Towards therapeutic electrophysiological neurofeedback in Parkinson's disease

Neurofeedback (NF) techniques support individuals to self-regulate specific features of brain activity, which has been shown to impact behavior and potentially ameliorate clinical symptoms. Electrophysiological NF (epNF) may be particularly impactful for patients with Parkinson's disease (PD), as evidence mounts to suggest a central role of pathological neural oscillations underlying symptoms in PD. Exaggerated beta oscillations (12-30 Hz) in the basal ganglia-cortical network are linked to motor symptoms (e.g., bradykinesia, rigidity), and beta is reduced by successful therapy with dopaminergic medication and Deep Brain Stimulation (DBS). PD patients also experience non-motor symptoms related to sleep, mood, motivation, and cognitive control. Although less is known about the mechanisms of non-motor symptoms in PD and how to successfully treat them, low frequency neural oscillations (1-12 Hz) in the basal ganglia-cortical network are particularly implicated in non-motor symptoms. Here, we review how cortical and subcortical epNF could be used to target motor and non-motor specific oscillations, and potentially serve as an adjunct therapy that enables PD patients to endogenously control their own pathological neural activities. Recent studies have demonstrated that epNF protocols can successfully support volitional control of cortical and subcortical beta rhythms. Importantly, this endogenous control of beta has been linked to changes in motor behavior. epNF for PD, as a casual intervention on neural signals, has the potential to increase understanding of the neurophysiology of movement, mood, and cognition and to identify new therapeutic approaches for motor and non-motor symptoms.

Multi-night cortico-basal recordings reveal mechanisms of NREM slow-wave suppression and spontaneous awakenings in Parkinson's disease

Sleep disturbance is a prevalent and disabling comorbidity in Parkinson's disease (PD). We performed multi-night (n = 57) at-home intracranial recordings from electrocorticography and subcortical electrodes using sensing-enabled Deep Brain Stimulation (DBS), paired with portable polysomnography in four PD participants and one with cervical dystonia (clinical trial: NCT03582891). Cortico-basal activity in delta increased and in beta decreased during NREM (N2 + N3) versus wakefulness in PD. DBS caused further elevation in cortical delta and decrease in alpha and low-beta compared to DBS OFF state. Our primary outcome demonstrated an inverse interaction between subcortical beta and cortical slow-wave during NREM. Our secondary outcome revealed subcortical beta increases prior to spontaneous awakenings in PD. We classified NREM vs. wakefulness with high accuracy in both traditional (30 s: 92.6 ± 1.7%) and rapid (5 s: 88.3 ± 2.1%) data epochs of intracranial signals. Our findings elucidate sleep neurophysiology and impacts of DBS on sleep in PD informing adaptive DBS for sleep dysfunction.

Proceedings of the 11th Annual Deep Brain Stimulation Think Tank: pushing the forefront of neuromodulation with functional network mapping, biomarkers for adaptive DBS, bioethical dilemmas, AI-guided neuromodulation, and translational advancements

The Deep Brain Stimulation (DBS) Think Tank XI was held on August 9-11, 2023 in Gainesville, Florida with the theme of "Pushing the Forefront of Neuromodulation". The keynote speaker was Dr. Nico Dosenbach from Washington University in St. Louis, Missouri. He presented his research recently published in Nature inn a collaboration with Dr. Evan Gordon to identify and characterize the somato-cognitive action network (SCAN), which has redefined the motor homunculus and has led to new hypotheses about the integrative networks underpinning therapeutic DBS. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers, and researchers (from industry and academia) can freely discuss current and emerging DBS technologies, as well as logistical and ethical issues facing the field. The group estimated that globally more than 263,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. This year's meeting was focused on advances in the following areas: cutting-edge translational neuromodulation, cutting-edge physiology, advances in neuromodulation from Europe and Asia, neuroethical dilemmas, artificial intelligence and computational modeling, time scales in DBS for mood disorders, and advances in future neuromodulation devices.

Motor network gamma oscillations in chronic home recordings predict dyskinesia in Parkinson's disease

In Parkinson's disease, imbalances between "antikinetic" and "prokinetic" patterns of neuronal oscillatory activity are related to motor dysfunction. Invasive brain recordings from the motor network have suggested that medical or surgical therapy can promote a prokinetic state by inducing narrowband gamma rhythms (65-90 Hz). Excessive narrowband gamma in the motor cortex promotes dyskinesia in rodent models, but the relationship between narrowband gamma and dyskinesia in humans has not been well established. To assess this relationship, we used a sensing-enabled deep brain stimulator system, attached to both motor cortex and basal ganglia (subthalamic or pallidal) leads, paired with wearable devices that continuously tracked motor signs in the contralateral upper limbs. We recorded 984 hours of multisite field potentials in 30 hemispheres of 16 subjects with Parkinson's disease (2/16 female, mean age 57 ± 12 years) while at home on usual antiparkinsonian medications. Recordings were done two to four weeks after implantation, prior to starting therapeutic stimulation. Narrowband gamma was detected in the precentral gyrus, subthalamic nucleus, or both structures on at least one side of 92% of subjects with a clinical history of dyskinesia. Narrowband gamma was not detected in the globus pallidus. Narrowband gamma spectral power in both structures co-fluctuated similarly with contralateral wearable dyskinesia scores (mean correlation coefficient of ρ=0.48 with a range of 0.12-0.82 for cortex, ρ=0.53 with a range of 0.5-0.77 for subthalamic nucleus). Stratification analysis showed the correlations were not driven by outlier values, and narrowband gamma could distinguish "on" periods with dyskinesia from "on" periods without dyskinesia. Time lag comparisons confirmed that gamma oscillations herald dyskinesia onset without a time lag in either structure when using 2-minute epochs. A linear model incorporating the three oscillatory bands (beta, theta/alpha, and narrowband gamma) increased the predictive power of dyskinesia for several subject hemispheres. We further identified spectrally distinct oscillations in the low gamma range (40-60 Hz) in three subjects, but the relationship of low gamma oscillations to dyskinesia was variable. Our findings support the hypothesis that excessive oscillatory activity at 65-90 Hz in the motor network tracks with dyskinesia similarly across both structures, without a detectable time lag. This rhythm may serve as a promising control signal for closed-loop deep brain stimulation using either cortical or subthalamic detection.

Beta and theta oscillations track effort and previous reward in human basal ganglia and prefrontal cortex during decision making

Choosing whether to exert effort to obtain rewards is fundamental to human motivated behavior. However, the neural dynamics underlying the evaluation of reward and effort in humans is poorly understood. Here, we investigate this with chronic intracranial recordings from prefrontal cortex (PFC) and basal ganglia (BG; subthalamic nuclei and globus pallidus) in people with Parkinson's disease performing a decision-making task with offers that varied in levels of reward and physical effort required. This revealed dissociable neural signatures of reward and effort, with BG beta (12-20 Hz) oscillations tracking subjective effort on a single trial basis and PFC theta (4-7 Hz) signaling previous trial reward. Stimulation of PFC increased overall acceptance of offers in addition to increasing the impact of reward on choices. This work uncovers oscillatory mechanisms that guide fundamental decisions to exert effort for reward across BG and PFC, as well as supporting a causal role of PFC for such choices.

Multi-night cortico-basal recordings reveal mechanisms of NREM slow wave suppression and spontaneous awakenings at high-temporal resolution in Parkinson's disease

Background

Sleep disturbance is a prevalent and highly disabling comorbidity in individuals with Parkinson's disease (PD) that leads to worsening of daytime symptoms, reduced quality of life and accelerated disease progression.

Objectives

We aimed to record naturalistic overnight cortico-basal neural activity in people with PD, in order to determine the neurophysiology of spontaneous awakenings and slow wave suppression in non-rapid eye movement (NREM) sleep, towards the development of novel sleep-targeted neurostimulation therapies.

Methods

Multi-night (n=58) intracranial recordings were performed at-home, from chronic electrocorticography and subcortical electrodes, with sensing-enabled Deep Brain Stimulation (DBS), paired with portable polysomnography. Four participants with PD and one participant with cervical dystonia were evaluated to determine the neural structures, signals and functional connectivity modulated during NREM sleep and prior to spontaneous awakenings. Intracranial recordings were performed both ON and OFF DBS to evaluate the impact of stimulation. Sleep staging was then classified with machine-learning models using intracranial cortico-basal signals on classical (30 s) and rapid (5 s) timescales.

Results

We demonstrate an increase in cortico-basal slow wave delta (1-4 Hz) activity and a decrease in beta (13-31 Hz) activity during NREM (N2 and N3) versus wakefulness in PD. Cortical-basal ganglia coherence was also found to be higher in the delta range and lower in the beta range during NREM. DBS stimulation resulted in a further elevation in cortical delta and a decrease in alpha (8-13 Hz) and low beta (13-15 Hz) power compared to the OFF stimulation state. Within NREM sleep, we observed a strong inverse interaction between subcortical beta and cortical slow wave activity and found that subcortical beta increases prior to spontaneous awakenings at high-temporal resolution (5s). Our machine-learning models trained on intracranial cortical or subcortical power features achieved high accuracy in both traditional (30s) and rapid (5s) time windows for NREM vs. wakefulness classification (30s: 92.6±1.7%; 5s: 88.3±2.1%).

Conclusions

Chronic, multi-night recordings in PD reveal increased cortico-basal slow wave, decreased beta activity, and changes in functional connectivity in NREM vs wakefulness, effects that are enhanced in the presence of DBS. Within NREM, subcortical beta and cortical delta are strongly inversely correlated and subcortical beta power increases prior to spontaneous awakenings. Our findings elucidate the network-level neurophysiology of sleep dysfunction in PD and the mechanistic impact of conventional DBS. Additionally, through accurate machine-learning classification of spontaneous awakenings, this study also provides a foundation for future personalized adaptive DBS therapies for sleep dysfunction in PD.

Uncovering the neurophysiology of mood, motivation and behavioral symptoms in Parkinson's disease through intracranial recordings

Neuropsychiatric mood and motivation symptoms (depression, anxiety, apathy, impulse control disorders) in Parkinson's disease (PD) are highly disabling, difficult to treat and exacerbated by current medications and deep brain stimulation therapies. High-resolution intracranial recording techniques have the potential to undercover the network dysfunction and cognitive processes that drive these symptoms, towards a principled re-tuning of circuits. We highlight intracranial recording as a valuable tool for mapping and desegregating neural networks and their contribution to mood, motivation and behavioral symptoms, via the ability to dissect multiplexed overlapping spatial and temporal neural components. This technique can be powerfully combined with behavioral paradigms and emerging computational techniques to model underlying latent behavioral states. We review the literature of intracranial recording studies investigating mood, motivation and behavioral symptomatology with reference to 1) emotional processing, 2) executive control 3) subjective valuation (reward & cost evaluation) 4) motor control and 5) learning and updating. This reveals associations between different frequency specific network activities and underlying cognitive processes of reward decision making and action control. If validated, these signals represent potential computational biomarkers of motivational and behavioural states and could lead to principled therapy development for mood, motivation and behavioral symptoms in PD.

Adaptive Deep Brain Stimulation for sleep stage targeting in Parkinson's disease

BACKGROUND

Sleep dysfunction is disabling in people with Parkinson's disease and is linked to worse motor and non-motor outcomes. Sleep-specific adaptive Deep Brain Stimulation has the potential to target pathophysiologies of sleep.

OBJECTIVE

Develop an adaptive Deep Brain Stimulation algorithm that modulates stimulation parameters in response to intracranially classified sleep stages.

METHODS

We performed at-home, multi-night intracranial electrocorticography and polysomnogram recordings to train personalized linear classifiers for discriminating the N3 NREM sleep stage. Classifiers were embedded into investigational Deep Brain Stimulators for N3 specific adaptive DBS.

RESULTS

We report high specificity of embedded, autonomous, intracranial electrocorticography N3 sleep stage classification across two participants and provide proof-of-principle of successful sleep stage specific adaptive Deep Brain Stimulation.

CONCLUSION

Multi-night cortico-basal recordings and sleep specific adaptive Deep Brain Stimulation provide an experimental framework to investigate sleep pathophysiology and mechanistic interactions with stimulation, towards the development of therapeutic neurostimulation paradigms directly targeting sleep dysfunction.

Views on minimum unit pricing for alcohol before its introduction among people with alcohol dependence in Scotland: A qualitative interview study

INTRODUCTION

Scotland implemented a minimum price per unit of alcohol (MUP) of £0.50 in May 2018 (1 UK unit = 10 mL/8 g ethanol). Some stakeholders expressed concerns about the policy having potential negative consequences for people with alcohol dependence. This study aimed to investigate anticipated impacts of MUP on people presenting to alcohol treatment services in Scotland before policy implementation.

METHODS

Qualitative interviews were conducted with 21 people with alcohol dependence accessing alcohol treatment services in Scotland between November 2017 and April 2018. Interviews examined respondents' current and anticipated patterns of drinking and spending, effects on their personal life, and their views on potential policy impact. Interview data were thematically analysed using a constant comparison method.

RESULTS

Three key themes were identified: (i) strategies used to manage the cost of alcohol and anticipated responses to MUP; (ii) broader effects of MUP; and (iii) awareness and preparedness for MUP. Respondents expected to be impacted by MUP, particularly those on low incomes and those with more severe dependence symptoms. They anticipated using familiar strategies including borrowing and reprioritising spending to keep alcohol affordable. Some respondents anticipated negative consequences. Respondents were sceptical about the short-term benefits of MUP for current drinkers but felt it might prevent harm for future generations. Respondents had concerns about the capacity of treatment services to meet support needs.

DISCUSSION AND CONCLUSIONS

People with alcohol dependence identified immediate concerns alongside potential long-term benefits of MUP before its introduction. They also had concerns over the preparedness of service providers.

Sub-harmonic entrainment of cortical gamma oscillations to deep brain stimulation in Parkinson's disease: Model based predictions and validation in three human subjects

OBJECTIVES

The exact mechanisms of deep brain stimulation (DBS) are still an active area of investigation, in spite of its clinical successes. This is due in part to the lack of understanding of the effects of stimulation on neuronal rhythms. Entrainment of brain oscillations has been hypothesised as a potential mechanism of neuromodulation. A better understanding of entrainment might further inform existing methods of continuous DBS, and help refine algorithms for adaptive methods. The purpose of this study is to develop and test a theoretical framework to predict entrainment of cortical rhythms to DBS across a wide range of stimulation parameters.

MATERIALS AND METHODS

We fit a model of interacting neural populations to selected features characterising PD patients' off-stimulation finely-tuned gamma rhythm recorded through electrocorticography. Using the fitted models, we predict basal ganglia DBS parameters that would result in 1:2 entrainment, a special case of sub-harmonic entrainment observed in patients and predicted by theory.

RESULTS

We show that the neural circuit models fitted to patient data exhibit 1:2 entrainment when stimulation is provided across a range of stimulation parameters. Furthermore, we verify key features of the region of 1:2 entrainment in the stimulation frequency/amplitude space with follow-up recordings from the same patients, such as the loss of 1:2 entrainment above certain stimulation amplitudes.

CONCLUSION

Our results reveal that continuous, constant frequency DBS in patients may lead to nonlinear patterns of neuronal entrainment across stimulation parameters, and that these responses can be predicted by modelling. Should entrainment prove to be an important mechanism of therapeutic stimulation, our modelling framework may reduce the parameter space that clinicians must consider when programming devices for optimal benefit.

Personalized chronic adaptive deep brain stimulation outperforms conventional stimulation in Parkinson's disease

Deep brain stimulation is a widely used therapy for Parkinson's disease (PD) but currently lacks dynamic responsiveness to changing clinical and neural states. Feedback control has the potential to improve therapeutic effectiveness, but optimal control strategy and additional benefits of "adaptive" neurostimulation are unclear. We implemented adaptive subthalamic nucleus stimulation, controlled by subthalamic or cortical signals, in three PD patients (five hemispheres) during normal daily life. We identified neurophysiological biomarkers of residual motor fluctuations using data-driven analyses of field potentials over a wide frequency range and varying stimulation amplitudes. Narrowband gamma oscillations (65-70 Hz) at either site emerged as the best control signal for sensing during stimulation. A blinded, randomized trial demonstrated improved motor symptoms and quality of life compared to clinically optimized standard stimulation. Our approach highlights the promise of personalized adaptive neurostimulation based on data-driven selection of control signals and may be applied to other neurological disorders.

Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation

Adaptive deep brain stimulation (aDBS) shows promise for improving treatment for neurological disorders such as Parkinson's disease (PD). aDBS uses symptom-related biomarkers to adjust stimulation parameters in real-time to target symptoms more precisely. To enable these dynamic adjustments, parameters for an aDBS algorithm must be determined for each individual patient. This requires time-consuming manual tuning by clinical researchers, making it difficult to find an optimal configuration for a single patient or to scale to many patients. Furthermore, the long-term effectiveness of aDBS algorithms configured in-clinic while the patient is at home remains an open question. To implement this therapy at large scale, a methodology to automatically configure aDBS algorithm parameters while remotely monitoring therapy outcomes is needed. In this paper, we share a design for an at-home data collection platform to help the field address both issues. The platform is composed of an integrated hardware and software ecosystem that is open-source and allows for at-home collection of neural, inertial, and multi-camera video data. To ensure privacy for patient-identifiable data, the platform encrypts and transfers data through a virtual private network. The methods include time-aligning data streams and extracting pose estimates from video recordings. To demonstrate the use of this system, we deployed this platform to the home of an individual with PD and collected data during self-guided clinical tasks and periods of free behavior over the course of 1.5 years. Data were recorded at sub-therapeutic, therapeutic, and supra-therapeutic stimulation amplitudes to evaluate motor symptom severity under different therapeutic conditions. These time-aligned data show the platform is capable of synchronized at-home multi-modal data collection for therapeutic evaluation. This system architecture may be used to support automated aDBS research, to collect new datasets and to study the long-term effects of DBS therapy outside the clinic for those suffering from neurological disorders.

HTS driven by fluorescence lifetime detection of FRET identifies activators and inhibitors of cardiac myosin

Small molecules that bind to allosteric sites on target proteins to alter protein function are highly sought in drug discovery. High-throughput screening (HTS) assays are needed to facilitate the direct discovery of allosterically active compounds. We have developed technology for high-throughput time-resolved fluorescence lifetime detection of fluorescence resonance energy transfer (FRET), which enables the detection of allosteric modulators by monitoring changes in protein structure. We tested this approach at the industrial scale by adapting an allosteric FRET sensor of cardiac myosin to high-throughput screening (HTS), based on technology provided by Photonic Pharma and the University of Minnesota, and then used the sensor to screen 1.6 million compounds in the HTS facility at Bristol Myers Squibb. The results identified allosteric activators and inhibitors of cardiac myosin that do not compete with ATP binding, demonstrating high potential for FLT-based drug discovery.

Adaptive Deep Brain Stimulation: From Experimental Evidence Toward Practical Implementation

Closed-loop adaptive deep brain stimulation (aDBS) can deliver individualized therapy at an unprecedented temporal precision for neurological disorders. This has the potential to lead to a breakthrough in neurotechnology, but the translation to clinical practice remains a significant challenge. Via bidirectional implantable brain-computer-interfaces that have become commercially available, aDBS can now sense and selectively modulate pathophysiological brain circuit activity. Pilot studies investigating different aDBS control strategies showed promising results, but the short experimental study designs have not yet supported individualized analyses of patient-specific factors in biomarker and therapeutic response dynamics. Notwithstanding the clear theoretical advantages of a patient-tailored approach, these new stimulation possibilities open a vast and mostly unexplored parameter space, leading to practical hurdles in the implementation and development of clinical trials. Therefore, a thorough understanding of the neurophysiological and neurotechnological aspects related to aDBS is crucial to develop evidence-based treatment regimens for clinical practice. Therapeutic success of aDBS will depend on the integrated development of strategies for feedback signal identification, artifact mitigation, signal processing, and control policy adjustment, for precise stimulation delivery tailored to individual patients. The present review introduces the reader to the neurophysiological foundation of aDBS for Parkinson's disease (PD) and other network disorders, explains currently available aDBS control policies, and highlights practical pitfalls and difficulties to be addressed in the upcoming years. Finally, it highlights the importance of interdisciplinary clinical neurotechnological research within and across DBS centers, toward an individualized patient-centered approach to invasive brain stimulation. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

High precision magnetoencephalography reveals increased right-inferior frontal gyrus beta power during response conflict

Flexibility of behavior and the ability to rapidly switch actions is critical for adaptive living in humans. It is well established that the right-inferior frontal gyrus (R-IFG) is recruited during outright action-stopping, relating to increased beta (12-30 Hz) power. It has also been posited that inhibiting incorrect response tendencies and switching is central to motor flexibility. However, it is not known if the commonly reported R-IFG beta signature of response inhibition in action-stopping is also recruited during response conflict, which would suggest overlapping networks for stopping and switching. In the current study, we analyzed high precision magnetoencephalography (hpMEG) data recorded with multiple within subject recording sessions (trials n > 10,000) from 8 subjects during different levels of response conflict. We hypothesized that a R-IFG-triggered network for response inhibition is domain general and therefore also involved in mediating response conflict. We tested whether R-IFG showed increased beta power dependent on the level of response conflict. Using event-related spectral perturbations and linear mixed modeling, we found that R-IFG beta power increased for response conflict trials. The R-IFG beta increase was specific to trials with strong response conflict, and increased R-IFG beta power related to less error. This supports a more generalized role for R-IFG beta, beyond simple stopping behavior towards response switching.

A Realist Review of How Community-Based Drug Checking Services Could Be Designed and Implemented to Promote Engagement of People Who Use Drugs

With rising numbers of drug-related deaths in the UK and globally, exploration of interventions that seek to reduce drug-related harm is essential. Drug checking services (DCS) allow people to submit drug samples for chemical analysis and receive feedback about the sample, as well as harm reduction advice. The use of DCS is often linked to festival and/or nightlife settings and to so-called 'recreational' drug use, but research has also shown the potential of community-based DCS as an intervention serving more varied demographics of people who use drugs, including more marginalised individuals and those experiencing drug dependence. Whilst there is a growing evidence base on the effectiveness of drug checking as a harm reduction intervention, there is still limited evidence of the underlying mechanisms and processes within DCS which may aid implementation and subsequent engagement of people who use drugs. This presents a challenge to understanding why engagement differs across types of DCS, and how best to develop and deliver services across different contexts and for different populations. To explore the contexts and mechanisms which impact engagement in community-based DCS, a realist review was undertaken to synthesise the international evidence for the delivery and implementation of DCS. There were 133 sources included in the review. From these sources the underlying contexts, mechanisms, and outcomes relating to DCS implementation and engagement were developed and refined into seven programme theories. The findings of this review are theoretically novel and hold practical relevance for the design of DCS, with implications for optimisation, tailoring, and implementing services to reach individuals in different settings.

An Integrated Testing Strategy for Ecotoxicity (ITS-ECO) Assessment in the Marine Environmental Compartment using Mytilus spp.: A Case Study using Pristine and Coated CuO and TiO2 Nanomaterials

An integrated testing strategy for ecotoxicity assessment (ITS-ECO) was developed to aid in the hazard and fate assessment of engineered nanomaterials (ENMs) deposited in marine environments using the bivalve Mytilus spp. as a test species. The ENMs copper(II) oxide (CuO) and titanium dioxide (TiO₂ ), either in pristine form (core) or with functionalized coatings (polyethylene glycol [PEG], carboxyl [COOH], and ammonia [NH₃ ]) were selected as case study materials based on their production levels and use. High-throughput in vitro testing in Tier 1 of the ITS-ECO revealed CuO ENMs to elicit cytotoxic effects on lysosomes of hemocytes of mussels, with the hazard potential CuO PEG > CuO COOH > CuO NH₃  > CuO core, whereas TiO₂ ENMs were not cytotoxic. Genotoxicity in hemocytes as well as gill cells of mussels following in vivo exposure (48 h) to CuO ENMs was also seen. Longer in vivo exposures in Tier 2 (48 h-21 days) revealed subacute and chronic oxidative effects for both CuO and TiO₂ ENMs, in some cases leading to lipid peroxidation (core TiO₂ ENMs). In Tier 3 bioaccumulation studies, distinct patterns of uptake for Cu (predominantly in gills) and Ti (predominantly in digestive glands) and between the different core and coated ENMs were found. Clear NM-specific and coating-dependent effects on hazard and fate were seen. Overall, using a tiered testing approach, the ITS-ECO was able to differentiate the hazard (acute, subacute, and chronic effects) posed by ENMs of different compositions and coatings and to provide information on fate for environmental risk assessment of these ENMs. Environ Toxicol Chem 2022;41:1390-1406. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Dual-system Involvement: Exploring the Overlap Between Domestic and Family Violence and Child Maltreatment Perpetration

This longitudinal population-based study aimed to understand the dual-system involvement of males who engage in domestic and family violence (DFV) and child maltreatment perpetration. Specifically, this study investigates the prevalence of male perpetration of DFV, child maltreatment, and dual-system involvement, and compares the frequency and nature of perpetration by males with single-system contact and males with dual-system contact. The effect of race (Indigenous Australian/non-Indigenous Australian) and parental status on dual-system involvement is also examined. This study utilizes data from the Queensland Cross-sector Research Collaboration repository. These data contain each contact an individual male born in 1983 or 1984 had with the Queensland civil court system as a respondent to a domestic violence order (DVO) and the Queensland statutory child protection system, where, after an investigation, they were identified as the person responsible for substantiated harm to a child. The results from the study show a high level of overlap between males who perpetrate DFV and child maltreatment. Of males identified as being responsible for substantiated harm to a child, 58% have also been a respondent of a DVO, and approximately 16% of male DVO respondents were identified as being responsible for substantiated harm to a child. Differences were found in the frequency and nature of perpetration by males with dual-system involvement compared to those with single-system contact for either DFV or child maltreatment. Our results also show differences for Indigenous status, parental status, number of maltreatment events, harm type, number of DVOs, and number of breaches of a DVO. The implications of this study for both policy and practice are discussed.

Proceedings of the Ninth Annual Deep Brain Stimulation Think Tank: Advances in Cutting Edge Technologies, Artificial Intelligence, Neuromodulation, Neuroethics, Pain, Interventional Psychiatry, Epilepsy, and Traumatic Brain Injury

DBS Think Tank IX was held on August 25-27, 2021 in Orlando FL with US based participants largely in person and overseas participants joining by video conferencing technology. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers and researchers (from industry and academia) can freely discuss current and emerging deep brain stimulation (DBS) technologies as well as the logistical and ethical issues facing the field. The consensus among the DBS Think Tank IX speakers was that DBS expanded in its scope and has been applied to multiple brain disorders in an effort to modulate neural circuitry. After collectively sharing our experiences, it was estimated that globally more than 230,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. As such, this year's meeting was focused on advances in the following areas: neuromodulation in Europe, Asia and Australia; cutting-edge technologies, neuroethics, interventional psychiatry, adaptive DBS, neuromodulation for pain, network neuromodulation for epilepsy and neuromodulation for traumatic brain injury.

Concurrent stimulation and sensing in bi-directional brain interfaces: a multi-site translational experience

OBJECTIVE

To provide a design analysis and guidance framework for the implementation of concurrent stimulation and sensing during adaptive deep brain stimulation (aDBS) with particular emphasis on artifact mitigations.

APPROACH

We defined a general architecture of feedback-enabled devices, identified key components in the signal chain which might result in unwanted artifacts and proposed methods that might ultimately enable improved aDBS therapies. We gathered data from research subjects chronically-implanted with an investigational aDBS system, Summit RC+S, to characterize and explore artifact mitigations arising from concurrent stimulation and sensing. We then used a prototype investigational implantable device, DyNeuMo, and a bench-setup that accounts for tissue-electrode properties, to confirm our observations and verify mitigations. The strategies to reduce transient stimulation artifacts and improve performance during aDBS were confirmed in a chronic implant using updated configuration settings.

MAIN RESULTS

We derived and validated a "checklist" of configuration settings to improve system performance and areas for future device improvement. Key considerations for the configuration include 1) active instead of passive recharge, 2) sense-channel blanking in the amplifier, 3) high-pass filter settings, 4) tissue-electrode impedance mismatch management, 5) time-frequency trade-offs in the classifier, 6) algorithm blanking and transition rate limits. Without proper channel configuration, the aDBS algorithm was susceptible to limit-cycles of oscillating stimulation independent of physiological state. By applying the checklist, we could optimize each block's performance characteristics within the overall system. With system-level optimization, a 'fast' aDBS prototype algorithm was demonstrated to be feasible without reentrant loops, and with noise performance suitable for subcortical brain circuits.

SIGNIFICANCE

We present a framework to study sources and propose mitigations of artifacts in devices that provide chronic aDBS. This work highlights the trade-offs in performance as novel sensing devices translate to the clinic. Finding the appropriate balance of constraints is imperative for successful translation of aDBS therapies.

Artifact Characterization and a Multipurpose Template-Based Offline Removal Solution for a Sensing-Enabled Deep Brain Stimulation Device

BACKGROUND

The Medtronic "Percept" is the first FDA-approved deep brain stimulation (DBS) device with sensing capabilities during active stimulation. Its real-world signal-recording properties have yet to be fully described.

OBJECTIVE

This study details three sources of artifact (and potential mitigations) in local field potential (LFP) signals collected by the Percept and assesses the potential impact of artifact on the future development of adaptive DBS (aDBS) using this device.

METHODS

LFP signals were collected from 7 subjects in both experimental and clinical settings. The presence of artifacts and their effect on the spectral content of neural signals were evaluated in both the stimulation ON and OFF states using three distinct offline artifact removal techniques.

RESULTS

Template subtraction successfully removed multiple sources of artifact, including (1) electrocardiogram (ECG), (2) nonphysiologic polyphasic artifacts, and (3) ramping-related artifacts seen when changing stimulation amplitudes. ECG removal from stimulation ON (at 0 mA) signals resulted in spectral shapes similar to OFF stimulation spectra (averaged difference in normalized power in theta, alpha, and beta bands ≤3.5%). ECG removal using singular value decomposition was similarly successful, though required subjective researcher input. QRS interpolation produced similar recovery of beta-band signal but resulted in residual low-frequency artifact.

CONCLUSIONS

Artifacts present when stimulation is enabled notably affected the spectral properties of sensed signals using the Percept. Multiple discrete artifacts could be successfully removed offline using an automated template subtraction method. The presence of unrejected artifact likely influences online power estimates, with the potential to affect aDBS algorithm performance.

Exploring dual-system involvement for domestic violence victimisation and child maltreatment perpetration: An exploration by gender and race/ethnicity

BACKGROUND

There is an historical separation between system responses to domestic violence (DV) and child maltreatment. Concerns have been noted that DV victims may be over-represented as parents responsible for harm to children in the child protection system. Although there is a growing acknowledgement of the high overlap between DV and child maltreatment within families, little empirical research has been conducted on this relationship.

OBJECTIVE

This study aims to longitudinally examine the overlap of being a victim of DV and a perpetrator of child maltreatment, along with the impact of dual-system involvement on the nature and frequency of the violence experienced.

METHODS

The data are linked longitudinal administrative data from the Queensland Cross-sector Research Collaboration (QCRC) repository. These data contain each contact every individual born in Queensland in 1983 or 1984 had with the Queensland DV civil court system and the Queensland statutory child protection system.

RESULTS

Of individuals identified as a perpetrator of child maltreatment, 45% have also been a victim of DV and approximately 22% of DV victims were identified as a perpetrator of child maltreatment. Our results also show differences based on Indigenous status, gender, parental status, number of substantiations, frequency of violence, harm type, and number of domestic violence orders.

CONCLUSIONS

There is considerable overlap between individuals who are victims of DV and individuals who perpetrate child maltreatment. This overlap was influenced by both gender and race/ethnicity. The implications of this study for both policy and practice are discussed.

Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson's Disease Using a Wrist-Worn Accelerometer

Motor fluctuations in Parkinson’s disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson’s patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson’s patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.

Laminar dynamics of high amplitude beta bursts in human motor cortex

Motor cortical activity in the beta frequency range is one of the strongest and most studied movement-related neural signals. At the single trial level, beta band activity is often characterized by transient, high amplitude, bursting events rather than slowly modulating oscillations. The timing of these bursting events is tightly linked to behavior, suggesting a more dynamic functional role for beta activity than previously believed. However, the neural mechanisms underlying beta bursts in sensorimotor circuits are poorly understood. To address this, we here leverage and extend recent developments in high precision MEG for temporally resolved laminar analysis of burst activity, combined with a neocortical circuit model that simulates the biophysical generators of the electrical currents which drive beta bursts. This approach pinpoints the generation of beta bursts in human motor cortex to distinct excitatory synaptic inputs to deep and superficial cortical layers, which drive current flow in opposite directions. These laminar dynamics of beta bursts in motor cortex align with prior invasive animal recordings within the somatosensory cortex, and suggest a conserved mechanism for somatosensory and motor cortical beta bursts. More generally, we demonstrate the ability for uncovering the laminar dynamics of event-related neural signals in human non-invasive recordings. This provides important constraints to theories about the functional role of burst activity for movement control in health and disease, and crucial links between macro-scale phenomena measured in humans and micro-circuit activity recorded from animal models.

Prefrontal Physiomarkers of Anxiety and Depression in Parkinson's Disease

Objective: Anxiety and depression are prominent non-motor symptoms of Parkinson’s disease (PD), but their pathophysiology remains unclear. We sought to understand their neurophysiological correlates from chronic invasive recordings of the prefrontal cortex (PFC).

Methods: We studied four patients undergoing deep brain stimulation (DBS) for their motor signs, who had comorbid mild to moderate anxiety and/or depressive symptoms. In addition to their basal ganglia leads, we placed a permanent prefrontal subdural 4-contact lead. These electrodes were attached to an investigational pulse generator with the capability to sense and store field potential signals, as well as deliver therapeutic neurostimulation. At regular intervals over 3–5 months, participants paired brief invasive neural recordings with self-ratings of symptoms related to depression and anxiety.

Results: Mean age was 61 ± 7 years, mean disease duration was 11 ± 8 years and a mean Unified Parkinson’s Disease Rating Scale, with part III (UPDRS-III) off medication score of 37 ± 13. Mean Beck Depression Inventory (BDI) score was 14 ± 5 and Beck Anxiety Index was 16.5 ± 5. Prefrontal cortex spectral power in the beta band correlated with patient self-ratings of symptoms of depression and anxiety, with r-values between 0.31 and 0.48. Mood scores showed negative correlation with beta spectral power in lateral locations, and positive correlation with beta spectral power in a mesial recording location, consistent with the dichotomous organization of reward networks in PFC.

Interpretation: These findings suggest a physiological basis for anxiety and depression in PD, which may be useful in the development of neurostimulation paradigms for these non-motor disease features.

Sleep-Aware Adaptive Deep Brain Stimulation Control: Chronic Use at Home With Dual Independent Linear Discriminate Detectors

Adaptive deep brain stimulation (aDBS) is a promising new technology with increasing use in experimental trials to treat a diverse array of indications such as movement disorders (Parkinson’s disease, essential tremor), psychiatric disorders (depression, OCD), chronic pain and epilepsy. In many aDBS trials, a neural biomarker of interest is compared with a predefined threshold and stimulation amplitude is adjusted accordingly. Across indications and implant locations, potential biomarkers are greatly influenced by sleep. Successful chronic embedded adaptive detectors must incorporate a strategy to account for sleep, to avoid unwanted or unexpected algorithm behavior. Here, we show a dual algorithm design with two independent detectors, one used to track sleep state (wake/sleep) and the other used to track parkinsonian motor state (medication-induced fluctuations). Across six hemispheres (four patients) and 47 days, our detector successfully transitioned to sleep mode while patients were sleeping, and resumed motor state tracking when patients were awake. Designing “sleep aware” aDBS algorithms may prove crucial for deployment of clinically effective fully embedded aDBS algorithms.

Noncontrast MRI Protocol for Selected Pediatric Pituitary Endocrinopathies: A Procedure with High Diagnostic Yield and Potential to Reduce Anesthesia and Gadolinium-Based Contrast Exposure

BACKGROUND AND PURPOSE

Although many pediatric neuroradiology practices empirically use noncontrast brain and pituitary MR imaging for evaluation of growth hormone deficiency, central precocious puberty, and short stature, there are currently insufficient published data to support this practice in an evidence-based fashion. Therefore, the use of contrast-enhanced MR imaging for all pediatric pituitary endocrinopathies remains widespread. We evaluated whether noncontrast MR imaging has adequate diagnostic yield for the evaluation of pediatric growth hormone deficiency, central precocious puberty, and short stature.

MATERIALS AND METHODS

Pituitary MR imaging studies obtained for growth hormone deficiency, central precocious puberty, or short stature in patients 0-18 years of age from 2010 to 2019 were analyzed. Separate blinded review of noncontrast images in cases with abnormalities on the original radiology report was performed by 2 subspecialty-trained pediatric neuroradiologists, with discrepancies resolved by consensus.

RESULTS

Of the 134/442 MR imaging studies obtained for growth hormone deficiency, central precocious puberty, or short stature with hypothalamic-pituitary region abnormalities, there was 70% concordance with the original reports on blinded review of noncontrast images. Twenty-two of 40 discrepancies were deemed unrelated to the indication, and 9 cases originally interpreted as possible microadenoma were read as having normal findings on blinded review. Only 9 of 40 discrepancies required contrast for further characterization.

CONCLUSIONS

In our study, most relevant radiologic findings in patients with growth hormone deficiency, central precocious puberty, and short stature were detectable without contrast, providing evidence that contrast can be avoided in routine MR imaging evaluation of these indications. We propose a "rapid noncontrast pituitary" MR imaging protocol for pediatric patients presenting with growth hormone deficiency, central precocious puberty, or short stature, which may increase efficiency and decrease contrast and anesthesia exposure.

Long-term wireless streaming of neural recordings for circuit discovery and adaptive stimulation in individuals with Parkinson's disease

Neural recordings using invasive devices in humans can elucidate the circuits underlying brain disorders, but have so far been limited to short recordings from externalized brain leads in a hospital setting or from implanted sensing devices that provide only intermittent, brief streaming of time series data. Here, we report the use of an implantable two-way neural interface for wireless, multichannel streaming of field potentials in five individuals with Parkinson's disease (PD) for up to 15 months after implantation. Bilateral four-channel motor cortex and basal ganglia field potentials streamed at home for over 2,600 h were paired with behavioral data from wearable monitors for the neural decoding of states of inadequate or excessive movement. We validated individual-specific neurophysiological biomarkers during normal daily activities and used those patterns for adaptive deep brain stimulation (DBS). This technological approach may be widely applicable to brain disorders treatable by invasive neuromodulation.

Chronic Sensing of Subthalamic Local Field Potentials: Comparison of First and Second Generation Implantable Bidirectional Systems Within a Single Subject

BACKGROUND

Many adaptative deep brain stimulation (DBS) paradigms rely upon the ability to sense neural signatures of specific clinical signs or symptoms in order to modulate therapeutic stimulation. In first-generation bidirectional neurostimulators, the ability to sense neural signals during active stimulation was often limited by artifact. Newer devices, with improved design specifications for sensing, have recently been developed and are now clinically available.

OBJECTIVE

To compare the sensing capabilities of the first-generation Medtronic PC + S and second-generation Percept PC neurostimulators within a single patient.

METHODS

A 42-year-old man with Parkinson's disease was initially implanted with left STN DBS leads connected to a PC + S implantable pulse generator. Four years later, the PC + S was replaced with the Percept PC. Local field potential (LFP) signals were recorded, both with stimulation OFF and ON, at multiple timepoints with each device and compared. Offline processing of time series data included artifact removal using digital filtering and template subtraction, before subsequent spectral analysis. With Percept PC, embedded processing of spectral power within a narrow frequency band was also utilized.

RESULTS

In the absence of stimulation, both devices demonstrated a peak in the beta range (approximately 20 Hz), which was stable throughout the 4-year period. Similar to previous reports, recordings with the PC + S during active stimulation demonstrated significant stimulation artifact, limiting the ability to recover meaningful LFP signal. In contrast, the Percept PC, using the same electrodes and stimulation settings, produced time series data during stimulation with spectral analysis revealing a peak in the beta-band. Online analysis by the Percept demonstrated a reduction in beta-band activity with increasing stimulation amplitude.

CONCLUSION

This report highlights recent advances in implantable neurostimulator technology for DBS, demonstrating improvements in sensing capabilities during active stimulation between first- and second-generation devices. The ability to reliably sense during stimulation is an important step toward both the clinical implementation of adaptive algorithms and the further investigation into the neurophysiology underlying movement disorders.

Embedded adaptive deep brain stimulation for cervical dystonia controlled by motor cortex theta oscillations

Dystonia is a disabling movement disorder characterized by excessive muscle contraction for which the underlying pathophysiology is incompletely understood and treatment interventions limited in efficacy. Here we utilize a novel, sensing-enabled, deep brain stimulator device, implanted in a patient with cervical dystonia, to record local field potentials from chronically implanted electrodes in the sensorimotor cortex and subthalamic nuclei bilaterally. This rechargeable device was able to record large volumes of neural data at home, in the naturalistic environment, during unconstrained activity. We confirmed the presence of theta (3-7 Hz) oscillatory activity, which was coherent throughout the cortico-subthalamic circuit and specifically suppressed by high-frequency stimulation. Stimulation also reduced the duration, rate and height of theta bursts. These findings motivated a proof-of-principle trial of a new form of adaptive deep brain stimulation - triggered by theta-burst activity recorded from the motor cortex. This facilitated increased peak stimulation amplitudes without induction of dyskinesias and demonstrated improved blinded clinical ratings compared to continuous DBS, despite reduced total electrical energy delivered. These results further strengthen the pathophysiological role of low frequency (theta) oscillations in dystonia and demonstrate the potential for novel adaptive stimulation strategies linked to cortico-basal theta bursts.

The Neurophysiology of Sleep in Parkinson's Disease

Sleep disturbances are among the most common nonmotor complications of Parkinson's disease (PD), can present in prodromal stages, and progress with advancing disease. In addition to being a symptom of neurodegeneration, sleep disturbances may also contribute to disease progression. Currently, limited options exist to modulate sleep disturbances in PD. Studying the neurophysiological changes that affect sleep in PD at the cortical and subcortical level may yield new insights into mechanisms for reversal of sleep disruption. In this article, we review cortical and subcortical recording studies of sleep in PD with a particular focus on dissecting reported electrophysiological changes. These studies show that slow-wave sleep and rapid eye movement sleep are both notably disrupted in PD. We further explore the impact of these electrophysiological changes and discuss the potential for targeting sleep via stimulation therapy to modify PD-related motor and nonmotor symptoms. © 2021 International Parkinson and Movement Disorder Society.

Proceedings of the Eighth Annual Deep Brain Stimulation Think Tank: Advances in Optogenetics, Ethical Issues Affecting DBS Research, Neuromodulatory Approaches for Depression, Adaptive Neurostimulation, and Emerging DBS Technologies

We estimate that 208,000 deep brain stimulation (DBS) devices have been implanted to address neurological and neuropsychiatric disorders worldwide. DBS Think Tank presenters pooled data and determined that DBS expanded in its scope and has been applied to multiple brain disorders in an effort to modulate neural circuitry. The DBS Think Tank was founded in 2012 providing a space where clinicians, engineers, researchers from industry and academia discuss current and emerging DBS technologies and logistical and ethical issues facing the field. The emphasis is on cutting edge research and collaboration aimed to advance the DBS field. The Eighth Annual DBS Think Tank was held virtually on September 1 and 2, 2020 (Zoom Video Communications) due to restrictions related to the COVID-19 pandemic. The meeting focused on advances in: (1) optogenetics as a tool for comprehending neurobiology of diseases and on optogenetically-inspired DBS, (2) cutting edge of emerging DBS technologies, (3) ethical issues affecting DBS research and access to care, (4) neuromodulatory approaches for depression, (5) advancing novel hardware, software and imaging methodologies, (6) use of neurophysiological signals in adaptive neurostimulation, and (7) use of more advanced technologies to improve DBS clinical outcomes. There were 178 attendees who participated in a DBS Think Tank survey, which revealed the expansion of DBS into several indications such as obesity, post-traumatic stress disorder, addiction and Alzheimer’s disease. This proceedings summarizes the advances discussed at the Eighth Annual DBS Think Tank.

A space-time continuum in DBS: structural and functional advances in Parkinson's disease

This scientific commentary refers to ‘Modulation of beta bursts in subthalamic sensorimotor circuits predicts improvement in bradykinesia’, by Kehnemouyi et al. (doi:10.1093/brain/awaa394).

Acute waterborne and chronic sediment toxicity of silver and titanium dioxide nanomaterials towards the oligochaete, Lumbriculus variegatus

The use of silver (Ag) and titanium dioxide (TiO₂) nanomaterials (NMs) in industrial processes and consumer products has experienced considerable growth since the late 20th century. Throughout their lifecycle, both Ag NM and TiO₂NM are released into the environment, with benthic systems anticipated to be the final sink. Their potential toxicity towards benthic species is therefore of major concern. This study investigated the toxicity of silver (Ag; NM-300 K) and titanium dioxide (TiO₂; NM-104) NMs to the freshwater oligochaete, Lumbriculus variegatus in acute (0-96-h) waterborne and chronic (28-d) sediment studies. Toxicity was investigated via assessment of mortality, behaviour, and antioxidant enzyme activity. The 96-h LC₅₀ for Ag NMs in water was 0.51 mg/l (95% CI, 0.45-0.56), with L. variegatus displaying inhibited predation-avoidance behaviour compared to controls (6.66 ± 10%) successful response at 24-h), as well as significant increases (p < 0.05) in catalase (CAT) activity at sub-lethal concentrations at 24-h. Behavioural improvement and the return of antioxidant enzymes to control levels was observed after 48 and 72-h. AgNO₃ exposure proved more toxic than Ag NM (96-h LC₅₀ = 0.034 mg/l, 95% CI, 0.031-0.037) but resulted in no changes to antioxidant enzymes following sub-lethal exposure. Furthermore, Ag dissolution from Ag NM (~2-4%) could not account for the full extent of toxicity observed, suggesting a nano-specific effect. Increased environmental relevance via the inclusion of Suwannee River Humic Acid (SRHA, 5 mg/l) alleviated sub-lethal Ag NM toxicity despite a comparable 96-h LC₅₀ (0.54 mg/l, 95% CI, 0.51-0.57). Significant effects of Ag NMs in formulated sediments (mortality, biomass) were only recorded according to OECD 225 at the highest test concentration (1333 mg/kg) for Ag NM indicating a potential attenuating effect of sediments towards toxicity. No toxicity was observed for TiO₂ NM in aquatic or sediment exposures up to concentrations of 2000 mg/l and 1333 mg/kg, respectively.

Glutamatergic Contribution to Probabilistic Reasoning and Jumping to Conclusions in Schizophrenia: A Double-Blind, Randomized Experimental Trial

BACKGROUND

Impaired probabilistic reasoning and the jumping-to-conclusions reasoning bias are hallmark features of schizophrenia (SCZ), yet the neuropharmacological basis of these deficits remains unclear. Here we tested the hypothesis that glutamatergic neurotransmission specifically contributes to jumping to conclusions and impaired probabilistic reasoning in SCZ.

METHODS

A total of 192 healthy participants received either NMDA receptor agonists/antagonists (D-cycloserine/dextromethorphan), dopamine type 2 receptor agonists/antagonists (bromocriptine/haloperidol), or placebo in a randomized, double-blind, between-subjects design. In addition, we tested 32 healthy control participants matched to 32 psychotic inpatients with SCZ-a state associated with compromised probabilistic reasoning due to reduced glutamatergic neurotransmission. All experiments employed two versions of a probabilistic reasoning (beads) task, which required participants to either sample individual amounts of sensory information to infer correct decisions or provide explicit probability estimates for presented sensory information. Our task instantiations assessed both information sampling and explicit probability estimates in different probabilistic contexts (easy vs. difficult conditions) and changing sensory information through random transitions among easy, difficult, and ambiguous trial types.

RESULTS

Following administration of D-cycloserine, haloperidol, and bromocriptine, healthy participants displayed data-gathering behavior that was normal compared with placebo and was adequate in the context of all employed task conditions and trial level difficulties. However, healthy participants receiving dextromethorphan displayed a jumping-to-conclusions bias, abnormally increased probability estimates, and overweighting of sensory information. These effects were mirrored in patients with SCZ performing the same versions of the beads task.

CONCLUSIONS

Our findings provide novel neuropharmacological evidence linking reduced glutamatergic neurotransmission to impaired information sampling and to disrupted probabilistic reasoning, namely to overweighting of sensory evidence, in patients with SCZ.

Artificial intelligence-guided image acquisition on patients with implanted electrophysiological devices: results from a pivotal prospective multi-center clinical trial

Background

A novel, recently FDA-authorized software uses deep learning (DL) to provide prescriptive transthoracic echocardiography (TTE) guidance, allowing novices to acquire standard TTE views. The DL model was trained by &gt;5,000,000 observations of the impact of probe motion on image orientation/quality. This study evaluated whether novice-acquired TTE images guided by this software were of diagnostic quality in patients with and without implanted electrophysiological (EP) devices, focusing on RV size and function, which were thought to be sensitive to EP devices. Some aspects of the study have previously been presented.

Methods

240 patients (61±16 years old, 58% male, 33% BMI &gt;30 kg/m2, 91% with cardiac pathology) were recruited. 8 nurses without echo experience each acquired 10 view TTEs in 30 patients guided by the software. 235 of the patients were also scanned by a trained sonographer without assistance from the software. 5 Level 3 echocardiographers independently assessed the diagnostic quality of the TTEs acquired by the nurses and sonographers to evaluate the effect of EP devices on DL software performance.

Results

Nurses using the AI-guided acquisition software acquired TTEs of sufficient quality to make qualitative assessments of right ventricular (RV) size and function in greater than 80% of cases for patients with and without implanted EP devices (Table). There was no significant difference between nurse- and sonographer-acquired scans.

Conclusion

These results indicate that new DL software can guide novices to obtain TTEs that enable qualitative assessment of RV size even in the presence of implanted EP devices. The results of the comparison to sonographer-acquired exams indicate the software performance is robust to presence of pacemaker/ICD leads visible in the images (Figure).

Nurse-acquired TTE with visible ICD lead

Funding Acknowledgement

Type of funding source: Private company. Main funding source(s): Caption Health, Inc.

Contingent negative variation: a biomarker of abnormal attention in functional movement disorders

BACKGROUND AND PURPOSE

Contingent negative variation (CNV) is a negative cortical wave that precedes a pre-cued imperative stimulus requiring a quick motor response. It has been related to motor preparation and anticipatory attention. The aim was to ascertain whether the clinical improvement of functional movement disorders after physiotherapy would be associated with faster reaction times and modulation of CNV.

METHODS

Motor performance and CNV were analysed during a pre-cued choice reaction time task with varying cue validity. Twenty-one patients with functional movement disorders and 13 healthy controls at baseline were compared. Patients then underwent physiotherapy. At follow-up after physiotherapy, patients were categorized as clinically improved (responders) or not improved (non-responders) and retested.

RESULTS

At baseline, patients did not generate CNV, contrary to controls [mean amplitude (µV) at the end of preparation to move: patients -0.47 (95% CI -1.94, 1.00) versus controls -2.59 (95% CI -4.46, -0.72)]. Responders performed faster after physiotherapy [mean natural logarithm (ln) reaction time (RT) (ms): follow-up 6.112 (95% CI 5.923, 6.301) versus baseline 6.206 (95% CI 6.019, 6.394), P = 0.010], contrary to non-responders. Simultaneously, responders showed a recovery of CNV after physiotherapy [follow-up -1.95 (95% CI -3.49, -0.41) versus baseline -0.19 (95% CI -1.73, 1.35), P < 0.001], contrary to non-responders [follow-up -0.32 (95% CI -1.79, 1.14) versus baseline -0.72 (95% CI -2.19, 0.75), P = 0.381].

CONCLUSIONS

Clinical improvement of functional movement disorders after physiotherapy was associated with faster reaction times and normalization of CNV, which was absent at baseline. These findings suggest that CNV may constitute a useful neurophysiological biomarker related to abnormal attention in functional movement disorders.

Case report: Headache and neurological deficits with CSF lymphocytosis (HaNDL) associated with P/Q type voltage-gated calcium channel antibodies (CACNA1A)

BACKGROUND

Headache and Neurological Deficits with cerebrospinal fluid (CSF) Lymphocytosis (HaNDL) is an increasingly recognised syndrome but the aetiology remains unclear. HaNDL has striking clinical features similar to Familial Hemiplegic Migraine (FHM), commonly related to gene mutations encoding the P/Q-type voltage-gated calcium channel (VGCC).

CASE REPORT

We report a case of HaNDL associated with high P/Q-type voltage-gated calcium channel antibodies. Extensive investigations excluded alternative diagnoses and CSF lymphocytosis resolved within 3 months. The case was complicated by raised intracranial pressure resulting in an enlarged blind spot, papilloedema and bilateral lateral rectus palsies.

CONCLUSION

This novel association of P/Q-type voltage-gated calcium channel antibodies with HaNDL has implications for the pathology of HaNDL and spectrum of voltage-gated calcium channel-antibody disorders. We compare the clinical features of FHM and HaNDL and the potential pathological role of these antibodies. This case also highlights that raised intracranial pressure is a common feature of HaNDL, rarely resulting in serious complications.

Debugging Adaptive Deep Brain Stimulation for Parkinson's Disease

Deep brain stimulation (DBS) is a successful treatment for patients with Parkinson's disease. In adaptive DBS, stimulation is titrated according to feedback about clinical state and underlying pathophysiology. This contrasts with conventional stimulation, which is fixed and continuous. In acute trials, adaptive stimulation matches the efficacy of conventional stimulation while delivering about half the electrical energy. The latter means potentially fewer side-effects. The next step is to determine the long-term efficacy, efficiency, and side-effect profile of adaptive stimulation, and chronic trials are currently being considered by the medical devices industry. However, there are several different approaches to adaptive DBS, and several possible limitations have been highlighted. Here we review the findings to date to ascertain how and who to stimulate in chronic trials designed to establish the long-term utility of adaptive DBS. © 2020 International Parkinson and Movement Disorder Society.

Toward adaptive deep brain stimulation for dystonia

The presence of abnormal neural oscillations within the cortico-basal ganglia-thalamo-cortical (CBGTC) network has emerged as one of the current principal theories to explain the pathophysiology of movement disorders. In theory, these oscillations can be used as biomarkers and thereby serve as a feedback signal to control the delivery of deep brain stimulation (DBS). This new form of DBS, dependent on different characteristics of pathological oscillations, is called adaptive DBS (aDBS), and it has already been applied in patients with Parkinson's disease. In this review, the authors summarize the scientific research to date on pathological oscillations in dystonia and address potential biomarkers that might be used as a feedback signal for controlling aDBS in patients with dystonia.

Parkinsonian Tremor Detection from Subthalamic Nucleus Local Field Potentials for Closed-Loop Deep Brain Stimulation

Deep Brain Stimulation (DBS) is a widely used therapy to ameliorate symptoms experienced by patients with Parkinson's Disease (PD). Conventional DBS is continuously ON even though PD symptoms fluctuate over time leading to undesirable side-effects and high energy requirements. This study investigates the use of a Iogistic regression-based classifier to identify periods when PD patients have rest tremor exploiting Local Field Potentials (LFPs) recorded with DBS electrodes implanted in the Subthalamic Nucleus in 7 PD patients (8 hemispheres). Analyzing 36.1 minutes of data with a 512 milliseconds non-overlapping window, the classification accuracy was well above chance-level for all patients, with Area Under the Curve (AUC) ranging from 0.67 to 0.93. The features with the most discriminative ability were, in descending order, power in the 31-45 Hz, 5-7 Hz, 21-30 Hz, 46-55 Hz, and 56-95 Hz frequency bands. These results suggest that using a machine learning-based classifier, such as the one proposed in this study, can form the basis for on-demand DBS therapy for PD tremor, with the potential to reduce side-effects and lower battery consumption.

Human motor cortical beta bursts relate to movement planning and response errors

Motor cortical beta activity (13-30 Hz) is a hallmark signature of healthy and pathological movement, but its behavioural relevance remains unclear. Using high-precision magnetoencephalography (MEG), we show that during the classical event-related desynchronisation (ERD) and event-related synchronisation (ERS) periods, motor cortical beta activity in individual trials (n > 12,000) is dominated by high amplitude, transient, and infrequent bursts. Beta burst probability closely matched the trial-averaged beta amplitude in both the pre- and post-movement periods, but individual bursts were spatially more focal than the classical ERS peak. Furthermore, prior to movement (ERD period), beta burst timing was related to the degree of motor preparation, with later bursts resulting in delayed response times. Following movement (ERS period), the first beta burst was delayed by approximately 100 milliseconds when an incorrect response was made. Overall, beta burst timing was a stronger predictor of single trial behaviour than beta burst rate or single trial beta amplitude. This transient nature of motor cortical beta provides new constraints for theories of its role in information processing within and across cortical circuits, and its functional relevance for behaviour in both healthy and pathological movement.

Adaptive deep brain stimulation as advanced Parkinson's disease treatment (ADAPT study): protocol for a pseudo-randomised clinical study

INTRODUCTION

Adaptive deep brain stimulation (aDBS), based on the detection of increased beta oscillations in the subthalamic nucleus (STN), has been assessed in patients with Parkinson's disease (PD) during the immediate postoperative setting. In these studies, aDBS was shown to be at least as effective as conventional DBS (cDBS), while stimulation time and side effects were reduced. However, the effect of aDBS on motor symptoms and stimulation-induced side effects during the chronically implanted phase (after the stun effect of DBS placement has disappeared) has not yet been determined.

METHODS AND ANALYSIS

This protocol describes a single-centre clinical study in which aDBS will be tested in 12 patients with PD undergoing battery replacement, with electrodes implanted in the STN, and as a proof of concept in the internal globus pallidus. Patients included will be allocated in a pseudo-randomised fashion to a three-condition (no stimulation/cDBS/ aDBS), cross-over design. A battery of tests will be conducted and recorded during each condition, which aim to measure the severity of motor symptoms and side effects. These tests include a tablet-based tapping test, a subscale of the Movement Disorder Society-unified Parkinson's disease rating scale (subMDS-UPDRS), the Speech Intelligibility Test (SIT) and a tablet-based version of the Stroop test. SubMDS-UPDRS and SIT recordings will be blindly assessed by independent raters. Data will be analysed using a linear mixed-effects model.

ETHICS AND DISSEMINATION

This protocol was approved by the Ethical Committee of the University Medical Centre Groningen, where the study will be carried out. Data management and compliance to research policies and standards of our centre, including data privacy, storage and veracity, will be controlled by an independent monitor. All the scientific findings derived from this protocol are aimed to be made public through publication of articles in international journals.

TRIAL REGISTRATION NUMBER

NTR 5456; Pre-results.

Induction of M2 Macrophages Prevents Bone Loss in Murine Periodontitis Models

Periodontitis is characterized by the progressive destruction of tooth-supporting alveolar bone, which is mainly caused by chronic inflammation in response to persistent bacterial insult. It has recently become clear that the pathogenesis of periodontitis is associated with a high ratio of proinflammatory M1 (classically activated) macrophages to anti-inflammatory M2 (alternatively activated). To decrease the inflammatory activity, we locally delivered the C-C motif chemokine ligand 2 (CCL2) using controlled-release microparticles (MPs). CCL2 is known to promote chemotaxis of M0 or M2 phenotype macrophages to the inflamed site and induce M2 phenotype polarization locally. Our in vitro data showed that CCL2 increased the number of M2 phenotype macrophages, decreased TNF-α secretion, and enhanced chemotaxis of RAW264.7 cells toward CCL2 MPs. Moreover, we induced periodontal disease in 2 animal models through inoculation of Porphyromonas gingivalis and ligature around the murine molar. Micro-computed tomography analysis showed significant reduction of alveolar bone loss in the CCL2 MP treatment group when compared with a blank MP group and a no-treatment periodontitis group in both models. Immunohistologic analysis showed a significant increase in the M2 phenotype subset and a decrease in the M1 phenotype subset in the CCL2 MP group of the P. gingivalis-induced model. Also, in both models, tartrate-resistant acidic phosphatase staining showed significantly fewer numbers of osteoclasts in the CCL2 MP group in alveolar bone area. Moreover, quantitative polymerase chain reaction results showed a significant increase in IL-1RA (interleukin 1 receptor antagonist) mRNA expression and a decrease in RANKL (receptor activator of nuclear factor kappa-Β ligand) mRNA expression in the CCL2 MP group in the ligature model. In summary, manipulation of endogenous M2 phenotype macrophages with CCL2 MPs decreased the M1 phenotype:M2 phenotype ratio and prevented alveolar bone loss in mouse periodontitis models. The delivery of CCL2 MPs provides a novel approach to treat periodontal disease.

Quantifying the performance of MEG source reconstruction using resting state data

In magnetoencephalography (MEG) research there are a variety of inversion methods to transform sensor data into estimates of brain activity. Each new inversion scheme is generally justified against a specific simulated or task scenario. The choice of this scenario will however have a large impact on how well the scheme performs. We describe a method with minimal selection bias to quantify algorithm performance using human resting state data. These recordings provide a generic, heterogeneous, and plentiful functional substrate against which to test different MEG recording and reconstruction approaches. We used a Hidden Markov model to spatio-temporally partition data into self-similar dynamic states. To test the anatomical precision that could be achieved, we then inverted these data onto libraries of systematically distorted subject-specific cortical meshes and compared the quality of the fit using cross validation and a Free energy metric. This revealed which inversion scheme was able to identify the least distorted (most accurate) anatomical models, and allowed us to quantify an upper bound on the mean anatomical distortion accordingly. We used two resting state datasets, one recorded with head-casts and one without. In the head-cast data, the Empirical Bayesian Beamformer (EBB) algorithm showed the best mean anatomical discrimination (3.7 mm) compared with Minimum Norm/LORETA (6.0 mm) and Multiple Sparse Priors (9.4 mm). This pattern was replicated in the second (conventional dataset) although with a marginally poorer (non-significant) prediction of the missing (cross-validated) data. Our findings suggest that the abundant resting state data now commonly available could be used to refine and validate MEG source reconstruction methods and/or recording paradigms.

Lamina-specific cortical dynamics in human visual and sensorimotor cortices

Distinct anatomical and spectral channels are thought to play specialized roles in the communication within cortical networks. While activity in the alpha and beta frequency range (7 - 40 Hz) is thought to predominantly originate from infragranular cortical layers conveying feedback-related information, activity in the gamma range (>40 Hz) dominates in supragranular layers communicating feedforward signals. We leveraged high precision MEG to test this proposal, directly and non-invasively, in human participants performing visually cued actions. We found that visual alpha mapped onto deep cortical laminae, whereas visual gamma predominantly occurred more superficially. This lamina-specificity was echoed in movement-related sensorimotor beta and gamma activity. These lamina-specific pre- and post- movement changes in sensorimotor beta and gamma activity suggest a more complex functional role than the proposed feedback and feedforward communication in sensory cortex. Distinct frequency channels thus operate in a lamina-specific manner across cortex, but may fulfill distinct functional roles in sensory and motor processes.

Restorative Justice Conferencing: Not a Panacea for the Overrepresentation of Australia's Indigenous Youth in the Criminal Justice System

Restorative justice conferencing is a police diversionary strategy used extensively in Australian jurisdictions to channel young offenders away from formal court processing. Advocates view conferencing as culturally appropriate and a means to reduce the overrepresentation of Indigenous young people because it is rooted in Indigenous justice traditions. However, whether conferencing is effective at reducing recidivism by Indigenous young people compared with non-Indigenous young people remains unknown. We examine this using a longitudinal cohort of youth offenders from Australia. Propensity score matching was used to match Indigenous and non-Indigenous young people at their first conference and examined reoffending outcomes to explore its efficacy at reducing recidivism ( n = 394). Results indicate that, despite statistically controlling for factors related to reoffending, recidivism levels postconference were significantly higher for Indigenous young people. These results suggest that conferencing is unlikely to address the problem of Indigenous overrepresentation within Australia's youth justice system.