Insights and opportunities for deep brain stimulation as a brain circuit intervention

Disentangling Bradykinesia and Rigidity in Parkinson's Disease: Evidence from Short- and Long-Term Subthalamic Nucleus Deep Brain Stimulation

OBJECTIVE

Bradykinesia and rigidity are considered closely related motor signs in Parkinson disease (PD), but recent neurophysiological findings suggest distinct pathophysiological mechanisms. This study aims to examine and compare longitudinal changes in bradykinesia and rigidity in PD patients treated with bilateral subthalamic nucleus deep brain stimulation (STN-DBS).

METHODS

In this retrospective cohort study, the clinical progression of appendicular and axial bradykinesia and rigidity was assessed up to 15 years after STN-DBS in the best treatment conditions (ON medication and ON stimulation). The severity of bradykinesia and rigidity was examined using ad hoc composite scores from specific subitems of the Unified Parkinson's Disease Rating Scale motor part (UPDRS-III). Short- and long-term predictors of bradykinesia and rigidity were analyzed through linear regression analysis, considering various preoperative demographic and clinical data, including disease duration and severity, phenotype, motor and cognitive scores (eg, frontal score), and medication.

RESULTS

A total of 301 patients were examined before and 1 year after surgery. Among them, 101 and 56 individuals were also evaluated at 10-year and 15-year follow-ups, respectively. Bradykinesia significantly worsened after surgery, especially in appendicular segments (p < 0.001). Conversely, rigidity showed sustained benefit, with unchanged clinical scores compared to preoperative assessment (p > 0.05). Preoperative motor disability (eg, composite scores from the UPDRS-III) predicted short- and long-term outcomes for both bradykinesia and rigidity (p < 0.01). Executive dysfunction was specifically linked to bradykinesia but not to rigidity (p < 0.05).

INTERPRETATION

Bradykinesia and rigidity show long-term divergent progression in PD following STN-DBS and are associated with independent clinical factors, supporting the hypothesis of partially distinct pathophysiology. ANN NEUROL 2024;96:234-246.

A longitudinal electrophysiological and behavior dataset for PD rat in response to deep brain stimulation

Here we presented an electrophysiological dataset collected from layer V of the primary motor cortex (M1) and the corresponding behavior dataset from normal and hemi-parkinson rats over 5 consecutive weeks. The electrophysiological dataset was constituted by the raw wideband signal, neuronal spikes, and local field potential (LFP) signal. The open-field test was done and recorded to evaluate the behavior variation of rats among the entire experimental cycle. We conducted technical validation of this dataset through sorting the spike data to form action potential waveforms and analyzing the spectral power of LFP data, then based on these findings a closed-loop DBS protocol was developed by the oscillation activity response of M1 LFP signal. Additionally, this protocol was applied to the hemi-parkinson rat for five consecutive days while simultaneously recording the electrophysiological data. This dataset is currently the only publicly available dataset that includes longitudinal closed-loop DBS recordings, which can be utilized to investigate variations of neuronal activity within the M1 following long-term closed-loop DBS, and explore additional reliable biomarkers.

U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis

Introduction

The blood oxygen level-dependent (BOLD) signal derived from functional neuroimaging is commonly used in brain network analysis and dementia diagnosis. Missing the BOLD signal may lead to bad performance and misinterpretation of findings when analyzing neurological disease. Few studies have focused on the restoration of brain functional time-series data.

Methods

In this paper, a novel U-shaped convolutional transformer GAN (UCT-GAN) model is proposed to restore the missing brain functional time-series data. The proposed model leverages the power of generative adversarial networks (GANs) while incorporating a U-shaped architecture to effectively capture hierarchical features in the restoration process. Besides, the multi-level temporal-correlated attention and the convolutional sampling in the transformer-based generator are devised to capture the global and local temporal features for the missing time series and associate their long-range relationship with the other brain regions. Furthermore, by introducing multi-resolution consistency loss, the proposed model can promote the learning of diverse temporal patterns and maintain consistency across different temporal resolutions, thus effectively restoring complex brain functional dynamics.

Results

We theoretically tested our model on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and our experiments demonstrate that the proposed model outperforms existing methods in terms of both quantitative metrics and qualitative assessments. The model's ability to preserve the underlying topological structure of the brain functional networks during restoration is a particularly notable achievement.

Conclusion

Overall, the proposed model offers a promising solution for restoring brain functional time-series and contributes to the advancement of neuroscience research by providing enhanced tools for disease analysis and interpretation.

Galvanic vs. pulsatile effects on decision-making networks: reshaping the neural activation landscape

Objective. Primarily due to safety concerns, biphasic pulsatile stimulation (PS) is the present standard for electrical excitation of neural tissue with a diverse set of applications. While pulses have been shown to be effective to achieve functional outcomes, they have well-known deficits. Due to recent technical advances, galvanic stimulation (GS), delivery of current for extended periods of time (>1 s), has re-emerged as an alternative to PS.Approach. In this paper, we use a winner-take-all decision-making cortical network model to investigate differences between pulsatile and GS in the context of a perceptual decision-making task.Main results. Based on previous work, we hypothesized that GS would produce more spatiotemporally distributed, network-sensitive neural responses, while PS would produce highly synchronized activation of a limited group of neurons. Our results in-silico support these hypotheses for low-amplitude GS but deviate when galvanic amplitudes are large enough to directly activate or block nearby neurons.Significance. We conclude that with careful parametrization, GS could overcome some limitations of PS to deliver more naturalistic firing patterns in the group of targeted neurons.

Neuroscience fundamentals relevant to neuromodulation: Neurobiology of deep brain stimulation in Parkinson's disease

Deep Brain Stimulation (DBS) has become a pivotal therapeutic approach for Parkinson's Disease (PD) and various neuropsychiatric conditions, impacting over 200,000 patients. Despite its widespread application, the intricate mechanisms behind DBS remain a subject of ongoing investigation. This article provides an overview of the current knowledge surrounding the local, circuit, and neurobiochemical effects of DBS, focusing on the subthalamic nucleus (STN) as a key target in PD management. The local effects of DBS, once thought to mimic a reversible lesion, now reveal a more nuanced interplay with myelinated axons, neurotransmitter release, and the surrounding microenvironment. Circuit effects illuminate the modulation of oscillatory activities within the basal ganglia and emphasize communication between the STN and the primary motor cortex. Neurobiochemical effects, encompassing changes in dopamine levels and epigenetic modifications, add further complexity to the DBS landscape. Finally, within the context of understanding the mechanisms of DBS in PD, the article highlights the controversial question of whether DBS exerts disease-modifying effects in PD. While preclinical evidence suggests neuroprotective potential, clinical trials such as EARLYSTIM face challenges in assessing long-term disease modification due to enrollment timing and methodology limitations. The discussion underscores the need for robust biomarkers and large-scale prospective trials to conclusively determine DBS's potential as a disease-modifying therapy in PD.

Pathological and physiological functional cross-talks of α-synuclein and tau in the central nervous system

α-Synuclein and tau are abundant multifunctional brain proteins that are mainly expressed in the presynaptic and axonal compartments of neurons, respectively. Previous works have revealed that intracellular deposition of α-synuclein and/or tau causes many neurodegenerative disorders, including Alzheimer's disease and Parkinson's disease. Despite intense investigation, the normal physiological functions and roles of α-synuclein and tau are still unclear, owing to the fact that mice with knockout of either of these proteins do not present apparent phenotypes. Interestingly, the co-occurrence of α-synuclein and tau aggregates was found in post-mortem brains with synucleinopathies and tauopathies, some of which share similarities in clinical manifestations. Furthermore, the direct interaction of α-synuclein with tau is considered to promote the fibrillization of each of the proteins in vitro and in vivo. On the other hand, our recent findings have revealed that α-synuclein and tau are cooperatively involved in brain development in a stage-dependent manner. These findings indicate strong cross-talk between the two proteins in physiology and pathology. In this review, we provide a summary of the recent findings on the functional roles of α-synuclein and tau in the physiological conditions and pathogenesis of neurodegenerative diseases. A deep understanding of the interplay between α-synuclein and tau in physiological and pathological conditions might provide novel targets for clinical diagnosis and therapeutic strategies to treat neurodegenerative diseases.

Neuromodulation for the treatment of Prader-Willi syndrome - A systematic review

Prader-Willi syndrome (PWS) is a complex, genetic disorder characterized by multisystem involvement, including hyperphagia, maladaptive behaviors and endocrinological derangements. Recent developments in advanced neuroimaging have led to a growing understanding of PWS as a neural circuit disorder, as well as subsequent interests in the application of neuromodulatory therapies. Various non-invasive and invasive device-based neuromodulation methods, including vagus nerve stimulation (VNS), transcranial direct current stimulation (tDCS), repetitive transcranial magnetic stimulation (rTMS), and deep brain stimulation (DBS) have all been reported to be potentially promising treatments for addressing the major symptoms of PWS. In this systematic literature review, we summarize the recent literature that investigated these therapies, discuss the underlying circuits which may underpin symptom manifestations, and cover future directions of the field. Through our comprehensive search, there were a total of 47 patients who had undergone device-based neuromodulation therapy for PWS. Two articles described VNS, 4 tDCS, 1 rTMS and 2 DBS, targeting different symptoms of PWS, including aberrant behavior, hyperphagia and weight. Multi-center and multi-country efforts will be required to advance the field given the low prevalence of PWS. Finally, given the potentially vulnerable population, neuroethical considerations and dialogue should guide the field.

Modulation of DBS-induced cortical responses and movement by the directionality and magnitude of current administered

Subthalamic deep brain stimulation (STN-DBS) is an effective therapy for alleviating motor symptoms in people with Parkinson's disease (PwP), although some may not receive optimal clinical benefits. One potential mechanism of STN-DBS involves antidromic activation of the hyperdirect pathway (HDP), thus suppressing cortical beta synchrony to improve motor function, albeit the precise mechanisms underlying optimal DBS parameters are not well understood. To address this, 18 PwP with STN-DBS completed a 2 Hz monopolar stimulation of the left STN during MEG. MEG data were imaged in the time-frequency domain using minimum norm estimation. Peak vertex time series data were extracted to interrogate the directional specificity and magnitude of DBS current on evoked and induced cortical responses and accelerometer metrics of finger tapping using linear mixed-effects models and mediation analyses. We observed increases in evoked responses (HDP ~ 3-10 ms) and synchronization of beta oscillatory power (14-30 Hz, 10-100 ms) following DBS pulse onset in the primary sensorimotor cortex (SM1), supplementary motor area (SMA) and middle frontal gyrus (MFG) ipsilateral to the site of stimulation. DBS parameters significantly modulated neural and behavioral outcomes, with clinically effective contacts eliciting significant increases in medium-latency evoked responses, reductions in induced SM1 beta power, and better movement profiles compared to suboptimal contacts, often regardless of the magnitude of current applied. Finally, HDP-related improvements in motor function were mediated by the degree of SM1 beta suppression in a setting-dependent manner. Together, these data suggest that DBS-evoked brain-behavior dynamics are influenced by the level of beta power in key hubs of the basal ganglia-cortical loop, and this effect is exacerbated by the clinical efficacy of DBS parameters. Such data provides novel mechanistic and clinical insight, which may prove useful for characterizing DBS programming strategies to optimize motor symptom improvement in the future.

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.

Dopaminergic reinforcement in the motor system: Implications for Parkinson's disease and deep brain stimulation

Millions of people suffer from dopamine-related disorders spanning disturbances in movement, cognition and emotion. These changes are often attributed to changes in striatal dopamine function. Thus, understanding how dopamine signalling in the striatum and basal ganglia shapes human behaviour is fundamental to advancing the treatment of affected patients. Dopaminergic neurons innervate large-scale brain networks, and accordingly, many different roles for dopamine signals have been proposed, such as invigoration of movement and tracking of reward contingencies. The canonical circuit architecture of cortico-striatal loops sparks the question, of whether dopamine signals in the basal ganglia serve an overarching computational principle. Such a holistic understanding of dopamine functioning could provide new insights into symptom generation in psychiatry to neurology. Here, we review the perspective that dopamine could bidirectionally control neural population dynamics, increasing or decreasing their strength and likelihood to reoccur in the future, a process previously termed neural reinforcement. We outline how the basal ganglia pathways could drive strengthening and weakening of circuit dynamics and discuss the implication of this hypothesis on the understanding of motor signs of Parkinson's disease (PD), the most frequent dopaminergic disorder. We propose that loss of dopamine in PD may lead to a pathological brain state where repetition of neural activity leads to weakening and instability, possibly explanatory for the fact that movement in PD deteriorates with repetition. Finally, we speculate on how therapeutic interventions such as deep brain stimulation may be able to reinstate reinforcement signals and thereby improve treatment strategies for PD in the future.

Kinematic Effects of Combined Subthalamic and Dorsolateral Nigral Deep Brain Stimulation in Parkinson's Disease

Background

Additional stimulation of the substantia nigra (SNr) has been proposed to target axial symptoms and gait impairment in patients with Parkinson's disease (PD).

Objective

This study aimed to characterize effects of combined deep brain stimulation (DBS) of the subthalamic nucleus (STN) and SNr on gait performance in PD and to map stimulation sites within the SNr.

Methods

In a double-blinded crossover design, 10 patients with PD and gait impairment underwent clinical examination and kinematic assessment with STN DBS, combined STN+SNr DBS and OFF DBS 30 minutes after reprogramming. To confirm stimulation within the SNr, electrodes, active contacts, and stimulation volumes were modeled in a common space and overlap with atlases of SNr was computed.

Results

Overlap of stimulation volumes with dorsolateral SNr was confirmed for all patients. UPDRS III, scoring of freezing during turning and transitioning, stride length, stride velocity, and range of motion of shank, knee, arm, and trunk as well as peak velocities during turning and transitions and turn duration were improved with STN DBS compared to OFF. On cohort level, no further improvement was observed with combined STN+SNr DBS but additive improvement of spatiotemporal gait parameters was observed in individual subjects.

Conclusions

Combined high frequency DBS of the STN and dorsolateral SNr did not consistently result in additional short-term kinematic or clinical benefit compared to STN DBS. Stimulation intervals, frequency, and patient selection for target symptoms as well as target region within the SNr need further refinement in future trials.

Machine learning for adaptive deep brain stimulation in Parkinson's disease: closing the loop

Parkinson's disease (PD) is the second most common neurodegenerative disease bearing a severe social and economic impact. So far, there is no known disease modifying therapy and the current available treatments are symptom oriented. Deep Brain Stimulation (DBS) is established as an effective treatment for PD, however current systems lag behind today's technological potential. Adaptive DBS, where stimulation parameters depend on the patient's physiological state, emerges as an important step towards "smart" DBS, a strategy that enables adaptive stimulation and personalized therapy. This new strategy is facilitated by currently available neurotechnologies allowing the simultaneous monitoring of multiple signals, providing relevant physiological information. Advanced computational models and analytical methods are an important tool to explore the richness of the available data and identify signal properties to close the loop in DBS. To tackle this challenge, machine learning (ML) methods applied to DBS have gained popularity due to their ability to make good predictions in the presence of multiple variables and subtle patterns. ML based approaches are being explored at different fronts such as the identification of electrophysiological biomarkers and the development of personalized control systems, leading to effective symptom relief. In this review, we explore how ML can help overcome the challenges in the development of closed-loop DBS, particularly its role in the search for effective electrophysiology biomarkers. Promising results demonstrate ML potential for supporting a new generation of adaptive DBS, with better management of stimulation delivery, resulting in more efficient and patient-tailored treatments.

Pathological pallidal beta activity in Parkinson's disease is sustained during sleep and associated with sleep disturbance

Parkinson's disease (PD) is associated with excessive beta activity in the basal ganglia. Brain sensing implants aim to leverage this biomarker for demand-dependent adaptive stimulation. Sleep disturbance is among the most common non-motor symptoms in PD, but its relationship with beta activity is unknown. To investigate the clinical potential of beta activity as a biomarker for sleep quality in PD, we recorded pallidal local field potentials during polysomnography in PD patients off dopaminergic medication and compared the results to dystonia patients. PD patients exhibited sustained and elevated beta activity across wakefulness, rapid eye movement (REM), and non-REM sleep, which was correlated with sleep disturbance. Simulation of adaptive stimulation revealed that sleep-related beta activity changes remain unaccounted for by current algorithms, with potential negative outcomes in sleep quality and overall quality of life for patients.

Low intensity focused ultrasound: a new prospect for the treatment of Parkinson's disease

Background: As a chronic and progressive neurodegenerative disease, Parkinson's disease (PD) still lacks effective and safe targeted drug therapy. Low-intensity focused ultrasound (LIFU), a new method to stimulate the brain and open the blood-brain barrier (BBB), has been widely concerned by PD researchers due to its non-invasive characteristics.Methods: PubMed was searched for the past 10 years using the terms 'focused ultrasound', 'transcranial ultrasound', 'pulse ultrasound', and 'Parkinson's disease'. Relevant citations were selected from the authors' references. After excluding articles describing high-intensity focused ultrasound or non-Parkinson's disease applications, we found more than 100 full-text analyses for pooled analysis.Results: Current preclinical studies have shown that LIFU could improve PD motor symptoms by regulating microglia activation, increasing neurotrophic factors, reducing oxidative stress, and promoting nerve repair and regeneration, while LIFU combined with microbubbles (MBs) can promote drugs to cross the BBB, which may become a new direction of PD treatment. Therefore, finding an efficient drug carrier system is the top priority of applying LIFU with MBs to deliver drugs.Conclusions: This article aims to review neuro-modulatory effect of LIFU and the possible biophysical mechanism in the treatment of PD, summarize the latest progress in delivering vehicles with MBs, and discuss its advantages and limitations.