OBJECTIVE: The goal of this study was to decode sensory information from the dorsal root ganglia (DRG) in real time, and to use this information to adapt the control of unilateral stepping with a state-based control algorithm consisting of both feed-forward and feedback components. APPROACH: In five anesthetized cats, hind limb stepping on a walkway or treadmill was produced by patterned electrical stimulation of the spinal cord through implanted microwire arrays, while neuronal activity was recorded from the DRG. Different parameters, including distance and tilt of the vector between hip and limb endpoint, integrated gyroscope and ground reaction force were modelled from recorded neural firing rates. These models were then used for closed-loop feedback. MAIN RESULTS: Overall, firing-rate-based predictions of kinematic sensors (limb endpoint, integrated gyroscope) were the most accurate with variance accounted for >60% on average. Force prediction had the lowest prediction accuracy (48 ± 13%) but produced the greatest percentage of successful rule activations (96.3%) for stepping under closed-loop feedback control. The prediction of all sensor modalities degraded over time, with the exception of tilt. SIGNIFICANCE: Sensory feedback from moving limbs would be a desirable component of any neuroprosthetic device designed to restore walking in people after a spinal cord injury. This study provides a proof-of-principle that real-time feedback from the DRG is possible and could form part of a fully implantable neuroprosthetic device with further development.
OBJECTIVE: The goal of this study was to decode sensory information from the dorsal root ganglia (DRG) in real time, and to use this information to adapt the control of unilateral stepping with a state-based control algorithm consisting of both feed-forward and feedback components. APPROACH: In five anesthetized cats, hind limb stepping on a walkway or treadmill was produced by patterned electrical stimulation of the spinal cord through implanted microwire arrays, while neuronal activity was recorded from the DRG. Different parameters, including distance and tilt of the vector between hip and limb endpoint, integrated gyroscope and ground reaction force were modelled from recorded neural firing rates. These models were then used for closed-loop feedback. MAIN RESULTS: Overall, firing-rate-based predictions of kinematic sensors (limb endpoint, integrated gyroscope) were the most accurate with variance accounted for >60% on average. Force prediction had the lowest prediction accuracy (48 ± 13%) but produced the greatest percentage of successful rule activations (96.3%) for stepping under closed-loop feedback control. The prediction of all sensor modalities degraded over time, with the exception of tilt. SIGNIFICANCE: Sensory feedback from moving limbs would be a desirable component of any neuroprosthetic device designed to restore walking in people after a spinal cord injury. This study provides a proof-of-principle that real-time feedback from the DRG is possible and could form part of a fully implantable neuroprosthetic device with further development.
Authors: Jorge G Cham; Edward A Branchaud; Zoran Nenadic; Bradley Greger; Richard A Andersen; Joel W Burdick Journal: J Neurophysiol Date: 2004-06-30 Impact factor: 2.714
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Authors: Kevin A Mazurek; Bradley J Holinski; Dirk G Everaert; Vivian K Mushahwar; Ralph Etienne-Cummings Journal: IEEE Trans Biomed Circuits Syst Date: 2016-03-09 Impact factor: 3.833
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Authors: Nikolaus Wenger; Eduardo Martin Moraud; Jerome Gandar; Pavel Musienko; Marco Capogrosso; Laetitia Baud; Camille G Le Goff; Quentin Barraud; Natalia Pavlova; Nadia Dominici; Ivan R Minev; Leonie Asboth; Arthur Hirsch; Simone Duis; Julie Kreider; Andrea Mortera; Oliver Haverbeck; Silvio Kraus; Felix Schmitz; Jack DiGiovanna; Rubia van den Brand; Jocelyne Bloch; Peter Detemple; Stéphanie P Lacour; Erwan Bézard; Silvestro Micera; Grégoire Courtine Journal: Nat Med Date: 2016-01-18 Impact factor: 53.440