Neural prosthetics may provide a promising solution to restore visual perception in some forms of blindness. The restored prosthetic percept is rudimentary compared to normal vision and can be optimized with a variety of image preprocessing techniques to maximize relevant information transfer. Extracting the most useful features from a visual scene is a nontrivial task and optimal preprocessing choices strongly depend on the context. Despite rapid advancements in deep learning, research currently faces a difficult challenge in finding a general and automated preprocessing strategy that can be tailored to specific tasks or user requirements. In this paper, we present a novel deep learning approach that explicitly addresses this issue by optimizing the entire process of phosphene generation in an end-to-end fashion. The proposed model is based on a deep auto-encoder architecture and includes a highly adjustable simulation module of prosthetic vision. In computational validation experiments, we show that such an approach is able to automatically find a task-specific stimulation protocol. The results of these proof-of-principle experiments illustrate the potential of end-to-end optimization for prosthetic vision. The presented approach is highly modular and our approach could be extended to automated dynamic optimization of prosthetic vision for everyday tasks, given any specific constraints, accommodating individual requirements of the end-user.

Neuroprosthetic implants are a promising technology for restoring some form of vision in people with visual impairments via electrical neurostimulation in the visual pathway. Although an artificially generated prosthetic percept is relatively limited compared with normal vision, it may provide some elementary perception of the surroundings, re-enabling daily living functionality. For mobility in particular, various studies have investigated the benefits of visual neuroprosthetics in a simulated prosthetic vision paradigm with varying outcomes. The previous literature suggests that scene simplification via image processing, and particularly contour extraction, may potentially improve the mobility performance in a virtual environment. In the current simulation study with sighted participants, we explore both the theoretically attainable benefits of strict scene simplification in an indoor environment by controlling the environmental complexity, as well as the practically achieved improvement with a deep learning-based surface boundary detection implementation compared with traditional edge detection. A simulated electrode resolution of 26 × 26 was found to provide sufficient information for mobility in a simple environment. Our results suggest that, for a lower number of implanted electrodes, the removal of background textures and within-surface gradients may be beneficial in theory. However, the deep learning-based implementation for surface boundary detection did not improve mobility performance in the current study. Furthermore, our findings indicate that, for a greater number of electrodes, the removal of within-surface gradients and background textures may deteriorate, rather than improve, mobility. Therefore, finding a balanced amount of scene simplification requires a careful tradeoff between informativity and interpretability that may depend on the number of implanted electrodes.

The mechanisms for the emergence and transmission of synchronized oscillations in Parkinson’s disease, which are potentially causal to motor deficits, remain debated. Aside from the motor cortex and the subthalamic nucleus, the external globus pallidus (GPe) has been shown to be essential for the maintenance of these oscillations and plays a major role in sculpting neural network activity in the basal ganglia (BG). While neural activity of the healthy GPe shows almost no correlations between pairs of neurons, prominent synchronization in the β frequency band arises after dopamine depletion. Several studies have proposed that this shift is due to network interactions between the different BG nuclei, including the GPe. However, recent studies demonstrate an important role for the properties of neurons within the GPe. In this review, we will discuss these intrinsic GPe properties and review proposed mechanisms for activity decorrelation within the dopamine-intact GPe. Failure of the GPe to desynchronize correlated inputs can be a possible explanation for synchronization in the whole BG. Potential triggers of synchronization involve the enhancement of GPe-GPe inhibition and changes in ion channel function in GPe neurons.

Background: The relationship between orthostatic hypotension and clinical outcome in older adults is poorly understood. Blood pressure drop rate (ie, speed of blood pressure drop) may particularly reflect the imposed challenge to the baroreflex and the associated clinical outcome (ie, frailty and number of falls). This study aimed to compare orthostatic blood pressure drop rate and drop magnitude with regard to their association with frailty and number of falls. Methods and Results: Blood pressure was measured continuously during a standardized active stand task in 168 patients (mean age 81.4±7.0; 55.4% female) who visited a geriatric outpatient clinic for cognitive or mobility problems. The association of orthostatic blood pressure drop rate, blood pressure drop magnitude, and baroreflex sensitivity (ie, increase in heart rate divided by systolic blood pressure drop magnitude) with frailty (Fried criteria and 4 frailty markers) and self‐reported number of falls was assessed using linear regression models, adjusting for age and sex. Systolic blood pressure drop rate had the strongest association with frailty according to the 4 frailty markers (β 0.30; 95% CI , 0.11–0.49; P =0.003) and number of falls (β 1.09; 95% CI , 0.19–1.20; P =0.018); diastolic blood pressure drop magnitude was most strongly associated with frailty according to the Fried criteria (β 0.37; 95% CI , 0.15–0.60; P <0.001). Baroreflex sensitivity was associated with neither frailty nor number of falls. Conclusions: Orthostatic blood pressure drop rate was associated with frailty and falls and may reflect the challenge to the baroreflex rather than drop magnitude.

Purpose: Cerebral autoregulation (CA) aims to attenuate the effects of blood pressure variation on cerebral blood flow. This study assessed the criterion validity of CA derived from near-infrared spectroscopy (NIRS) as an alternative for Transcranial Doppler (TCD). Methods: Measurements of continuous blood pressure (BP), oxygenated hemoglobin (O₂Hb) using NIRS and cerebral blood flow velocity (CBFV) using TCD (gold standard) were performed in 82 controls, 27 patients with hypertension and 94 cognitively impaired patients during supine rest (all individuals) and repeated sit to stand transitions (cognitively impaired patients). The BP-CBFV and BP-O₂Hb transfer function phase shifts (TF_φ) were computed as CA measures. Spearman correlations (ρ) and Bland Altman limits of agreement (BAloa) between NIRS- and TCD-derived CA measures were computed. BAloa separation < 50° was considered a high absolute agreement. Results: NIRS- and TCD-derived CA estimates were significantly correlated during supine rest (ρ = 0.22–0.30, N = 111–120) and repeated sit-to-stand transitions (ρ = 0.46–0.61, N = 19–32). BAloa separation ranged between 87° and 112° (supine rest) and 65°–77° (repeated sit to stand transitions). Conclusion: Criterion validity of NIRS-derived CA measures allows for comparison between groups but was insufficient for clinical application in individuals.