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.

Reliability of the TMS-evoked potential in dorsolateral prefrontal cortex.

We currently lack a reliable method to probe cortical excitability noninvasively from the human dorsolateral prefrontal cortex (dlPFC). We recently found that the strength of early and local dlPFC transcranial magnetic stimulation (TMS)-evoked potentials (EL-TEPs) varied widely across dlPFC subregions. Despite these differences in response amplitude, reliability at each target is unknown. Here we quantified within-session reliability of dlPFC EL-TEPs after TMS to six left dlPFC subregions in 15 healthy subjects. We evaluated reliability (concordance correlation coefficient [CCC]) across targets, time windows, quantification methods, regions of interest, sensor- vs. source-space, and number of trials. On average, the medial target was most reliable (CCC = 0.78) and the most anterior target was least reliable (CCC = 0.24). However, all targets except the most anterior were reliable (CCC > 0.7) using at least one combination of the analytical parameters tested. Longer (20 to 60 ms) and later (30 to 60 ms) windows increased reliability compared to earlier and shorter windows. Reliable EL-TEPs (CCC up to 0.86) were observed using only 25 TMS trials at a medial dlPFC target. Overall, medial dlPFC targeting, wider windows, and peak-to-peak quantification improved reliability. With careful selection of target and analytic parameters, highly reliable EL-TEPs can be extracted from the dlPFC after only a small number of trials.

Neural effects of TMS trains on the human prefrontal cortex.

How does a train of TMS pulses modify neural activity in humans? Despite adoption of repetitive TMS (rTMS) for the treatment of neuropsychiatric disorders, we still do not understand how rTMS changes the human brain. This limited understanding stems in part from a lack of methods for noninvasively measuring the neural effects of a single TMS train-a fundamental building block of treatment-as well as the cumulative effects of consecutive TMS trains. Gaining this understanding would provide foundational knowledge to guide the next generation of treatments. Here, to overcome this limitation, we developed methods to noninvasively measure causal and acute changes in cortical excitability and evaluated this neural response to single and sequential TMS trains. In 16 healthy adults, standard 10 Hz trains were applied to the dorsolateral prefrontal cortex in a randomized, sham-controlled, event-related design and changes were assessed based on the TMS-evoked potential (TEP), a measure of cortical excitability. We hypothesized that single TMS trains would induce changes in the local TEP amplitude and that those changes would accumulate across sequential trains, but primary analyses did not indicate evidence in support of either of these hypotheses. Exploratory analyses demonstrated non-local neural changes in sensor and source space and local neural changes in phase and source space. Together these results suggest that single and sequential TMS trains may not be sufficient to modulate local cortical excitability indexed by typical TEP amplitude metrics but may cause neural changes that can be detected outside the stimulation area or using phase or source space metrics. This work should be contextualized as methods development for the monitoring of transient noninvasive neural changes during rTMS and contributes to a growing understanding of the neural effects of rTMS.

Reliability of the TMS-evoked potential in dorsolateral prefrontal cortex.

Background: We currently lack a robust and reliable method to probe cortical excitability noninvasively from the human dorsolateral prefrontal cortex (dlPFC), a region heavily implicated in psychiatric disorders. We recently found that the strength of early and local dlPFC single pulse transcranial magnetic stimulation (TMS)-evoked potentials (EL-TEPs) varied widely depending on the anatomical subregion probed, with more medial regions eliciting stronger responses than anterolateral sites. Despite these differences in amplitude of response, the reliability at each target is not known. Objective: To evaluate the reliability of EL-TEPs across the dlPFC. Methods: In 15 healthy subjects, we quantified within-session reliability of dlPFC EL-TEPs after single pulse TMS to six dlPFC subregions. We evaluated the concordance correlation coefficient (CCC) across targets and analytical parameters including time window, quantification method, region of interest, sensor-vs. source-space, and number of trials. Results: At least one target in the anterior and posterior dlPFC produced reliable EL-TEPs (CCC>0.7). The medial target was most reliable (CCC = 0.78) and the most anterior target was least reliable (CCC = 0.24). ROI size and type (sensor vs. source space) did not affect reliability. Longer (20-60 ms, CCC = 0.62) and later (30-60 ms, CCC = 0.61) time windows resulted in higher reliability compared to earlier and shorter (20-40 ms, CCC 0.43; 20-50 ms, CCC = 0.55) time windows. Peak-to-peak quantification resulted in higher reliability than the mean of the absolute amplitude. Reliable EL-TEPs (CCC up to 0.86) were observed using only 25 TMS trials for a medial dlPFC target. Conclusions: Medial TMS location, wider time window (20-60ms), and peak-to-peak quantification improved reliability. Highly reliable EL-TEPs can be extracted from dlPFC after only a small number of trials. Highlights: Medial dlPFC target improved EL-TEP reliability compared to anterior targets.After optimizing analytical parameters, at least one anterior and one posterior target was reliable (CCC>0.7).Longer (20-60 ms) and later (30-60 ms) time windows were more reliable than earlier and shorter (20-40 ms or 20-50 ms) latencies.Peak-to-peak quantification resulted in higher reliability compared to the mean of the absolute amplitude.As low as 25 trials can yield reliable EL-TEPs from the dlPFC.

Reliability and Validity of Transcranial Magnetic Stimulation-Electroencephalography Biomarkers.

Noninvasive brain stimulation and neuroimaging have revolutionized human neuroscience with a multitude of applications, including diagnostic subtyping, treatment optimization, and relapse prediction. It is therefore particularly relevant to identify robust and clinically valuable brain biomarkers linking symptoms to their underlying neural mechanisms. Brain biomarkers must be reproducible (i.e., have internal reliability) across similar experiments within a laboratory and be generalizable (i.e., have external reliability) across experimental setups, laboratories, brain regions, and disease states. However, reliability (internal and external) is not alone sufficient; biomarkers also must have validity. Validity describes closeness to a true measure of the underlying neural signal or disease state. We propose that these metrics, reliability and validity, should be evaluated and optimized before any biomarker is used to inform treatment decisions. Here, we discuss these metrics with respect to causal brain connectivity biomarkers from coupling transcranial magnetic stimulation (TMS) with electroencephalography (EEG). We discuss controversies around TMS-EEG stemming from the multiple large off-target components (noise) and relatively weak genuine brain responses (signal), as is unfortunately often the case in noninvasive human neuroscience. We review the current state of TMS-EEG recordings, which consist of a mix of reliable noise and unreliable signal. We describe methods for evaluating TMS-EEG biomarkers, including how to assess internal and external reliability across facilities, cognitive states, brain networks, and disorders and how to validate these biomarkers using invasive neural recordings or treatment response. We provide recommendations to increase reliability and validity, discuss lessons learned, and suggest future directions for the field.

Personalized Repetitive Transcranial Magnetic Stimulation for Depression.

Personalized treatments are gaining momentum across all fields of medicine. Precision medicine can be applied to neuromodulatory techniques, in which focused brain stimulation treatments such as repetitive transcranial magnetic stimulation (rTMS) modulate brain circuits and alleviate clinical symptoms. rTMS is well tolerated and clinically effective for treatment-resistant depression and other neuropsychiatric disorders. Despite its wide stimulation parameter space (location, angle, pattern, frequency, and intensity can be adjusted), rTMS is currently applied in a one-size-fits-all manner, potentially contributing to its suboptimal clinical response (∼50%). In this review, we examine components of rTMS that can be optimized to account for interindividual variability in neural function and anatomy. We discuss current treatment options for treatment-resistant depression, the neural mechanisms thought to underlie treatment, targeting strategies, stimulation parameter selection, and adaptive closed-loop treatment. We conclude that a better understanding of the wide and modifiable parameter space of rTMS will greatly improve the clinical outcome.

Neural effects of TMS trains on the human prefrontal cortex.

How does a train of TMS pulses modify neural activity in humans? Despite adoption of repetitive TMS (rTMS) for the treatment of neuropsychiatric disorders, we still do not understand how rTMS changes the human brain. This limited understanding stems in part from a lack of methods for noninvasively measuring the neural effects of a single TMS train - a fundamental building block of treatment - as well as the cumulative effects of consecutive TMS trains. Gaining this understanding would provide foundational knowledge to guide the next generation of treatments. Here, to overcome this limitation, we developed methods to noninvasively measure causal and acute changes in cortical excitability and evaluated this neural response to single and sequential TMS trains. In 16 healthy adults, standard 10 Hz trains were applied to the dorsolateral prefrontal cortex (dlPFC) in a randomized, sham-controlled, event-related design and changes were assessed based on the TMS-evoked potential (TEP), a measure of cortical excitability. We hypothesized that single TMS trains would induce changes in the local TEP amplitude and that those changes would accumulate across sequential trains, but primary analyses did not indicate evidence in support of either of these hypotheses. Exploratory analyses demonstrated non-local neural changes in sensor and source space and local neural changes in phase and source space. Together these results suggest that single and sequential TMS trains may not be sufficient to modulate local cortical excitability indexed by typical TEP amplitude metrics but may cause neural changes that can be detected outside the stimulation area or using phase or source space metrics. This work should be contextualized as methods development for the monitoring of transient noninvasive neural changes during rTMS and contributes to a growing understanding of the neural effects of rTMS.

Mapping cortical excitability in the human dorsolateral prefrontal cortex.

Objective: To characterize early TEPs anatomically and temporally (20-50 ms) close to the TMS pulse (EL-TEPs), as well as associated muscle artifacts (<20 ms), across the dlPFC. We hypothesized that TMS location and angle influence EL-TEPs, and that EL-TEP amplitude is inversely related to muscle artifact. Additionally, we sought to determine an optimal group-level TMS target and angle, while investigating the potential benefits of a personalized approach. Methods: In 16 healthy participants, we applied single-pulse TMS to six targets within the dlPFC at two coil angles and measured EEG responses. Results: Stimulation location significantly influenced EL-TEPs, with posterior and medial targets yielding larger EL-TEPs. Regions with high EL-TEP amplitude had less muscle artifact, and vice versa. The best group-level target yielded 102% larger EL-TEP responses compared to other dlPFC targets. Optimal dlPFC target differed across subjects, suggesting that a personalized targeting approach might boost the EL-TEP by an additional 36%. Significance: Early local TMS-evoked potentials (EL-TEPs) can be probed without significant muscle-related confounds in posterior-medial regions of the dlPFC. The identification of an optimal group-level target and the potential for further refinement through personalized targeting hold significant implications for optimizing depression treatment protocols. Highlights: Early local TMS-evoked potentials (EL-TEPs) varied significantly across the dlPFC as a function of TMS target.TMS targets with less muscle artifact had significantly larger EL-TEPs.Selection of a postero-medial target increased EL-TEPs by 102% compared to anterior targets.

Mindfulness Improves Brain-Computer Interface Performance by Increasing Control Over Neural Activity in the Alpha Band.

Brain-computer interfaces (BCIs) are promising tools for assisting patients with paralysis, but suffer from long training times and variable user proficiency. Mind-body awareness training (MBAT) can improve BCI learning, but how it does so remains unknown. Here, we show that MBAT allows participants to learn to volitionally increase alpha band neural activity during BCI tasks that incorporate intentional rest. We trained individuals in mindfulness-based stress reduction (MBSR; a standardized MBAT intervention) and compared performance and brain activity before and after training between randomly assigned trained and untrained control groups. The MBAT group showed reliably faster learning of BCI than the control group throughout training. Alpha-band activity in electroencephalogram signals, recorded in the volitional resting state during task performance, showed a parallel increase over sessions, and predicted final BCI performance. The level of alpha-band activity during the intentional resting state correlated reliably with individuals' mindfulness practice as well as performance on a breath counting task. Collectively, these results show that MBAT modifies a specific neural signal used by BCI. MBAT, by increasing patients' control over their brain activity during rest, may increase the effectiveness of BCI in the large population who could benefit from alternatives to direct motor control.

EEG electrode digitization with commercial virtual reality hardware.

Accurate spatial co-registration of EEG electrode positions with individual head models is an important component for EEG source localization and imaging. Due to variations in head shape between individuals, this requires measurements of electrode locations in each individual. Existing hardware for digitization can be accurate, but also relatively expensive. With the goal of making digitization more accessible for a range of research laboratories, we have developed an open-source software tool that can make use of less expensive consumer virtual reality hardware for EEG electrode digitization. Here we describe our developed VRDigitizer system and compare it to existing digitization solutions. Experimental evaluations were performed in a phantom head model and in 12 human subjects. In our comparison experiments, VRDigitizer was able to measure electrode positions with a mean error of 3.74 mm, compared to 1.73 mm and 2.98 mm for the commercial systems tested.

Exploring Cognitive Flexibility With a Noninvasive BCI Using Simultaneous Steady-State Visual Evoked Potentials and Sensorimotor Rhythms.

EEG-based brain-computer interface (BCI) technology creates non-biological pathways for conveying a user's mental intent solely through noninvasively measured neural signals. While optimizing the performance of a single task has long been the focus of BCI research, in order to translate this technology into everyday life, realistic situations, in which multiple tasks are performed simultaneously, must be investigated. In this paper, we explore the concept of cognitive flexibility, or multitasking, within the BCI framework by utilizing a 2-D cursor control task, using sensorimotor rhythms (SMRs), and a four-target visual attention task, using steady-state visual evoked potentials (SSVEPs), both individually and simultaneously. We found no significant difference between the accuracy of the tasks when executing them alone (SMR-57.9% ± 15.4% and SSVEP-59.0% ± 14.2%) and simultaneously (SMR-54.9% ± 17.2% and SSVEP-57.5% ± 15.4%). These modest decreases in performance were supported by similar, non-significant changes in the electrophysiology of the SSVEP and SMR signals. In this sense, we report that multiple BCI tasks can be performed simultaneously without a significant deterioration in performance; this finding will help drive these systems toward realistic daily use in which a user's cognition will need to be involved in multiple tasks at once.

AIR-MRF: Accelerated iterative reconstruction for magnetic resonance fingerprinting.

Existing approaches for reconstruction of multiparametric maps with magnetic resonance fingerprinting (MRF) are currently limited by their estimation accuracy and reconstruction time. We aimed to address these issues with a novel combination of iterative reconstruction, fingerprint compression, additional regularization, and accelerated dictionary search methods. The pipeline described here, accelerated iterative reconstruction for magnetic resonance fingerprinting (AIR-MRF), was evaluated with simulations as well as phantom and in vivo scans. We found that the AIR-MRF pipeline provided reduced parameter estimation errors compared to non-iterative and other iterative methods, particularly at shorter sequence lengths. Accelerated dictionary search methods incorporated into the iterative pipeline reduced the reconstruction time at little cost of quality.

Improved receiver arrays and optimized parallel imaging accelerations applied to time-resolved 3D fluoroscopically tracked peripheral runoff CE-MRA.

OBJECTIVES: Three-station stepping-table time-resolved 3D contrast-enhanced magnetic resonance angiography has conflicting demands in the need to limit acquisition time in proximal stations to match the speed of the advancing contrast bolus and in the distal-most station to avoid venous contamination while still providing clinically useful spatial resolution. This work describes improved receiver coil arrays which address this issue by allowing increased acceleration factors, providing increased spatial resolution per unit time. MATERIALS AND METHODS: Receiver coil arrays were constructed for each station (pelvis, thigh, calf) and then integrated into a 48-element array for three-station peripheral CE-MRA. Coil element sizes and array configurations for these three stations were designed to improve SENSE-type parallel imaging taking advantage of an increase in coil count for all stations versus the previous 32 channel capability. At each station either acceleration apportionment or optimal CAIPIRINHA selection was used to choose the optimum acceleration parameters for each subject. Results were evaluated in both single- and multi-station studies. RESULTS: Single-station studies showed that SENSE acceleration in the thigh station could be readily increased from R=8 to R=10, allowing reduction of the frame time from 2.5 to 2.1 s to better image the typically rapidly advancing bolus at this station. Similarly, the improved coil array for the calf station permitted acceleration increase from R=8 to R=12, providing a 4.0 vs. 5.2 s frame time. Results in three-station studies suggest an improved ability to track the contrast bolus in peripheral CE-MRA. CONCLUSIONS: Modified receiver coil arrays and individualized parameter optimization have been used to provide improved acceleration at all stations in multi-station peripheral CE-MRA and provide high spatial resolution with frame times as short as 2.1 s.

Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms.

Brain-computer interfaces (BCIs) have been explored in the field of neuroengineering to investigate how the brain can use these systems to control external devices. We review the principles and approaches we have taken to develop a sensorimotor rhythm EEG based brain-computer interface (BCI). The methods include developing BCI systems incorporating the control of physical devices to increase user engagement, improving BCI systems by inversely mapping scalp-recorded EEG signals to the cortical source domain, integrating BCI with noninvasive neuromodulation strategies to improve learning, and incorporating mind-body awareness training to enhance BCI learning and performance. The challenges and merits of these strategies are discussed, together with recent findings. Our work indicates that the sensorimotor-rhythm-based noninvasive BCI has the potential to provide communication and control capabilities as an alternative to physiological motor pathways.

Subject-specific optimization of channel currents for multichannel transcranial magnetic stimulation.

The goal of this work is to develop a focal transcranial magnetic stimulation (TMS) system using a multichannel coil array for high-resolution neuromodulation. We proposed a novel spatially-distributed stimulation strategy to significantly improve the focality of TMS. Computer simulations were conducted to evaluate the proposed approach and test the merits of multichannel TMS. Three different multichannel coil arrays were modeled in addition to a conventional figure-8 coil for comparison. Simulations were performed on finite element head models of six subjects constructed from anatomical MR images via an automated pipeline. Multichannel TMS arrays exhibited significantly more focal induced electric field magnitudes compared to the figure-8 coil. Additionally, electrical steering of stimulation sites without physical movement of the coil array was demonstrated.