The initiation of movement, whether triggered by an internal (self-paced) or external stimulus (cued), relies on complex neural circuitry involving the pre-motor and motor cortex, basal ganglia, cerebellum, midbrain, and thalamus. The motor thalamus (mThal) integrates inputs from these structures to orchestrate the shift of cortical activity from a preparatory into an execution mode, resulting in action initiation. However, it is unclear whether the activity in the mThal that releases the motor program is different for externally cued and self-paced movements. Projections from the cerebellum and from the midbrain to the mThal have a critical role in cued movement initiation, while substantia nigra compacta (SNc) dopamine neurons, which send important projections to the basal ganglia, have been show to gate and invigorate self-paced actions. The loss of these neurons in Parkinson’s disease (PD) perturbs basal ganglia inputs to the mThal, leading to impairments in movement initiation. Interestingly, PD disrupts self-paced movements more than those triggered by external cues. We propose that self-paced movement initiation depends on the basal ganglia projections to the mThal, whereas external cues can engage the mThal through cerebellar and/or midbrain circuits to promote movement initiation. To test this hypothesis, we propose a novel behaviour task where mice initiate the same action in a cued or a self-paced manner, combined with recordings and optogenetic manipulation of input-defined mThal populations. We expand this approach to a mouse model of PD to further test if our hypothesis can explain why the use of external cues has a beneficial effect in PD. Our ground-breaking experiments will advance the fundamental understanding of the neural circuits that support self-paced and cued movement initiations, while establishing a framework to understand the positive effect of cues in PD.

Non-invasive Brain Machine Interfaces (BMI) bring great promise for neuro-rehabilitation and neuro-prosthesis, as well as for brain control of everyday devices and performance of simple tasks. Over the last 15 years the interest in BMIs has grown substantially, and a variety of interfaces have been developed. The field has been growing dramatically, and market studies reveal an estimated market size of $1.46 billion by 2020. However, non-invasive BMIs have failed to reach the impressive control seen by BMIs implanted in the brain. To date, they require considerable training to reach a moderate level of control, they are susceptible to noise and interference, do not generalize between people and devices, and performance does not show long-term consolidation. Results from our ERC-funded work uncovered a new paradigm that dramatically improves these issues. We propose to develop a prototype for a novel, standalone, non-invasive, noise-resistant BMI, based on an unexplored BMI learning paradigm. In this POC we will 1) refine the brain signal interface (decoder) to be automatically customizable to each individual and produces faster training, 2) implement our BMI technology into a portable hardware-based system, and 3) develop a virtual reality/gaming training platform that will increase learning, performance and consolidation of BMI control. In addition to these technical aims, we propose to explore commercial opportunities and societal benefits, in particular in the health sector. We will conduct market analysis and develop a business case for this product, while expanding industry contacts for production and commercialization. The work proposed in this PoC grant will permit, for the first time to our knowledge, the development of a portable, stand-alone, noise-resistant, and easy to learn BMI, applicable across a wide set of devices, which will bring a significant social impact in health, entertainment and other applications.

Five leading European supercomputing centres are committed to develop, within their respective national programs and service portfolios, a set of services that will be federated across a consortium. The work will be undertaken by the following supercomputing centres, which form the High Performance Analytics and Computing (HPAC) Platform of the Human Brain Project (HBP): ▪ Barcelona Supercomputing Centre (BSC) in Spain, ▪ The Italian supercomputing centre CINECA, ▪ The Swiss National Supercomputing Centre CSCS, ▪ The Jülich Supercomputing Centre in Germany, and ▪ Commissariat à l'énergie atomique et aux énergies alternatives (CEA), France (joining in April 2018). The new consortium will be called Fenix and it aims at providing scalable compute and data services in a federated manner. The neuroscience community is of particular interest in this context and the HBP represents a prioritised driver for the Fenix infrastructure design and implementation. The Interactive Computing E-Infrastructure for the HBP (ICEI) project will realise key elements of this Fenix infrastructure that are targeted to meet the needs of the neuroscience community. The participating sites plan for cloud-like services that are compatible with the work cultures of scientific computing and data science. Specifically, this entails developing interactive supercomputing capabilities on the available extreme computing and data systems. Key features of the ICEI infrastructure are: ▪ Scalable compute resources; ▪ A federated data infrastructure; and ▪ Interactive Compute Services providing access to the federated data infrastructure as well as elastic access to the scalable compute resources. The ICEI e-infrastructure will be realised through a coordinated procurement of equipment and R&D services. Furthermore, significant additional parts of the infrastructure and R&D services will be realised within the ICEI project through in-kind contributions from the participating supercomputing centres.

Thorough, quantitative analysis of behaviour is essential for advancing neuroscience and neurological disorder research and has the potential to significantly improve the characterization of phenotypes in genetic, pharmacological and toxicological screens commonly employed in the pharmaceutical and biotech industries. Achieving high-throughput screening, without compromising the richness of phenotypic data requires scalable, standardized and cost-effective solutions for behavioral recording that also allow seamless integration across labs. To address this need, we developed megabouts.ai (Jouary et al., Biorxiv, 2024) an AI-based pipeline for behavioral analysis capable of reliably inferring high-speed behavioral kinematics from diverse and low-resolution data sources. Complementing this, we have designed a low-cost, modular behavioral testing system optimized to leverage the strengths of the megabouts.ai pipeline, and enable sophisticated behavioral experiments compatible with high-throughput screening approaches. In this proposal, we will develop and validate these tools across a range of behavioral screening paradigms, exploring their potential applications in both basic and translational research. Our goal is to disseminate these innovations, promoting their effective use in academic and industry settings, unlocking new experimental possibilities and enhancing the capabilities of existing commercial and custom-built solutions.