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Creators/Authors contains: "Thakor, Nitish V."

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  1. ; ; ; ; ; ; ; ; ; ; et al ( , Communications Medicine)
    Abstract Background

    Tension in the spinal cord is a trademark of tethered cord syndrome. Unfortunately, existing tests cannot quantify tension across the bulk of the cord, making the diagnostic evaluation of stretch ambiguous. A potential non-destructive metric for spinal cord tension is ultrasound-derived shear wave velocity (SWV). The velocity is sensitive to tissue elasticity and boundary conditions including strain. We use the term Ultrasound Tensography to describe the acoustic evaluation of tension with SWV.

     Methods

    Our solution Tethered cord Assessment with Ultrasound Tensography (TAUT) was utilized in three sub-studies: finite element simulations, a cadaveric benchtop validation, and a neurosurgical case series. The simulation computed SWV for given tensile forces. The cadaveric model with induced tension validated the SWV-tension relationship. Lastly, SWV was measured intraoperatively in patients diagnosed with tethered cords who underwent treatment (spinal column shortening). The surgery alleviates tension by decreasing the vertebral column length.

     Results

    Here we observe a strong linear relationship between tension and squared SWV across the preclinical sub-studies. Higher tension induces faster shear waves in the simulation (R2 = 0.984) and cadaveric (R2 = 0.951) models. The SWV decreases in all neurosurgical procedures (p < 0.001). Moreover, TAUT has a c-statistic of 0.962 (0.92-1.00), detecting all tethered cords.

     Conclusions

    This study presents a physical, clinical metric of spinal cord tension. Strong agreement among computational, cadaveric, and clinical studies demonstrates the utility of ultrasound-induced SWV for quantitative intraoperative feedback. This technology is positioned to enhance tethered cord diagnosis, treatment, and postoperative monitoring as it differentiates stretched from healthy cords.

     
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  2. ; ; ; ; ; ( , Muscle & Nerve)
    Abstract

    After an amputation, advanced prosthetic limbs can be used to interface with the nervous system and restore motor function. Despite numerous breakthroughs in the field, many of the recent research advancements have not been widely integrated into clinical practice. This review highlights recent innovations in neuromuscular implants—specifically those that interface with skeletal muscle—which could improve the clinical translation of prosthetic technologies. Skeletal muscle provides a physiologic gateway to harness and amplify signals from the nervous system. Recent surgical advancements in muscle reinnervation surgeries leverage the “bio‐amplification” capabilities of muscle, enabling more intuitive control over a greater number of degrees of freedom in prosthetic limbs than previously achieved. We anticipate that state‐of‐the‐art implantable neuromuscular interfaces that integrate well with skeletal muscle and novel surgical interventions will provide a long‐term solution for controlling advanced prostheses. Flexible electrodes are expected to play a crucial role in reducing foreign body responses and improving the longevity of the interface. Additionally, innovations in device miniaturization and ongoing exploration of shape memory polymers could simplify surgical procedures for implanting such interfaces. Once implanted, wireless strategies for powering and transferring data from the interface can eliminate bulky external wires, reduce infection risk, and enhance day‐to‐day usability. By outlining the current limitations of neuromuscular interfaces along with potential future directions, this review aims to guide continued research efforts and future collaborations between engineers and specialists in the field of neuromuscular and musculoskeletal medicine.

     
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  3. ; ; ; ; ( , IEEE Transactions on Neural Systems and Rehabilitation Engineering)
  4. ; ( , Proceedings of the IEEE)
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  5. ; ; ; ; ; ; ; ; ( , 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC))
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  6. ; ; ; ; ; ( , IEEE Transactions on Biomedical Circuits and Systems)
    null (Ed.)
    A major issue with upper limb prostheses is the disconnect between sensory information perceived by the user and the information perceived by the prosthesis. Advances in prosthetic technology introduced tactile information for monitoring grasping activity, but visual information, a vital component in the human sensory system, is still not fully utilized as a form of feedback to the prosthesis. For able-bodied individuals, many of the decisions for grasping or manipulating an object, such as hand orientation and aperture, are made based on visual information before contact with the object. We show that inclusion of neuromorphic visual information, combined with tactile feedback, improves the ability and efficiency of both able-bodied and amputee subjects to pick up and manipulate everyday objects.We discovered that combining both visual and tactile information in a real-time closed loop feedback strategy generally decreased the completion time of a task involving picking up and manipulating objects compared to using a single modality for feedback. While the full benefit of the combined feedback was partially obscured by experimental inaccuracies of the visual classification system, we demonstrate that this fusion of neuromorphic signals from visual and tactile sensors can provide valuable feedback to a prosthetic arm for enhancing real-time function and usability. 
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  7. ; ; ; ; ( , Proceedings of IEEE Biomedical Circuits and Systems)
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    In this work, we investigated the classification of texture by neuromorphic tactile encoding and an unsupervised learning method. Additionally, we developed an adaptive classification algorithm to detect and characterize the presence of new texture data. The neuromorphic tactile encoding of textures from a multilayer tactile sensor was based on the physical structure and afferent spike signaling of human glabrous skin mechanoreceptors. We explored different neuromorphic spike pattern metrics and dimensionality reduction techniques in order to maximize classification accuracy while improving computational efficiency. Using a dataset composed of 3 textures, we showed that unsupervised learning of the neuromorphic tactile encoding data had high classification accuracy (mean=86.46%, sd=5 .44%). Moreover, the adaptive classification algorithm was successful at determining that there were 3 underlying textures in the training dataset. In this work, tactile information is transformed into neuromorphic spiking activity that can be used as a stimulation pattern to elicit texture sensation for prosthesis users. Furthermore, we provide the basis for identifying new textures adaptively which can be used to actively modify stimulation patterns to improve texture discrimination for the user. 
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  8. ; ; ; ; ; ; ; ; ; ; et al ( , Journal of Neural Engineering)
  9. ; ; ; ; ; ; ; ; ( , Proceedings Volume 10482, Optogenetics and Optical Manipulation 2018)
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