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ObjectiveThis experiment tested if balance performance differed between a standardized treadmill surface perturbation task and a clinical pull test and was affected by medication or the presence of body weight support in people with Parkinson's disease (PD).MethodsTwenty-seven individuals were tested (14 PD in both ON- and OFF-medication states). Clinical pull test and rapid forward (backward fall) translations of the support surface were applied to induce postural reactions requiring at least 1 step to restore balance. The effects of pull type (clinical vs. treadmill), partial bodyweight support (0 vs 20% body weight) and group (control, PD ON-meds and PD OFF-meds) on reactive stepping as well as practice/learning effect were examined. The number of steps taken and the first step duration were entered in linear repeated-measures mixed-effect models separately.ResultsThe effects of pull type, group, and bodyweight support were all significant in both metrics, as was ON- vs. OFF-medication. A significant interaction term (group x pull type) was found in the first step duration, showing that the group difference was greater in treadmill compared to the clinical pull test. A significant practice effect was also observed within and across testing sessions.ConclusionsA standardized treadmill perturbation performed slightly better than the classical pull test in distinguishing between groups, and partial weight support did not substantially degrade the test's performance to detect the balance deficits in people with PD.

The ability of humans to coordinate stereotyped, alternating movements between the two legs during bipedal walking is a complex motor behavior that requires precisely timed activities across multiple nodes of the supraspinal network. Understanding of the neural network dynamics that underlie natural walking in humans is limited. We investigated cortical and subthalamic neural activities during overground walking and evaluated spectral biomarkers to decode the gait cycle in three patients with Parkinson's disease without gait disturbances. Patients were implanted with chronic bilateral deep brain stimulation (DBS) leads in the subthalamic nucleus (STN) and electrocorticography paddles overlaying the primary motor and somatosensory cortices. Local field potentials were recorded from these areas while the participants performed overground walking and synchronized to external gait kinematic sensors. We found that the STN displays increased low-frequency (4-12 Hz) spectral power during the period before contralateral leg swing. Furthermore, STN shows increased theta frequency (4-8 Hz) coherence with the primary motor through the initiation and early phase of contralateral leg swing. Additional analysis revealed that each patient had specific frequency bands that could detect a significant difference between left and right initial leg swing. Our findings indicate that there are alternating spectral changes between the two hemispheres in accordance with the gait cycle. In addition, we identified patient-specific, gait-related biomarkers in both the STN and cortical areas at discrete frequency bands that may be used to drive adaptive DBS to improve gait dysfunction in patients with Parkinson's disease.

Closed-loop neurostimulation is a promising therapy being tested and clinically implemented in a growing number of neurological and psychiatric indications. This therapy is enabled by chronically implanted, bidirectional devices including the Medtronic Summit RC+S system. In order to successfully optimize therapy for patients implanted with these devices, analyses must be conducted offline on the recorded neural data, in order to inform optimal sense and stimulation parameters. The file format, volume, and complexity of raw data from these devices necessitate conversion, parsing, and time reconstruction ahead of time-frequency analyses and modeling common to standard neuroscientific analyses. Here, we provide an open-source toolbox written in Matlab which takes raw files from the Summit RC+S and transforms these data into a standardized format amenable to conventional analyses. Furthermore, we provide a plotting tool which can aid in the visualization of multiple data streams and sense, stimulation, and therapy settings. Finally, we describe an analysis module which replicates RC+S on-board power computations, a functionality which can accelerate biomarker discovery. This toolbox aims to accelerate the research and clinical advances made possible by longitudinal neural recordings and adaptive neurostimulation in people with neurological and psychiatric illnesses.

Little is known about the electrophysiological activity of the spinal cord during voluntary movement control in humans. We present a novel method for recording electrophysiological activity from the human spinal cord using implanted epidural electrodes during naturalistic movements including overground walking. Spinal electrograms (SEGs) were recorded from epidural electrodes implanted as part of a test trial for patients with chronic pain undergoing evaluation for spinal cord stimulation. Externalized ends of the epidural leads were connected to an external amplifier to capture SEGs. Electromyographic and accelerometry data from the upper and lower extremities were collected using wireless sensors and synchronized to the SEG data. Patients were instructed to perform various arm and leg movements while SEG and kinematic data were collected. This study proves the safety and feasibility of performing epidural spinal recordings from human subjects performing movement tasks.