An update on adaptive deep brain stimulation in Parkinson's disease

Recent progress in the applications of presynaptic dopaminergic positron emission tomography imaging in parkinsonism

Nowadays, presynaptic dopaminergic positron emission tomography, which assesses deficiencies in dopamine synthesis, storage, and transport, is widely utilized for early diagnosis and differential diagnosis of parkinsonism. This review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest parkinsonism. We conducted a thorough literature search using reputable databases such as PubMed and Web of Science. Selection criteria involved identifying peer-reviewed articles published within the last 5 years, with emphasis on their relevance to clinical applications. The findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting prognosis. Moreover, when employed in conjunction with other imaging modalities and advanced analytical methods, presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo biomarker. This validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic depletion. In summary, the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials, ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders.

Inhibition of NLRP3 inflammasome ameliorates LPS-induced neuroinflammatory injury in mice via PINK1/Parkin pathway

Parkinson's disease (PD) is characterized by the severe loss of dopaminergic neurons in the substantia nigra pars compacta, leading to motor dysfunction. The onset of PD is often accompanied by neuroinflammation and α-Synuclein aggregation, and extensive research has focused on the activation of microglial NLRP3 inflammasomes in PD, which promotes the death of dopaminergic neurons. In this study, a model of cerebral inflammatory response was constructed in wild-type and Parkin＋/－ mice through bilateral intraventricular injection of LPS. LPS-induced activation of the NLRP3 inflammasome in wild-type mice promotes the progression of PD. The use of MCC950 in wild mice injected with LPS induces activation of Parkin/PINK and improves autophagy, which in turn improves mitochondrial turnover. It also inhibits LPS-induced inflammatory responses, improves motor function, protects dopaminergic neurons, and inhibits microglia activation. Furthermore, Parkin＋/－ mice exhibited motor dysfunction, loss of dopaminergic neurons, activation of the NLRP3 inflammasome, and α-Synuclein aggregation beginning at an early age. Parkin ± mice exhibited more pronounced microglia activation, greater NLRP3 inflammasome activation, more severe autophagy dysfunction, and more pronounced motor dysfunction after LPS injection compared to wild-type mice. Notably, the use of MCC950 in Parkin ± mice did not ameliorate NLRP3 inflammasome activation, autophagy dysfunction, or α-synuclein aggregation. Thus, MCC950 can only exert its effects in the presence of Parkin/PINK1, and targeting Parkin-mediated NLRP3 inflammasome activation is expected to be a potential therapeutic strategy for Parkinson's disease.

A Comprehensive Approach to Parkinson's Disease: Addressing Its Molecular, Clinical, and Therapeutic Aspects

Parkinson's disease (PD) is a gradually worsening neurodegenerative disorder affecting the nervous system, marked by a slow progression and varied symptoms. It is the second most common neurodegenerative disease, affecting over six million people in the world. Its multifactorial etiology includes environmental, genomic, and epigenetic factors. Clinical symptoms consist of non-motor and motor symptoms, with motor symptoms being the classic presentation. Therapeutic approaches encompass pharmacological, non-pharmacological, and surgical interventions. Traditional pharmacological treatment consists of administering drugs (MAOIs, DA, and levodopa), while emerging evidence explores the potential of antidiabetic agents for neuroprotection and gene therapy for attenuating parkinsonian symptoms. Non-pharmacological treatments, such as exercise, a calcium-rich diet, and adequate vitamin D supplementation, aim to slow disease progression and prevent complications. For those patients who have medically induced side effects and/or refractory symptoms, surgery is a therapeutic option. Deep brain stimulation is the primary surgical option, associated with motor symptom improvement. Levodopa/carbidopa intestinal gel infusion through percutaneous endoscopic gastrojejunostomy and a portable infusion pump succeeded in reducing "off" time, where non-motor and motor symptoms occur, and increasing "on" time. This article aims to address the general aspects of PD and to provide a comparative comprehensive review of the conventional and the latest therapeutic advancements and emerging treatments for PD. Nevertheless, further studies are required to optimize treatment and provide suitable alternatives.

Genomics of severe and treatment-resistant obsessive-compulsive disorder treated with deep brain stimulation: A preliminary investigation

Individuals with severe and treatment-resistant obsessive-compulsive disorder (trOCD) represent a small but severely disabled group of patients. Since trOCD cases eligible for deep brain stimulation (DBS) probably comprise the most severe end of the OCD spectrum, we hypothesize that they may be more likely to have a strong genetic contribution to their disorder. Therefore, while the worldwide population of DBS-treated cases may be small (~300), screening these individuals with modern genomic methods may accelerate gene discovery in OCD. As such, we have begun to collect DNA from trOCD cases who qualify for DBS, and here we report results from whole exome sequencing and microarray genotyping of our first five cases. All participants had previously received DBS in the bed nucleus of stria terminalis (BNST), with two patients responding to the surgery and one showing a partial response. Our analyses focused on gene-disruptive rare variants (GDRVs; rare, predicted-deleterious single-nucleotide variants or copy number variants overlapping protein-coding genes). Three of the five cases carried a GDRV, including a missense variant in the ion transporter domain of KCNB1, a deletion at 15q11.2, and a duplication at 15q26.1. The KCNB1 variant (hg19 chr20-47991077-C-T, NM_004975.3:c.1020G>A, p.Met340Ile) causes substitution of methionine for isoleucine in the trans-membrane region of neuronal potassium voltage-gated ion channel KV2.1. This KCNB1 substitution (Met340Ile) is located in a highly constrained region of the protein where other rare missense variants have previously been associated with neurodevelopmental disorders. The patient carrying the Met340Ile variant responded to DBS, which suggests that genetic factors could potentially be predictors of treatment response in DBS for OCD. In sum, we have established a protocol for recruiting and genomically characterizing trOCD cases. Preliminary results suggest that this will be an informative strategy for finding risk genes in OCD.

On-Off and Proportional Closed-Loop Adaptive Deep Brain Stimulation Reduces Motor Symptoms in Freely Moving Hemiparkinsonian Rats

OBJECTIVES

Closed-loop adaptive deep brain stimulation (aDBS) continuously adjusts stimulation parameters, with the potential to improve efficacy and reduce side effects of deep brain stimulation (DBS) for Parkinson's disease (PD). Rodent models can provide an effective platform for testing aDBS algorithms and establishing efficacy before clinical investigation. In this study, we compare two aDBS algorithms, on-off and proportional modulation of DBS amplitude, with conventional DBS in hemiparkinsonian rats.

MATERIALS AND METHODS

DBS of the subthalamic nucleus (STN) was delivered wirelessly in freely moving male and female hemiparkinsonian (N = 7) and sham (N = 3) Wistar rats. On-off and proportional aDBS, based on STN local field potential beta power, were compared with conventional DBS and three control stimulation algorithms. Behavior was assessed during cylinder tests (CT) and stepping tests (ST). Successful model creation was confirmed via apomorphine-induced rotation test and Tyrosine Hydroxylase-immunocytochemistry. Electrode location was histologically confirmed. Data were analyzed using linear mixed models.

RESULTS

Contralateral paw use in parkinsonian rats was reduced to 20% and 25% in CT and ST, respectively. Conventional, on-off, and proportional aDBS significantly improved motor function, restoring contralateral paw use to approximately 45% in both tests. No improvement in motor behavior was observed with either randomly applied on-off or low-amplitude continuous stimulation. Relative STN beta power was suppressed during DBS. Relative power in the alpha and gamma bands decreased and increased, respectively. Therapeutically effective adaptive DBS used approximately 40% less energy than did conventional DBS.

CONCLUSIONS

Adaptive DBS, using both on-off and proportional control schemes, is as effective as conventional DBS in reducing motor symptoms of PD in parkinsonian rats. Both aDBS algorithms yield substantial reductions in stimulation power. These findings support using hemiparkinsonian rats as a viable model for testing aDBS based on beta power and provide a path to investigate more complex closed-loop algorithms in freely behaving animals.

Wireless closed-loop deep brain stimulation using microelectrode array probes

Deep brain stimulation (DBS), including optical stimulation and electrical stimulation, has been demonstrated considerable value in exploring pathological brain activity and developing treatments for neural disorders. Advances in DBS microsystems based on implantable microelectrode array (MEA) probes have opened up new opportunities for closed-loop DBS (CL-DBS) in situ. This technology can be used to detect damaged brain circuits and test the therapeutic potential for modulating the output of these circuits in a variety of diseases simultaneously. Despite the success and rapid utilization of MEA probe-based CL-DBS microsystems, key challenges, including excessive wired communication, need to be urgently resolved. In this review, we considered recent advances in MEA probe-based wireless CL-DBS microsystems and outlined the major issues and promising prospects in this field. This technology has the potential to offer novel therapeutic options for psychiatric disorders in the future.

High-Gamma Activity Is Coupled to Low-Gamma Oscillations in Precentral Cortices and Modulates with Movement and Speech

Planning and executing motor behaviors requires coordinated neural activity among multiple cortical and subcortical regions of the brain. Phase-amplitude coupling between the high-gamma band amplitude and the phase of low frequency oscillations (theta, alpha, beta) has been proposed to reflect neural communication, as has synchronization of low-gamma oscillations. However, coupling between low-gamma and high-gamma bands has not been investigated. Here, we measured phase-amplitude coupling between low- and high-gamma in monkeys performing a reaching task and in humans either performing finger-flexion or word-reading tasks. We found significant coupling between low-gamma phase and high-gamma amplitude in multiple sensorimotor and premotor cortices of both species during all tasks. This coupling modulated with the onset of movement. These findings suggest that interactions between the low and high gamma bands are markers of network dynamics related to movement and speech generation.

The Application of Deep Brain Stimulation for Parkinson's Disease on the Motor Pathway: A Bibliometric Analysis across 10 Years

BACKGROUND AND OBJECTIVE

Since its initial report by James Parkinson in 1817, Parkinson's disease (PD) has remained a central subject of research and clinical advancement. The disease is estimated to affect approximately 1% of adults aged 60 and above. Deep brain stimulation, emerging as an alternative therapy for end-stage cases, has offered a lifeline to numerous patients. This review aimed to analyze publications pertaining to the impact of deep brain stimulation on the motor pathway in patients with PD over the last decade.

METHODS

Data were obtained from the Web of Science Core Collection through the library of Huazhong University of Science and Technology (China). The search strategy encompassed the following keywords: "deep brain stimulation", "Parkinson's disease", "motor pathway", and "human", from January 1, 2012, to December 1, 2022. Additionally, this review visualized the findings using the Citespace software.

RESULTS

The results indicated that the United States, the United Kingdom, Germany, and China were the primary contributors to this research field. University College London, Capital Medical University, and Maastricht University were the top 3 research institutions in the research area. Tom Foltynie ranked first with 6 publications, and the journals of Brain and Brain Stimulation published the greatest number of relevant articles. The prevailing research focal points in this domain, as determined by keywords "burst analysis", "encompassed neuronal activity", "nucleus", "hyper direct pathway", etc. CONCLUSION: This study has provided a new perspective through bibliometric analysis of the deep brain stimulation therapy for treating patients with PD, which can shed light on future research to advance our comprehension of this particular field of study.

Relationship between serum uric acid levels and the outcome of STN-DBS in Parkinson's disease

BACKGROUND

Uric acid is a natural antioxidant and it has been shown that low levels of uric acid may be a risk factor for the development of Parkinson's disease. We aimed to investigate the relationship between uric acid and improvement of motor symptoms in patients with Parkinson's disease after subthalamic nucleus deep brain stimulation.

METHODS

We analyzed the correlation between serum uric acid levels in 64 patients with Parkinson's disease and the rate of improvement of motor symptoms 2 years after subthalamic nucleus deep brain stimulation.

RESULTS

A non-linear correlation was observed between uric acid levels and the rate of motor symptom improvement after subthalamic nucleus deep brain stimulation, during both the drug-off and drug-on periods.

CONCLUSIONS

Uric acid is positively associated with the rate of motor symptom improvement in subthalamic nucleus deep brain stimulation within a certain range.

EEG-based biomarkers for optimizing deep brain stimulation contact configuration in Parkinson's disease

Objective

Subthalamic deep brain stimulation (STN-DBS) is a neurosurgical therapy to treat Parkinson's disease (PD). Optimal therapeutic outcomes are not achieved in all patients due to increased DBS technological complexity; programming time constraints; and delayed clinical response of some symptoms. To streamline the programming process, biomarkers could be used to accurately predict the most effective stimulation configuration. Therefore, we investigated if DBS-evoked potentials (EPs) combined with imaging to perform prediction analyses could predict the best contact configuration.

Methods

In 10 patients, EPs were recorded in response to stimulation at 10 Hz for 50 s on each DBS-contact. In two patients, we recorded from both hemispheres, resulting in recordings from a total of 12 hemispheres. A monopolar review was performed by stimulating on each contact and measuring the therapeutic window. CT and MRI data were collected. Prediction models were created to assess how well the EPs and imaging could predict the best contact configuration.

Results

EPs at 3 ms and at 10 ms were recorded. The prediction models showed that EPs can be combined with imaging data to predict the best contact configuration and hence, significantly outperformed random contact selection during a monopolar review.

Conclusion

EPs can predict the best contact configuration. Ultimately, these prediction tools could be implemented into daily practice to ease the DBS programming of PD patients.

Conformational Fluctuations and Induced Orientation of a Protein, Its Solvation Shell, and Bulk Water in Weak Non-Unfolding External Electric Fields

Extreme external electric fields have been reported to disrupt the tertiary structure of stably folded proteins; however, the effects of weaker electric fields on many biomolecules, especially net-uncharged proteins, and on the surrounding aqueous environment have been rarely discussed. To explore these effects at the atomic level, here, we have used molecular dynamics simulations to estimate rotational motion and induced structural fluctuations in the model protein ubiquitin and its hydration layer due to applied non-unfolding electrostatic fields. When exposed to weak electric fields of up to 0.2 V nm-1, ubiquitin displayed competition between internal structure-maintaining molecular interactions and the external orienting force, which disrupted the local structure in certain regions of the protein. Moreover, relative to hydration water, bulk water showed a greater tendency to align with the electric field, indicating that the presence of protein caused hydration water to acquire rotational mobility different from that in a pure-water system. The differential influence of the applied electric field on the hydration and bulk water surrounding ubiquitin will be common to almost all (nonmembrane) biomacromolecules. Our findings highlight the importance of local dipoles and their electric polarizability even in net-uncharged biomolecules.

Oscillatory beta dynamics inform biomarker-driven treatment optimization for Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by loss of dopaminergic neurons and dysregulation of the basal ganglia. Cardinal motor symptoms include bradykinesia, rigidity, and tremor. Deep brain stimulation (DBS) of select subcortical nuclei is standard of care for medication-refractory PD. Conventional open-loop DBS delivers continuous stimulation with fixed parameters that do not account for a patient's dynamic activity state or medication cycle. In comparison, closed-loop DBS, or adaptive DBS (aDBS), adjusts stimulation based on biomarker feedback that correlates with clinical state. Recent work has identified several neurophysiological biomarkers in local field potential recordings from PD patients, the most promising of which are 1) elevated beta (∼13-30 Hz) power in the subthalamic nucleus (STN), 2) increased beta synchrony throughout basal ganglia-thalamocortical circuits, notably observed as coupling between the STN beta phase and cortical broadband gamma (∼50-200 Hz) amplitude, and 3) prolonged beta bursts in the STN and cortex. In this review, we highlight relevant frequency and time domain features of STN beta measured in PD patients and summarize how spectral beta power, oscillatory beta synchrony, phase-amplitude coupling, and temporal beta bursting inform PD pathology, neurosurgical targeting, and DBS therapy. We then review how STN beta dynamics inform predictive, biomarker-driven aDBS approaches for optimizing PD treatment. We therefore provide clinically useful and actionable insight that can be applied toward aDBS implementation for PD.

Antiparkinsonian Effects of Polyphenols: A Narrative Review with a Focus on the Modulation of the Gut-brain Axis

Polyphenols, which are naturally occurring bioactive compounds in fruits and vegetables, are emerging as potential therapeutics for neurological disorders such as Parkinson's disease (PD). Polyphenols have diverse biological activities, such as anti-oxidative, anti-inflammatory, anti-apoptotic, and α-synuclein aggregation inhibitory effects, which could ameliorate PD pathogenesis. Studies have shown that polyphenols are capable of regulating the gut microbiota (GM) and its metabolites; in turn, polyphenols are extensively metabolized by the GM, resulting in the generation of bioactive secondary metabolites. These metabolites may regulate various physiological processes, including inflammatory responses, energy metabolism, intercellular communication, and host immunity. With increasing recognition of the importance of the microbiota-gut-brain axis (MGBA) in PD etiology, polyphenols have attracted growing attention as MGBA regulators. In order to address the potential therapeutic role of polyphenolic compounds in PD, we focused on MGBA. DATA AVAILABILITY: Data will be made available on request.

Insights into Advanced Neurological Dysfunction Mechanisms Following DBS Surgery in Parkinson's Patients: Neuroinflammation and Pyroptosis

Parkinson's disease is a severe neurodegenerative disorder. Currently, deep brain electrical stimulation (DBS) is the first line of surgical treatment. However, serious neurological impairments such as speech disorders, disturbances of consciousness, and depression after surgery limit the efficacy of treatment. In this review, we summarize the recent experimental and clinical studies that have explored the possible causes of neurological deficits after DBS. Furthermore, we tried to identify clues from oxidative stress and pathological changes in patients that could lead to the activation of microglia and astrocytes in DBS surgical injury. Notably, reliable evidence supports the idea that neuroinflammation is caused by microglia and astrocytes, which may contribute to caspase-1 pathway-mediated neuronal pyroptosis. Finally, existing drugs and treatments may partially ameliorate the loss of neurological function in patients following DBS surgery by exerting neuroprotective effects.

The efficacy of stereotactic minimally invasive thrombolysis at different catheter positions in the treatment of small- and medium-volume basal ganglia hemorrhage (SMITDCP I): a randomized, controlled, and blinded endpoint phase 1 trial

Objective

The aim of this study was to evaluate the effects of stereotactic minimally invasive puncture with different catheter placement positions when combined with urokinase thrombolysis for the treatment of small- and medium-volume basal ganglia hemorrhage. Our goal was to identify the best minimally invasive catheter placement position to enhance therapeutic efficacy for patients with cerebral hemorrhage.

Methods

The stereotactic minimally invasive thrombolysis at different catheter positions in the treatment of small- and medium-volume basal ganglia hemorrhage (SMITDCPI) was a randomized, controlled, and endpoint phase 1 trial. We recruited patients with spontaneous ganglia hemorrhage (medium-to-small and medium volume) who were treated in our hospital. All patients received stereotactic, minimally invasive punctures combined with an intracavitary thrombolytic injection of urokinase hematoma. A randomized number table method was used to divide the patients into two groups concerning the location of catheterization: a penetrating hematoma long-axis group and a hematoma center group. The general conditions of the two groups of patients were compared, and the data were analyzed, including the time of catheterization, the dosage of urokinase, the amount of residual hematoma, the hematoma clearance rate, complications, and the National Institute of Health stroke scale (NIHSS) score data at 1 month after surgery.

Results

Between June 2019 and March 2022, 83 patients were randomly recruited and assigned to the two groups as follows: 42 cases (50.60%) to the penetrating hematoma long-axis group and 41 cases (49.40%) to the hematoma center group. Compared with the hematoma center group, the long-axis group was associated with a significantly shorter catheterization time, a lower urokinase dose, a lower residual hematoma volume, a higher hematoma clearance rate, and fewer complications (P < 0.05). However, there were no significant differences between the two groups in terms of the NIHSS scores when tested 1 month after surgery (P > 0.05).

Conclusion

Stereotactic minimally invasive puncture combined with urokinase for the treatment of small- and medium-volume hemorrhage in the basal ganglia, including catheterization through the long axis of the hematoma, led to significantly better drainage effects and fewer complications. However, there was no significant difference in short-term NIHSS scores between the two types of catheterization.

Closed-Loop Adaptive Deep Brain Stimulation in Parkinson's Disease: Procedures to Achieve It and Future Perspectives

Parkinson's disease (PD) is a neurodegenerative disease with a heavy burden on patients, families, and society. Deep brain stimulation (DBS) can improve the symptoms of PD patients for whom medication is insufficient. However, current open-loop uninterrupted conventional DBS (cDBS) has inherent limitations, such as adverse effects, rapid battery consumption, and a need for frequent parameter adjustment. To overcome these shortcomings, adaptive DBS (aDBS) was proposed to provide responsive optimized stimulation for PD. This topic has attracted scientific interest, and a growing body of preclinical and clinical evidence has shown its benefits. However, both achievements and challenges have emerged in this novel field. To date, only limited reviews comprehensively analyzed the full framework and procedures for aDBS implementation. Herein, we review current preclinical and clinical data on aDBS for PD to discuss the full procedures for its achievement and to provide future perspectives on this treatment.

Genomics of severe and treatment-resistant obsessive-compulsive disorder treated with deep brain stimulation: a preliminary investigation

Individuals with severe and treatment-resistant obsessive-compulsive disorder (trOCD) represent a small but severely disabled group of patients. Since trOCD cases eligible for deep brain stimulation (DBS) probably comprise the most severe end of the OCD spectrum, we hypothesize that they may be more likely to have a strong genetic contribution to their disorder. Therefore, while the worldwide population of DBS-treated cases may be small (~300), screening these individuals with modern genomic methods may accelerate gene discovery in OCD. As such, we have begun to collect DNA from trOCD cases who qualify for DBS, and here we report results from whole exome sequencing and microarray genotyping of our first five cases. All participants had previously received DBS in the bed nucleus of stria terminalis (BNST), with two patients responding to the surgery and one showing a partial response. Our analyses focused on gene-disruptive rare variants (GDRVs; rare, predicted-deleterious single-nucleotide variants or copy number variants overlapping protein-coding genes). Three of the five cases carried a GDRV, including a missense variant in the ion transporter domain of KCNB1, a deletion at 15q11.2, and a duplication at 15q26.1. The KCNB1 variant (hg19 chr20-47991077-C-T, NM_004975.3:c.1020G>A, p.Met340Ile) causes substitution of methionine for isoleucine in the trans-membrane region of neuronal potassium voltage-gated ion channel KV2.1. This KCNB1 substitution (Met340Ile) is located in a highly constrained region of the protein where other rare missense variants have previously been associated with neurodevelopmental disorders. The patient carrying the Met340Ile variant responded to DBS, which suggests that genetic factors could potentially be predictors of treatment response in DBS for OCD. In sum, we have established a protocol for recruiting and genomically characterizing trOCD cases. Preliminary results suggest that this will be an informative strategy for finding risk genes in OCD.

The effect of deep brain stimulation in Parkinson's disease reflected in EEG microstates

Mechanisms of deep brain stimulation (DBS) on cortical networks were explored mainly by fMRI. Advanced analysis of high-density EEG is a source of additional information and may provide clinically useful biomarkers. The presented study evaluates EEG microstates in Parkinson's disease and the effect of DBS of the subthalamic nucleus (STN). The association between revealed spatiotemporal dynamics of brain networks and changes in oscillatory activity and clinical examination were assessed. Thirty-seven patients with Parkinson's disease treated by STN-DBS underwent two sessions (OFF and ON stimulation conditions) of resting-state EEG. EEG microstates were analyzed in patient recordings and in a matched healthy control dataset. Microstate parameters were then compared across groups and were correlated with clinical and neuropsychological scores. Of the five revealed microstates, two differed between Parkinson's disease patients and healthy controls. Another microstate differed between ON and OFF stimulation conditions in the patient group and restored parameters in the ON stimulation state toward to healthy values. The mean beta power of that microstate was the highest in patients during the OFF stimulation condition and the lowest in healthy controls; sources were localized mainly in the supplementary motor area. Changes in microstate parameters correlated with UPDRS and neuropsychological scores. Disease specific alterations in the spatiotemporal dynamics of large-scale brain networks can be described by EEG microstates. The approach can reveal changes reflecting the effect of DBS on PD motor symptoms as well as changes probably related to non-motor symptoms not influenced by DBS.

Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease Treatment

Deep brain stimulation (DBS) has shown great promise toward treating motor symptoms caused by Parkinson's disease (PD), by delivering electrical pulses to the Basal Ganglia (BG) region of the brain. However, DBS devices approved by the U.S. Food and Drug Administration (FDA) can only deliver continuous DBS (cDBS) stimuli at a fixed amplitude; this energy inefficient operation reduces battery lifetime of the device, cannot adapt treatment dynamically for activity, and may cause significant side-effects (e.g., gait impairment). In this work, we introduce an offline reinforcement learning (RL) framework, allowing the use of past clinical data to train an RL policy to adjust the stimulation amplitude in real time, with the goal of reducing energy use while maintaining the same level of treatment (i.e., control) efficacy as cDBS. Moreover, clinical protocols require the safety and performance of such RL controllers to be demonstrated ahead of deployments in patients. Thus, we also introduce an offline policy evaluation (OPE) method to estimate the performance of RL policies using historical data, before deploying them on patients. We evaluated our framework on four PD patients equipped with the RC+S DBS system, employing the RL controllers during monthly clinical visits, with the overall control efficacy evaluated by severity of symptoms (i.e., bradykinesia and tremor), changes in PD biomakers (i.e., local field potentials), and patient ratings. The results from clinical experiments show that our RL-based controller maintains the same level of control efficacy as cDBS, but with significantly reduced stimulation energy. Further, the OPE method is shown effective in accurately estimating and ranking the expected returns of RL controllers.

Adaptive deep brain stimulation for Parkinson's disease: looking back at the past decade on motor outcomes

BACKGROUND

Adaptive deep brain stimulation (aDBS) has been reported to be an effective treatment for motor symptoms in patients with Parkinson's disease (PD). However, it remains unclear whether and in which motor domain aDBS provides greater/less benefits than conventional DBS (cDBS).

OBJECTIVE

To conduct a meta-analysis and systematic review to explore the improvement of the motor symptoms of PD patients undergoing aDBS and the comparison between aDBS and cDBS.

METHODS

Nineteen studies from PubMed, Embase, and the Cochrane Library database were eligible for the main analysis. Twelve studies used quantitative plus qualitative analysis; seven studies were only qualitatively analyzed. The efficacy of aDBS was evaluated and compared to cDBS through overall motor function improvements, changes in symptoms of rigidity-bradykinesia, dyskinesia, tremor, and speech function, and total electrical energy delivered (TEED). The overall motor improvement and TEED were investigated through meta-analyses, while other variables were investigated by systematic review.

RESULTS

Quantitative analysis showed that aDBS, with a reduction of TEED (55% of that of cDBS), significantly improved motor functions (33.9%, p < 0.01) and may be superior to cDBS in overall motor improvement (p = 0.002). However, significant publication bias was detected regarding the superiority (p = 0.006, Egger's test). In the qualitative analysis, rigidity-bradykinesia, dyskinesia, and speech function outcomes after aDBS and cDBS were comparable. Beta-based aDBS may not be as efficient as cDBS for tremor control.

CONCLUSIONS

aDBS can effectively relieve the clinical symptoms of advanced PD as did cDBS, at least in acute trials, delivering less stimulation than cDBS. Specific symptoms including tremor and axial disability remain to be compared between aDBS and cDBS in long-term studies.

Association of subthalamic beta frequency sub-bands to symptom severity in patients with Parkinson's disease: A systematic review

OBJECTIVE

Local field potentials (LFP), specifically beta (13-30Hz) frequency measures, have been found to be associated with motor dysfunction in people with Parkinson's disease (PwPD). A consensus on beta subband (low- and high-beta) relationships to clinical state or therapy response has yet to be determined. The objective of this review is to synthesize literature reporting the association of low- and high-beta characteristics to clinical ratings of motor symptoms in PwPD.

METHODS

A systematic search of existing literature was completed using EMBASE. Articles which collected subthalamic nucleus (STN) LFPs using macroelectrodes in PwPD, analyzed low- (13-20 Hz) and high-beta (21-35 Hz) bands, collected UPDRS-III, and reported correlational strength or predictive capacity of LFPs to UPDRS-III scores.

RESULTS

The initial search yielded 234 articles, with 11 articles achieving inclusion. Beta measures included power spectral density, peak characteristics, and burst characteristics. High-beta was a significant predictor of UPDRS-III responses to therapy in 5 (100%) articles. Low-beta was significantly associated with UPDRS-III total score in 3 (60%) articles. Low- and high-beta associations to UPDRS-III subscores were mixed.

CONCLUSION

This systematic review reinforces previous reports that beta band oscillatory measures demonstrate a consistent relationship to Parkinsonian motor symptoms and ability to predict motor response to therapy. Specifically, high-beta, demonstrated a consistent ability to predict UPDRS-III responses to common PD therapies, while low-beta measures were associated with general Parkinsonian symptom severity. Continued research is needed to determine which beta subband demonstrates the greatest association to motor symptom subtypes and potentially offers clinical utility toward LFP-guided DBS programming and adaptive DBS.

Altered Neurovascular Coupling for Multidisciplinary Intensive Rehabilitation in Parkinson's Disease

Effective rehabilitation in Parkinson's disease (PD) is related to brain reorganization with restoration of cortico-subcortical networks and compensation of frontoparietal networks; however, further neural rehabilitation evidence from a multidimensional perspective is needed. To investigate how multidisciplinary intensive rehabilitation treatment affects neurovascular coupling, 31 PD patients (20 female) before and after treatment and 30 healthy controls (17 female) underwent blood oxygenation level-dependent functional magnetic resonance imaging and arterial spin labeling scans. Cerebral blood flow (CBF) was used to measure perfusion, and fractional amplitude of low-frequency fluctuation (fALFF) was used to measure neural activity. The global CBF-fALFF correlation and regional CBF/fALFF ratio were calculated as neurovascular coupling. Dynamic causal modeling (DCM) was used to evaluate treatment-related alterations in the strength and directionality of information flow. Treatment reduced CBF-fALFF correlations. The altered CBF/fALFF exhibited increases in the left angular gyrus and the right inferior parietal gyrus and decreases in the bilateral thalamus and the right superior frontal gyrus. The CBF/fALFF alteration in right superior frontal gyrus showed correlations with motor improvement. Further, DCM indicated increases in connectivity from the superior frontal gyrus and decreases from the thalamus to the inferior parietal gyrus. The benefits of rehabilitation were reflected in the dual mechanism, with restoration of executive control occurring in the initial phase of motor learning and compensation of information integration occurring in the latter phase. These findings may yield multimodal insights into the role of rehabilitation in disease modification and identify the dorsolateral superior frontal gyrus as a potential target for noninvasive neuromodulation in PD.SIGNIFICANCE STATEMENT Although rehabilitation has been proposed as a promising supplemental treatment for PD as it results in brain reorganization, restoring cortico-subcortical networks and eliciting compensatory activation of frontoparietal networks, further multimodal evidence of the neural mechanisms underlying rehabilitation is needed. We measured the ratio of perfusion and neural activity derived from arterial spin labeling and blood oxygenation level-dependent fMRI data and found that benefits of rehabilitation seem to be related to the dual mechanism, restoring executive control in the initial phase of motor learning and compensating for information integration in the latter phase. We also identified the dorsolateral superior frontal gyrus as a potential target for noninvasive neuromodulation in PD patients.

High-gamma activity is coupled to low-gamma oscillations in precentral cortices and modulates with movement and speech

Planning and executing motor behaviors requires coordinated neural activity among multiple cortical and subcortical regions of the brain. Phase-amplitude coupling between the high-gamma band amplitude and the phase of low frequency oscillations (theta, alpha, beta) has been proposed to reflect neural communication, as has synchronization of low-gamma oscillations. However, coupling between low-gamma and high-gamma bands has not been investigated. Here, we measured phase-amplitude coupling between low- and high-gamma in monkeys performing a reaching task and in humans either performing finger movements or speaking words aloud. We found significant coupling between low-gamma phase and high-gamma amplitude in multiple sensorimotor and premotor cortices of both species during all tasks. This coupling modulated with the onset of movement. These findings suggest that interactions between the low and high gamma bands are markers of network dynamics related to movement and speech generation.

A comparison of methods to suppress electrocardiographic artifacts in local field potential recordings

OBJECTIVE

Local field potential (LFP) recordings from deep brain stimulation (DBS) electrodes are often contaminated with electrocardiographic (ECG) artifacts that hinder the detection of disease-specific electrical brain activity.

METHODS

Three ECG suppression methods were evaluated: (1) QRS interpolation of the Perceive toolbox, (2) template subtraction, and (3) singular value decomposition (SVD). LFPs were recorded with the Medtronic Percept^TM PC system in nine Parkinson's disease patients with stimulation OFF ("OFF-DBS"; anode disconnected) and ON at 0 mA ("ON-DBS 0 mA"; anode connected). Findings were verified with simulated ECG-contaminated time series.

RESULTS

ECG artifacts were present in 10 out of 18 ON-DBS 0 mA recordings. All ECG suppression methods drastically reduced artifact-induced beta band (13-35 Hz) power and at least partly recovered the beta peak and beta burst dynamics. Using external ECG recordings and lengthening artifact epoch length improved the performance of the suppression methods. Increasing epoch length, however, elevated the risk of flattening the beta peak and losing beta burst dynamics.

CONCLUSIONS

The SVD method formed the preferred trade-off between artifact cleaning and signal loss, as long as its parameter settings are adequately chosen.

SIGNIFICANCE

ECG suppression methods enable analysis of disease-specific neural activity from signals affected by ECG artifacts.

Effect and Relationship of Gait on Subcortical Local Field Potentials in Parkinson's Disease: A Systematic Review

OBJECTIVES

Developments in deep brain stimulation (DBS) technology have enabled the ability to detect local field potentials (LFPs) in Parkinson disease (PD). Gait dysfunction is one of the most prevalent deficits seen in PD. However, no consensus has been reached on the effect of gait on LFPs and the relationship between LFPs and clinical measures of gait. The objective of this systematic review was to synthesize existing research regarding the relationship between gait dysfunction and LFPs in PD.

METHODS

A systematic search of the literature yielded a total of ten articles, including 132 patients with PD, which met the criteria for inclusion.

RESULTS

Beta frequency band measures showed low-to-strong correlation to clinical gait measures (r = -0.50 to 0.82). Two studies found decreased beta power during gait; one found increased beta frequency peaks during gait; and one found higher beta power during dual-task gait than during single-task gait. One of the three studies comparing patients with and without freezing found significantly increased beta burst duration and power during gait in freezers compared with nonfreezers. All studies showed moderate-to-high methodologic quality.

CONCLUSIONS

This review highlights the need to consider the effect of gait on LFP recordings, particularly when used to guide DBS programming. Although sample sizes were small, it appears LFPs are associated to and modulated by gait in patients with PD. This evidence suggests that LFPs have the potential to be used as a biomarker of gait dysfunction in PD.

Morphologic brain network predicts levodopa responsiveness in Parkinson disease

Background

The levodopa challenge test (LCT) has been routinely used in Parkinson disease (PD) evaluation and predicts the outcome of deep brain stimulation (DBS). Guidelines recommend that patients with an improvement in Unified Parkinson's Disease Rating Scale (UPDRS)-III score > 33% in the LCT receive DBS treatment. However, LCT results are affected by many factors, and only provide information on the immediate effectiveness of dopamine. The aim of the present study was to investigate the relationship between LCT outcome and brain imaging features of PD patients to determine whether the latter can be used to identify candidates for DBS.

Methods

A total of 38 PD patients were enrolled in the study. Based on improvement in UPDRS-III score in the LCT, patients were divided into low improvement (PD-LCT-L) and high improvement (PD-LCT-H) groups. Each patient's neural network was reconstructed based on T1-weighted magnetic resonance imaging data using the Jensen-Shannon divergence similarity estimation method. The network was established with the multiple kernel support vector machine technique. We analyzed differences in individual morphologic brain networks and their global and local metrics to determine whether there were differences in the connectomes of PD-LCT-L and PD-LCT-H groups.

Results

The 2 groups were similar in terms of demographic and clinical characteristics. Mean ± SD levodopa responsiveness was 26.52% ± 3.47% in the PD-LCT-L group (N = 13) and 58.66% ± 4.09% in the PD-LCT-H group (N = 25). There were no significant differences between groups in global and local metrics. There were 43 consensus connections that were affected in both groups; in PD-LCT-L patients, most of these connections were decreased whereas those related to the dorsolateral superior frontal gyrus and left cuneus were significantly increased.

Conclusion

Morphologic brain network assessment is a valuable method for predicting levodopa responsiveness in PD patients, which can facilitate the selection of candidates for DBS.

Emerging Materials, Wearables, and Diagnostic Advancements in Therapeutic Treatment of Brain Diseases

Among the most critical health issues, brain illnesses, such as neurodegenerative conditions and tumors, lower quality of life and have a significant economic impact. Implantable technology and nano-drug carriers have enormous promise for cerebral brain activity sensing and regulated therapeutic application in the treatment and detection of brain illnesses. Flexible materials are chosen for implantable devices because they help reduce biomechanical mismatch between the implanted device and brain tissue. Additionally, implanted biodegradable devices might lessen any autoimmune negative effects. The onerous subsequent operation for removing the implanted device is further lessened with biodegradability. This review expands on current developments in diagnostic technologies such as magnetic resonance imaging, computed tomography, mass spectroscopy, infrared spectroscopy, angiography, and electroencephalogram while providing an overview of prevalent brain diseases. As far as we are aware, there hasn't been a single review article that addresses all the prevalent brain illnesses. The reviewer also looks into the prospects for the future and offers suggestions for the direction of future developments in the treatment of brain diseases.

Molecular and Cellular Interactions in Pathogenesis of Sporadic Parkinson Disease

An increasing number of the population all around the world suffer from age-associated neurodegenerative diseases including Parkinson’s disease (PD). This disorder presents different signs of genetic, epigenetic and environmental origin, and molecular, cellular and intracellular dysfunction. At the molecular level, α-synuclein (αSyn) was identified as the principal molecule constituting the Lewy bodies (LB). The gut microbiota participates in the pathogenesis of PD and may contribute to the loss of dopaminergic neurons through mitochondrial dysfunction. The most important pathogenetic link is an imbalance of Ca²⁺ ions, which is associated with redox imbalance in the cells and increased generation of reactive oxygen species (ROS). In this review, genetic, epigenetic and environmental factors that cause these disorders and their cause-and-effect relationships are considered. As a constituent of environmental factors, the example of organophosphates (OPs) is also reviewed. The role of endothelial damage in the pathogenesis of PD is discussed, and a ‘triple hit hypothesis’ is proposed as a modification of Braak’s dual hit one. In the absence of effective therapies for neurodegenerative diseases, more and more evidence is emerging about the positive impact of nutritional structure and healthy lifestyle on the state of blood vessels and the risk of developing these diseases.

Implants with Sensing Capabilities

Because of the aging human population and increased numbers of surgical procedures being performed, there is a growing number of biomedical devices being implanted each year. Although the benefits of implants are significant, there are risks to having foreign materials in the body that may lead to complications that may remain undetectable until a time at which the damage done becomes irreversible. To address this challenge, advances in implantable sensors may enable early detection of even minor changes in the implants or the surrounding tissues and provide early cues for intervention. Therefore, integrating sensors with implants will enable real-time monitoring and lead to improvements in implant function. Sensor integration has been mostly applied to cardiovascular, neural, and orthopedic implants, and advances in combined implant-sensor devices have been significant, yet there are needs still to be addressed. Sensor-integrating implants are still in their infancy; however, some have already made it to the clinic. With an interdisciplinary approach, these sensor-integrating devices will become more efficient, providing clear paths to clinical translation in the future.

Nanoscale geometry determines mechanical biocompatibility of vertically aligned nanofibers

Vertically aligned carbon nanofibers (VACNFs) are promising material candidates for neural biosensors due to their ability to detect neurotransmitters in physiological concentrations. However, the expected high rigidity of CNFs could induce mechanical mismatch with the brain tissue, eliciting formation of a glial scar around the electrode and thus loss of functionality. We have evaluated mechanical biocompatibility of VACNFs by growing nickel-catalyzed carbon nanofibers of different lengths and inter-fiber distances. Long nanofibers with large inter-fiber distance prevented maturation of focal adhesions, thus constraining cells from obtaining a highly spread morphology that is observed when astrocytes are being contacted with stiff materials commonly used in neural implants. A silicon nanopillar array with 500 nm inter-pillar distance was used to reveal that this inhibition of focal adhesion maturation occurs due to the surface nanoscale geometry, more precisely the inter-fiber distance. Live cell atomic force microscopy was used to confirm astrocytes being significantly softer on the long Ni-CNFs compared to other surfaces, including a soft gelatin hydrogel. We also observed hippocampal neurons to mature and form synaptic contacts when being cultured on both long and short carbon nanofibers, without having to use any adhesive proteins or a glial monoculture, indicating high cytocompatibility of the material also with neuronal population. In contrast, neurons cultured on a planar tetrahedral amorphous carbon sample showed immature neurites and indications of early-stage apoptosis. Our results demonstrate that mechanical biocompatibility of biomaterials is greatly affected by their nanoscale surface geometry, which provides means for controlling how the materials and their mechanical properties are perceived by the cells. STATEMENT OF SIGNIFICANCE: Our research article shows, how nanoscale surface geometry determines mechanical biocompatibility of apparently stiff materials. Specifically, astrocytes were prevented from obtaining highly spread morphology when their adhesion site maturation was inhibited, showing similar morphology on nominally stiff vertically aligned carbon fiber (VACNF) substrates as when being cultured on ultrasoft surfaces. Furthermore, hippocampal neurons matured well and formed synapses on these carbon nanofibers, indicating high biocompatibility of the materials. Interestingly, the same VACNF materials that were used in this study have earlier also been proven to be capable for electrophysiological recordings and sensing neurotransmitters at physiological concentrations with ultra-high sensitivity and selectivity, thus providing a platform for future neural probes or smart culturing surfaces with superior sensing performance and biocompatibility.

Deep brain stimulation for phantom limb pain

Phantom limb pain is a rare cause of chronic pain in children but it is associated with extremely refractory pain and disability. The reason for limb amputation is often due to treatment for cancer or trauma and it has a lower incidence compared to adults. The mechanism of why phantom pain exists remains uncertain and may be a result of cortical reorganisation as well as ectopic peripheral input. Treatment is aimed at reducing both symptoms as well as managing pain related disability and functional restoration. Neuromodulatory approaches using deep brain stimulation for phantom limb pain is reserved for only the most refractory cases. The targets for brain stimulation include the thalamic nuclei and motor cortex. Novel targets such as the anterior cingulate cortex remain experimental as cases of serious adverse effects such as seziures have limited their widespread uptake. A multidisciplinary approach is crucial to successful rehabilitation using a biopsychosocial pain management approach.

Design of a Parkinsonian Biomarkers Combination Optimization Method Using Rodent Model

Adaptive Deep Brain Stimulation (aDBS) has been proposed in literature to avoid the negative consequences associated with the continuous stimulation delivered through traditional deep brain stimulation. This work seeks to determine a group of neural biomarkers that a classification algorithm could use on an aDBS device using rodent animal models. The neural activities were acquired from the primary motor cortex of four Parkinsonian model rats and four healthy rats from a control group. To overcome the variability introduced from the small rat sample size, this work proposes a novel method for combining and running Genetic Feature Selection and Forward Stepwise Feature Selection in an environment where classification accuracy varies greatly based on how the folds are organized before cross-validation. Three separate classification algorithms, Logistic Regression, k-Nearest Neighbor, and Random Forest are used to verify the proposed method. For Logistic Regression, the set of Alpha Power (7-12 Hz), High Beta Power (20-30 Hz), and 55-95 Hz Gamma Power shows the best performance in classification. For k-Nearest Neighbor, the characterizing features are Low Beta Power (12-20 Hz), High Beta Power, All Beta Power (12-30 Hz), 55-95 Hz Gamma Power, and 95-105 Hz Gamma Power. For Random Forest, they are High Beta Power, All Beta Power, 55-95 Hz Gamma Power, 95-105 Hz Gamma Power, and 300-350 Hz High-Frequency Oscillations Power. With the selected feature set, experimental results show an increasing classification accuracy from 59.08% to 77.69% for Logistic Regression, from 49.53% to 73.44% for k-Nearest Neighbor, and from 54.10% to 71.15% for Random Forest. Clinical Relevance- This experiment provides a method for determining the most effective biomarkers from a larger set for classifying Parkinsonian behavior for an aDBS device.

Deep brain stimulation and stereotactic-assisted brain graft injection targeting fronto-striatal circuits for Huntington's disease: an update

INTRODUCTION

Huntington's Disease as progressive neurological disorders associated with motor, behavioral, and cognitive impairment poses a therapeutic challenge in case of limited responsiveness to established therapeutics. Pallidal deep brain stimulation and neurorestorative strategies (brain grafts) scoping to modulate fronto-striatal circuits have gained increased recognition for the treatment of refractory Huntington's disease (HD).

AREAS COVERED

A review (2000-2022) was performed in PubMed, Embase, and Cochrane Library covering clinical trials conceptualized to determine the efficacy and safety of invasive, stereotactic-guided deep-brain stimulation and intracranial brain-graft injection targeting the globus pallidus and adjunct structures (striatum).

EXPERT OPINION

Stereotactic brain-grafting strategies were performed in few HD patients with inconsistent findings and mild-to-moderate clinical responsiveness with a recently published large, randomized-controlled trial (NCT00190450) yielding negative results. We identified 19 in-human DBS trials (uncontrolled) targeting the globus pallidus internus/externus along with randomized-controlled trial pending report (NCT02535884). We did not detect any significant changes in the UHDRS total score after restorative injections, while in contrast, the use of deep-brain stimulation resulted in a significant reduction of chorea. GPi-DBS should be considered in cases where selective chorea is present. However, both invasive therapies remain experimental and are not ready for the implementation in clinical use.

Use of Artificial Intelligence in Clinical Neurology

Artificial intelligence is already innovating in the provision of neurologic care. This review explores key artificial intelligence concepts; their application to neurologic diagnosis, prognosis, and treatment; and challenges that await their broader adoption. The development of new diagnostic biomarkers, individualization of prognostic information, and improved access to treatment are among the plethora of possibilities. These advances, however, reflect only the tip of the iceberg for the ways in which artificial intelligence may transform neurologic care in the future.

Enriched Environment and Exercise Enhance Stem Cell Therapy for Stroke, Parkinson’s Disease, and Huntington’s Disease

Stem cells, specifically embryonic stem cells (ESCs), mesenchymal stem cells (MSCs), induced pluripotent stem cells (IPSCs), and neural progenitor stem cells (NSCs), are a possible treatment for stroke, Parkinson’s disease (PD), and Huntington’s disease (HD). Current preclinical data suggest stem cell transplantation is a potential treatment for these chronic conditions that lack effective long-term treatment options. Finding treatments with a wider therapeutic window and harnessing a disease-modifying approach will likely improve clinical outcomes. The overarching concept of stem cell therapy entails the use of immature cells, while key in recapitulating brain development and presents the challenge of young grafted cells forming neural circuitry with the mature host brain cells. To this end, exploring strategies designed to nurture graft-host integration will likely enhance the reconstruction of the elusive neural circuitry. Enriched environment (EE) and exercise facilitate stem cell graft-host reconstruction of neural circuitry. It may involve at least a two-pronged mechanism whereby EE and exercise create a conducive microenvironment in the host brain, allowing the newly transplanted cells to survive, proliferate, and differentiate into neural cells; vice versa, EE and exercise may also train the transplanted immature cells to learn the neurochemical, physiological, and anatomical signals in the brain towards better functional graft-host connectivity.

Seed-Based Connectivity Prediction of Initial Outcome of Subthalamic Nuclei Deep Brain Stimulation

Subthalamic nuclei deep brain stimulation (STN-DBS) is a well-established treatment for Parkinson's disease (PD). Some studies have confirmed the long-term efficacy is associated with brain connectivity; however, whether the initial outcome is associated with brain connectivity and efficacy of prediction based on these factors has not been well investigated. In the present study, a total of 98 patients were divided into a training set (n = 78) and a test set (n = 20). The stimulation and medication responses were calculated based on the motor performance. The functional and structural connectomes were established based on a public database and used to measure the association between stimulation response and brain connectivity. The prediction of initial outcome was achieved via a machine learning algorithm-support vector machine based on the model established with the training set. It was found that the initial outcome of STN-DBS was associated with functional/structural connectivities between the volume of tissue activated and multiple brain regions, including the supplementary motor area, precentral and frontal areas, cingulum, temporal cortex, and striatum. These factors could be used to predict the initial outcome, with an r value of 0.4978 (P = 0.0255). Our study demonstrates a correlation between a specific connectivity pattern and initial outcome of STN-DBS, which could be used to predict the initial outcome of DBS.

Rapid development of an integrated remote programming platform for neuromodulation systems through the biodesign process

Treating chronic symptoms for pain and movement disorders with neuromodulation therapies involves fine-tuning of programming parameters over several visits to achieve and maintain symptom relief. This, together with challenges in access to trained specialists, has led to a growing need for an integrated wireless remote care platform for neuromodulation devices. In March of 2021, we launched the first neuromodulation device with an integrated remote programming platform. Here, we summarize the biodesign steps taken to identify the unmet patient need, invent, implement, and test the new technology, and finally gain market approval for the remote care platform. Specifically, we illustrate how agile development aligned with the evolving regulatory requirements can enable patient-centric digital health technology in neuromodulation, such as the remote care platform. The three steps of the biodesign process applied for remote care platform development are: (1) Identify, (2) Invent, and (3) Implement. First, we identified the unmet patient needs through market research and voice-of-customer (VOC) process. Next, during the concept generation phase of the invention step, we integrated the results from the VOC into defining requirements for prototype development. Subsequently, in the concept screening phase, ten subjects with PD participated in a clinical pilot study aimed at characterizing the safety of the remote care prototype. Lastly, during the implementation step, lessons learned from the pilot experience were integrated into final product development as new features. Following final product development, we completed usability testing to validate the full remote care system and collected preliminary data from the limited market release experience. The VOC data, during prototype development, helped us identify thresholds for video quality and needs priorities for clinicians and patients. During the pilot study, one subject reported anticipated remote-care-related adverse events that were resolved without sequelae. For usability analysis following final product development, the failure rates for task completion for both user groups were about 1%. Lastly, during the initial 4 weeks of the limited market release experience, a total of 858 remote care sessions were conducted with a 93% success rate. Overall, we developed a remote care platform by adopting a user-centric approach. Although the system intended to address pre-COVID19 challenges associated with disease management, the unforeseen overlap of the study with the pandemic elevated the importance of such a system and an innovative development process enabled us to advance a patient-centric platform to gain regulatory approval and successfully launch the remote care platform to market.

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.

Review of Noninvasive or Minimally Invasive Deep Brain Stimulation

Brain stimulation is a critical technique in neuroscience research and clinical application. Traditional transcranial brain stimulation techniques, such as transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), and deep brain stimulation (DBS) have been widely investigated in neuroscience for decades. However, TMS and tDCS have poor spatial resolution and penetration depth, and DBS requires electrode implantation in deep brain structures. These disadvantages have limited the clinical applications of these techniques. Owing to developments in science and technology, substantial advances in noninvasive and precise deep stimulation have been achieved by neuromodulation studies. Second-generation brain stimulation techniques that mainly rely on acoustic, electronic, optical, and magnetic signals, such as focused ultrasound, temporal interference, near-infrared optogenetic, and nanomaterial-enabled magnetic stimulation, offer great prospects for neuromodulation. This review summarized the mechanisms, development, applications, and strengths of these techniques and the prospects and challenges in their development. We believe that these second-generation brain stimulation techniques pave the way for brain disorder therapy.

Directional Deep Brain Stimulation in the Treatment of Parkinson's Disease

Deep brain stimulation (DBS) is a treatment modality that has been shown to improve the clinical outcomes of individuals with movement disorders, including Parkinson's disease. Directional DBS represents an advance in the field that allows clinicians to better modulate the electrical stimulation to increase therapeutic gains while minimizing side effects. In this review, we summarize the principles of directional DBS, including available technologies and stimulation paradigms, and examine the growing clinical study data with respect to its use in Parkinson's disease.

A New Implantable Closed-Loop Clinical Neural Interface: First Application in Parkinson's Disease

Deep brain stimulation (DBS) is used for the treatment of movement disorders, including Parkinson’s disease, dystonia, and essential tremor, and has shown clinical benefits in other brain disorders. A natural path for the improvement of this technique is to continuously observe the stimulation effects on patient symptoms and neurophysiological markers. This requires the evolution of conventional deep brain stimulators to bidirectional interfaces, able to record, process, store, and wirelessly communicate neural signals in a robust and reliable fashion. Here, we present the architecture, design, and first use of an implantable stimulation and sensing interface (AlphaDBS^R System) characterized by artifact-free recording and distributed data management protocols. Its application in three patients with Parkinson’s disease (clinical trial n. NCT04681534) is shown as a proof of functioning of a clinically viable implanted brain-computer interface (BCI) for adaptive DBS. Reliable artifact free-recordings, and chronic long-term data and neural signal management are in place.

Predictive accuracy of CNN for cortical oscillatory activity in an acute rat model of parkinsonism

In neurological and neuropsychiatric disorders neuronal oscillatory activity between basal ganglia and cortical circuits are altered, which may be useful as biomarker for adaptive deep brain stimulation. We investigated whether changes in the spectral power of oscillatory activity in the motor cortex (MCtx) and the sensorimotor cortex (SMCtx) of rats after injection of the dopamine (DA) receptor antagonist haloperidol (HALO) would be similar to those observed in Parkinson disease. Thereafter, we tested whether a convolutional neural network (CNN) model would identify brain signal alterations in this acute model of parkinsonism. A sixteen channel surface micro-electrocorticogram (ECoG) recording array was placed under the dura above the MCtx and SMCtx areas of one hemisphere under general anaesthesia in rats. Seven days after surgery, micro ECoG was recorded in individual free moving rats in three conditions: (1) basal activity, (2) after injection of HALO (0.5 mg/kg), and (3) with additional injection of apomorphine (APO) (1 mg/kg). Furthermore, a CNN-based classification consisting of 23,530 parameters was applied on the raw data. HALO injection decreased oscillatory theta band activity (4-8 Hz) and enhanced beta (12-30 Hz) and gamma (30-100 Hz) in MCtx and SMCtx, which was compensated after APO injection (P ¡ 0.001). Evaluation of classification performance of the CNN model provided accuracy of 92%, sensitivity of 90% and specificity of 93% on one-dimensional signals. The CNN proposed model requires a minimum of sensory hardware and may be integrated into future research on therapeutic devices for Parkinson disease, such as adaptive closed loop stimulation, thus contributing to more efficient way of treatment.

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.

Should the Globus Pallidus Targeting Be Refined in Dystonia?

BACKGROUND AND STUDY AIMS

 Deep brain stimulation (DBS) of the globus pallidus internus (GPi) is a highly effective therapy for primary generalized and focal dystonias, but therapeutic success is compromised by a nonresponder rate of up to 20%. Variability in electrode placement and in tissue stimulated inside the GPi may explain in part different outcomes among patients. Refinement of the target within the pallidal area could be helpful for surgery planning and clinical outcomes. The objective of this study was to discuss current and potential methodological (somatotopy, neuroimaging, and neurophysiology) aspects that might assist neurosurgical targeting of the GPi, aiming to treat generalized or focal dystonia.

METHODS

 We selected published studies by searching electronic databases and scanning the reference lists for articles that examined the anatomical and electrophysiologic aspects of the GPi in patients with idiopathic/inherited dystonia who underwent functional neurosurgical procedures.

RESULTS

 The sensorimotor sector of the GPi was the best target to treat dystonic symptoms, and was localized at its lateral posteroventral portion. The effective volume of tissue activated (VTA) to treat dystonia had a mean volume of 153 mm³ in the posterior GPi area. Initial tractography studies evaluated the close relation between the electrode localization and pallidothalamic tract to control dystonic symptoms.Regarding the somatotopy, the more ventral, lateral, and posterior areas of the GPi are associated with orofacial and cervical representation. In contrast, the more dorsal, medial, and anterior areas are associated with the lower limbs; between those areas, there is the representation of the upper limb. Excessive pallidal synchronization has a peak at the theta band of 3 to 8 Hz, which might be responsible for generating dystonic symptoms.

CONCLUSIONS

 Somatotopy assessment of posteroventral GPi contributes to target-specific GPi sectors related to segmental body symptoms. Tractography delineates GPi output pathways that might guide electrode implants, and electrophysiology might assist in pointing out areas of excessive theta synchronization. Finally, the identification of oscillatory electrophysiologic features that correlate with symptoms might enable closed-loop approaches in the future.

Deep brain stimulation: is it time to change gears by closing the loop?

Objective.Adaptive deep brain stimulation (aDBS) is a form of invasive stimulation that was conceived to overcome the technical limitations of traditional DBS, which delivers continuous stimulation of the target structure without considering patients' symptoms or status in real-time. Instead, aDBS delivers on-demand, contingency-based stimulation. So far, aDBS has been tested in several neurological conditions, and will be soon extensively studied to translate it into clinical practice. However, an exhaustive description of technical aspects is still missing.Approach.in this topical review, we summarize the knowledge about the current (and future) aDBS approach and control algorithms to deliver the stimulation, as reference for a deeper undestending of aDBS model.Main results.We discuss the conceptual and functional model of aDBS, which is based on the sensing module (that assesses the feedback variable), the control module (which interpretes the variable and elaborates the new stimulation parameters), and the stimulation module (that controls the delivery of stimulation), considering both the historical perspective and the state-of-the-art of available biomarkers.Significance.aDBS modulates neuronal circuits based on clinically relevant biofeedback signals in real-time. First developed in the mid-2000s, many groups have worked on improving closed-loop DBS technology. The field is now at a point in conducting large-scale randomized clinical trials to translate aDBS into clinical practice. As we move towards implanting brain-computer interfaces in patients, it will be important to understand the technical aspects of aDBS.

Identification of effective features of LFP signal for making closed-loop deep brain stimulation in parkinsonian rats

Traditional deep brain stimulation (DBS) is one of the acceptable methods to relieve the clinical symptoms of Parkinson's disease in its advanced stages. Today, the use of closed-loop DBS to increase stimulation efficiency and patient satisfaction is one of the most important issues under investigation. The present study was aimed to find local field potential (LFP) features of parkinsonian rats, which can determine the timing of stimulation with high accuracy. The LFP signals from rats were recorded in three groups of parkinsonian rat models receiving stimulation (stimulation), without getting stimulation (off-stimulation), and sham-controlled group. The frequency domain and chaotic features of signals were extracted for classifying three classes by support vector machine (SVM) and neural networks. The best combination of features was selected using the genetic algorithm (GA). Finally, the effective features were introduced to determine the on/off stimulation time, and the optimal stimulation parameters were identified. It was found that a combination of frequency domain and chaotic features with an accuracy of about 99% was able to determine the time the DBS must switch on. In about 80.67% of the 1861 different stimulation parameters, the brain was able to maintain its state for about 3 min after stimulation discontinuation.

Continuously Decoding Grasping Movements using Stereotactic Depth Electrodes

Brain-Computer Interfaces (BCIs) that decode a patient's movement intention to control a prosthetic device could restore some independence to paralyzed patients. An important step on the road towards naturalistic prosthetic control is to decode movement continuously with low-latency. BCIs based on intracortical micro-arrays provide continuous control of robotic arms, but require a minor craniotomy. Surface recordings of neural activity using EEG have made great advances over the last years, but suffer from high noise levels and large intra-session variance. Here, we investigate the use of minimally invasive recordings using stereotactically implanted EEG (sEEG). These electrodes provide a sparse sampling across many brain regions. So far, promising decoding results have been presented using data measured from the subthalamic nucleus or trial-to-trial based methods using depth electrodes. In this work, we demonstrate that grasping movements can continuously be decoded using sEEG electrodes, as well. Beta and high-gamma activity was extracted from eight participants performing a grasping task. We demonstrate above chance level decoding of movement vs rest and left vs right, from both frequency bands with accuracies up to 0.94 AUC. The vastly different electrode locations between participants lead to large variability. In the future, we hope that sEEG recordings will provide additional information for the decoding process in neuroprostheses.

Patient, Caregiver, and Decliner Perspectives on Whether to Enroll in Adaptive Deep Brain Stimulation Research

This research study provides patient and caregiver perspectives as to whether or not to undergo adaptive deep brain stimulation (aDBS) research. A total of 51 interviews were conducted in a multi-site study including patients undergoing aDBS and their respective caregivers along with persons declining aDBS. Reasons highlighted for undergoing aDBS included hopes for symptom alleviation, declining quality of life, desirability of being in research, and altruism. The primary reasons for not undergoing aDBS issues were practical rather than specific to aDBS technology, although some persons highlighted a desire to not be the first to trial the new technology. These themes are discussed in the context of "push" factors wherein any form of surgical intervention is preferable to none and "pull" factors wherein opportunities to contribute to science combine with hopes and/or expectations for the alleviation of symptoms. We highlight the significance of study design in decision making. aDBS is an innovative technology and not a completely new technology. Many participants expressed value in being part of research as an important consideration. We suggest that there are important implications when comparing patient perspectives vs. theoretical perspectives on the choice for or against aDBS. Additionally, it will be important how we communicate with patients especially in reference to the complexity of study design. Ultimately, this study reveals that there are benefits and potential risks when choosing a research study that involves implantation of a medical device.