1
|
Payne SC, Osborne PB, Thompson A, Eiber CD, Keast JR, Fallon JB. Selective recording of physiologically evoked neural activity in a mixed autonomic nerve using a minimally invasive array. APL Bioeng 2023; 7:046110. [PMID: 37928642 PMCID: PMC10625482 DOI: 10.1063/5.0164951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 10/16/2023] [Indexed: 11/07/2023] Open
Abstract
Real-time closed-loop control of neuromodulation devices requires long-term monitoring of neural activity in the peripheral nervous system. Although many signal extraction methods exist, few are both clinically viable and designed for extracting small signals from fragile peripheral visceral nerves. Here, we report that our minimally invasive recording and analysis technology extracts low to negative signal to noise ratio (SNR) neural activity from a visceral nerve with a high degree of specificity for fiber type and class. Complex activity was recorded from the rat pelvic nerve that was physiologically evoked during controlled bladder filling and voiding, in an extensively characterized in vivo model that provided an excellent test bed to validate our technology. Urethane-anesthetized male rats (n = 12) were implanted with a four-electrode planar array and the bladder instrumented for continuous-flow cystometry, which measures urodynamic function by recording bladder pressure changes during constant infusion of saline. We demonstrated that differential bipolar recordings and cross-correlation analyses extracts afferent and efferent activity, and discriminated between subpopulations of fibers based on conduction velocity. Integrated Aδ afferent fiber activity correlated with bladder pressure during voiding (r2: 0.66 ± 0.06) and was not affected by activating nociceptive afferents with intravesical capsaicin (r2: 0.59 ± 0.14, P = 0.54, and n = 3). Collectively, these results demonstrate our minimally invasive recording and analysis technology is selective in extracting mixed neural activity with low/negative SNR. Furthermore, integrated afferent activity reliably correlates with bladder pressure and is a promising first step in developing closed-loop technology for bladder control.
Collapse
Affiliation(s)
| | - Peregrine B. Osborne
- Department of Anatomy and Physiology, University of Melbourne, Victoria 3010, Australia
| | | | - Calvin D. Eiber
- Department of Anatomy and Physiology, University of Melbourne, Victoria 3010, Australia
| | - Janet R. Keast
- Department of Anatomy and Physiology, University of Melbourne, Victoria 3010, Australia
| | | |
Collapse
|
2
|
Sevcencu C. Single-interface bioelectronic medicines - concept, clinical applications and preclinical data. J Neural Eng 2022; 19. [PMID: 35533654 DOI: 10.1088/1741-2552/ac6e08] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 05/08/2022] [Indexed: 11/12/2022]
Abstract
Presently, large groups of patients with various diseases are either intolerant, or irresponsive to drug therapies and also intractable by surgery. For several diseases, one option which is available for such patients is the implantable neurostimulation therapy. However, lacking closed-loop control and selective stimulation capabilities, the present neurostimulation therapies are not optimal and are therefore used as only "third" therapeutic options when a disease cannot be treated by drugs or surgery. Addressing those limitations, a next generation class of closed-loop controlled and selective neurostimulators generically named bioelectronic medicines seems within reach. A sub-class of such devices is meant to monitor and treat impaired functions by intercepting, analyzing and modulating neural signals involved in the regulation of such functions using just one neural interface for those purposes. The primary objective of this review is to provide a first broad perspective on this type of single-interface devices for bioelectronic therapies. For this purpose, the concept, clinical applications and preclinical studies for further developments with such devices are here analyzed in a narrative manner.
Collapse
Affiliation(s)
- Cristian Sevcencu
- National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, Cluj-Napoca, 400293, ROMANIA
| |
Collapse
|
3
|
Closed-loop sacral neuromodulation for bladder function using dorsal root ganglia sensory feedback in an anesthetized feline model. Med Biol Eng Comput 2022; 60:1527-1540. [PMID: 35349032 PMCID: PMC9124066 DOI: 10.1007/s11517-022-02554-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 03/16/2022] [Indexed: 10/18/2022]
Abstract
Overactive bladder patients suffer from a frequent, uncontrollable urge to urinate, which can lead to a poor quality of life. We aim to improve open-loop sacral neuromodulation therapy by developing a conditional stimulation paradigm using neural recordings from dorsal root ganglia (DRG) as sensory feedback. Experiments were performed in 5 anesthetized felines. We implemented a Kalman filter-based algorithm to estimate the bladder pressure in real-time using sacral-level DRG neural recordings and initiated sacral root electrical stimulation when the algorithm detected an increase in bladder pressure. Closed-loop neuromodulation was performed during continuous cystometry and compared to bladder fills with continuous and no stimulation. Overall, closed-loop stimulation increased bladder capacity by 13.8% over no stimulation (p < 0.001) and reduced stimulation time versus continuous stimulation by 57.7%. High-confidence bladder single units had a reduced sensitivity during stimulation, with lower linear trendline fits and higher pressure thresholds for firing observed during stimulation trials. This study demonstrates the utility of decoding bladder pressure from neural activity for closed-loop control of sacral neuromodulation. An underlying mechanism for sacral neuromodulation may be a reduction in bladder sensory neuron activity during stimulation. Real-time validation during behavioral studies is necessary prior to clinical translation of closed-loop sacral neuromodulation.
Collapse
|
4
|
Multitask neural networks for predicting bladder pressure with time series data. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
5
|
Sperry ZJ, Na K, Jun J, Madden LR, Socha A, Yoon E, Seymour JP, Bruns TM. High-density neural recordings from feline sacral dorsal root ganglia with thin-film array. J Neural Eng 2021; 18. [PMID: 33545709 DOI: 10.1088/1741-2552/abe398] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 02/05/2021] [Indexed: 12/26/2022]
Abstract
Objective. Dorsal root ganglia (DRG) are promising sites for recording sensory activity. Current technologies for DRG recording are stiff and typically do not have sufficient site density for high-fidelity neural data techniques.Approach. In acute experiments, we demonstrate single-unit neural recordings in sacral DRG of anesthetized felines using a 4.5µm thick, high-density flexible polyimide microelectrode array with 60 sites and 30-40µm site spacing. We delivered arrays into DRG with ultrananocrystalline diamond shuttles designed for high stiffness affording a smaller footprint. We recorded neural activity during sensory activation, including cutaneous brushing and bladder filling, as well as during electrical stimulation of the pudendal nerve and anal sphincter. We used specialized neural signal analysis software to sort densely packed neural signals.Main results. We successfully delivered arrays in five of six experiments and recorded single-unit sensory activity in four experiments. The median neural signal amplitude was 55μV peak-to-peak and the maximum unique units recorded at one array position was 260, with 157 driven by sensory or electrical stimulation. In one experiment, we used the neural analysis software to track eight sorted single units as the array was retracted ∼500μm.Significance. This study is the first demonstration of ultrathin, flexible, high-density electronics delivered into DRG, with capabilities for recording and tracking sensory information that are a significant improvement over conventional DRG interfaces.
Collapse
Affiliation(s)
- Zachariah J Sperry
- Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.,Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States of America
| | - Kyounghwan Na
- Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States of America
| | - James Jun
- Flatiron Institute, Simons Foundation, New York City, NY, United States of America
| | - Lauren R Madden
- Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.,Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States of America
| | - Alec Socha
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States of America.,Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States of America
| | - Eusik Yoon
- Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.,Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States of America.,Center for Nanomedicine, Institute for Basic Science (IBS) and Graduate Program of Nano Biomedical Engineering (NanoBME), Advanced Science Institute, Yonsei University, Seoul, Republic of Korea
| | - John P Seymour
- Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.,Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States of America.,University of Texas Health Science Center, Department of Neurosurgery, Houston, TX, United States of America.,Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States of America
| | - Tim M Bruns
- Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States of America.,Biointerfaces Institute, University of Michigan, Ann Arbor, MI, United States of America
| |
Collapse
|
6
|
Cracchiolo M, Ottaviani MM, Panarese A, Strauss I, Vallone F, Mazzoni A, Micera S. Bioelectronic medicine for the autonomic nervous system: clinical applications and perspectives. J Neural Eng 2021; 18. [PMID: 33592597 DOI: 10.1088/1741-2552/abe6b9] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 02/16/2021] [Indexed: 12/11/2022]
Abstract
Bioelectronic medicine (BM) is an emerging new approach for developing novel neuromodulation therapies for pathologies that have been previously treated with pharmacological approaches. In this review, we will focus on the neuromodulation of autonomic nervous system (ANS) activity with implantable devices, a field of BM that has already demonstrated the ability to treat a variety of conditions, from inflammation to metabolic and cognitive disorders. Recent discoveries about immune responses to ANS stimulation are the laying foundation for a new field holding great potential for medical advancement and therapies and involving an increasing number of research groups around the world, with funding from international public agencies and private investors. Here, we summarize the current achievements and future perspectives for clinical applications of neural decoding and stimulation of the ANS. First, we present the main clinical results achieved so far by different BM approaches and discuss the challenges encountered in fully exploiting the potential of neuromodulatory strategies. Then, we present current preclinical studies aimed at overcoming the present limitations by looking for optimal anatomical targets, developing novel neural interface technology, and conceiving more efficient signal processing strategies. Finally, we explore the prospects for translating these advancements into clinical practice.
Collapse
Affiliation(s)
- Marina Cracchiolo
- The BioRobotics Institute and Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Matteo Maria Ottaviani
- The BioRobotics Institute and Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Alessandro Panarese
- The BioRobotics Institute and Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Ivo Strauss
- The BioRobotics Institute and Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Fabio Vallone
- The BioRobotics Institute and Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Alberto Mazzoni
- The BioRobotics Institute and Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Silvestro Micera
- The BioRobotics Institute and Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.,Bertarelli Foundation Chair in Translational NeuroEngineering, Centre for Neuroprosthetics and Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| |
Collapse
|
7
|
A Computationally-Efficient, Online-Learning Algorithm for Detecting High-Voltage Spindles in the Parkinsonian Rats. Ann Biomed Eng 2020; 48:2809-2820. [PMID: 33200261 DOI: 10.1007/s10439-020-02680-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 10/22/2020] [Indexed: 10/23/2022]
Abstract
Abnormally-synchronized, high-voltage spindles (HVSs) are associated with motor deficits in 6-hydroxydopamine-lesioned parkinsonian rats. The non-stationary, spike-and-wave HVSs (5-13 Hz) represent the cardinal parkinsonian state in the local field potentials (LFPs). Although deep brain stimulation (DBS) is an effective treatment for the Parkinson's disease, continuous stimulation results in cognitive and neuropsychiatric side effects. Therefore, an adaptive stimulator able to stimulate the brain only upon the occurrence of HVSs is demanded. This paper proposes an algorithm not only able to detect the HVSs with low latency but also friendly for hardware realization of an adaptive stimulator. The algorithm is based on autoregressive modeling at interval, whose parameters are learnt online by an adaptive Kalman filter. In the LFPs containing 1131 HVS episodes from different brain regions of four parkinsonian rats, the algorithm detects all HVSs with 100% sensitivity. The algorithm also achieves higher precision (96%) and lower latency (61 ms), while requiring less computation time than the continuous wavelet transform method. As the latency is much shorter than the mean duration of an HVS episode (4.3 s), the proposed algorithm is suitable for realization of a smart neuromodulator for mitigating HVSs effectively by closed-loop DBS.
Collapse
|
8
|
Lubba CH, Ouyang A, Jones NS, Bruns TM, Schultz S. Bladder pressure encoding by sacral dorsal root ganglion fibres: implications for decoding. J Neural Eng 2020; 18. [PMID: 33202396 DOI: 10.1088/1741-2552/abcb14] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 11/17/2020] [Indexed: 11/11/2022]
Abstract
OBJECTIVE We aim at characterising the encoding of bladder pressure (intravesical pressure) by a population of sensory fibres. This research is motivated by the possibility to restore bladder function in elderly patients or after spinal cord injury using implanted devices, so called bioelectronic medicines. For these devices, nerve-based estimation of intravesical pressure can enable a personalized and on-demand stimulation paradigm, which has promise of being more effective and efficient. In this context, a better understanding of the encoding strategies employed by the body might in the future be exploited by informed decoding algorithms that enable a precise and robust bladder-pressure estimation. APPROACH To this end, we apply information theory to microelectrode-array recordings from the cat sacral dorsal root ganglion while filling the bladder, conduct surrogate data studies to augment the data we have, and finally decode pressure in a simple informed approach. MAIN RESULTS We find an encoding scheme by different main bladder neuron types that we divide into three response types (slow tonic, phasic, and derivative fibres). We show that an encoding by different bladder neuron types, each represented by multiple cells, offers reliability through within-type redundancy and high information rates through semi-independence of different types. Our subsequent decoding study shows a more robust decoding from mean responses of homogeneous cell pools. SIGNIFICANCE We have here, for the first time, established a link between an information theoretic analysis of the encoding of intravesical pressure by a population of sensory neurons to an informed decoding paradigm. We show that even a simple adapted decoder can exploit the redundancy in the population to be more robust against cell loss. This work thus paves the way towards principled encoding studies in the periphery and towards a new generation of informed peripheral nerve decoders for bioelectronic medicines.
Collapse
Affiliation(s)
- Carl Henning Lubba
- Bioengineering, Imperial College London, Royal School of Mines, Exhibition Road, London, SW7 2AZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Aileen Ouyang
- Department of Biomedical Engineering, University of Michigan , Ann Arbor, Michigan, UNITED STATES
| | - Nick S Jones
- Department of Mathematics, Imperial College London, London, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Tim M Bruns
- Department of Biomedical Engineering, University of Michigan , Ann Arbor, Michigan, UNITED STATES
| | - Simon Schultz
- Imperial College London, London, SW7 2AZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| |
Collapse
|
9
|
Estimation of Bladder Pressure and Volume from the Neural Activity of Lumbosacral Dorsal Horn Using a Long-Short-Term-Memory-based Deep Neural Network. Sci Rep 2019; 9:18128. [PMID: 31792247 PMCID: PMC6889392 DOI: 10.1038/s41598-019-54144-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 11/09/2019] [Indexed: 12/30/2022] Open
Abstract
In this paper, we propose a deep recurrent neural network (DRNN) for the estimation of bladder pressure and volume from neural activity recorded directly from spinal cord gray matter neurons. The model was based on the Long Short-Term Memory (LSTM) architecture, which has emerged as a general and effective model for capturing long-term temporal dependencies with good generalization performance. In this way, training the network with the data recorded from one rat could lead to estimating the bladder status of different rats. We combined modeling of spiking and local field potential (LFP) activity into a unified framework to estimate the pressure and volume of the bladder. Moreover, we investigated the effect of two-electrode recording on decoding performance. The results show that the two-electrode recordings significantly improve the decoding performance compared to single-electrode recordings. The proposed framework could estimate bladder pressure and volume with an average normalized root-mean-squared (NRMS) error of 14.9 ± 4.8% and 19.7 ± 4.7% and a correlation coefficient (CC) of 83.2 ± 3.2% and 74.2 ± 6.2%, respectively. This work represents a promising approach to the real-time estimation of bladder pressure/volume in the closed-loop control of bladder function using functional electrical stimulation.
Collapse
|
10
|
Annals of Biomedical Engineering 2018 Year in Review. Ann Biomed Eng 2019; 47:2343-2345. [DOI: 10.1007/s10439-019-02420-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
11
|
Yan D, Bruns TM, Wu Y, Zimmerman LL, Stephan C, Cameron AP, Yoon E, Seymour JP. Ultracompliant Carbon Nanotube Direct Bladder Device. Adv Healthc Mater 2019; 8:e1900477. [PMID: 31556241 PMCID: PMC6893921 DOI: 10.1002/adhm.201900477] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 08/08/2019] [Indexed: 12/20/2022]
Abstract
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.
Collapse
Affiliation(s)
- Dongxiao Yan
- Department of Electrical and Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Tim M. Bruns
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Yuting Wu
- Department of Electrical and Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Lauren L. Zimmerman
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Chris Stephan
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Anne P. Cameron
- Department of Urology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Euisik Yoon
- Department of Electrical and Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, 03722, Korea
- Graduate Program of Nano Biomedical Engineering (Nano BME), Yonsei-IBS Institute, Yonsei University, Seoul, 03722, Korea
| | - John P. Seymour
- Department of Electrical and Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| |
Collapse
|
12
|
Ouyang Z, Sperry ZJ, Barrera ND, Bruns TM. Real-Time Bladder Pressure Estimation for Closed-Loop Control in a Detrusor Overactivity Model. IEEE Trans Neural Syst Rehabil Eng 2019; 27:1209-1216. [PMID: 31021771 DOI: 10.1109/tnsre.2019.2912374] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Overactive bladder (OAB) patients suffer from a frequent urge to urinate, which can lead to a poor quality of life. Current neurostimulation therapy uses open-loop electrical stimulation to alleviate symptoms. Continuous stimulation facilitates habituation of neural pathways and consumes battery power. Sensory feedback-based closed-loop stimulation may offer greater clinical benefit by driving bladder relaxation only when bladder contractions are detected, leading to increased bladder capacity. Effective delivery of such sensory feedback, particularly in real-time, is necessary to accomplish this goal. We implemented a Kalman filter-based model to estimate bladder pressure in real-time using unsorted neural recordings from sacral-level dorsal root ganglia, achieving a 0.88 ± 0.16 correlation coefficient fit across 35 normal and simulated OAB bladder fills in five experiments. We also demonstrated closed-loop neuromodulation using the estimated pressure to trigger pudendal nerve stimulation, which increased bladder capacity by 40% in two trials. An offline analysis indicated that unsorted neural signals had a similar stability over time as compared to sorted single units, which would require a higher computational load. We believe this paper demonstrates the utility of decoding bladder pressure from neural activity for closed-loop control; however, real-time validation during behavioral studies is necessary prior to clinical translation.
Collapse
|
13
|
Kashkoush AI, Gaunt RA, Fisher LE, Bruns TM, Weber DJ. Recording single- and multi-unit neuronal action potentials from the surface of the dorsal root ganglion. Sci Rep 2019; 9:2786. [PMID: 30808921 PMCID: PMC6391375 DOI: 10.1038/s41598-019-38924-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Accepted: 01/03/2019] [Indexed: 12/30/2022] Open
Abstract
The dorsal root ganglia (DRG) contain cell bodies of primary afferent neurons, which are frequently studied by recording extracellularly with penetrating microelectrodes inserted into the DRG. We aimed to isolate single- and multi-unit activity from primary afferents in the lumbar DRG using non-penetrating electrode arrays and to characterize the relationship of that activity with limb position and movement. The left sixth and seventh lumbar DRG (L6-L7) were instrumented with penetrating and non-penetrating electrode arrays to record neural activity during passive hindlimb movement in 7 anesthetized cats. We found that the non-penetrating arrays could record both multi-unit and well-isolated single-unit activity from the surface of the DRG, although with smaller signal to noise ratios (SNRs) compared to penetrating electrodes. Across all recorded units, the median SNR was 1.1 for non-penetrating electrodes and 1.6 for penetrating electrodes. Although the non-penetrating arrays were not anchored to the DRG or surrounding tissues, the spike amplitudes did not change (<1% change from baseline spike amplitude) when the limb was moved passively over a limited range of motion (~20 degrees at the hip). Units of various sensory fiber types were recorded, with 20% of units identified as primary muscle spindles, 37% as secondary muscle spindles, and 24% as cutaneous afferents. Our study suggests that non-penetrating electrode arrays can record modulated single- and multi-unit neural activity of various sensory fiber types from the DRG surface.
Collapse
Affiliation(s)
- Ahmed I Kashkoush
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Robert A Gaunt
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.,Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America
| | - Lee E Fisher
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.,Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America
| | - Tim M Bruns
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America.,Biointerfaces Institute, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Douglas J Weber
- Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America. .,Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America. .,Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania, United States of America.
| |
Collapse
|
14
|
Detecting Bladder Biomarkers for Closed-Loop Neuromodulation: A Technological Review. Int Neurourol J 2018; 22:228-236. [PMID: 30599493 PMCID: PMC6312967 DOI: 10.5213/inj.1836246.123] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Accepted: 12/11/2018] [Indexed: 12/19/2022] Open
Abstract
Neuromodulation was introduced for patients with poor outcomes from the existing traditional treatment approaches. It is well-established as an alternative, novel treatment option for voiding dysfunction. The current system of neuromodulation uses an open-loop system that only delivers continuous stimulation without considering the patient's state changes. Though the conventional open-loop system has shown positive clinical results, it can cause problems such as decreased efficacy over time due to neural habituation, higher risk of tissue damage, and lower battery life. Therefore, there is a need for a closed-loop system to overcome the disadvantages of existing systems. The closed-loop neuromodulation includes a system to monitor and stimulate micturition reflex pathways from the lower urinary tract, as well as the central nervous system. In this paper, we reviewed the current technological status to measure biomarker for closed-loop neuromodulation systems for voiding dysfunction.
Collapse
|
15
|
Rutter EM, Langdale CL, Hokanson JA, Hamilton F, Tran H, Grill WM, Flores KB. Detection of Bladder Contractions From the Activity of the External Urethral Sphincter in Rats Using Sparse Regression. IEEE Trans Neural Syst Rehabil Eng 2018; 26:1636-1644. [PMID: 30004881 DOI: 10.1109/tnsre.2018.2854675] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Bladder overactivity and incontinence and dysfunction can be mitigated by electrical stimulation of the pudendal nerve applied at the onset of a bladder contraction. Thus, it is important to predict accurately both bladder pressure and the onset of bladder contractions. We propose a novel method for prediction of bladder pressure using a time-dependent spectrogram representation of external urethral sphincter electromyographic (EUS EMG) activity and a least absolute shrinkage and selection operator regression model. There was a statistically significant improvement in prediction of bladder pressure compared with methods based on the firing rate of EUS EMG activity. This approach enabled prediction of the onset of bladder contractions with 91% specificity and 96% sensitivity and may be suitable for closed-loop control of bladder continence.
Collapse
|
16
|
Shi J, Wu B, Song B, Song J, Li S, Trau D, Lu WF. Learning-Based Cell Injection Control for Precise Drop-on-Demand Cell Printing. Ann Biomed Eng 2018; 46:1267-1279. [PMID: 29873013 DOI: 10.1007/s10439-018-2054-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 05/16/2018] [Indexed: 12/21/2022]
Abstract
Drop-on-demand (DOD) printing is widely used in bioprinting for tissue engineering because of little damage to cell viability and cost-effectiveness. However, satellite droplets may be generated during printing, deviating cells from the desired position and affecting printing position accuracy. Current control on cell injection in DOD printing is primarily based on trial-and-error process, which is time-consuming and inflexible. In this paper, a novel machine learning technology based on Learning-based Cell Injection Control (LCIC) approach is demonstrated for effective DOD printing control while eliminating satellite droplets automatically. The LCIC approach includes a specific computational fluid dynamics (CFD) simulation model of piezoelectric DOD print-head considering inverse piezoelectric effect, which is used instead of repetitive experiments to collect data, and a multilayer perceptron (MLP) network trained by simulation data based on artificial neural network algorithm, using the well-known classification performance of MLP to optimize DOD printing parameters automatically. The test accuracy of the LCIC method was 90%. With the validation of LCIC method by experiments, satellite droplets from piezoelectric DOD printing are reduced significantly, improving the printing efficiency drastically to satisfy requirements of manufacturing precision for printing complex artificial tissues. The LCIC method can be further used to optimize the structure of DOD print-head and cell behaviors.
Collapse
Affiliation(s)
- Jia Shi
- School of Mechanical Engineering and Automation, Northeastern University, Shenyang, Liaoning, China.,Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 1197576, Singapore
| | - Bin Wu
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 1197576, Singapore
| | - Bin Song
- Singapore Institute of Manufacturing Technology, Singapore, Singapore
| | - Jinchun Song
- School of Mechanical Engineering and Automation, Northeastern University, Shenyang, Liaoning, China
| | - Shihao Li
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
| | - Dieter Trau
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
| | - Wen F Lu
- Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 1197576, Singapore.
| |
Collapse
|
17
|
Mulugeta L, Drach A, Erdemir A, Hunt CA, Horner M, Ku JP, Myers JG, Vadigepalli R, Lytton WW. Credibility, Replicability, and Reproducibility in Simulation for Biomedicine and Clinical Applications in Neuroscience. Front Neuroinform 2018; 12:18. [PMID: 29713272 PMCID: PMC5911506 DOI: 10.3389/fninf.2018.00018] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 03/29/2018] [Indexed: 12/27/2022] Open
Abstract
Modeling and simulation in computational neuroscience is currently a research enterprise to better understand neural systems. It is not yet directly applicable to the problems of patients with brain disease. To be used for clinical applications, there must not only be considerable progress in the field but also a concerted effort to use best practices in order to demonstrate model credibility to regulatory bodies, to clinics and hospitals, to doctors, and to patients. In doing this for neuroscience, we can learn lessons from long-standing practices in other areas of simulation (aircraft, computer chips), from software engineering, and from other biomedical disciplines. In this manuscript, we introduce some basic concepts that will be important in the development of credible clinical neuroscience models: reproducibility and replicability; verification and validation; model configuration; and procedures and processes for credible mechanistic multiscale modeling. We also discuss how garnering strong community involvement can promote model credibility. Finally, in addition to direct usage with patients, we note the potential for simulation usage in the area of Simulation-Based Medical Education, an area which to date has been primarily reliant on physical models (mannequins) and scenario-based simulations rather than on numerical simulations.
Collapse
Affiliation(s)
| | - Andrew Drach
- The Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States
| | - Ahmet Erdemir
- Department of Biomedical Engineering and Computational Biomodeling (CoBi) Core, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, United States
| | - C A Hunt
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States
| | | | - Joy P Ku
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Jerry G Myers
- NASA Glenn Research Center, Cleveland, OH, United States
| | - Rajanikanth Vadigepalli
- Department of Pathology, Anatomy and Cell Biology, Daniel Baugh Institute for Functional Genomics and Computational Biology, Thomas Jefferson University, Philadelphia, PA, United States
| | - William W Lytton
- Department of Neurology, SUNY Downstate Medical Center, The State University of New York, New York, NY, United States.,Department of Physiology and Pharmacology, SUNY Downstate Medical Center, The State University of New York, New York, NY, United States.,Department of Neurology, Kings County Hospital Center, New York, NY, United States
| |
Collapse
|