"Optimization of Transcranial Magnetic Stimulation (TMS) Parameters using concurrent TMS-EEG and TMS-fMRI"

Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation method that has shown diagnostic, therapeutic and research potential in the central nervous system (CNS), with research and clinical uses still evolving. Efficient TMS involves finding an optimal target location, i.e., coil position and orientation over a target brain region. Despite being an important procedure, there are no standard guidelines addressing the optimal procedure for TMS hotspot search. We used TMS evoked EEG potentials (TEPs) measured using concurrent TMS-EEG to propose a TMS hotspot search procedure. We also proposed the means to validate the TMS hotspot search procedure using TMS evoked BOLD response measured using concurrent TMS-fMRI. Although the presented TEPs evidence to characterize a genuine TMS hotspot is convincing, the TMS-fMRI-based evidence is insufficient and limited to claim any generalized validation of the proposed procedure. It reflects that future studies are needed to conduct further research validations.Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation method that has shown diagnostic, therapeutic and research potential in the central nervous system (CNS), with research and clinical uses still evolving. Efficient TMS involves finding an optimal target location, i.e., coil position and orientation over a target brain region. Despite being an important procedure, there are no standard guidelines addressing the optimal procedure for TMS hotspot search. We used TMS evoked EEG potentials (TEPs) measured using concurrent TMS-EEG to propose a TMS hotspot search procedure. We also proposed the means to validate the TMS hotspot search procedure using TMS evoked BOLD response measured using concurrent TMS-fMRI. Although the presented TEPs evidence to characterize a genuine TMS hotspot is convincing, the TMS-fMRI-based evidence is insufficient and limited to claim any generalized validation of the proposed procedure. It reflects that future studies are needed to conduct further research validations

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.Peer reviewe

Neuromodulatory effects of theta burst stimulation to the prefrontal cortex

Theta burst stimulation (TBS) is a new form of repetitive transcranial magnetic stimulation (TMS) capable of non-invasively modulating cortical excitability. In recent years TBS has been increasingly used as a neuroscientific investigative tool and therapeutic intervention for psychiatric disorders, in which the dorsolateral prefrontal cortex (DLPFC) is often the primary target. However, the neuromodulatory effects of TBS on prefrontal regions remain unclear. Here we share EEG and ECG recordings and structural MRI scans, including high-resolution DTI, from twenty-four healthy participants who received intermittent TBS (two sessions), continuous TBS (two sessions), and sham stimulation (one session) applied to the left DLPFC using a single-blinded crossover design. Each session includes eyes-open resting-state EEG and single-pulse TMS-EEG obtained before TBS and 2−, 15−, and 30-minutes post-stimulation. This dataset enables foundational basic science investigations into the neuromodulatory effects of TBS on the DLPFC

TAAC - TMS Adaptable Auditory Control: A universal tool to mask TMS clicks

Background: Coupling transcranial magnetic stimulation with electroencephalography (TMS-EEG) allows recording the EEG response to a direct, non-invasive cortical perturbation. However, obtaining a genuine TMS- evoked EEG potential requires controlling for several confounds, among which a main source is represented by the auditory evoked potentials (AEPs) associated to the TMS discharge noise (TMS click). This contaminating factor can be in principle prevented by playing a masking noise through earphones. New method: Here we release TMS Adaptable Auditory Control (TAAC), a highly flexible, open-source, Matlab®- based interface that generates in real-time customized masking noises. TAAC creates noises starting from the stimulator-specific TMS click and tailors them to fit the individual, subject-specific click perception by mixing and manipulating the standard noises in both time and frequency domains. Results: We showed that TAAC allows us to provide standard as well as customized noises able to effectively and safely mask the TMS click. Comparison with existing methods: Here, we showcased two customized noises by comparing them to two standard noises previously used in the TMS literature (i.e., a white noise and a noise generated from the stimulator-specific TMS click only). For each, we quantified the Sound Pressure Level (SPL; measured by a Head and Torso Simulator - HATS) required to mask the TMS click in a population of 20 healthy subjects. Both customized noises were effective at safe (according to OSHA and NIOSH safety guidelines) and lower SPLs with respect to standard noises. Conclusions: At odds with previous methods, TAAC allows creating effective and safe masking noises specifically tailored on each TMS device and subject. The combination of TAAC with tools for the real-time visualization of TEPs can help control the influence of auditory confounds also in non-compliant patients. Finally, TAAC is a highly flexible and open-source tool, so it can be further extended to meet different experimental requirements

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

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

Predicting Alzheimer’s disease severity by means of TMS–EEG coregistration

Clinical manifestations of Alzheimer's disease (AD) are associated with a breakdown in large-scale communication, such that AD may be considered as a “disconnection syndrome.” An established method to test effective connectivity is the combination of transcranial magnetic stimulation with electroencephalography (TMS–EEG) because the TMS-induced cortical response propagates to distant anatomically connected regions. To investigate whether prefrontal connectivity alterations may predict disease severity, we explored the relationship of dorsolateral prefrontal cortex connectivity (derived from TMS–EEG) with cognitive decline (measured with Mini Mental State Examination and a face–name association memory task) in 26 patients with AD. The amplitude of TMS–EEG evoked component P30, which was found to be generated in the right superior parietal cortex, predicted Mini Mental State Examination and face–name memory scores: higher P30 amplitudes predicted poorer cognitive and memory performances. The present results indicate that advancing disease severity might be associated with effective connectivity increase involving long-distance frontoparietal connections, which might represent a maladaptive pathogenic mechanism reflecting a damaged excitatory–inhibitory balance between anterior and posterior regions

Somatosensory input in the context of transcranial magnetic stimulation coupled with electroencephalography: An evidence-based overview

The transcranial evoked potential (TEP) is a powerful technique to investigate brain dynamics, but some methodological issues limit its interpretation. A possible contamination of the TEP by electroencephalographic (EEG) responses evoked by the somatosensory input generated by transcranial magnetic stimulation (TMS) has been postulated; nonetheless, a characterization of these responses is lacking. The aim of this work was to review current evidence about possible somatosensory evoked potentials (SEP) induced by sources of somatosensory input in the craniofacial region. Among these, only contraction of craniofacial muscle and stimulation of free cutaneous nerve endings may be able to induce EEG responses, but direct evidence is lacking due to experimental difficulties in isolating these inputs. Notably, EEG evoked activity in this context is represented by a N100/P200 complex, reflecting a saliency-related multimodal response, rather than specific activation of the primary somatosensory cortex. Strategies to minimize or remove these responses by EEG processing still yield uncertain results; therefore, data inspection is of paramount importance to judge a possible contamination of the TEP by multimodal potentials caused by somatosensory input