Bioelectronic medicine最新文献

Measuring the weak magnetic fields generated by spontaneous biological activity, such as those produced in the brain or heart, offers complementary information to conventional electrophysiological techniques, as electroencephalography and electrocardiography. Nevertheless, the widespread clinical use of biomagnetic sensing is hindered by the bulky and costly technology currently available, including SQUID-based and optically pumped magnetometers. In recent years magnetoelectric materials have been explored as highly sensitive, room-temperature magnetic field sensors, offering a compelling alternative to conventional approaches. Here, we investigate the feasibility of using resonant magnetoelectric nanoparticles (MENPs) as nanoscale magnetic sensors by exploiting the delta-E effect, in which magnetic-field-induced changes in elastic properties, i.e. Young's modulus, shift the nanoparticle's resonance frequency. Using a computational modeling approach, we first developed and characterized a core-shell MENP model. We then identified its natural resonance frequencies in the GHz range, evaluated its sensitivity to external magnetic field variations, and determined the optimal bias static magnetic field and core radius for maximum sensitivity. Finally, we assessed the performance of the optimized nanoparticle in detecting neural-level magnetic fields. Our simulations demonstrate that MENP can achieve a maximum sensitivity of 2.59 Hz/nT for a core diameter of 50 nm under a bias static magnetic field of 1000 Oe. These findings highlight both the feasibility of exploiting the delta-E effect in MENPs and the tunability of their structural parameters, which could be tailored for specific applications. In conclusion, this work set the theoretical groundwork for the development of cutting-edge, wireless and non-invasive nanoscale magnetic sensors for neural interfacing and biomedical signals sensing.

Background: Damage to the peripheral nervous system severely disrupts motor control, sensory perception, and organ function, often resulting in long-term disability. To restore such impairments, peripheral nerve interfaces (PNIs) aim to achieve precise stimulation selectivity, yet current approaches face several limitations. Most PNIs rely on metal-based electrodes, which introduce a mechanical mismatch with soft neural tissue and are limited by low charge-injection capacity. The device design of these PNIs also suffers from a fundamental trade-off: highly invasive approaches enable high selectivity but provoke strong foreign-body responses, while less invasive designs minimize tissue damage but fail to provide sufficient selectivity. Although current-steering strategies have been explored to enhance selectivity, their performance remains inadequate for clinical application. Significant advances in PNIs are required to safely achieve higher selectivity.

Methods: In this work, a novel array consisting of a single penetrating interfascicular electrode (SPIF) added to an extraneural cuff (EC) array, termed SPIFEC, was developed using laser-based fabrication and polymeric materials. Electrochemical properties were characterized, and ex vivo experiments using whole rat sciatic nerve were conducted to assess fascicular selectivity. The implantation was assessed through computed tomography (CT) imaging.

Results: The SPIFEC design includes seven extraneural electrodes and one double-sided interfascicular penetrating electrode. Electrochemical analysis revealed the polymeric electrodes had low impedance, high charge storage capacity and high charge-injection limit, when compared to previous reports on traditional metallic devices. Ex vivo studies demonstrated that the device achieved high fascicular selectivity, particularly in nerves with well-defined fascicles, outperforming a comparable non-penetrating cuff. CT imaging confirmed the interfascicular positioning of the penetrating electrode.

Conclusion: These results demonstrate the potential of this novel SPIFEC array in enhancing spatial selectivity for peripheral nerve applications. Further studies, including chronic in vivo testing, are required to fully evaluate long-term performance and clinical potential in neuroprosthetic systems.

Objective: The aim of this study was to identify and analyze all the relevant literature regarding the use of machine learning to predict response to neuromodulation therapies in patients diagnosed with drug-resistant epilepsy.

Material and methods: We systematically search PubMed, Embase, Scopus and Cochrane databases to identify all the studies that used machine learning models to predict response to neuromodulations. Prior to the search, the study was registered at the International prospective register of systematic reviews (PROSPERO, CRD42024543952). Quality assessment and risk of bias was done using PROBAST. A random effects model was used to calculate the pooled value of the AUROC. A sub-analysis was performed for population-specific scenarios.

Results: A total of 4,451 studies were identified after our initial search, from those, only 12 papers were included in the final analysis. The total number of patients across all the cohorts was 535. 11 studies focused on VNS and only one on ctDCS. Only five out of the 12 studies included an external cohort to validate the results. The most common population was pediatric (n = 7). The most common ML model used was the support vector machine. The pooled area under the receiver operating characteristic curve (AUROC) was 0.84 (95% IC, 079-0.88).

Conclusions: Our study suggests that multimodal ML approaches show promising performance in predicting response to neuromodulation strategies in patients with drug-resistant epilepsy. However, the limited number of studies, the scarcity of external validation and small cohorts highlight the need for larger, high-quality prospective investigations to confirm these findings and improve the generalizability of ML-based prediction models.

Background: Chronic overactivity of the renal nerves is a key pathophysiological attribute of drug-resistant hypertension. Indeed, catheter-based renal denervation can lower blood pressure by severing the brain-kidney (efferent nerves) and kidney-brain (afferent nerves) link, but its irreversibility and potential nerve reconnection limits adaptability and longevity, highlighting the need for alternative treatments. Kilohertz-frequency electrical stimulation is an approach known to reversibly inhibit peripheral nerve activity and has potential to reversibly modulate renal blood flow.

Methods: This study investigated how electrical stimulation of the renal nerves affects renal blood flow in a non-diseased anesthetized swine. Using unilateral hook electrodes around the renal artery-nerve complex, we performed parameter sweeps of stimulation frequency (20-15000 Hz) and measured blood flow and blood pressure changes in the ipsilateral kidney.

Results: Stimulation at low frequencies (≤ 100 Hz) resulted in a sustained reduction in renal blood flow. High stimulation frequencies (> 100 Hz) often resulted in an immediate decrease in blood flow through the kidneys, but the responses exhibited adaptation with continued isochronal pulsatile stimulation. Notably, while kilohertz-frequency stimulation did not directly increase renal blood flow in this experiment, it did induce a carryover effect on renal nerve sensitivities to low-frequency stimulation, reducing the effect of subsequent low-frequency stimulation on renal blood flow (from - 11.8% to -4.7%, median).

Conclusions: Responses to renal nerve stimulation depend on stimulation frequency with effects that can persist or adapt as well as effects that can range from decreasing renal blood flow to decreasing the sensitivity of the renal nerve signaling pathway. These findings have important implications for future development of bioelectronic interfaces with the renal nerve for modulation of kidney function.

The cochlear implant (CI) is considered one of the most successful neural prostheses, enabling deaf individuals to achieve intelligible speech perception. However, CI performance remains limited in noise and with complex acoustic scenes, including music and multi-talker speech. One major issue for CIs is the poor electrode-neural interface where electrodes are positioned within the bony cochlea and distant from the auditory nerve fibers. Due to recent advances with microelectrode technologies designed for peripheral nerves, there has been rekindled interest in the auditory nerve implant (ANI), in which a novel prosthesis with a microelectrode array has been developed for direct stimulation of the auditory nerve. Animal studies demonstrate that the ANI achieves substantially lower thresholds and more selective neural activation compared to CI stimulation, which could lead to greater hearing performance. To successfully translate the ANI to patients, the ANI device components need to be further designed for safe and reliable implantation in humans through development of alternative surgical techniques, and validated in chronic animal studies. New stimulation strategies also need to be developed, especially with the potential to insert tens to hundreds of microelectrodes across the spiraling tonotopy of the auditory nerve to activate more spatially and temporally distinct nerve fiber patterns than is possible with the CI. Once in humans, extensive perceptual experiments can be performed with the ANI to characterize thresholds, loudness growth functions, pitch patterns, temporal coding properties, and spectral selectivity, as well as evaluating novel stimulation strategies that will guide the development of the next generation ANI system.