ICM Paris Brain Institute

Observational data have become a popular source of evidence for causal effects when no randomized controlled trial exists, or to supplement information provided by those. In practice, a wide range of designs and analytical choices exist, and one recent approach relies on the target trial emulation framework. This framework is particularly well suited to mimic what could be obtained in a specific randomized controlled trial, while avoiding time-related selection biases. In this abstract, we present how this framework could be useful to emulate trials in malignant melanoma, and the challenges faced when planning such a study using longitudinal observational data from a cohort study. More specifically, two questions are envisaged: duration of immune checkpoint inhibitors, and trials comparing treatment strategies for BRAF V600-mutant patients (targeted therapy as 1st line, followed by immunotherapy as 2nd line, vs. immunotherapy as 2nd line followed by targeted therapy as 1st line). Using data from 1027 participants to the MELBASE cohort, we detail the results for the emulation of a trial where immune checkpoint inhibitor would be stopped at 6 months vs. continued, in patients in response or with stable disease.

Detecting the direction of image motion is important for visual navigation, predator avoidance and prey capture, and thus essential for the survival of all animals that have eyes. However, the direction of motion is not explicitly represented at the level of the photoreceptors: it rather needs to be computed by subsequent neural circuits. The exact nature of this process represents a classic example of neural computation and has been a longstanding question in the field. Our results obtained in the fruit fly Drosophila demonstrate that the local direction of motion is computed in two parallel ON and OFF pathways. Within each pathway, a retinotopic array of four direction-selective T4 (ON) and T5 (OFF) cells represents the four Cartesian components of local motion vectors (leftward, rightward, upward, downward). Since none of the presynaptic neurons is directionally selective, direction selectivity first emerges within T4 and T5 cells. Our present research focuses on the cellular and biophysical mechanisms by which the direction of image motion is computed in these neurons.

How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? I will propose a possible path towards autonomous intelligent agents, based on a new modular cognitive architecture and a somewhat new self-supervised training paradigm. The centerpiece of the proposed architecture is a configurable predictive world model that allows the agent to plan. Behavior and learning are driven by a set of differentiable intrinsic cost functions. The world model uses a new type of energy-based model architecture called H-JEPA (Hierarchical Joint Embedding Predictive Architecture). H-JEPA learns hierarchical abstract representations of the world that are simultaneously maximally informative and maximally predictable. The corresponding working paper is available here:https://openreview.net/forum?id=BZ5a1r-kVsf

Thoughts and emotions can impact physiology. This connection is evident by the emergence of disease following stress, psychosomatic disorders, or recovery in response to placebo treatment. Nevertheless, this fundamental aspect of physiology remains largely unexplored. In this talk, I will focus on the brain’s involvement in regulating the peripheral immune response and explore the question of how the brain evaluates and represents the state of the immune system it regulates.

The development of human induced pluripotent stem cells (iPSC) and their subsequent differentiation into neurons has provided new opportunities for the generation of physiologically-relevant, in vitro disease models. I will present our work using iPSC to modal familial Alzheimer's Disease (fAD) and Frontotemporal Dementia (FTD). We have investigated the mutation-specific effects of APP and PSEN1 mutations on Abeta generation in neurons generated from individuals with fAD, revealing distinct mechanisms that may contribute to clinical heterogeneity in disease. I will also discuss our work to understand the developmental and pathological changes to tau that occur in iPSC-neurons, particularly the challenges of understanding tau pathology in a developmental system, tau proteostasis and how iPSC-neurons may help us identify early signatures of tau pathology in disease.

Dementia with Lewy bodies (DLB) is a synucleinopathy but more than half of patients with DLB also have varying degrees of tau and amyloid-β co-pathology. Identifying and tracking the pathologic heterogeneity of DLB with multi-modal biomarkers is critical for the design of clinical trials that target each pathology early in the disease at a time when prevention or delaying the transition to dementia is possible. Furthermore, longitudinal evaluation of multi-modal biomarkers contributes to our understanding of the type and extent of the pathologic progression and serves to characterize the temporal emergence of the associated phenotypic expression. This talk will focus on the utility of multi-modal imaging in DLB.

Recent findings have revealed that mRNAs have a much more dynamic behaviour than initially described. This is particularly true in neurons, where mRNAs are transported to specific axonal and dendritic areas. The seminar will present our most recent findings unveiling complex mRNA processing dynamics driven by splicing proteins in developing axons.

I will present field experiments on vervet monkeys testing physical and social cognition, with a focus on social learning. The understanding of the emergence of cultural behaviours in animals has advanced significantly with contributions from complementary approaches: natural observations and controlled field experiments. Experiments with wild vervet monkeys highlight that monkeys are selective about ‘who’ they learn from socially and that they will abandon personal foraging preferences in favour of group norms new to them. The reported findings highlight the feasibility to study cognition under field conditions.

Lysosomal storage diseases are a group of over 70 inherited metabolic disorders, many of which have a neurodegenerative clinical course. Treatments have been developed for a subset of these disorders and are now in routine clinical use. We have found that some neurological and neurodegenerative diseases share unanticipated links to lysosomal storage diseases providing insights into disease pathogenesis. These links also suggest treatments developed for lysosomal disorders may have unanticipated utility in other rare and common diseases.

One of the major challenges in developing therapeutics for the neurodegenerative disorders is the blood-brain barrier, limiting the availability of systemically administered therapies such as recombinant proteins or monoclonal antibodies from reaching the brain. Direct central nervous system (CNS) gene therapy using adeno-associated virus vectors expressing a therapeutic protein, monoclonal antibody or inhibiting RNA-coding sequences has two characteristics ideal for therapy of neurodegenerative disorders: circumventing the blood-brain barrier by directly expressing the therapy in the brain and the ability to provide persistent therapy with only a single administration. There are several critical parameters relevant to successful CNS gene therapy, including choice of vector, design of the gene to be expressed, delivery/route of administration, dose and anti-vector immune responses. The presentation will focus on these issues, the current status of clinical trials of gene therapy for neurodegeneration and specific challenges that will need to be overcome to ensure the success of these therapies.

In a few months, human infants develop complex capacities in numerous cognitive domains. They learn their native language, recognize their parents, refine their numerical capacities and their perception of the world around them but are they conscious and how can we study consciousness when no verbal report is possible? One way to approach this question is to rely on the neural responses correlated with conscious perception in adults (i.e. a global increase of activity in notably frontal regions with top-down amplification of the sensory levels). We can thus study at what age the developing anatomical architecture might be mature enough to allow this type of responses, but moreover we can use similar experimental paradigms than in adults in which we expect to observe a similar pattern of functional responses.

Spontaneous and sensory-evoked cortical activity is highly state-dependent, promoting the functional flexibility of cortical circuits underlying perception and cognition. Using neural recordings in combination with behavioral state monitoring, we find that arousal and motor activity have complementary roles in regulating local cortical operations, providing dynamic control of sensory encoding. These changes in encoding are linked to altered performance on perceptual tasks. Neuromodulators, such as acetylcholine, may regulate this state-dependent flexibility of cortical network function. We therefore recently developed an approach for dual mesoscopic imaging of acetylcholine release and neural activity across the entire cortical mantle in behaving mice. We find spatiotemporally heterogeneous patterns of cholinergic signaling across the cortex. Transitions between distinct behavioral states reorganize the structure of large-scale cortico-cortical networks and differentially regulate the relationship between cholinergic signals and neural activity. Together, our findings suggest dynamic state-dependent regulation of cortical network operations at the levels of both local and large-scale circuits.

The establishment of synaptic connections is essential for normal brain function, yet the molecular mechanisms responsible for the precise connectivity of specific neural circuits remain largely unknown. Previous work has shown that the assembly of cortical circuits requires specific functions of molecular signalling complexes at different classes of synapses. In this talk, I will describe the molecular logic through which specific pyramidal cell-interneuron circuits are established in the cerebral cortex of the mouse, and how alterations in some of these connectivity motifs might be liked to disease.

The human cortical ribbon varies during the lifespan, from childhood to senescence. To study the effects of genetic and environmental factors on these dynamics, one needs to measure specific phenotypes (cortical volume, surface area, thickness, new neuroimaging phenotypes such as intracortical myelination or multimodal ones based on their combination, or their asymmetries) that characterize the cerebral grey matter accurately

Cross-sectional and longitudinal multimodal brain imaging studies using positron emission tomography (PET) and magnetic resonance imaging (MRI) have provided detailed insight into the pathophysiological progression of Alzheimer’s disease. It starts at an asymptomatic stage with widespread gradual accumulation of beta-amyloid and spread of pathological tau deposits. Subsequently changes of functional connectivity and glucose metabolism associated with mild cognitive impairment and brain atrophy may develop. However, the rate of progression to a symptomatic stage and ultimately dementia varies considerably between individuals. Mathematical models have been developed to describe disease progression, which may be used to identify markers that determine the current stage and likely rate of progression. Both are very important to improve the efficacy of clinical trials. In this lecture, I will provide an overview on current research and future perspectives in this area.

Neural stem cells reside in the adult mammalian brain. The ventricular-subventricular zone (V-SVZ) gives rise to olfactory bulb neurons, as well as small numbers of glia throughout life. Adult V-SVZ neural stem cells dynamically integrate intrinsic and extrinsic signals to either maintain the quiescent state or to become activated to divide and generate progeny. I will present our recent findings highlighting adult neural stem cell heterogeneity, including the identification of novel gliogenic domains and cell types, and the key roles of physiological state and long-range signals in the regulation of regionally distinct pools of adult neural stem cells.

Coping with threatening situations requires both identifying stimuli predicting danger and selecting adaptive behavioral responses in order to survive. The dorso medial prefrontal cortex (dmPFC) is a critical structure involved in the regulation of threat-related behaviour, yet it is still largely unclear how threat-predicting stimuli and defensive behaviours are associated within prefrontal networks in order to successfully drive adaptive responses. To address these questions, we used a combination of extracellular recordings, neuronal decoding approaches, and optogenetic manipulations to show that threat representations and the initiation of avoidance behaviour are dynamically encoded in the overall population activity of dmPFC neurons. These data indicate that although dmPFC population activity at stimulus onset encodes sustained threat representations and discriminates threat- from non-threat cues, it does not predict action outcome. In contrast, transient dmPFC population activity prior to action initiation reliably predicts avoided from non-avoided trials. Accordingly, optogenetic inhibition of prefrontal activity critically constrained the selection of adaptive defensive responses in a time-dependent manner. These results reveal that the adaptive selection of active fear responses relies on a dynamic process of information linking threats with defensive actions unfolding within prefrontal networks.

While the diversity of neurons in the adult mammalian brain is staggering, these cells emerge from a seemingly limited set of progenitors during development. This begs the question of how complexity emerges from a finite number of elements during dynamic biological processes. Here, I will discuss recent work from my laboratory addressing relationships between genetic diversity and connectivity in single-cell types, and how progenitor diversity may constrain adult brain cellular states during normal and abnormal brain development.

In this presentation I will show the opportunities and challenges of big data analytics with AI techniques in medical imaging, also in combination with genetic and clinical data. Both conventional machine learning techniques, such as radiomics for tumor characterization, and deep learning techniques for studying brain ageing and prognosis in dementia, will be addressed. Also the concept of deep imaging, a full integration of medical imaging and machine learning, will be discussed. Finally, I will address the challenges of how to successfully integrate these technologies in daily clinical workflow.