Hysteretic behavior of bladder afferent neurons in response to changes in bladder pressure

Abstract Background Mechanosensitive afferents innervating the bladder increase their firing rate as the bladder fills and pressure rises. However, the relationship between afferent firing rates and intravesical pressure is not a simple linear one. Firing rate responses to pressure can differ depending on prior activity, demonstrating hysteresis in the system. Though this hysteresis has been commented on in published literature, it has not been quantified. Results Sixty-six bladder afferents recorded from sacral dorsal root ganglia in five alpha-chloralose anesthetized felines were identified based on their characteristic responses to pressure (correlation coefficient ≥ 0.2) during saline infusion (2 ml/min). For saline infusion trials, we calculated a maximum hysteresis ratio between the firing rate difference at each pressure and the overall firing rate range (or Hmax) of 0.86 ± 0.09 (mean ± standard deviation) and mean hysteresis ratio (or Hmean) of 0.52 ± 0.13 (n = 46 afferents). For isovolumetric trials in two experiments (n = 33 afferents) Hmax was 0.72 ± 0.14 and Hmean was 0.40 ± 0.14. Conclusions A comprehensive state model that integrates these hysteresis parameters to determine the bladder state may improve upon existing neuroprostheses for bladder control.http://deepblue.lib.umich.edu/bitstream/2027.42/134628/1/12868_2016_Article_292.pd

Transcutaneous Electrical Nerve Stimulation to Improve Female Sexual Dysfunction Symptoms: A Pilot Study

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146481/1/ner12846.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146481/2/ner12846_am.pd

Interpatient differences in neural recruitment patterns during pudendal nerve stimulation – a computational investigation

Bladder dysfunction impairs the quality of life for millions of individuals around the world. Common causes of bladder dysfunction include aging, trauma, and neurological disorders. Due to inadequacies in conventional treatments, neuromodulation therapies to address bladder dysfunction, such as sacral nerve stimulation, have emerged. However, patient needs still remain unmet. Pudendal nerve stimulation (PNS) has recently gained clinical interest as a promising treatment for bladder dysfunction. While PNS has been extensively investigated in preclinical settings, there is a gap in our understanding of the mechanisms of action and efficacy of PNS as limited studies of PNS have been performed on human subjects. We developed patient-specific computational models for 10 participants receiving PNS as part of their clinical care to improve our understanding of this therapy. Our modeling approach consisted of segmentation of pre- and post-operative magnetic resonance and computed tomography images to create a volume conductor model of each participant’s pelvic anatomy and implanted stimulator. We used the finite element method to approximate the electric fields generated by PNS for each participant. We then simulated each participant’s neural recruitment during PNS by coupling the electric field solutions to multicompartment axon models placed within the pudendal nerve. We used this modeling approach to simulate the neural recruitment order for each participant over a select range of stimulation parameters. Our simulations demonstrated neural recruitment profiles in agreement with the experimental stimulation thresholds measured in each participant. Our results suggest that stimulation waveform parameters, contact selection, and electrode array placement all have a significant impact on the efficacy of PNS. PNS is an emerging neuromodulation therapy which may help address bladder dysfunction that is refractory to conventional treatments. In this study, we used computational models to account for patient-specific anatomy and electrode array placement to simulate the effects of PNS. Our model results were in line with experimental measurements and underscore the importance of electrode placement relative to the roots, trunk, and branches of the pudendal nerve. Future models could be enhanced by considering histological studies that describe the somatotopic organization of the pudendal nerve. Including such data could provide further insight into the therapeutic mechanisms of PNS and optimize its use in clinical applications

Ultracompliant Carbon Nanotube Direct Bladder Device

The bladder, stomach, intestines, heart, and lungs all move dynamically to achieve their purpose. A long‐term implantable device that can attach onto an organ, sense its movement, and deliver current to modify the organ function would be useful in many therapeutic applications. The bladder, for example, can suffer from incomplete contractions that result in urinary retention with patients requiring catheterization. Those affected may benefit from a combination of a strain sensor and electrical stimulator to better control bladder emptying. The materials and design of such a device made from thin layer carbon nanotube (CNT) and Ecoflex 00–50 are described and demonstrate its function with in vivo feline bladders. During bench‐top characterization, the resistive and capacitive sensors exhibit stability throughout 5000 stretching cycles under physiology conditions. In vivo measurements with piezoresistive devices show a high correlation between sensor resistance and volume. Stimulation driven from platinum‐silicone composite electrodes successfully induce bladder contraction. A method for reliable connection and packaging of medical grade wire to the CNT device is also presented. This work is an important step toward the translation of low‐durometer elastomers, stretchable CNT percolation, and platinum‐silicone composite, which are ideal for large‐strain bioelectric applications to sense or modulate dynamic organ states.An ultracompliant strain sensor and stimulation electrode array is described built from carbon nanotube, low‐durometer silicone, and a PDMS/Pt‐microparticle composite. This work provides an early demonstration of stretchable electronics to address the condition of underactive bladder by real‐time volume detection and evoked contractions with particular attention to fabrication and strain sensor performance in physiologic conditions.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152012/1/adhm201900477-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152012/2/adhm201900477.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152012/3/adhm201900477_am.pd

Electrical stimulation of renal nerves for modulating urine glucose excretion in rats

Abstract Background The role of the kidney in glucose homeostasis has gained global interest. Kidneys are innervated by renal nerves, and renal denervation animal models have shown improved glucose regulation. We hypothesized that stimulation of renal nerves at kilohertz frequencies, which can block propagation of action potentials, would increase urine glucose excretion. Conversely, we hypothesized that low frequency stimulation, which has been shown to increase renal nerve activity, would decrease urine glucose excretion. Methods We performed non-survival experiments on male rats under thiobutabarbital anesthesia. A cuff electrode was placed around the left renal artery, encircling the renal nerves. Ureters were cannulated bilaterally to obtain urine samples from each kidney independently for comparison. Renal nerves were stimulated at kilohertz frequencies (1–50 kHz) or low frequencies (2–5 Hz), with intravenous administration of a glucose bolus shortly into the 25–40-min stimulation period. Urine samples were collected at 5–10-min intervals, and colorimetric assays were used to quantify glucose excretion and concentration between stimulated and non-stimulated kidneys. A Kruskal-Wallis test was performed across all stimulation frequencies (α = 0.05), followed by a post-hoc Wilcoxon rank sum test with Bonferroni correction (α = 0.005). Results For kilohertz frequency trials, the stimulated kidney yielded a higher average total urine glucose excretion at 33 kHz (+ 24.5%; n = 9) than 1 kHz (− 5.9%; n = 6) and 50 kHz (+ 2.3%; n = 14). In low frequency stimulation trials, 5 Hz stimulation led to a lower average total urine glucose excretion (− 40.4%; n = 6) than 2 Hz (− 27.2%; n = 5). The average total urine glucose excretion between 33 kHz and 5 Hz was statistically significant (p < 0.005). Similar outcomes were observed for urine flow rate, which may suggest an associated response. No trends or statistical significance were observed for urine glucose concentrations. Conclusion To our knowledge, this is the first study to investigate electrical stimulation of renal nerves to modulate urine glucose excretion. Our experimental results show that stimulation of renal nerves may modulate urine glucose excretion, however, this response may be associated with urine flow rate. Future work is needed to examine the underlying mechanisms and identify approaches for enhancing regulation of glucose excretion.https://deepblue.lib.umich.edu/bitstream/2027.42/143868/1/42234_2018_Article_8.pd

Deep Learning from Dual-Energy Information for Whole-Heart Segmentation in Dual-Energy and Single-Energy Non-Contrast-Enhanced Cardiac CT

Deep learning-based whole-heart segmentation in coronary CT angiography (CCTA) allows the extraction of quantitative imaging measures for cardiovascular risk prediction. Automatic extraction of these measures in patients undergoing only non-contrast-enhanced CT (NCCT) scanning would be valuable. In this work, we leverage information provided by a dual-layer detector CT scanner to obtain a reference standard in virtual non-contrast (VNC) CT images mimicking NCCT images, and train a 3D convolutional neural network (CNN) for the segmentation of VNC as well as NCCT images. Contrast-enhanced acquisitions on a dual-layer detector CT scanner were reconstructed into a CCTA and a perfectly aligned VNC image. In each CCTA image, manual reference segmentations of the left ventricular (LV) myocardium, LV cavity, right ventricle, left atrium, right atrium, ascending aorta, and pulmonary artery trunk were obtained and propagated to the corresponding VNC image. These VNC images and reference segmentations were used to train 3D CNNs for automatic segmentation in either VNC images or NCCT images. Automatic segmentations in VNC images showed good agreement with reference segmentations, with an average Dice similarity coefficient of 0.897 \pm 0.034 and an average symmetric surface distance of 1.42 \pm 0.45 mm. Volume differences [95% confidence interval] between automatic NCCT and reference CCTA segmentations were -19 [-67; 30] mL for LV myocardium, -25 [-78; 29] mL for LV cavity, -29 [-73; 14] mL for right ventricle, -20 [-62; 21] mL for left atrium, and -19 [-73; 34] mL for right atrium, respectively. In 214 (74%) NCCT images from an independent multi-vendor multi-center set, two observers agreed that the automatic segmentation was mostly accurate or better. This method might enable quantification of additional cardiac measures from NCCT images for improved cardiovascular risk prediction