Robotics and Motor Control

Robot-Driven Locomotor Perturbations Reveal Synergy-Mediated, Context-Dependent Feedforward and Feedback Mechanisms of Adaptation

Humans respond to mechanical perturbations that affect their gait by changing their motor control strategy. Previous work indicates that adaptation during gait is context-dependent, and perturbations altering long-term stability are compensated for even at the cost of higher energy expenditure. However, it is unclear if gait adaptation is driven by unilateral or bilateral mechanisms, and what the roles of feedback and feedforward control are in the generation of compensatory responses. Here, we used a robot-based adaptation paradigm to investigate if feedback/feedforward and unilateral/bilateral contributions to locomotor adaptation are also context-dependent in healthy adults. A robot was used to induce two opposite unilateral mechanical perturbations affecting the step length over multiple gait cycles. Electromyographic signals were collected and analyzed to determine how muscle synergies change in response to perturbations. The results unraveled different unilateral modulation dynamics of the muscle-synergy activations during adaptation, characterized by the combination of a slow-progressive feedforward process and a fast-reactive feedback-driven process. The relative unilateral contributions of the two processes to motor-output adjustments, however, depended on which perturbation was delivered. Overall, these observations provide evidence that, in humans, both descending and afferent drives project onto the same spinal interneuronal networks that encode locomotor muscle synergies.

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Robot-induced perturbations of human walking reveal a selective generation of motor adaptation

The processes underlying the generation of motor adaptation in response to mechanical perturbations during human walking have been subject to debate. We used a robotic system to apply mechanical perturbations to step length and step height over consecutive gait cycles. Specifically, we studied perturbations affecting only step length, only step height, and step length and height in combination. Both step-length and step-height perturbations disrupt normal walking patterns, but step-length perturbations have a far greater impact on locomotor stability. We found a selective process of motor adaptation in that participants failed to adapt to step-height perturbations but strongly adapted to step-length perturbations, even when these adaptations increased metabolic cost. These results indicate that motor adaptation during human walking is primarily driven by locomotor stability, and only secondarily by energy expenditure and walking pattern preservation. These findings have substantial implications for the design of protocols for robot-assisted gait rehabilitation.

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Design of a gait training device for control of pelvic obliquity

This paper presents the design and testing of a novel device for the control of pelvic obliquity during gait. The device, called the Robotic Gait Rehabilitation (RGR) Trainer, consists of a single actuator system designed to target secondary gait deviations, such as hip-hiking, affecting the movement of the pelvis. Secondary gait deviations affecting the pelvis are generated in response to primary gait deviations (e.g. limited knee flexion during the swing phase) in stroke survivors and contribute to the overall asymmetrical gait pattern often observed in these patients. The proposed device generates a force field able to affect the obliquity of the pelvis (i.e. the rotation of the pelvis around the anteroposterior axis) by using an impedance-controlled single linear actuator acting on a hip orthosis. Tests showed that the RGR Trainer is able to induce changes in pelvic obliquity trajectories (hip-hiking) in healthy subjects. These results suggest that the RGR Trainer is suitable to test the hypothesis that has motivated our efforts toward developing the system, namely that addressing both primary and secondary gait deviations during robotic-assisted gait training may help promote a physiologically-sound gait behavior more effectively than when only primary deviations are addressed.

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Clinical Applications of Statistical Methods, Machine Learning, and Artificial Intelligence

Machine learning to predict passenger mortality and hospital length of stay following motor vehicle collision

Motor vehicle collisions (MVCs) account for 1.35 million deaths and cost $518 billion US dollars each year worldwide, disproportionately affecting young patients and low-income nations. The ability to successfully anticipate clinical outcomes will help physicians form effective management strategies and counsel families with greater accuracy. The authors aimed to train several classifiers, including a neural network model, to accurately predict MVC outcomes.

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Deep learning for robust detection of interictal epileptiform discharges

Automatic detection of interictal epileptiform discharges (IEDs, short as 'spikes') from an epileptic brain can help predict seizure recurrence and support the diagnosis of epilepsy. Developing fast, reliable, and robust detection methods for IEDs based on scalp or intracranial electroencephalogram (iEEG) may facilitate online seizure monitoring and closed-loop neurostimulation.

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Brain-Computer Interfaces

Brain-Computer Interface, Neuromodulation, and Neurorehabilitation Strategies for Spinal Cord Injury

As neural bypass interfacing, neuromodulation, and neurorehabilitation continue to evolve, there is growing recognition that combination therapies may achieve superior results. This article briefly introduces these broad areas of active research and lays out some of the current evidence for their use for patients with spinal cord injury.

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Implantable brain-computer interface for neuroprosthetic-enabled volitional hand grasp restoration in spinal cord injury

Loss of hand function after cervical spinal cord injury severely impairs functional independence. We describe a method for restoring volitional control of hand grasp in one 21-year-old male subject with complete cervical quadriplegia (C5 American Spinal Injury Association Impairment Scale A) using a portable fully implanted brain-computer interface within the home environment. The brain-computer interface consists of subdural surface electrodes placed over the dominant-hand motor cortex and connects to a transmitter implanted subcutaneously below the clavicle, which allows continuous reading of the electrocorticographic activity. Movement-intent was used to trigger functional electrical stimulation of the dominant hand during an initial 29-weeks laboratory study and subsequently via a mechanical hand orthosis during in-home use. Movement-intent information could be decoded consistently throughout the 29-week in-laboratory study with a mean accuracy of 89.0% (range 78-93.3%). Improvements were observed in both the speed and accuracy of various upper extremity tasks, including lifting small objects and transferring objects to specific targets. At-home decoding accuracy during open-loop trials reached an accuracy of 91.3% (range 80-98.95%) and an accuracy of 88.3% (range 77.6-95.5%) during closed-loop trials. Importantly, the temporal stability of both the functional outcomes and decoder metrics were not explored in this study. A fully implanted brain-computer interface can be safely used to reliably decode movement-intent from the motor cortex, allowing for accurate volitional control of hand grasp.

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Design-development of an at-home modular brain-computer interface (BCI) platform in a case study of cervical spinal cord injury

The objective of this study was to develop a portable and modular brain-computer interface (BCI) software platform independent of input and output devices. We implemented this platform in a case study of a subject with cervical spinal cord injury (C5 ASIA A).

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Ablation dynamics during laser interstitial thermal therapy for mesiotemporal epilepsy

The recent emergence of laser interstitial thermal therapy (LITT) as a frontline surgical tool in the management of brain tumors and epilepsy is a result of advances in MRI thermal imaging. A limitation to further improving LITT is the diversity of brain tissue thermoablative properties, which hinders our ability to predict LITT treatment-related effects. Utilizing the mesiotemporal lobe as a consistent anatomic model system, the goal of this study was to use intraoperative thermal damage estimate (TDE) maps to study short- and long-term effects of LITT and to identify preoperative variables that could be helpful in predicting tissue responses to thermal energy.

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Neural fragility as an EEG marker of the seizure onset zone

Over 15 million patients with epilepsy worldwide do not respond to drugs. Successful surgical treatment requires complete removal or disconnection of the seizure onset zone (SOZ), brain region(s) where seizures originate. Unfortunately, surgical success rates vary between 30 and 70% because no clinically validated biological marker of the SOZ exists. We develop and retrospectively validate a new electroencephalogram (EEG) marker-neural fragility-in a retrospective analysis of 91 patients by using neural fragility of the annotated SOZ as a metric to predict surgical outcomes. Fragility predicts 43 out of 47 surgical failures, with an overall prediction accuracy of 76% compared with the accuracy of clinicians at 48% (successful outcomes). In failed outcomes, we identify fragile regions that were untreated. When compared to 20 EEG features proposed as SOZ markers, fragility outperformed in predictive power and interpretability, which suggests neural fragility as an EEG biomarker of the SOZ.

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Long-term seizure and psychiatric outcomes following laser ablation of mesial temporal structures

Postsurgical seizure outcome following laser interstitial thermal therapy (LiTT) for the management of drug-resistant mesial temporal lobe epilepsy (MTLE) has been limited to 2 years. Furthermore, its impact on presurgical mood and anxiety disorders has not been investigated. The objectives of this study were (1) to identify seizure outcome changes over a period ranging from 18 to 81 months; (2) to investigate the seizure-free rate in the last follow-up year; (3) to identify the variables associated with seizure freedom; and (4) to identify the impact of LiTT on presurgical mood and anxiety disorders.

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Movement Disorders

Freezing of Gait in Parkinson's Disease: Invasive and Noninvasive Neuromodulation

Freezing of gait (FoG) is one of the most disabling yet poorly understood symptoms of Parkinson's disease (PD). FoG is an episodic gait pattern characterized by the inability to step that occurs on initiation or turning while walking, particularly with the perception of tight surroundings. This phenomenon impairs balance, increases falls, and reduces the quality of life.

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Dissecting Brainstem Locomotor Circuits: Converging Evidence for Cuneiform Nucleus Stimulation

There is a pressing and unmet need for effective therapies for freezing of gait (FOG) and other neurological gait disorders. Deep brain stimulation (DBS) of a midbrain target known as the pedunculopontine nucleus (PPN) was proposed as a potential treatment based on its postulated involvement in locomotor control as part of the mesencephalic locomotor region (MLR). However, DBS trials fell short of expectations, leading many clinicians to abandon this strategy. Here, we discuss the potential reasons for this failure and review recent clinical data along with preclinical optogenetics evidence to argue that another nearby nucleus, the cuneiform nucleus (CnF), may be a superior target.

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MR Tractography-Based Targeting and Physiological Identification of the Cuneiform Nucleus for Directional DBS in a Parkinson's Disease Patient With Levodopa-Resistant Freezing of Gait

Freezing of gait (FOG) is a debilitating motor deficit in a subset of Parkinson's Disease (PD) patients that is poorly responsive to levodopa or deep brain stimulation (DBS) of established PD targets. The proposal of a DBS target in the midbrain, known as the pedunculopontine nucleus (PPN), to address FOG was based on its observed neuropathology in PD and its hypothesized involvement in locomotor control as a part of the mesencephalic locomotor region (MLR). Initial reports of PPN DBS were met with enthusiasm; however, subsequent studies reported mixed results. A closer review of the MLR basic science literature, suggests that the closely related cuneiform nucleus (CnF), dorsal to the PPN, may be a superior site to promote gait. Although suspected to have a conserved role in the control of gait in humans, deliberate stimulation of a homolog to the CnF in humans using directional DBS electrodes has not been attempted.

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