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Background: Recent evidence demonstrates that manually triggered vagus nerve stimulation (VNS) combined with rehabilitation leads to increased recovery of upper limb motor function after stroke. This approach is premised on studies demonstrating that the timing of stimulation relative to movements is a key determinant in the effectiveness of this approach., Objective: The overall goal of the study was to identify an algorithm that could be used to automatically trigger VNS on the best movements during rehabilitative exercises while maintaining a desired interval between stimulations to reduce the burden of manual stimulation triggering., Methods: To develop the algorithm, we analyzed movement data collected from patients with a history of neurological injury. We applied 3 different algorithms to the signal, analyzed their triggering choices, and then validated the best algorithm by comparing triggering choices to those selected by a therapist delivering VNS therapy., Results: The dynamic algorithm triggered above the 95th percentile of maximum movement at a rate of 5.09 (interquartile range [IQR] = 0.74) triggers per minute. The periodic algorithm produces stimulation at set intervals but low movement selectivity (34.05%, IQR = 7.47), while the static threshold algorithm produces long interstimulus intervals (27.16 ± 2.01 seconds) with selectivity of 64.49% (IQR = 25.38). On average, the dynamic algorithm selects movements that are 54 ± 3% larger than therapist-selected movements., Conclusions: This study shows that a dynamic algorithm is an effective strategy to trigger VNS during the best movements at a reliable triggering rate., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: MPK has a financial interest in MicroTransponder, Inc., which is developing closed-loop VNS for stroke and tinnitus. RLR is the co-founder and CEO of XNerve, which is developing nerve stimulation therapies. The other authors declare that no competing interests exist.

Nerve damage can cause chronic, debilitating problems including loss of motor control and paresthesia, and generates maladaptive neuroplasticity as central networks attempt to compensate for the loss of peripheral connectivity. However, it remains unclear if this is a critical feature responsible for the expression of symptoms. Here, we use brief bursts of closed-loop vagus nerve stimulation (CL-VNS) delivered during rehabilitation to reverse the aberrant central plasticity resulting from forelimb nerve transection. CL-VNS therapy drives extensive synaptic reorganization in central networks paralleled by improved sensorimotor recovery without any observable changes in the nerve or muscle. Depleting cortical acetylcholine blocks the plasticity-enhancing effects of CL-VNS and consequently eliminates recovery, indicating a critical role for brain circuits in recovery. These findings demonstrate that manipulations to enhance central plasticity can improve sensorimotor recovery and define CL-VNS as a readily translatable therapy to restore function after nerve damage.

Background and Purpose: Chronic impairment of the arm and hand is a common consequence of stroke. Animal and human studies indicate that brief bursts of vagus nerve stimulation (VNS) in conjunction with rehabilitative training improve recovery of motor function after stroke. In this study, we tested whether VNS could promote generalization, long-lasting recovery, and structural plasticity in motor networks., Methods: Rats were trained on a fully automated, quantitative task that measures forelimb supination. On task proficiency, unilateral cortical and subcortical ischemic lesions were administered. One week after ischemic lesion, rats were randomly assigned to receive 6 weeks of rehabilitative training on the supination task with or without VNS. Rats then underwent 4 weeks of testing on a task assessing forelimb strength to test generalization of recovery. Finally, the durability of VNS benefits was tested on the supination task 2 months after the cessation of VNS. After the conclusion of behavioral testing, viral tracing was performed to assess synaptic connectivity in motor networks., Results: VNS enhances plasticity in corticospinal motor networks to increase synaptic connectivity to musculature of the rehabilitated forelimb. Adding VNS more than doubled the benefit of rehabilitative training, and the improvements lasted months after the end of VNS. Pairing VNS with supination training also significantly improved performance on a similar, but untrained task that emphasized volitional forelimb strength, suggesting generalization of forelimb recovery., Conclusions: This study provides the first evidence that VNS paired with rehabilitative training after stroke (1) doubles long-lasting recovery on a complex task involving forelimb supination, (2) doubles recovery on a simple motor task that was not paired with VNS, and (3) enhances structural plasticity in motor networks., (© 2018 American Heart Association, Inc.)

Myocardial ischemia is spontaneous, frequently asymptomatic, and contributes to fatal cardiovascular consequences. Importantly, myocardial sensory networks cannot reliably detect and correct myocardial ischemia on their own. Here, we demonstrate an artificially intelligent and responsive bioelectronic medicine, where an artificial neural network (ANN) supplements myocardial sensory networks, enabling reliable detection and correction of myocardial ischemia. ANNs were first trained to decode spontaneous cardiovascular stress and myocardial ischemia with an overall accuracy of ~92%. ANN-controlled vagus nerve stimulation (VNS) significantly mitigated major physiological features of myocardial ischemia, including ST depression and arrhythmias. In contrast, open-loop VNS or ANN-controlled VNS following a caudal vagotomy essentially failed to reverse cardiovascular pathophysiology. Last, variants of ANNs were used to meet clinically relevant needs, including interpretable visualizations and unsupervised detection of emerging cardiovascular stress. Overall, these preclinical results suggest that ANNs can potentially supplement deficient myocardial sensory networks via an artificially intelligent bioelectronic medicine system. [ABSTRACT FROM AUTHOR]