coupling to brain activity.

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Closed-loop set-up for guiding TMS with brain activity feedback.

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Automatic stimulus orientation optimization based on TMS-evoked EEG responses.

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Adjusting TMS parameters electronically allows fast and effortless procedures.

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TMS-evoked EEG responses depend on the stimulus orientation.

TMS with fast and accurate electronic control: Measuring the orientation sensitivity of corticomotor pathways

BACKGROUND

Transcranial magnetic stimulation (TMS) coils allow only a slow, mechanical adjustment of the stimulating electric field (E-field) orientation in the cerebral tissue. Fast E-field control is needed to synchronize the stimulation with the ongoing brain activity. Also, empirical models that fully describe the relationship between evoked responses and the stimulus orientation and intensity are still missing.

OBJECTIVE

We aimed to (1) develop a TMS transducer for manipulating the E-field orientation electronically with high accuracy at the neuronally meaningful millisecond-level time scale and (2) devise and validate a physiologically based model describing the orientation selectivity of neuronal excitability.

METHODS

We designed and manufactured a two-coil TMS transducer. The coil windings were computed with a minimum-energy optimization procedure, and the transducer was controlled with our custom-made electronics. The electronic E-field control was verified with a TMS characterizer. The motor evoked potential amplitude and latency of a hand muscle were mapped in 3° steps of the stimulus orientation in 16 healthy subjects for three stimulation intensities. We fitted a logistic model to the motor response amplitude.

RESULTS

The two-coil TMS transducer allows one to manipulate the pulse orientation accurately without manual coil movement. The motor response amplitude followed a logistic function of the stimulus orientation; this dependency was strongly affected by the stimulus intensity.

CONCLUSION

The developed electronic control of the E-field orientation allows exploring new stimulation paradigms and probing neuronal mechanisms. The presented model helps to disentangle the neuronal mechanisms of brain function and guide future non-invasive stimulation protocols.

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

BACKGROUND

Transcranial 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.

OBJECTIVE

To develop an mTMS system that allows adjusting the location and orientation of the E-field maximum within a cortical region.

METHODS

We 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.

RESULTS

The 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.

CONCLUSION

The developed mTMS system enables electronically targeted brain stimulation within a cortical region.

Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design

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We analyze spatial sampling of MEG and EEG using a realistic head model.

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On-scalp MEG may benefit from three times more samples than EEG and off-scalp MEG.

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We optimize sample positions to convey the most information from the brain.

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Optimized sampling can be useful when the sensor number is limited.

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The sample positions can be optimized to target a region of interest in the brain.

In this paper, we analyze spatial sampling of electro- (EEG) and magnetoencephalography (MEG), where the electric or magnetic field is typically sampled on a curved surface such as the scalp. By simulating fields originating from a representative adult-male head, we study the spatial-frequency content in EEG as well as in on- and off-scalp MEG. This analysis suggests that on-scalp MEG, off-scalp MEG and EEG can benefit from up to 280, 90 and 110 spatial samples, respectively. In addition, we suggest a new approach to obtain sensor locations that are optimal with respect to prior assumptions. The approach also allows to control, e.g., the uniformity of the sensor locations. Based on our simulations, we argue that for a low number of spatial samples, model-informed non-uniform sampling can be beneficial. For a large number of samples, uniform sampling grids yield nearly the same total information as the model-informed grids.

Trade-off between stimulation focality and the number of coils in multi-locus transcranial magnetic stimulation

Objective. Coils designed for transcranial magnetic stimulation (TMS) must incorporate trade-offs between the required electrical power or energy, focality and depth penetration of the induced electric field (E-field), coil size, and mechanical properties of the coil, as all of them cannot be optimally met at the same time. In multi-locus TMS (mTMS), a transducer consisting of several coils allows electronically targeted stimulation of the cortex without physically moving a coil. In this study, we aimed to investigate the relationship between the number of coils in an mTMS transducer, the focality of the induced E-field, and the extent of the cortical region within which the location and orientation of the maximum of the induced E-field can be controlled.Approach.We applied convex optimization to design planar and spherically curved mTMS transducers of different E-field focalities and analyzed their properties. We characterized the trade-off between the focality of the induced E-field and the extent of the cortical region that can be stimulated with an mTMS transducer with a given number of coils.Main results.At the expense of the E-field focality, one can, with the same number of coils, design an mTMS transducer that can control the location and orientation of the peak of the induced E-field within a wider cortical region.Significance. With E-fields of moderate focality, the problem of electronically targeted TMS becomes considerably easier compared with highly focal E-fields; this may speed up the development of mTMS and the emergence of new clinical and research applications.

Effect of stimulus orientation and intensity on short-interval intracortical inhibition (SICI) and facilitation (SICF): A multi-channel transcranial magnetic stimulation study

Besides stimulus intensities and interstimulus intervals (ISI), the electric field (E-field) orientation is known to affect both short-interval intracortical inhibition (SICI) and facilitation (SICF) in paired-pulse transcranial magnetic stimulation (TMS). However, it has yet to be established how distinct orientations of the conditioning (CS) and test stimuli (TS) affect the SICI and SICF generation. With the use of a multi-channel TMS transducer that provides electronic control of the stimulus orientation and intensity, we aimed to investigate how changes in the CS and TS orientation affect the strength of SICI and SICF. We hypothesized that the CS orientation would play a major role for SICF than for SICI, whereas the CS intensity would be more critical for SICI than for SICF. In eight healthy subjects, we tested two ISIs (1.5 and 2.7 ms), two CS and TS orientations (anteromedial (AM) and posteromedial (PM)), and four CS intensities (50, 70, 90, and 110% of the resting motor threshold (RMT)). The TS intensity was fixed at 110% RMT. The intensities were adjusted to the corresponding RMT in the AM and PM orientations. SICI and SICF were observed in all tested CS and TS orientations. SICI depended on the CS intensity in a U-shaped manner in any combination of the CS and TS orientations. With 70% and 90% RMT CS intensities, stronger PM-oriented CS induced stronger inhibition than weaker AM-oriented CS. Similar SICF was observed for any CS orientation. Neither SICI nor SICF depended on the TS orientation. We demonstrated that SICI and SICF could be elicited by the CS perpendicular to the TS, which indicates that these stimuli affected either overlapping or strongly connected neuronal populations. We concluded that SICI is primarily sensitive to the CS intensity and that CS intensity adjustment resulted in similar SICF for different CS orientations.

The impact of artifact removal approaches on TMS-EEG signal

Transcranial magnetic stimulation (TMS)-evoked potentials (TEPs) allow one to assess cortical excitability and effective connectivity in clinical and basic research. However, obtaining clean TEPs is challenging due to the various TMS-related artifacts that contaminate the electroencephalographic (EEG) signal when the TMS pulse is delivered. Different preprocessing approaches have been employed to remove the artifacts, but the degree of artifact reduction or signal distortion introduced in this phase of analysis is still unknown. Knowing and controlling this potential source of uncertainty will increase the inter-rater reliability of TEPs and improve the comparability between TMS-EEG studies. The goal of this study was to assess the variability in TEP waveforms due to of the use of different preprocessing pipelines. To accomplish this aim, we preprocessed the same TMS-EEG data with four different pipelines and compared the results. The dataset was obtained from 16 subjects in two identical recording sessions, each session consisting of both left dorsolateral prefrontal cortex and left inferior parietal lobule stimulation at 100% of the resting motor threshold. Considerable differences in TEP amplitudes and global mean field power (GMFP) were found between the preprocessing pipelines. Topographies of TEPs from the different pipelines were all highly correlated (ρ>0.8) at latencies over 100 ms. By contrast, waveforms at latencies under 100 ms showed a variable level of correlation, with ρ ranging between 0.2 and 0.9. Moreover, the test-retest reliability of TEPs depended on the preprocessing pipeline. Taken together, these results take us to suggest that the choice of the preprocessing approach has a marked impact on the final TEP, and that further studies are needed to understand advantages and disadvantages of the different approaches.

Safety and recommendations for TMS use in healthy subjects and patient populations, with updates on training, ethical and regulatory issues: Expert Guidelines

This article is based on a consensus conference, promoted and supported by the International Federation of Clinical Neurophysiology (IFCN), which took place in Siena (Italy) in October 2018. The meeting intended to update the ten-year-old safety guidelines for the application of transcranial magnetic stimulation (TMS) in research and clinical settings (Rossi et al., 2009). Therefore, only emerging and new issues are covered in detail, leaving still valid the 2009 recommendations regarding the description of conventional or patterned TMS protocols, the screening of subjects/patients, the need of neurophysiological monitoring for new protocols, the utilization of reference thresholds of stimulation, the managing of seizures and the list of minor side effects. New issues discussed in detail from the meeting up to April 2020 are safety issues of recently developed stimulation devices and pulse configurations; duties and responsibility of device makers; novel scenarios of TMS applications such as in the neuroimaging context or imaging-guided and robot-guided TMS; TMS interleaved with transcranial electrical stimulation; safety during paired associative stimulation interventions; and risks of using TMS to induce therapeutic seizures (magnetic seizure therapy). An update on the possible induction of seizures, theoretically the most serious risk of TMS, is provided. It has become apparent that such a risk is low, even in patients taking drugs acting on the central nervous system, at least with the use of traditional stimulation parameters and focal coils for which large data sets are available. Finally, new operational guidelines are provided for safety in planning future trials based on traditional and patterned TMS protocols, as well as a summary of the minimal training requirements for operators, and a note on ethics of neuroenhancement.

Signal-Space Projection Suppresses the tACS Artifact in EEG Recordings

Background

To probe the functional role of brain oscillations, transcranial alternating current stimulation (tACS) has proven to be a useful neuroscientific tool. Because of the excessive tACS-caused artifact at the stimulation frequency in electroencephalography (EEG) signals, tACS + EEG studies have been mostly limited to compare brain activity between recordings before and after concurrent tACS. Critically, attempts to suppress the artifact in the data cannot assure that the entire artifact is removed while brain activity is preserved. The current study aims to evaluate the feasibility of specific artifact correction techniques to clean tACS-contaminated EEG data.

New Method

In the first experiment, we used a phantom head to have full control over the signal to be analyzed. Driving pre-recorded human brain-oscillation signals through a dipolar current source within the phantom, we simultaneously applied tACS and compared the performance of different artifact-correction techniques: sine subtraction, template subtraction, and signal-space projection (SSP). In the second experiment, we combined tACS and EEG on one human subject to demonstrate the best-performing data-correction approach in a proof of principle.

Results

The tACS artifact was highly attenuated by SSP in the phantom and the human EEG; thus, we were able to recover the amplitude and phase of the oscillatory activity. In the human experiment, event-related desynchronization could be restored after correcting the artifact.

Comparison With Existing Methods

The best results were achieved with SSP, which outperformed sine subtraction and template subtraction.

Conclusion

Our results demonstrate the feasibility of SSP by applying it to a phantom measurement with pre-recorded signal and one human tACS + EEG dataset. For a full validation of SSP, more data are needed.

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.

Spatial extent of cortical motor hotspot in navigated transcranial magnetic stimulation

BACKGROUND

Motor mapping with navigated transcranial magnetic stimulation (nTMS) requires defining a "hotspot", a stimulation site consistently producing the highest-amplitude motor-evoked potentials (MEPs). The exact location of the hotspot is difficult to determine, and the spatial extent of high-amplitude MEPs usually remains undefined due to MEP variability and the spread of the TMS-induced electric field (E-field). Therefore, here we aim to define the hotspot as a sub-region of a motor map.

NEW METHOD

We analyzed MEP amplitude distributions in motor mappings of 30 healthy subjects in two orthogonal directions on the motor cortex. Based on the widths of these distributions, the hotspot extent was estimated as an elliptic area. In addition, E-field distributions induced by motor map edge stimulations were simulated for ten subjects, and the E-field attenuation was analyzed to obtain another estimate for hotspot extent.

RESULTS

The median MEP-based hotspot area was 13 mm² (95% confidence interval (CI) = [10, 18] mm²). The mean E-field-based hotspot area was 26 mm² (95% CI = [13, 38] mm²).

COMPARISON WITH EXISTING METHODS

In contrast to the conventional hotspot, the new definition considers its spatial extent, indicating the most easily excited area where subsequent nTMS stimuli should be targeted for maximal response. The E-field-based hotspot provides an estimate for the extent of cortical structures where the E-field is close to its maximum.

CONCLUSIONS

The nTMS hotspot should be considered as an area rather than a single qualitatively defined spot due to MEP variability and E-field spread.

Superconducting receiver arrays for magnetic resonance imaging

Superconducting QUantum-Interference Devices (SQUIDs) make magnetic resonance imaging (MRI) possible in ultra-low microtesla-range magnetic fields. In this work, we investigate the design parameters affecting the signal and noise performance of SQUID-based sensors and multichannel magnetometers for MRI of the brain. Besides sensor intrinsics, various noise sources along with the size, geometry and number of superconducting detector coils are important factors affecting the image quality. We derive figures of merit based on optimal combination of multichannel data, analyze different sensor array designs, and provide tools for understanding the signal detection and the different noise mechanisms. The work forms a guide to making design decisions for both imaging- and sensor-oriented readers.

EEG Artifact Removal in TMS Studies of Cortical Speech Areas

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) is commonly applied for studying the effective connectivity of neuronal circuits. The stimulation excites neurons, and the resulting TMS-evoked potentials (TEPs) are recorded with EEG. A serious obstacle in this method is the generation of large muscle artifacts from scalp muscles, especially when frontolateral and temporoparietal, such as speech, areas are stimulated. Here, TMS-EEG data were processed with the signal-space projection and source-informed reconstruction (SSP-SIR) artifact-removal methods to suppress these artifacts. SSP-SIR suppressed muscle artifacts according to the difference in frequency contents of neuronal signals and muscle activity. The effectiveness of SSP-SIR in rejecting muscle artifacts and the degree of excessive attenuation of brain EEG signals were investigated by comparing the processed versions of the recorded TMS-EEG data with simulated data. The calculated individual lead-field matrix describing how the brain signals spread on the cortex were used as simulated data. We conclude that SSP-SIR was effective in suppressing artifacts also when frontolateral and temporoparietal cortical sites were stimulated, but it may have suppressed also the brain signals near the stimulation site. Effective connectivity originating from the speech-related areas may be studied even when speech areas are stimulated at least on the contralateral hemisphere where the signals were not suppressed that much.

Automatic Spatial Calibration of Ultra-Low-Field MRI for High-Accuracy Hybrid MEG-MRI

With a hybrid magnetoencephalography (MEG)-MRI device that uses the same sensors for both modalities, the co-registration of MRI and MEG data can be replaced by an automatic calibration step. Based on the highly accurate signal model of ultra-low-field (ULF) MRI, we introduce a calibration method that eliminates the error sources of traditional co-registration. The signal model includes complex sensitivity profiles of the superconducting pickup coils. In the ULF MRI, the profiles are independent of the sample and therefore well-defined. In the most basic form, the spatial information of the profiles, captured in parallel ULF-MR acquisitions, is used to find the exact coordinate transformation required. We assessed our calibration method by simulations assuming a helmet-shaped pickup-coil-array geometry. Using a carefully constructed objective function and sufficient approximations, even with low-SNR images, sub-voxel and sub-millimeter calibration accuracy were achieved. After the calibration, distortion-free MRI and high spatial accuracy for MEG source localization can be achieved. For an accurate sensor-array geometry, the co-registration and associated errors are eliminated, and the positional error can be reduced to a negligible level.