There is an increasing interest in the potential role of influenza infection in evoking ischemic vascular events, including acute myocardial infarction (AMI). Such an etiological link can have important implications for both primary (influenza vaccination) and secondary (antiviral treatment or thromboprophylaxis) prevention strategies. However, establishing conclusive evidence is hindered by shortcomings of traditional epidemiological designs used to study this association including cohort, case-control and ecological studies. An alternative design, the self-controlled case series (SCCS), has important advantages over more traditional designs because 1) it eliminates fixed confounder bias as each study subject serves as its own control (i.e. self-controlled) and 2) is particularly suitable for studying assocations where both the exposure (influenza infection) and outcome (AMI) are relatively rare. This design was recently applied for the first time to study the link between influenza infection and AMI occurrence in Ontario, Canada (published: New England Journal of Medicine, January 2018). The case-series was created by linking several healthcare registries. A 6-fold increased risk of AMI during the week following a confirmed influenza infection was found, while for other respiratory viral pathogens, relative risks were much lower (2.8-3.5). This indicates that effects specific to influenza virus infection can trigger AMI, which has important implications for influenza prevention and treatment in cardiovascular risk-management. Yet, these findings need to be corroborated before guidelines and policies are updated accordingly. Hence, we propose to replicate this landmark study. We will follow similar approaches for data collection, linkage and analysis as in the original study, but the study cohort will consist of Dutch citizens instead. Importantly, we will be able to account for deaths during follow-up by linking with individual-level mortality data from deaths registries. This is a substantial improvement in comparison to the original study, where this was not accounted for.

For people who cannot talk due to paralysis, an alternative means of communication is important. In this project it is investigated whether overt or covert spoken words can be read directly from the brain. The findings can contribute to the development of brain implants that convert brain signals to speech.

Hundreds of children are born with severe physical impairment and are unable to communicate effectively. What if these children could use their brain signals to communicate and participate in society? An implantable communication Brain-Computer Interface (cBCI) would allow brain signals to be directly translated into computer commands, thereby enabling the user to control communication software. Although already possible for adults, the development of implanted cBCIs for children with disabilities has been left mostly untouched. This research aims to evaluate the feasibility of implantable cBCI technology to establish communication in children with severe physical impairments.

Urgency: An estimated 179 million individuals in Europe are currently suffering from a brain disorder. These disorders are often persistent, leading to significant emotional and financial burdens to patients, their family, and society at large. For many brain disorders, including depression, substance abuse, autism, schizophrenia, insomnia, and dementia, there is no cure. Available treatments address symptom relief and are only effective in subsets of patients. The World Economic Forum, the World Health Organization and the European Brain Council all urge for improved understanding of brain disorders. Problem definition: Most brain disorders have in common a so-called ‘complex’ aetiology: i.e., they are influenced by multiple genetic and environmental risk factors. Each factor contributes only a small proportion to the total disease risk, and each individual potentially carries a different combination of genetic risk factors. Recent genetic discovery studies provided unprecedented insight into the genetic architecture of brain disorders by revealing many of the genes involved. Despite this enormous success, these results have not translated into mechanistic insight. That is because the detected genetic effects are small and numerous, and their combined biological implications are unclear. This complex nature of brain disorders has so far seriously hampered mechanistic disease insight, a prerequisite for successfully developing treatments. Opportunity: Two major recent advancements are of high relevance: First, novel genomics’ technologies have led to large-scale initiatives that provide genetic and transcriptomic signature maps of the human brain, down to cellular resolution. These maps are radically changing our understanding of the brain, and contain enormous potential for the interpretation of the functional role of the hundreds of genes implicated in brain disorders, as they allow mapping of risk genes to cells via their cellular expression. Aligning results from genetic discovery studies with these novel cellular signature maps of the brain will translate genetic discoveries into actionable starting points for functional follow-up studies. Second, a recent revolution in tools and technologies in experimental neuroscience enables studying cells and circuitry with unprecedented resolution. These new precision tools facilitate rapid genome editing, targeted intervention of the activity of neurons in the brain and the study of human neurons derived from patient cells. They provide promising new avenues to functionally investigate the role of cells in circuitry and in causal relationships with disease-relevant behaviour. Taken together these recent advances provide unparalleled opportunities to gain mechanistic insight into specific brain (dys)function and lay a new foundation for designing innovative treatment options for brain disorders. Goal & Approach: Our primary goal is to gain insight into the molecular and cellular basis of complex brain disorders, by closely connecting genetics to neurobiology, facilitating new experimental approaches, and enabling the design of novel treatment strategies. First, we will develop algorithms to align results from genetic discovery studies with cellular signature maps of the brain and generate actionable hypotheses on the involvement of specific cell types (neurons and glia) in multiple brain disorders. Second, we will verify the involvement of these cell types in human and animal models relevant to selected brain disorders. Third, we will identify the neural circuitry in which identified cell types are involved. Fourth, we will determine the role of identified cell types and neural circuitry in behaviour relevant for the brain disorders. Fifth, at multiple stages of our project we will generate results that can potentially serve as starting points for novel treatment regimens – we will actively monitor this and push translation of our results. The project will build a computational and technological platform to translate genetic findings into mechanistic insights into brain disorders, so urgently needed. The consortium consists of 21 excellent researchers selected for their expertise representing the scientific fields that are crucial to meet the project’s goal. The project capitalizes on recent exciting advances in genetics and neurobiology and is highly timely; never before were the odds so much in favour of mechanistically understanding brain disorders. The BRAINSCAPE consortium is exceptionally well-positioned to successfully realize this unique opportunity.

The decision to eat does not solely depend on our current metabolic state, but also on the value of the available food. This value is determined by both the rewards caloric-content and hedonic value, the degree to which food is experienced as pleasurable. As such, hungry animals will work harder for calorie-dense foods than rewards that has little- or no nutritional value. Likewise, rewards that are considered "pleasurable" will increase the motivational drive more than neutral or aversive rewards. Although this behavior is well described and of great importance to our understanding of reward-seeking behavior and eating disorders, the neurobiological mechanisms that mediate these choices are poorly understood. The aim of the experiments described in this proposal is two-fold; First, I will elucidate how reward value affects neuronal activity and behavior, and second, I will identify specific brain areas that mediate the metabolic and hedonic properties of reward. Therefore, I will measure neuronal activity of individual neurons in the rat brain while the animal is performing a decision-making task to obtain food rewards. In this task, the caloric-, and hedonic value of the rewards and the metabolic state of the animal (food-deprived or satiated) will be varied and I will assess how energy balance affects both reward-signaling as well as behavioral performance during task-execution. Secondly, I will inhibit the activity of specific neurons in rats that work to obtain food in the decision-making task. This approach will allow me to assess the role of very specific brain areas in the signaling of reward-information and ultimately decision-making. These experiments bring together the unique combination of two state-of-the art technologies "optogentics and in vivo electrophysiology" to elucidate the neurobiological substrate with which our brain processes cognitive- and metabolic information about reward and the manner in which the decision to eat is made.