Motor neuroprosthesis for promoting recovery of function after stroke.

BACKGROUND: Motor neuroprosthesis (MN) involves electrical stimulation of neural structures by miniaturized devices to allow the performance of tasks in the natural environment in which people live (home and community context), as an orthosis. In this way, daily use of these devices could act as an environmental facilitator for increasing the activities and participation of people with stroke. OBJECTIVES: To assess the effects of MN for improving independence in activities of daily living (ADL), activities involving limbs, participation scales of health-related quality of life (HRQoL), exercise capacity, balance, and adverse events in people after stroke. SEARCH METHODS: We searched the Cochrane Stroke Group Trials Register (searched 19 August 2019), the Cochrane Central Register of Controlled Trials (CENTRAL) (August 2019), MEDLINE (1946 to 16 August 2019), Embase (1980 to 19 August 2019), and five additional databases. We also searched trial registries, databases, and websites to identify additional relevant published, unpublished, and ongoing trials. SELECTION CRITERIA: Randomized controlled trials (RCTs) and randomized controlled cross-over trials comparing MN for improving activities and participation versus other assistive technology device or MN without electrical stimulus (stimulator is turned off), or no treatment, for people after stroke. DATA COLLECTION AND ANALYSIS: Two review authors independently selected trials, extracted data, and assessed risk of bias of the included studies. Any disagreements were resolved through discussion with a third review author. We contacted trialists for additional information when necessary and performed all analyses using Review Manager 5. We used GRADE to assess the certainty of the evidence. MAIN RESULTS: We included four RCTs involving a total of 831 participants who were more than three months poststroke. All RCTs were of MN that applied electrical stimuli to the peroneal nerve. All studies included conditioning protocols to adapt participants to MN use, after which participants used MN from up to eight hours per day to all-day use for ambulation in daily activities performed in the home or community context. All studies compared the use of MN versus another assistive device (ankle-foot orthosis [AFO]). There was a high risk of bias for at least one assessed domain in three of the four included studies. No studies reported outcomes related to independence in ADL. There was low-certainty evidence that AFO was more beneficial than MN on activities involving limbs such as walking speed until six months of device use (mean difference (MD) -0.05 m/s, 95% confidence interval (CI) -0.10 to -0.00; P = 0.03; 605 participants; 2 studies; I2 = 0%; low-certainty evidence); however, this difference was no longer present in our sensitivity analysis (MD -0.07 m/s, 95% CI -0.16 to 0.02; P = 0.13; 110 participants; 1 study; I2 = 0%). There was low to moderate certainty that MN was no more beneficial than AFO on activities involving limbs such as walking speed between 6 and 12 months of device use (MD 0.00 m/s, 95% CI -0.05 to 0.05; P = 0.93; 713 participants; 3 studies; I2 = 17%; low-certainty evidence), Timed Up and Go (MD 0.51 s, 95% CI -4.41 to 5.43; P = 0.84; 692 participants; 2 studies; I2 = 0%; moderate-certainty evidence), and modified Emory Functional Ambulation Profile (MD 14.77 s, 95% CI -12.52 to 42.06; P = 0.29; 605 participants; 2 studies; I2 = 0%; low-certainty evidence). There was no significant difference in walking speed when MN was delivered with surface or implantable electrodes (test for subgroup differences P = 0.09; I2 = 65.1%). For our secondary outcomes, there was very low to moderate certainty that MN was no more beneficial than another assistive device for participation scales of HRQoL (standardized mean difference 0.26, 95% CI -0.22 to 0.74; P = 0.28; 632 participants; 3 studies; I2 = 77%; very low-certainty evidence), exercise capacity (MD -9.03 m, 95% CI -26.87 to 8.81; P = 0.32; 692 participants; 2 studies; I2 = 0%; low-certainty evidence), and balance (MD -0.34, 95% CI -1.96 to 1.28; P = 0.68; 692 participants; 2 studies; I2 = 0%; moderate-certainty evidence). Although there was low- to moderate-certainty evidence that the use of MN did not increase the number of serious adverse events related to intervention (risk ratio (RR) 0.35, 95% CI 0.04 to 3.33; P = 0.36; 692 participants; 2 studies; I2 = 0%; low-certainty evidence) or number of falls (RR 1.20, 95% CI 0.92 to 1.55; P = 0.08; 802 participants; 3 studies; I2 = 33%; moderate-certainty evidence), there was low-certainty evidence that the use of MN in people after stroke may increase the risk of participants dropping out during the intervention (RR 1.48, 95% CI 1.11 to 1.97; P = 0.007; 829 participants; 4 studies; I2 = 0%). AUTHORS' CONCLUSIONS: Current evidence indicates that MN is no more beneficial than another assistive technology device for improving activities involving limbs measured by Timed Up and Go, balance (moderate-certainty evidence), activities involving limbs measured by walking speed and modified Emory Functional Ambulation Profile, exercise capacity (low-certainty evidence), and participation scale of HRQoL (very low-certainty evidence). Evidence was insufficient to estimate the effect of MN on independence in ADL. In comparison to other assistive devices, MN does not appear to increase the number of falls (moderate-certainty evidence) or serious adverse events (low-certainty evidence), but may result in a higher number of dropouts during intervention period (low-certainty evidence).

Optimizing the detection of nonstationary signals by using recurrence analysis.

Recurrence analysis and its quantifiers are strongly dependent on the evaluation of the vicinity threshold parameter, i.e., the threshold to regard two points close enough in phase space to be considered as just one. We develop a new way to optimize the evaluation of the vicinity threshold in order to assure a higher level of sensitivity to recurrence quantifiers to allow the detection of even small changes in the dynamics. It is used to promote recurrence analysis as a tool to detect nonstationary behavior of time signals or space profiles. We show that the ability to detect small changes provides information about the present status of the physical process responsible to generate the signal and offers mechanisms to predict future states. Here, a higher sensitive recurrence analysis is proposed as a precursor, a tool to predict near future states of a particular system, based on just (experimentally) obtained signals of some available variables of the system. Comparisons with traditional methods of recurrence analysis show that the optimization method developed here is more sensitive to small variations occurring in a signal. The method is applied to numerically generated time series as well as experimental data from physiology.

Predictability of arousal in mouse slow wave sleep by accelerometer data.

Arousals can be roughly characterized by punctual intrusions of wakefulness into sleep. In a standard perspective, using human electroencephalography (EEG) data, arousals are associated to slow-wave rhythms and K-complex brain activity. The physiological mechanisms that give rise to arousals during sleep are not yet fully understood. Moreover, subtle body movement patterns, which may characterize arousals both in human and in animals, are usually not detectable by eye perception and are not in general present in sleep studies. In this paper, we focus attention on accelerometer records (AR) to characterize and predict arousal during slow wave sleep (SWS) stage of mice. Furthermore, we recorded the local field potentials (LFP) from the CA1 region in the hippocampus and paired with accelerometer data. The hippocampus signal was also used here to identify the SWS stage. We analyzed the AR dynamics of consecutive arousals using recurrence technique and the determinism (DET) quantifier. Recurrence is a fundamental property of dynamical systems, which can be exploited to characterize time series properties. The DET index evaluates how similar are the evolution of close trajectories: in this sense, it computes how accurate are predictions based on past trajectories. For all analyzed mice in this work, we observed, for the first time, the occurrence of a universal dynamic pattern a few seconds that precedes the arousals during SWS sleep stage based only on the AR signal. The predictability success of an arousal using DET from AR is nearly 90%, while similar analysis using LFP of hippocampus brain region reveal 88% of success. Noteworthy, our findings suggest an unique dynamical behavior pattern preceding an arousal of AR data during sleep. Thus, the employment of this technique applied to AR data may provide useful information about the dynamics of neuronal activities that control sleep-waking switch during SWS sleep period. We argue that the predictability of arousals observed through DET(AR) can be functionally explained by a respiratory-driven modification of neural states. Finally, we believe that the method associating AR data with other physiologic events such as neural rhythms can become an accurate, convenient and non-invasive way of studying the physiology and physiopathology of movement and respiratory processes during sleep.

On the photovoltaic effect in local field potential recordings.

Optogenetics allows light activation of genetically defined cell populations and the study of their link to specific brain functions. While it is a powerful method that has revolutionized neuroscience in the last decade, the shortcomings of directly stimulating electrodes and living tissue with light have been poorly characterized. Here, we assessed the photovoltaic effects in local field potential (LFP) recordings of the mouse hippocampus. We found that light leads to several artifacts that resemble genuine LFP features in animals with no opsin expression, such as stereotyped peaks at the power spectrum, phase shifts across different recording channels, coupling between low and high oscillation frequencies, and sharp signal deflections that are detected as spikes. Further, we tested how light stimulation affected hippocampal LFP recordings in mice expressing channelrhodopsin 2 in parvalbumin neurons (PV/ChR2 mice). Genuine oscillatory activity at the frequency of light stimulation could not be separated from light-induced artifacts. In addition, light stimulation in PV/ChR2 mice led to an overall decrease in LFP power. Thus, genuine LFP changes caused by the stimulation of specific cell populations may be intermingled with spurious changes caused by photovoltaic effects. Our data suggest that care should be taken in the interpretation of electrophysiology experiments involving light stimulation.

Beta2 oscillations (23-30 Hz) in the mouse hippocampus during novel object recognition.

The oscillatory activity of hippocampal neuronal networks is believed to play a role in memory acquisition and consolidation. Particular focus has been given to characterising theta (4-12 Hz), gamma (40-100 Hz) and ripple (150-250 Hz) oscillations. Beyond these well-described network states, few studies have investigated hippocampal beta2 (23-30 Hz) activity in vivo and its link to behaviour. A previous sudy showed that the exploration of novel environments may lead to the appearance of beta2 oscillations in the mouse hippocampus. In the present study we characterised hippocampal beta2 oscillations in mice during an object recognition task. We found prominent bursts of beta2 oscillations in the beginning of novel exploration sessions (four new objects), which could be readily observed by spectral analysis and visual inspection of local field potentials. Beta2 modulated hippocampal but not neocortical neurons and its power decreased along the session. We also found increased beta2 power in the beginning of a second exploration session performed 24 h later in a slightly modified environment (two new, two familiar objects), but to a lesser extent than in the first session. However, the increase in beta2 power in the second exploration session became similar to the first session when we pharmacologically impaired object recognition in a new set of experiments performed 1 week later. Our results suggest that hippocampal beta2 activity is associated with a dynamic network state tuned for novelty detection and which may allow new learning to occur.