Electroencephalogram-based adaptive closed-loop brain-computer interface in neurorehabilitation: a review.

Brain-computer interfaces (BCIs) represent a groundbreaking approach to enabling direct communication for individuals with severe motor impairments, circumventing traditional neural and muscular pathways. Among the diverse array of BCI technologies, electroencephalogram (EEG)-based systems are particularly favored due to their non-invasive nature, user-friendly operation, and cost-effectiveness. Recent advancements have facilitated the development of adaptive bidirectional closed-loop BCIs, which dynamically adjust to users' brain activity, thereby enhancing responsiveness and efficacy in neurorehabilitation. These systems support real-time modulation and continuous feedback, fostering personalized therapeutic interventions that align with users' neural and behavioral responses. By incorporating machine learning algorithms, these BCIs optimize user interaction and promote recovery outcomes through mechanisms of activity-dependent neuroplasticity. This paper reviews the current landscape of EEG-based adaptive bidirectional closed-loop BCIs, examining their applications in the recovery of motor and sensory functions, as well as the challenges encountered in practical implementation. The findings underscore the potential of these technologies to significantly enhance patients' quality of life and social interaction, while also identifying critical areas for future research aimed at improving system adaptability and performance. As advancements in artificial intelligence continue, the evolution of sophisticated BCI systems holds promise for transforming neurorehabilitation and expanding applications across various domains.

Experiences of adolescents and young adults with type 1 diabetes and chronically elevated glucose levels following the transition from multiple daily injections to advanced hybrid closed-loop: A qualitative study.

AIM: To understand experiences of using second-generation advanced hybrid closed-loop (AHCL) therapy in adolescents and young adults with chronically elevated glucose levels who were previously using multiple daily injections (MDI) therapy. METHOD: Semi-structured interviews with participants aged 13-25 years, on AHCL therapy for 3 months as part of a single-arm prospective study. Key inclusions: HbA1c ≥69 mmol/mol (8.5%); diabetes duration ≥1 year; and using MDI therapy prior to the study. Qualitative content analysis was used to identify themes and subthemes. RESULTS: Interviews were conducted among 14 participants with mean age 19.4 ± 4.3 years and mean baseline HbA1c 90 ± 25 mmol/mol (10.4 ± 4.5%). Three themes were identified: (1) substantially improved glucose levels improved perceptions of overall health; (2) features of AHCL aid in adoption and ongoing self-management; and (3) burden of care was reduced through automation of insulin delivery. Overall, there were positive impacts on physical, mental and social well-being. Participants were willing to overlook minor frustrations with AHCL because of the vast benefits that they had experienced. Four participants reported transient pseudo-hypoglycaemia: symptoms of hypoglycaemia when objectively measured glucose was in the clinically recommended range (3.9-10 mmol/L, 70-180 mg/dL). CONCLUSION: Transition to AHCL therapy positively impacted diabetes management in adolescents and youth with chronically elevated glucose levels. It appears to create a window of opportunity in which youth may re-engage with diabetes management. Pseudo-hypoglycaemia can occur during the transition to AHCL. This could be a barrier to AHCL uptake and is likely to require individualised support.

The neurophysiology of sensorimotor prosthetic control.

Movement is a central behavior of daily living; thus lost or compromised movement due to disease, injury, or amputation causes enormous loss of productivity and quality of life. While prosthetics have evolved enormously over the years, restoring natural sensorimotor (SM) control via a prosthesis is a difficult problem which neuroengineering has yet to solve. With a focus on upper limb prosthetics, this perspective article discusses the neurophysiology of motor control under healthy conditions and after amputation, the development of upper limb prostheses from early generations to current state-of-the art sensorimotor neuroprostheses, and how postinjury changes could complicate prosthetic control. Current challenges and future development of smart sensorimotor neuroprostheses are also discussed.

Closed-Loop Recycling of Wearable Electronic Textiles.

Wearable electronic textiles (e-textiles) are transforming personalized healthcare through innovative applications. However, integrating electronics into textiles for e-textile manufacturing exacerbates the rapidly growing issues of electronic waste (e-waste) and textile recycling due to the complicated recycling and disposal processes needed for mixed materials, including textile fibers, electronic materials, and components. Here, first closed-loop recycling for wearable e-textiles is reported by incorporating the thermal-pyrolysis of graphene-based e-textiles to convert them into graphene-like electrically conductive recycled powders. A scalable pad-dry coating technique is then used to reproduce graphene-based wearable e-textiles and demonstrate their potential healthcare applications as wearable electrodes for capturing electrocardiogram (ECG) signals and temperature sensors. Additionally, recycled graphene-based textile supercapacitor highlights their potential as sustainable energy storage devices, maintaining notable durability and retaining ≈94% capacitance after 1000 cycles with an areal capacitance of 4.92 mF cm⁻2. Such sustainable closed-loop recycling of e-textiles showcases the potential for their repurposing into multifunctional applications, promoting a circular approach that potentially prevents negative environmental impact and reduces landfill disposal.

Incidental Carcinoid Tumor Arising in a Meckel's Diverticulum.

This case report describes a patient who presented with concern for a closed-loop small bowel obstruction (SBO). During exploratory laparotomy, an area of ischemic bowel due to closed loop obstruction was resected, along with an incidentally discovered inflamed-appearing Meckel's diverticulum (MD). The resected specimen contained a well-differentiated carcinoid tumor of benign behavior with a maximum diameter of 0.6 cm, which invaded the submucosal layer (pT1b and pN0). Over the last several years, there has been a debate with little consensus regarding the proper surgical management in the case of an asymptomatic MD that is discovered incidentally during abdominal exploration. The intention of sharing this case is to underline the importance of the decision-making process in treating patients with this intraabdominal pathologic condition found incidentally at the time of surgery.

Artificial intelligence-driven transformations in diabetes care: a comprehensive literature review.

Artificial intelligence (AI) has been applied in healthcare for diagnosis, treatments, disease management, and for studying underlying mechanisms and disease complications in diseases like diabetes and metabolic disorders. This review is a comprehensive overview of various applications of AI in the healthcare system for managing diabetes. A literature search was conducted on PubMed to locate studies integrating AI in the diagnosis, treatment, management and prevention of diabetes. As diabetes is now considered a pandemic now so employing AI and machine learning approaches can be applied to limit diabetes in areas with higher prevalence. Machine learning algorithms can visualize big datasets, and make predictions. AI-powered mobile apps and the closed-loop system automated glucose monitoring and insulin delivery can lower the burden on insulin. AI can help identify disease markers and potential risk factors as well. While promising, AI's integration in the medical field is still challenging due to privacy, data security, bias, and transparency. Overall, AI's potential can be harnessed for better patient outcomes through personalized treatment.

Novel murine closed-loop auditory stimulation paradigm elicits macrostructural sleep benefits in neurodegeneration.

Boosting slow-wave activity (SWA) by modulating slow waves through closed-loop auditory stimulation (CLAS) might provide a powerful non-pharmacological tool to investigate the link between sleep and neurodegeneration. Here, we established mouse CLAS (mCLAS)-mediated SWA enhancement and explored its effects on sleep deficits in neurodegeneration, by targeting the up-phase of slow waves in mouse models of Alzheimer's disease (AD, Tg2576) and Parkinson's disease (PD, M83). We found that tracking a 2 Hz component of slow waves leads to highest precision of non-rapid eye movement (NREM) sleep detection in mice, and that its combination with a 30° up-phase target produces a significant 15-30% SWA increase from baseline in wild-type (WTAD) and transgenic (TGAD) mice versus a mock stimulation group. Conversely, combining 2 Hz with a 40° phase target yields a significant increase ranging 30-35% in WTPD and TGPD mice. Interestingly, these phase-target-triggered SWA increases are not genotype dependent but strain specific. Sleep alterations that may contribute to disease progression and burden were described in AD and PD lines. Notably, pathological sleep traits were rescued by mCLAS, which elicited a 14% decrease of pathologically heightened NREM sleep fragmentation in TGAD mice, accompanied by a steep decrease in microarousal events during both light and dark periods. Overall, our results indicate that model-tailored phase targeting is key to modulate SWA through mCLAS, prompting the acute alleviation of key neurodegeneration-associated sleep phenotypes and potentiating sleep regulation and consolidation. Further experiments assessing the long-term effect of mCLAS in neurodegeneration may majorly impact the establishment of sleep-based therapies.

Flexible Temperature Sensor with High Reproducibility and Wireless Closed-Loop System for Decoupled Multimodal Health Monitoring and Personalized Thermoregulation.

Temperature and pulse waves are two fundamental indicators of body health. Specifically, thermoresistive flexible temperature sensors are one of the most applied sensors. However, they suffer from poor reproducibility of resistivity; and decoupling temperature from pressure/strain is still challenging. Besides, autonomous thermoregulation by wearable sensory systems is in high demand, but conventional commercial apparatuses are cumbersome and not suitable for long-term portable use. Here, a material-design strategy is developed to overcome the problem of poor reproducibility of resistivity by tuning the thermal expansion coefficient to nearly zero, precluding the detriment caused by shape expansion/shrinkage with temperature variation and achieving high reproducibility. The strategy also obtains more reliable sensitivity and higher stability, and the designed thermoresistive fiber has strain-insensitive sensing performance and fast response/recovery time. A smart textile woven by the thermoresistive fiber can decouple temperature and pulse without crosstalk; and a flexible wireless closed-loop system comprising the smart textile, a heating textile, a flexible diminutive control patch, and a smartphone is designed and constructed to monitor health status in real-time and autonomously regulate body temperature. This work offers a new route to circumvent temperature-sensitive effects for flexible sensors and new insights for personalized thermoregulation.

Variational analysis of sensory feedback mechanisms in powerstroke-recovery systems.

Although the raison d'etre of the brain is the survival of the body, there are relatively few theoretical studies of closed-loop rhythmic motor control systems. In this paper we provide a unified framework, based on variational analysis, for investigating the dual goals of performance and robustness in powerstroke-recovery systems. To demonstrate our variational method, we augment two previously published closed-loop motor control models by equipping each model with a performance measure based on the rate of progress of the system relative to a spatially extended external substrate-such as a long strip of seaweed for a feeding task, or progress relative to the ground for a locomotor task. The sensitivity measure quantifies the ability of the system to maintain performance in response to external perturbations, such as an applied load. Motivated by a search for optimal design principles for feedback control achieving the complementary requirements of efficiency and robustness, we discuss the performance-sensitivity patterns of the systems featuring different sensory feedback architectures. In a paradigmatic half-center oscillator-motor system, we observe that the excitation-inhibition property of feedback mechanisms determines the sensitivity pattern while the activation-inactivation property determines the performance pattern. Moreover, we show that the nonlinearity of the sigmoid activation of feedback signals allows the existence of optimal combinations of performance and sensitivity. In a detailed hindlimb locomotor system, we find that a force-dependent feedback can simultaneously optimize both performance and robustness, while length-dependent feedback variations result in significant performance-versus-sensitivity tradeoffs. Thus, this work provides an analytical framework for studying feedback control of oscillations in nonlinear dynamical systems, leading to several insights that have the potential to inform the design of control or rehabilitation systems.

Maximal Analgesic Effect Attained by the Use of Objective Neurophysiological Measurements With Closed-Loop Spinal Cord Stimulation.

OBJECTIVES: Spinal cord stimulation (SCS) has been challenged by the lack of neurophysiologic data to guide therapy optimization. Current SCS programming by trial-and-error results in suboptimal and variable therapeutic effects. A novel system with a physiologic closed-loop feedback mechanism using evoked-compound action potentials enables the optimization of physiologic neural dose by consistently and accurately activating spinal cord fibers. We aimed to identify neurophysiologic dose metrics and their ranges that resulted in clinically meaningful treatment responses. MATERIALS AND METHODS: Subjects from 3 clinical studies (n = 180) with baseline back and leg pain ≥60 mm visual analog scale and physical function in the severe to crippled category were included. Maximal analgesic effect (MAE) was operationally defined as the greatest percent reduction in pain intensity or as the greatest cumulative responder score (minimal clinically important differences [MCIDs]) obtained within the first 3 months of SCS implant. The physiologic metrics that produced the MAE were analyzed. RESULTS: We showed that a neural dose regimen with a high neural dose accuracy of 2.8µV and dose ratio of 1.4 resulted in a profound clinical benefit to chronic pain patients (MAE of 79 ± 1% for pain reduction and 12.5 ± 0.4 MCIDs). No differences were observed for MAE or neurophysiological dose metrics between the trial phase and post-implant MAE visit. CONCLUSION: For the first time, an evidence-based neural dose regimen is available for a neurostimulation intervention as a starting point to enable optimization of clinical benefit, monitoring of adherence, and management of the therapy.

A Closed-Loop Nanosystem Based on Piezoelectric Sensor and Pd-Nanoshell Photothermal Ablation for Renal Denervation to Treat Hypertension.

Renal sympathetic nerves play a crucial role in the pathogenesis of hypertension, and renal denervation (RDN) is a new solution for patients with refractory hypertension. However, current RDN techniques show inconsistent results in clinical application probably owing to incomplete endovascular ablation of the sympathetic nerves and a lack of measures to localize and assess efficacy. In this study, a closed-loop RDN system consisting of a sensing unit with a piezoelectric thin-film sensor (PTFS) and a treatment unit with a hollow Pd nanoparticle shell (PdNPS) with a diameter of 202.0 nm for photothermal neural ablation is constructed. The PTFS can monitor and collect arterial pulsation and blood pressure (BP) and direct PdNPS to maximize RDN. PdNPS maintains a local temperature of 58-62 °C under near-infrared-II irradiation (1,064 nm) to achieve effective RDN within a range of 90-120 s treatment window. Photothermal ablation significantly inhibits the activities of renal sympathetic nerves post-procedure and after one month and reduces the elevation of BP by > 50%. The novel closed-loop system enables safe and efficient targeting, dynamic monitoring, and ablation of the renal sympathetic nerves. This closed-loop system provides a new strategy for RDN technology and even for treating sympathetic nerve-related chronic diseases.

Insulin Delivery Technology for Treatment of Infants with Neonatal Diabetes Mellitus: A Systematic Review.

Neonatal diabetes mellitus is a rare disorder of glucose metabolism with onset within the first 6 months of life. The initial treatment is based on insulin infusion. The technologies for diabetes treatment can be very helpful, even if guidelines are still lacking. The current study aimed to provide a comprehensive review of the literature about the safety and efficacy of insulin treatment with technology for diabetes to support clinicians in the management of infants with neonatal diabetes mellitus. A total of 22 papers were included, most of them case reports or case series. The first infants with neonatal diabetes mellitus treated with insulin pumps were described nearly two decades ago. Over the years, continuous glucose monitoring systems were added to treat these individuals, allowing for a better customization of insulin administration. Insulin was diluted in some cases to further minimize the doses. Improvement in technology for diabetes prompted clinicians to use new devices and algorithms for insulin delivery in infants with neonatal diabetes as well. These systems are safe and effective, may shorten hospital stay, and help clinicians weaning insulin during the remission phase in the transient forms or switching from insulin to sulfonylurea when suggested by the molecular diagnosis. New technologies for insulin delivery in infants with neonatal diabetes can be used safely and closed-loop algorithms can work properly in these situations, optimizing blood glucose control.

Efficacy and Safety of Different Hybrid Closed Loop Systems for Automated Insulin Delivery in People With Type 1 Diabetes: A Systematic Review and Network Meta-Analysis.

AIMS: To compare the efficacy and safety of different hybrid closed loop (HCL) systems in people with diabetes through a network meta-analysis. METHODS: We searched MEDLINE, EMBASE, CENTRAL and PubMed for randomised clinical trials (RCTs) enrolling children, adolescents and/or adults with type 1 or type 2 diabetes, evaluating Minimed 670G, Minimed 780G, Control-IQ, CamAPS Fx, DBLG-1, DBLHU, and Omnipod 5 HCL systems against other types of insulin therapy, and reporting time in target range (TIR) as outcome. RESULTS: A total of 28 RCTs, all enrolling people with type 1 diabetes, were included. HCL systems significantly increased TIR compared with subcutaneous insulin therapy without continuous glucose monitoring (SIT). Minimed 780G achieved the highest TIR ahead of Control IQ (mean difference (MD) 5.1%, 95% confidence interval (95% CI) [0.68; 9.52], low certainty), Minimed 670G (MD 7.48%, 95% CI [4.27; 10.7], moderate certainty), CamAPS Fx (MD 8.94%, 95% CI [4.35; 13.54], low certainty), and DBLG1 (MD 10.69%, 95% CI [5.73; 15.65], low certainty). All HCL systems decreased time below target range, with DBLG1 (MD -3.69%, 95% CI [-5.2; -2.19], high certainty), Minimed 670G (MD -2.9%, 95% CI [-3.77; -2.04], moderate certainty) and Minimed 780G (MD -2.79%, 95% CI [-3.94; -1.64], high certainty) exhibiting the largest reductions compared to SIT. The risk of severe hypoglycaemia and diabetic ketoacidosis was similar to other types of insulin therapy. CONCLUSIONS: We show a hierarchy of efficacy among the different HCL systems in people with type 1 diabetes, thus providing support to clinical decision-making. TRIAL REGISTRATION: PROSPERO CRD42023453717.

Adaptive closed-loop modulation of cortical theta oscillations: Insights into the neural dynamics of navigational decision-making.

Navigational decision-making tasks, such as spatial working memory (SWM), rely highly on information integration from several cortical and sub-cortical regions. Performance in SWM tasks is associated with theta rhythm, including low-frequency oscillations related to movement and memory. The interaction of the ventral hippocampus (vHPC) and medial prefrontal cortex (mPFC), reflected in theta synchrony, is essential in various steps of information processing during SWM. We used a closed-loop neurofeedback (CLNF) system to upregulate theta power in the mPFC and investigate its effects on circuit dynamics and behavior in animal models. Specifically, we hypothesized that enhancing the power of the theta rhythm in the mPFC might improve SWM performance. Animals were divided into three groups: closed-loop (CL), random-loop (RL), and OFF (without stimulation). We recorded local field potential (LFP) in the mPFC while electrical reward stimulation contingent on cortical theta activity was delivered to the lateral hypothalamus (LH), which is considered one of the central reward-associated regions. We also recorded LFP in the vHPC to evaluate the related subcortical neural changes. Results revealed a sustained increase in the theta power in both mPFC and vHPC for the CL group. Our analysis also revealed an increase in mPFC-vHPC synchronization in the theta range over the stimulation sessions in the CL group, as measured by coherence and cross-correlation in the theta frequency band. The reinforcement of this circuit improved spatial decision-making performance in the subsequent behavioral results. Our findings provide direct evidence of the relationship between specific theta upregulation and SWM performance and suggest that theta oscillations are integral to cognitive processes. Overall, this study highlights the potential of adaptive CLNF systems in investigating neural dynamics in various brain circuits.

Dye-Based Fluorescent Organic Nanoparticles, New Promising Tools for Optogenetics.

Dye-based fluorescent organic nanoparticles are a specific class of nanoparticles obtained by nanoprecipitation in water of pure dyes only. While the photophysical and colloidal properties of the nanoparticles strongly depend on the nature of the aggregated dyes, their excellent brightness in the visible and in the near infrared make these nanoparticles a unique and versatile platform for in vivo application. This article examines the promising utilization of these nanoparticles for in vivo optogenetics applications. Their photophysical properties as well as their biocompatibility and their capacity to activate Chrimson opsin in vivo through the fluorescence reabsorption process are demonstrated. Additionally, an illustrative example of employing these nanoparticles in fear reduction in mice through closed-loop stimulation is presented. Through an optogenetic methodology, the nanoparticles demonstrate an ability to selectively manipulate neurons implicated in the fear response and diminish the latter. Dye-based fluorescent organic nanoparticles represent a promising and innovative strategy for optogenetic applications, holding substantial potential in the domain of translational neuroscience. This work paves the way for novel therapeutic modalities for neurological and neuropsychiatric disorders.

Intussusception Within a Jejunostomy With Closed Loop Obstruction: A Case Report.

Jejunojejunal intussusception is a rare yet severe complication of Roux-en-Y gastric bypass (RYGBP) surgery. We are presenting a unique case of retrograde jejunal intussusception with a closed-loop blockage and an associated abdominal herniation that occurred two years after a laparoscopic RYGBP. The patient presented with abdominal pain, nausea, and vomiting, prompting a clinical diagnosis and a biphasic contrast-enhanced computed tomography (CT) scan, which later revealed a complicated jejunal intussusception with signs of ischemia showing decreased wall enhancement and distal collapsed jejunal walls with complete closed-loop bowel obstruction. The case was successfully managed through emergent laparoscopy to repair the hernia and reduce the intussusception, after which the postoperative period was unremarkable. This article aims to raise awareness about this rare but significant postoperative complication and stress the importance of early medical attention in similar cases.

A Recyclable Ionogel with High Mechanical Robustness Based on Covalent Adaptable Networks.

Ionogels are an emerging class of soft materials for flexible electronics, with high ionic conductivity, low volatility, and mechanical stretchability. Recyclable ionogels are recently developed to address the sustainability crisis of current electronics, through the introduction of non-covalent bonds. However, this strategy sacrifices mechanical robustness and chemical stability, severely diminishing the potential for practical application. Here, covalent adaptable networks (CANs) are incorporated into ionogels, where dynamic covalent crosslinks endow high strength (11.3 MPa tensile strength), stretchability (2396% elongation at break), elasticity (energy loss coefficient of 0.055 at 100% strain), and durability (5000 cycles of 150% strain). The reversible nature of CANs allows the ionogel to be closed-loop recyclable for up to ten times. Additionally, the ionogel is toughened by physical crosslinks between conducting ions and polymer networks, breaking the common dilemma in enhancing mechanical properties and electrical conductivity. The ionogel demonstrates robust strain sensing performance under harsh mechanical treatments and is applied for reconfigurable multimodal sensing based on its recyclability. This study provides insights into improving the mechanical and electrical properties of ionogels toward functionally reliable and environmentally sustainable bioelectronics.