Huntington’s disease (HD) is a neurodegenerative disorder characterized by progressive motor symptoms that are preceded by cognitive deficits and is considered as a disorder that primarily affects forebrain striatal neurons. To gain a better understanding of the molecular and cellular mechanisms associated with disease progression, we analyzed the expression of proteins involved in GABAergic neurotransmission in the striatum of the R6/1 transgenic mouse model. Western blot, quantitative PCR and immunohistochemical analyses were conducted on male R6/1 mice and age-matched wild type littermates. Analyses were performed on 2 and 6 month-old animals, respectively, before and after the onset of motor symptoms. Expression of GAD 67, GAD 65, NL2, or gephyrin proteins, involved in GABA synthesis or synapse formation did not display major changes. In contrast, expression of α1, α3 and α5 GABAAR subunits was increased while the expression of δ was decreased, suggesting a change in tonic- and phasic inhibitory transmission. Western blot analysis of the striatum from 8 month-old Hdh Q111, a knock-in mouse model of HD with mild deficits, confirmed the α1 subunit increased expression. From immunohistochemical analyses, we also found that α1 subunit expression is increased in medium-sized spiny projection neurons (MSN) and decreased in parvalbumin (PV)-expressing interneurons at 2 and 6 months in R6/1 mice. Moreover, α2 subunit labeling on the PV and MSN cell membranes was increased at 2 months and decreased at 6 months. Alteration of gene expression in the striatum and modification of GABAA receptor subtypes in both interneurons and projection neurons suggested that HD mutation has a profound effect on synaptic plasticity at an early stage, before the onset of motor symptoms. These results also indicate that cognitive and other behavioral deficits may be associated with changes in GABAergic neurotransmission that consequently could be a relevant target for early therapeutic treatment.

To this day, despite the increasing motor capability of robotic devices, elaborating efficient control strategies is still a key challenge in the field of humanoid robotic arms. In particular, providing a human “pilot” with efficient ways to drive such a robotic arm requires thorough testing prior to integration into a finished system. Additionally, when it is needed to preserve anatomical consistency between pilot and robot, such testing requires to employ devices showing human-like features. To fulfill this need for a biomimetic test platform, we present Reachy, a human-like life-scale robotic arm with seven joints from shoulder to wrist. Although Reachy does not include a poly-articulated hand and is therefore more suitable for studying reaching than manipulation, a robotic hand prototype from available third-party projects could be integrated to it. Its 3D-printed structure and off-the-shelf actuators make it inexpensive relatively to the price of an industrial-grade robot. Using an open-source architecture, its design makes it broadly connectable and customizable, so it can be integrated into many applications. To illustrate how Reachy can connect to external devices, this paper presents several proofs of concept where it is operated with various control strategies, such as tele-operation or gaze-driven control. In this way, Reachy can help researchers to explore, develop and test innovative control strategies and interfaces on a human-like robot.

Background Prosthetic restoration of reach and grasp function after a trans-humeral amputation requires control of multiple distal degrees of freedom in elbow, wrist and fingers. However, such a high level of amputation reduces the amount of available myoelectric and kinematic information from the residual limb. Methods To overcome these limits, we added contextual information about the target’s location and orientation such as can now be extracted from gaze tracking by computer vision tools. For the task of picking and placing a bottle in various positions and orientations in a 3D virtual scene, we trained artificial neural networks to predict postures of an intact subject’s elbow, forearm and wrist (4 degrees of freedom) either solely from shoulder kinematics or with additional knowledge of the movement goal. Subjects then performed the same tasks in the virtual scene with distal joints predicted from the context-aware network. Results Average movement times of 1.22s were only slightly longer than the naturally controlled movements (0.82 s). When using a kinematic-only network, movement times were much longer (2.31s) and compensatory movements from trunk and shoulder were much larger. Integrating contextual information also gave rise to motor synergies closer to natural joint coordination. Conclusions Although notable challenges remain before applying the proposed control scheme to a real-world prosthesis, our study shows that adding contextual information to command signals greatly improves prediction of distal joint angles for prosthetic control.