Timing-dependent synergies between motor cortex and posterior spinal stimulation in humans.

Volitional movement requires descending input from the motor cortex and sensory feedback through the spinal cord. We previously developed a paired brain and spinal electrical stimulation approach in rats that relies on convergence of the descending motor and spinal sensory stimuli in the cervical cord. This approach strengthened sensorimotor circuits and improved volitional movement through associative plasticity. In humans, it is not known whether posterior epidural spinal cord stimulation targeted at the sensorimotor interface or anterior epidural spinal cord stimulation targeted within the motor system is effective at facilitating brain evoked responses. In 59 individuals undergoing elective cervical spine decompression surgery, the motor cortex was stimulated with scalp electrodes and the spinal cord was stimulated with epidural electrodes, with muscle responses being recorded in arm and leg muscles. Spinal electrodes were placed either posteriorly or anteriorly, and the interval between cortex and spinal cord stimulation was varied. Pairing stimulation between the motor cortex and spinal sensory (posterior) but not spinal motor (anterior) stimulation produced motor evoked potentials that were over five times larger than brain stimulation alone. This strong augmentation occurred only when descending motor and spinal afferent stimuli were timed to converge in the spinal cord. Paired stimulation also increased the selectivity of muscle responses relative to unpaired brain or spinal cord stimulation. Finally, clinical signs suggest that facilitation was observed in both injured and uninjured segments of the spinal cord. The large effect size of this paired stimulation makes it a promising candidate for therapeutic neuromodulation. KEY POINTS: Pairs of stimuli designed to alter nervous system function typically target the motor system, or one targets the sensory system and the other targets the motor system for convergence in cortex. In humans undergoing clinically indicated surgery, we tested paired brain and spinal cord stimulation that we developed in rats aiming to target sensorimotor convergence in the cervical cord. Arm and hand muscle responses to paired sensorimotor stimulation were more than five times larger than brain or spinal cord stimulation alone when applied to the posterior but not anterior spinal cord. Arm and hand muscle responses to paired stimulation were more selective for targeted muscles than the brain- or spinal-only conditions, especially at latencies that produced the strongest effects of paired stimulation. Measures of clinical evidence of compression were only weakly related to the paired stimulation effect, suggesting that it could be applied as therapy in people affected by disorders of the central nervous system.

Biomarkers predict the efficacy of closed-loop rTMS treatment for refractory depression.

Transcranial magnetic stimulation (TMS) is a non-invasive FDA-approved therapy for major depressive disorder (MDD), specifically for treatment-resistant depression (TRD). Though offering promise for those with TRD, its effectiveness is less than one in two patients (i.e., less than 50%). Limits on efficacy may be due to individual patient variability, but to date, there are no established biomarkers or measures of target engagement that can predict efficacy. Additionally, TMS efficacy is typically not assessed until a six-week treatment ends, precluding interim re-evaluations of the treatment. Here, we report results using a closed-loop phase-locked repetitive TMS (rTMS) treatment that synchronizes the delivery of rTMS based on the timing of the pulses relative to a patient's individual electroencephalographic (EEG) prefrontal alpha oscillation indexed by functional magnetic resonance imaging (fMRI). Among responders, synchronized rTMS produces two systematic changes in brain dynamics: a reduction in global cortical excitability and enhanced phase entrainment of cortical dynamics. These effects predict clinical outcomes in the synchronized treatment group but not in an active-treatment unsynchronized control group. The systematic decrease in excitability and increase in entrainment correlated with treatment efficacy at the endpoint and intermediate weeks during the synchronized treatment. Specifically, we show that weekly biomarker tracking enables efficacy prediction and dynamic adjustments through a treatment course, improving the overall response rates. This innovative approach advances the prospects of individualized medicine in MDD and holds potential for application in other neuropsychiatric disorders.

Increased entrainment and decreased excitability predict efficacious treatment of closed-loop phase-locked rTMS for treatment-resistant depression.

Transcranial magnetic stimulation (TMS) is an FDA-approved therapy for major depressive disorder (MDD), specifically for patients who have treatment-resistant depression (TRD). However, TMS produces response or remission in about 50% of patients but is ineffective for the other 50%. Limits on efficacy may be due to individual patient variability, but to date, there are no good biomarkers or measures of target engagement. In addition, TMS efficacy is typically not assessed until a six-week treatment ends, precluding the evaluation of intermediate improvements during the treatment duration. Here, we report on results using a closed-loop phase-locked repetitive TMS (rTMS) treatment that synchronizes the delivery of rTMS based on the timing of the pulses relative to a patient's individual electroencephalographic (EEG) prefrontal alpha oscillation informed by functional magnetic resonance imaging (fMRI). We find that, in responders, synchronized delivery of rTMS produces two systematic changes in brain dynamics. The first change is a decrease in global cortical excitability, and the second is an increase in the phase entrainment of cortical dynamics. These two effects predict clinical outcomes in the synchronized treatment group but not in an active-treatment unsynchronized control group. The systematic decrease in excitability and increase in entrainment correlated with treatment efficacy at the endpoint and intermediate weeks during the synchronized treatment. Specifically, we show that weekly tracking of these biomarkers allows for efficacy prediction and potential of dynamic adjustments through a treatment course, improving the overall response rates.

Timing dependent synergies between motor cortex and posterior spinal stimulation in humans.

Volitional movement requires descending input from motor cortex and sensory feedback through the spinal cord. We previously developed a paired brain and spinal electrical stimulation approach in rats that relies on convergence of the descending motor and spinal sensory stimuli in the cervical cord. This approach strengthened sensorimotor circuits and improved volitional movement through associative plasticity. In humans it is not known whether dorsal epidural SCS targeted at the sensorimotor interface or anterior epidural SCS targeted within the motor system is effective at facilitating brain evoked responses. In 59 individuals undergoing elective cervical spine decompression surgery, the motor cortex was stimulated with scalp electrodes and the spinal cord with epidural electrodes while muscle responses were recorded in arm and leg muscles. Spinal electrodes were placed either posteriorly or anteriorly, and the interval between cortex and spinal cord stimulation was varied. Pairing stimulation between the motor cortex and spinal sensory (posterior) but not spinal motor (anterior) stimulation produced motor evoked potentials that were over five times larger than brain stimulation alone. This strong augmentation occurred only when descending motor and spinal afferent stimuli were timed to converge in the spinal cord. Paired stimulation also increased the selectivity of muscle responses relative to unpaired brain or spinal cord stimulation. Finally, paired stimulation effects were present regardless of the severity of myelopathy as measured by clinical signs or spinal cord imaging. The large effect size of this paired stimulation makes it a promising candidate for therapeutic neuromodulation.

Intraoperative electrical stimulation of the human dorsal spinal cord reveals a map of arm and hand muscle responses.

Although epidural stimulation of the lumbar spinal cord has emerged as a powerful modality for recovery of movement, how it should be targeted to the cervical spinal cord to activate arm and hand muscles is not well understood, particularly in humans. We sought to map muscle responses to posterior epidural cervical spinal cord stimulation in humans. We hypothesized that lateral stimulation over the dorsal root entry zone would be most effective and responses would be strongest in the muscles innervated by the stimulated segment. Twenty-six people undergoing clinically indicated cervical spine surgery consented to mapping of motor responses. During surgery, stimulation was performed in midline and lateral positions at multiple exposed segments; six arm and three leg muscles were recorded on each side of the body. Across all segments and muscles tested, lateral stimulation produced stronger muscle responses than midline despite similar latency and shape of responses. Muscles innervated at a cervical segment had the largest responses from stimulation at that segment, but responses were also observed in muscles innervated at other cervical segments and in leg muscles. The cervical responses were clustered in rostral (C4-C6) and caudal (C7-T1) cervical segments. Strong responses to lateral stimulation are likely due to the proximity of stimulation to afferent axons. Small changes in response sizes to stimulation of adjacent cervical segments argue for local circuit integration, and distant muscle responses suggest activation of long propriospinal connections. This map can help guide cervical stimulation to improve arm and hand function.NEW & NOTEWORTHY A map of muscle responses to cervical epidural stimulation during clinically indicated surgery revealed strongest activation when stimulating laterally compared to midline and revealed differences to be weaker than expected across different segments. In contrast, waveform shapes and latencies were most similar when stimulating midline and laterally, indicating activation of overlapping circuitry. Thus, a map of the cervical spinal cord reveals organization and may help guide stimulation to activate arm and hand muscles strongly and selectively.

Spatiospectral brain networks reflective of improvisational experience.

Musical improvisers are trained to categorize certain musical structures into functional classes, which is thought to facilitate improvisation. Using a novel auditory oddball paradigm (Goldman et al., 2020) which enables us to disassociate a deviant (i.e. musical chord inversion) from a consistent functional class, we recorded scalp EEG from a group of musicians who spanned a range of improvisational and classically trained experience. Using a spatiospectral based inter and intra network connectivity analysis, we found that improvisers showed a variety of differences in connectivity within and between large-scale cortical networks compared to classically trained musicians, as a function of deviant type. Inter-network connectivity in the alpha band, for a time window leading up to the behavioural response, was strongly linked to improvisation experience, with the default mode network acting as a hub. Spatiospectral networks post response were substantially different between improvisers and classically trained musicians, with greater inter-network connectivity (specific to the alpha and beta bands) seen in improvisers whereas those with more classical training had largely reduced inter-network activity (mostly in the gamma band). More generally, we interpret our findings in the context of network-level correlates of expectation violation as a function of subject expertise, and we discuss how these may generalize to other and more ecologically valid scenarios.

Targeting Sensory and Motor Integration for Recovery of Movement After CNS Injury.

The central nervous system (CNS) integrates sensory and motor information to acquire skilled movements, known as sensory-motor integration (SMI). The reciprocal interaction of the sensory and motor systems is a prerequisite for learning and performing skilled movement. Injury to various nodes of the sensorimotor network causes impairment in movement execution and learning. Stimulation methods have been developed to directly recruit the sensorimotor system and modulate neural networks to restore movement after CNS injury. Part 1 reviews the main processes and anatomical interactions responsible for SMI in health. Part 2 details the effects of injury on sites critical for SMI, including the spinal cord, cerebellum, and cerebral cortex. Finally, Part 3 reviews the application of activity-dependent plasticity in ways that specifically target integration of sensory and motor systems. Understanding of each of these components is needed to advance strategies targeting SMI to improve rehabilitation in humans after injury.

Ballistocardiogram Artifact Reduction in Simultaneous EEG-fMRI Using Deep Learning.

OBJECTIVE: The concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution. However, the ballistocardiogram (BCG), a large-amplitude artifact caused by cardiac induced movement contaminates the EEG during EEG-fMRI recordings. Removal of BCG in software has generally made use of linear decompositions of the corrupted EEG. This is not ideal as the BCG signal propagates in a manner which is non-linearly dependent on the electrocardiogram (ECG). In this paper, we present a novel method for BCG artifact suppression using recurrent neural networks (RNNs). METHODS: EEG signals were recovered by training RNNs on the nonlinear mappings between ECG and the BCG corrupted EEG. We evaluated our model's performance against the commonly used Optimal Basis Set (OBS) method at the level of individual subjects, and investigated generalization across subjects. RESULTS: We show that our algorithm can generate larger average power reduction of the BCG at critical frequencies, while simultaneously improving task relevant EEG based classification. CONCLUSION: The presented deep learning architecture can be used to reduce BCG related artifacts in EEG-fMRI recordings. SIGNIFICANCE: We present a deep learning approach that can be used to suppress the BCG artifact in EEG-fMRI without the use of additional hardware. This method may have scope to be combined with current hardware methods, operate in real-time and be used for direct modeling of the BCG.

Decomposing Simon task BOLD activation using a drift-diffusion model framework.

The Simon effect is observed in spatial conflict tasks where the response time of subjects is increased if stimuli are presented in a lateralized manner so that they are incongruous with the response information that they represent symbolically. Previous studies have used fMRI to investigate this phenomenon, and while some have been driven by considerations of an underlying model, none have attempted to directly tie model and BOLD response together. It is likely that this is due to Simon models having been predominantly descriptive of the phenomenon rather than capturing the full spectrum of behavior at the level of individual subjects. Sequential sampling models (SSM) which capture full response distributions for correct and incorrect responses have recently been extended to capture conflict tasks. In this study we use our freely available framework for fitting and comparing non-standard SSMs to fit the Simon effect SSM (SE-SSM) to behavioral data. This model extension includes specific estimates of automatic response bias and a conflict counteraction parameter to individual subject behavioral data. We apply this approach in order to investigate whether our task specific model parameters have a correlate in BOLD response. Under the assumption that the SE-SSM reflects aspects of neural processing in this task, we go on to examine the BOLD correlates with the within trial expected decision-variable. We find that the SE-SSM captures the behavioral data and that our two conflict specific model parameters have clear across subject BOLD correlates, while other model parameters, as well as more standard behavioral measures do not. We also find that examining BOLD in terms of the expected decision-variable leads to a specific pattern of activation that would not be otherwise possible to extract.