A hallmark of our survival in the real world is our ability to show behavioral adaptation. Adaptation of behavior can be necessary for a number of reasons, making the study of the process challenging. Two classes of event can signal a need for adaptation: 1) Events caused by one’s own actions and specifically FeedBack –FB– from those actions (e.g. we adapt our strategy after an erroneous choice), and 2) Events not linked to our actions, specifically Action-InDependent Events –AiDE– (e.g. we adapt our strategy after a change of rule). These two types of events frequently occur concurrently and a critical part of adapting appropriately involves resolving the difference between the two. Our task is made even more complex by the fact that the dynamics of evidence accumulation after FB vs AiDE are very different. So, the crucial dilemma is this: after an unwanted outcome, should we adapt as if we made an error and received a negative FB, or should we continue to accumulate evidence as if there has been an AiDE to which we need to know how to adapt. Primates in general and humans in particular are able to resolve this credit assignment problem. Through the primate order, this ability has evolved together with particular brain networks including the frontal cortex. In human, a breakdown of this ability to link unexpected events to their correct cause would seem to be at the source of impairments in a wide range of psychological and neurological disorders. Yet the neural basis of this process and how it evolved through primate evolution are currently unknown. DYNADAPT aims to provide unprecedented characterization of 1) the mode of functioning of brain systems critically involved in this process and 2) their evolution through the primate order to reach its highest level of complexity in human. DYNADAPT is based on knowledge that I acquired recently in humans and non-human primates and makes the following hypotheses: 1) Relationships between FB, AiDE, and behavioral adaptation are built through learning within a frontal network including the midcingulate cortex (MCC) and the dorsolateral prefrontal cortex (DLPFC) and depend on the integrity of this network. 2) The anatomo-functional organization of this network has evolved from macaque, chimpanzee, to human in such a way that chimpanzees display a very similar organization to humans whereas macaques display all the first-fruits of this organization, strongly suggesting that i) macaque are good models to assess the causal role of this network on behavioral adaptation and on large-scale network functioning, and ii) understanding the organization of this network in chimpanzee is a critical step to bridge the gap between macaque and human and to grasp what makes the human brain unique. DYNADAPT will demonstrate the validity of these hypotheses in both human and non-human primates thanks to 4 approaches: 1) the various nodes of the network involved will be uncovered in human thanks to functional magnetic resonance imaging (fMRI) and a new adaptive task. 2) the macaque and chimpanzee homologue of these human nodes will be identified thanks to the coupling of RestingState-fMRI (RS-fMRI) and connectivity matching fingerprint analysis. 3) the causal role of these nodes on a) whole brain activity and b) adaptive behavior will be identified in macaque thanks to the coupling of local pharmacological perturbations in these nodes and a) RS-fMRI and b) the assessment of deficits in performance in the adaptive task, respectively. DYNADAPT will therefore provide critical insights into how the MCC-DLPFC network is organized to produce optimal adaptive behavior through the primate order to reach its highest level of organization in human. As such, it will provide a breakthrough in heterogeneous domains in fundamental, comparative, applied cognitive, and clinical neurosciences.

Our understanding of the neural dynamics of prefrontal cortex (PFC) and its interactions in complex cognitive tasks derives largely from recordings made in monkeys extensively trained on the task at hand. Whilst providing invaluable information, this approach ignores crucial learning and dynamic components of PFC function that occur during training. My previous work and that of others suggests that these components are central to the function of the PFC, and so we lack important information on a key role of PFC. Understanding PFC’s role in task acquisition is therefore an important scientific challenge in of itself, but it will also have important impacts down the line for the domains of cognitive training for education and lesion recovery. PREDYCT will study how the properties of PFC network dynamics change throughout cognitive training in monkeys. To do so I will develop a novel paradigm: longitudinal chronic neurophysiology in free-access cognitive testing combined with regular DREADD (Designer Receptors Exclusively Activated by Designer Drugs) inactivation of prefrontal interactions. I will make longitudinal recordings in monkeys at every stage of cognitive training, starting with naïve animals. I will make concurrent recordings at the single cell, local field, and brain surface levels, to access multi-scale encoding and dynamics, and so that changes in local circuitry can be directly compared to surface recordings from non-invasive methods in humans. I will make these recordings in PFC but also simultaneously in posterior parietal cortex (PPC) to study how this cortical interaction, known to be part of the putative multiple demand network and implicated in cognitive training, is altered during training. I will allow monkeys to self-pace their training using a free-access training apparatus, to make training as naturalistic as possible and extract data about motivation as well as performance. Motivation is a critical element for cognitive training and frontal neurophysiology. To understand causal contributions of cortical interactions I will use chemo-genetic DREADD inactivation punctually throughout training, a technique at the forefront of intervention technology. Together PREDYCT represents a unique methodological combination necessary to address my objectives, and with my research experience and supportive laboratory and team, I am perfectly placed to develop such an approach. The methodological advances also represent a highly significant refinement in the context of the 3Rs for electrophysiological recordings in the monkey. PREDYCT will therefore provide detailed fundamental mechanistic and causal data on the neural basis of cognitive training, data critically absent from the literature. This fundamental information will be applicable in a number of important fields in medicine and human sciences.

Induced pluripotent stem cells (iPSCs) open avenues for the development of new technologies in veterinary medicine and animal production. However, the efficiency of iPSC reprogramming remains low, especially in livestock. Additionally, the iPSC generated are in a primed (restricted embryonic contribution) rather than naïve (full pluripotency) state, hence restricting their potential. The project aims at promoting the transition to the naïve state in rabbits iPSC. We will interfere with the epigenetic status of iPSC using a focused CRISPR perturbation screen as well as small molecule inhibitors to manipulate chromatin modifiers' level or activity, respectively. Using our fluorescent reporter cell lines enabling tracking of primed to naïve state transition, we will screen for combinations of epigenetic modulators that efficiently promote the emergence of the naive state; a strategy that could then be applied to other livestock species.