Preconditioning Stimulus Intensity Alters Paired-Pulse TMS Evoked Potentials

Investigating cortical excitability and inhibition in patients with schizophrenia: A TMS-EEG study

BACKGROUND

Transcranial magnetic stimulation (TMS) combined with electromyography (EMG) has widely been used as a non-invasive brain stimulation tool to assess excitation/inhibition (E/I) balance. E/I imbalance is a putative mechanism underlying symptoms in patients with schizophrenia. Combined TMS-electroencephalography (TMS-EEG) provides a detailed examination of cortical excitability to assess the pathophysiology of schizophrenia. This study aimed to investigate differences in TMS-evoked potentials (TEPs), TMS-related spectral perturbations (TRSP) and intertrial coherence (ITC) between patients with schizophrenia and healthy controls.

MATERIALS AND METHODS

TMS was applied over the motor cortex during EEG recording. Differences in TEPs, TRSP and ITC between the patient and healthy subjects were analysed for all electrodes at each time point, by applying multiple independent sample t-tests with a cluster-based permutation analysis to correct for multiple comparisons.

RESULTS

Patients demonstrated significantly reduced amplitudes of early and late TEP components compared to healthy controls. Patients also showed a significant reduction of early delta (50-160 ms) and theta TRSP (30-250ms),followed by a reduction in alpha and beta suppression (220-560 ms; 190-420 ms). Patients showed a reduction of both early (50-110 ms) gamma increase and later (180-230 ms) gamma suppression. Finally, the ITC was significantly lower in patients in the alpha band, from 30 to 260 ms.

CONCLUSION

Our findings support the putative role of impaired GABA-receptor mediated inhibition in schizophrenia impacting excitatory neurotransmission. Further studies can usefully elucidate mechanisms underlying specific symptoms clusters using TMS-EEG biometrics.

Stability of transcranial magnetic stimulation electroencephalogram evoked potentials in pediatric epilepsy

Transcranial magnetic stimulation paired with electroencephalography (TMS-EEG) can measure local excitability and functional connectivity. To address trial-to-trial variability, responses to multiple TMS pulses are recorded to obtain an average TMS evoked potential (TEP). Balancing adequate data acquisition to establish stable TEPs with feasible experimental duration is critical when applying TMS-EEG to clinical populations. Here we aim to investigate the minimum number of pulses (MNP) required to achieve stable TEPs in children with epilepsy. Eighteen children with Self-Limited Epilepsy with Centrotemporal Spikes, a common epilepsy arising from the motor cortices, underwent multiple 100-pulse blocks of TMS to both motor cortices over two days. TMS was applied at 120% of resting motor threshold (rMT) up to a maximum of 100% maximum stimulator output. The average of all 100 pulses was used as a "gold-standard" TEP to which we compared "candidate" TEPs obtained by averaging subsets of pulses. We defined TEP stability as the MNP needed to achieve a concordance correlation coefficient of 80% between the candidate and "gold-standard" TEP. We additionally assessed whether experimental or clinical factors affected TEP stability. Results show that stable TEPs can be derived from fewer than 100 pulses, a number typically used for designing TMS-EEG experiments. The early segment (15-80 ms) of the TEP was less stable than the later segment (80-350 ms). Global mean field amplitude derived from all channels was less stable than local TEP derived from channels overlying the stimulated site. TEP stability did not differ depending on stimulated hemisphere, block order, or antiseizure medication use, but was greater in older children. Stimulation administered with an intensity above the rMT yielded more stable local TEPs. Studies of TMS-EEG in pediatrics have been limited by the complexity of experimental set-up and time course. This study serves as a critical starting point, demonstrating the feasibility of designing efficient TMS-EEG studies that use a relatively small number of pulses to study pediatric epilepsy and potentially other pediatric groups.

Changes in Cortical Activation by Transcranial Magnetic Stimulation Due to Coil Rotation Are Not Attributable to Cranial Muscle Activation

Transcranial magnetic stimulation coupled with electroencephalography (TMS-EEG) allows for the study of brain dynamics in health and disease. Cranial muscle activation can decrease the interpretability of TMS-EEG signals by masking genuine EEG responses and increasing the reliance on preprocessing methods but can be at least partly prevented by coil rotation coupled with the online monitoring of signals; however, the extent to which changing coil rotation may affect TMS-EEG signals is not fully understood. Our objective was to compare TMS-EEG data obtained with an optimal coil rotation to induce motor evoked potentials (M1standard) while rotating the coil to minimize cranial muscle activation (M1emg). TMS-evoked potentials (TEPs), TMS-related spectral perturbation (TRSP), and intertrial phase clustering (ITPC) were calculated in both conditions using two different preprocessing pipelines based on independent component analysis (ICA) or signal-space projection with source-informed reconstruction (SSP-SIR). Comparisons were performed with cluster-based correction. The concordance correlation coefficient was computed to measure the similarity between M1standard and M1emg TMS-EEG signals. TEPs, TRSP, and ITPC were significantly larger in M1standard than in M1emg conditions; a lower CCC than expected was also found. These results were similar across the preprocessing pipelines. While rotating the coil may be advantageous to reduce cranial muscle activation, it may result in changes in TMS-EEG signals; therefore, this solution should be tailored to the specific experimental context.

The effectiveness of intermittent theta burst stimulation for upper limb motor recovery after stroke: a systematic review and meta-analysis of randomized controlled trials

Background

Intermittent theta burst stimulation (iTBS) is a promising noninvasive therapy to restore the excitability of the cortex, and subsequently improve the function of the upper extremities. Several studies have demonstrated the effectiveness of iTBS in restoring upper limb function and modulating cortical excitability. We aimed to evaluate the effects of iTBS on upper limb motor recovery after stroke.

Objective

The purpose of this article is to evaluate the influence of intermittent theta-burst stimulation on upper limb motor recovery and improve the quality of life.

Method

A literature search was conducted using PubMed, EMBASE, MEDLINE, The Cochrane Library, Web of Science, and CBM, including only English studies, to identify studies that investigated the effects of iTBS on upper limb recovery, compared with sham iTBS used in control groups. Effect size was reported as standardized mean difference (SMD) or weighted mean difference (WMD).

Results

Ten studies were included in the meta-analysis. The results of the meta-analysis indicated that when compared to the control group, the iTBS group had a significant difference in the Fugl-Meyer Assessment (FMA) and Action Research Arm Test (ARAT) (WMD: 3.20, 95% CI: 1.42 to 4.97; WMD: 3.72, 95% CI: 2.13 to 5.30, respectively). In addition, there was also a significant improvement in the modified Ashworth scale (MAS) compared to the sham group (WMD: -0.56; 95% CI: -0.85 to -0.28). More evidence is still needed to confirm the effect of Barthel Index (BI) scores after interventions. However, no significant effect was found for the assessment of Motor Evoked Potential (MEP) amplitude and MEP latency (SMD: 0.35; 95% CI: -0.21 to 0.90; SMD: 0.35, 95% CI: -0.18 to 0.87; SMD: 0.03, 95% CI: -0.49 to 0.55; respectively).

Conclusion

Our results showed that iTBS significantly improved motor impairment, functional activities, and reduced muscle tone of upper limbs, thereby increasing the ability to perform Activities of Daily Living (ADL) in stroke patients, while there were no significant differences in MEPs. In conclusion, iTBS is a promising non-invasive brain stimulation as an adjunct to therapy and enhances the therapeutic effect of conventional physical therapy. In the future, more randomized controlled trials with large sample sizes, high quality, and follow-up are necessary to explore the neurophysiological effects.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023392739.

Motor potentials evoked by transcranial magnetic stimulation: interpreting a simple measure of a complex system

Transcranial magnetic stimulation (TMS) is a non-invasive technique that is increasingly used to study the human brain. One of the principal outcome measures is the motor-evoked potential (MEP) elicited in a muscle following TMS over the primary motor cortex (M1), where it is used to estimate changes in corticospinal excitability. However, multiple elements play a role in MEP generation, so even apparently simple measures such as peak-to-peak amplitude have a complex interpretation. Here, we summarize what is currently known regarding the neural pathways and circuits that contribute to the MEP and discuss the factors that should be considered when interpreting MEP amplitude measured at rest in the context of motor processing and patients with neurological conditions. In the last part of this work, we also discuss how emerging technological approaches can be combined with TMS to improve our understanding of neural substrates that can influence MEPs. Overall, this review aims to highlight the capabilities and limitations of TMS that are important to recognize when attempting to disentangle sources that contribute to the physiological state-related changes in corticomotor excitability.

Machine Learning-Based Classification to Disentangle EEG Responses to TMS and Auditory Input

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) offers an unparalleled opportunity to study cortical physiology by characterizing brain electrical responses to external perturbation, called transcranial-evoked potentials (TEPs). Although these reflect cortical post-synaptic potentials, they can be contaminated by auditory evoked potentials (AEPs) due to the TMS click, which partly show a similar spatial and temporal scalp distribution. Therefore, TEPs and AEPs can be difficult to disentangle by common statistical methods, especially in conditions of suboptimal AEP suppression. In this work, we explored the ability of machine learning algorithms to distinguish TEPs recorded with masking of the TMS click, AEPs and non-masked TEPs in a sample of healthy subjects. Overall, our classifier provided reliable results at the single-subject level, even for signals where differences were not shown in previous works. Classification accuracy (CA) was lower at the group level, when different subjects were used for training and test phases, and when three stimulation conditions instead of two were compared. Lastly, CA was higher when average, rather than single-trial TEPs, were used. In conclusion, this proof-of-concept study proposes machine learning as a promising tool to separate pure TEPs from those contaminated by sensory input.

EEG responses induced by cerebellar TMS at rest and during visuomotor adaptation

BACKGROUND

Connections between the cerebellum and the cortex play a critical role in learning and executing complex behaviours. Dual-coil transcranial magnetic stimulation (TMS) can be used non-invasively to probe connectivity changes between the lateral cerebellum and motor cortex (M1) using the motor evoked potential as an outcome measure (cerebellar-brain inhibition, CBI). However, it gives no information about cerebellar connections to other parts of cortex.

OBJECTIVES

We used electroencephalography (EEG) to investigate whether it was possible to detect activity evoked in any areas of cortex by single-pulse TMS of the cerebellum (cerebellar TMS evoked potentials, cbTEPs). A second experiment tested if these responses were influenced by the performance of a cerebellar-dependent motor learning paradigm.

METHODS

In the first series of experiments, TMS was applied over either the right or left cerebellar cortex, and scalp EEG was recorded simultaneously. Control conditions that mimicked auditory and somatosensory inputs associated with cerebellar TMS were included to identify responses due to non-cerebellar sensory stimulation. We conducted a follow-up experiment that evaluated whether cbTEPs are behaviourally sensitive by assessing individuals before and after learning a visuomotor reach adaptation task.

RESULTS

A TMS pulse over the lateral cerebellum evoked EEG responses that could be distinguished from those caused by auditory and sensory artefacts. Significant positive (P80) and negative peaks (N110) over the contralateral frontal cerebral area were identified with a mirrored scalp distribution after left vs. right cerebellar stimulation. The P80 and N110 peaks were replicated in the cerebellar motor learning experiment and changed amplitude at different stages of learning. The change in amplitude of the P80 peak was associated with the degree of learning that individuals retained following adaptation. Due to overlap with sensory responses, the N110 should be interpreted with caution.

CONCLUSIONS

Cerebral potentials evoked by TMS of the lateral cerebellum provide a neurophysiological probe of cerebellar function that complements the existing CBI method. They may provide novel insight into mechanisms of visuomotor adaptation and other cognitive processes.

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.

Identifying novel biomarkers with TMS-EEG - Methodological possibilities and challenges

Biomarkers are essential for understanding the underlying pathologies in brain disorders and for developing effective treatments. Combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) is an emerging neurophysiological tool that can be used for biomarker development. This method can identify biomarkers associated with the function and dynamics of the inhibitory and excitatory neurotransmitter systems and effective connectivity between brain areas. In this review, we outline the current state of the TMS-EEG biomarker field by summarizing the existing protocols and the possibilities and challenges associated with this methodology.

Action Selection and Motor Decision Making: Insights from Transcranial Magnetic Stimulation

In everyday life, goal-oriented motor behaviour relies on the estimation of the rewards/costs associated with alternative actions and on the appropriate selection of movements. Motor decision making is defined as the process by which a motor plan is chosen among a set of competing actions based on the expected value. In the present literature review we discuss evidence from transcranial magnetic stimulation (TMS) studies of motor control. We focus primarily on studies of action selection for instructed movements and motor decision making. In the first section, we delve into the usefulness of various TMS paradigms to characterise the contribution of motor areas and distributed brain networks to cued action selection. Then, we address the influence of motivational information (e.g., reward and biomechanical cost) in guiding action choices based on TMS findings. Finally, we conclude that TMS represents a powerful tool for elucidating the neurophysiological mechanisms underlying action choices in humans.

TMS-EEG responses across the lifespan: Measurement, methods for characterisation and identified responses

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) allows probing of the neurophysiology of any neocortical brain area in vivo with millisecond accuracy. TMS-EEG is particularly unique compared with other available neurophysiological methods, as it can measure the state and dynamics of excitatory and inhibitory systems separately. Because of these capabilities, TMS-EEG responses are sensitive to the brain state, and the responses are influenced by brain maturation and ageing, making TMS-EEG a suitable method to study age-specific pathophysiology. In this review, we outline the TMS-EEG measurement procedure, the existing methods used for characterising TMS-EEG responses and the challenges associated with identifying the responses. We also summarise the findings thus far on how TMS-EEG responses change across the lifespan and the TMS-EEG features that separate typical and atypical brain maturation and ageing. Finally, we give an overview of the gaps in current knowledge to provide directions for future studies.

The effect of stimulation frequency on transcranial evoked potentials

Introduction

Transcranial magnetic stimulation-evoked electroencephalography potentials (TEPs) have been used to study motor cortical excitability in healthy subjects and several neurological conditions. However, optimal recording parameters for TEPs are still debated. Stimulation rates could affect TEP amplitude due to plasticity effects, thus confounding the assessment of cortical excitability. We tested whether short interpulse intervals (IPIs) affect TEP amplitude.

Methods

We investigated possible changes in TEP amplitude and global mean field amplitude (GMFA) obtained with stimulation of the primary motor cortex at IPIs of 1.1-1.4 s in a group of healthy subjects.

Results

We found no differences in TEP amplitude or GMFA between the first, second and last third of trials.

Discussion

Short IPIs do not affect TEP size and can be used without the risk of confounding effects due to short-term plasticity.

Modulating Frontal Networks' Timing-Dependent-Like Plasticity With Paired Associative Stimulation Protocols: Recent Advances and Future Perspectives

Starting from the early 2000s, paired associative stimulation (PAS) protocols have been used in humans to study brain connectivity in motor and sensory networks by exploiting the intrinsic properties of timing-dependent cortical plasticity. In the last 10 years, PAS have also been developed to investigate the plastic properties of complex cerebral systems, such as the frontal ones, with promising results. In the present work, we review the most recent advances of this technique, focusing on protocols targeting frontal cortices to investigate connectivity and its plastic properties, subtending high-order cognitive functions like memory, decision-making, attentional, or emotional processing. Overall, current evidence reveals that PAS can be effectively used to assess, enhance or depress physiological connectivity within frontal networks in a timing-dependent way, in turn modulating cognitive processing in healthy and pathological conditions.