Topic: Biomedical research

NF1 exon 51 alternative splicing: functional implications in Central Nervous System (CNS) Cells

Gene regulation networks in nervous system cancers: identification of novel drug targets

LRRK2 – a master regulator of neurodegeneration: acting on multiple systems including neuroinflammatory signaling, vesicular trafficking, and cell death pathways

Rett syndrome, MECP2 and therapeutic strategies

The development of the iPS cell technology has revolutionized our ability to study development and diseases in defined in vitro cell culture systems. The talk will focus on Rett Syndrome and discuss two topics: (i) the use of gene editing as an approach to therapy and (ii) the role of MECP2 in gene expression (i) The mutation of the X-linked MECP2 gene is causative for the disease. In a female patient, every cell has a wt copy that is, however, in 50% of the cells located on the inactive X chromosome. We have used epigenetic gene editing tools to activate the wt MECP2 allele on the inactive X chromosome. (ii) MECP2 is thought to act as repressor of gene expression. I will present data which show that MECP2 binds to Pol II and acts as an activator for thousands of genes. The target genes are significantly enriched for Autism related genes. Our data challenge the established model of MECP2’s role in gene expression and suggest novel therapeutic approaches.

Dynamic neurochemistry in conscious humans during stereoEEG monitoring

Alternative Splicing and Isoforms: role in brain function and pathology

Predictive processing: a circuit approach to psychosis

Predictive processing is a computational framework that aims to explain how the brain processes sensory information by making predictions about the environment and minimizing prediction errors. It can also be used to explain some of the key symptoms of psychotic disorders such as schizophrenia. In my talk, I will provide an overview of our progress in this endeavor.

Alpha synuclein in parkinson's Disease: From the bedside to the bench and back again

The role of extracellular vesicles in sickness and in health

From the guts to the brain through adaptive immunity in the prevention of Alzheimer’ disease

Dr. Pasinetti is the Saunders Family Chair and Professor of Neurology at Icahn School of medicine at Mount Sinai, New York. His studies allowed him to develop novel therapeutic approaches through investigation of preventable risk factors including mood disorders in the promotion of resilience against neurodegenerative disorder. In his presentation Dr. Pasinetti will discuss novel concepts about the gut-brain axis in mechanisms associated to peripheral adaptive immunity as therapeutic targets to mitigate the onset and the progression of Alzheimer’s disease and other form of dementia.

Hebbian Plasticity Supports Predictive Self-Supervised Learning of Disentangled Representations​

Discriminating distinct objects and concepts from sensory stimuli is essential for survival. Our brains accomplish this feat by forming meaningful internal representations in deep sensory networks with plastic synaptic connections. Experience-dependent plasticity presumably exploits temporal contingencies between sensory inputs to build these internal representations. However, the precise mechanisms underlying plasticity remain elusive. We derive a local synaptic plasticity model inspired by self-supervised machine learning techniques that shares a deep conceptual connection to Bienenstock-Cooper-Munro (BCM) theory and is consistent with experimentally observed plasticity rules. We show that our plasticity model yields disentangled object representations in deep neural networks without the need for supervision and implausible negative examples. In response to altered visual experience, our model qualitatively captures neuronal selectivity changes observed in the monkey inferotemporal cortex in-vivo. Our work suggests a plausible learning rule to drive learning in sensory networks while making concrete testable predictions.

Inhibitory connectivity and computations in olfaction

We use the olfactory system and forebrain of (adult) zebrafish as a model to analyze how relevant information is extracted from sensory inputs, how information is stored in memory circuits, and how sensory inputs inform behavior. A series of recent findings provides evidence that inhibition has not only homeostatic functions in neuronal circuits but makes highly specific, instructive contributions to behaviorally relevant computations in different brain regions. These observations imply that the connectivity among excitatory and inhibitory neurons exhibits essential higher-order structure that cannot be determined without dense network reconstructions. To analyze such connectivity we developed an approach referred to as “dynamical connectomics” that combines 2-photon calcium imaging of neuronal population activity with EM-based dense neuronal circuit reconstruction. In the olfactory bulb, this approach identified specific connectivity among co-tuned cohorts of excitatory and inhibitory neurons that can account for the decorrelation and normalization (“whitening”) of odor representations in this brain region. These results provide a mechanistic explanation for a fundamental neural computation that strictly requires specific network connectivity.

Identification and treatment of advanced, rupture-prone plaques to reduce cardiovascular mortality

Atherosclerosis is the underlying cause of major cardiovascular events, including heart attack and stroke. The build-up of plaque in coronary arteries can be a major risk for events, but risk is significantly higher in patients with vulnerable rather than stable plaque. Diagnostic imaging of vulnerable plaque is extremely useful for both stratifying patient risk and for determining effectiveness of experimental intervention in reducing cardiovascular risk. In the preclinical setting, being able to distinguish between stable and vulnerable plaque development and pair this with biochemical measures is critical for identification of new experimental candidates. In this webinar, Professor Stephen Nicholls and Dr Kristen Bubb from the Victorian Heart Institute will discuss the benefits of being able to visualise vulnerable plaque for both clinical and preclinical research. Professor Stephen Nicholls is a clinician-researcher and the Head of the Victorian Heart Institute. He is the lead investigator on multiple large, international, cardiovascular outcomes trials. He has attracted over $100 million in direct research funding and published more than 400 peer-reviewed manuscripts. He is focused on both therapeutic intervention to reduce vascular inflammation and lipid accumulation and precision medicine approaches to prevent cardiovascular mortality. Dr Kristen Bubb is a biomedical researcher and Group Leader within the Monash Biomedicine Discovery Institute Cardiovascular Program and Victorian Heart Institute. She focuses on preclinical/translational research into mechanisms underlying vascular pathologies including atherosclerosis and endothelium-driven hypertension within specific vascular systems, including pulmonary and pregnancy-induced. She has published >30 high impact papers in leading cardiovascular journals and attracted category 1&2 funding of >$750,000.

Optimal initialization strategies for Deep Spiking Neural Networks

Recent advances in neuromorphic hardware and Surrogate Gradient (SG) learning highlight the potential of Spiking Neural Networks (SNNs) for energy-efficient signal processing and learning. Like in Artificial Neural Networks (ANNs), training performance in SNNs strongly depends on the initialization of synaptic and neuronal parameters. While there are established methods of initializing deep ANNs for high performance, effective strategies for optimal SNN initialization are lacking. Here, we address this gap and propose flexible data-dependent initialization strategies for SNNs.

Structures in space and time - Hierarchical network dynamics in the amygdala

In addition to its role in the learning and expression of conditioned behavior, the amygdala has long been implicated in the regulation of persistent states, such as anxiety and drive. Yet, it is not evident what projections of the neuronal activity capture the functional role of the network across such different timescales, specifically when behavior and neuronal space are complex and high-dimensional. We applied a data-driven dynamical approach for the analysis of calcium imaging data from the basolateral amygdala, collected while mice performed complex, self-paced behaviors, including spatial exploration, free social interaction, and goal directed actions. The seemingly complex network dynamics was effectively described by a hierarchical, modular structure, that corresponded to behavior on multiple timescales. Our results describe the response of the network activity to perturbations along different dimensions and the interplay between slow, state-like representation and the fast processing of specific events and actions schemes. We suggest hierarchical dynamical models offer a unified framework to capture the involvement of the amygdala in transitions between persistent states underlying such different functions as sensory associative learning, action selection and emotional processing. * Work done in collaboration with Jan Gründemann, Sol Fustinana, Alejandro Tsai and Julien Courtin (@theLüthiLab)

Finding the needle in the haystack – Functional circuit and network models for neuroscience

Start of the talk will be 17:15h (CEST). This session is a double feature of the Cologne Theoretical Neuroscience Forum and the BCCN Berlin.

Biomarkers for Addiction Treatment Development: fMRI Drug Cue Reactivity as an Example

This webinar is mainly focused on “Biomarkers for Addiction Treatment Development: fMRI Drug Cue Reactivity as an Example”. Biomarkers and Biotypes of Drug Addiction: funding opportunities at NIDA, Tanya Ramey (NIDA, US) Neuroimaging-based Biomarker Development for Clinical Trials, Owen Carmicheal (Pennington Biomedical Research Center, USA) ENIGMA-Addiction Cue Reactivity Initiative (ACRI) and Checklist, Hamed Ekhtiari (Laureate Institute for Brain Research, USA) ENIGMA-ACRI Checklist: Participant Characteristics, General fMRI Information, General Task Information, Cue Information, Task-related Assessments, Pre-Post Scanning Consideration (James Prisciandaro, Medical University of South Carolina, USA; Marc Kaufman, McLean Hospital/Harvard Medical School, USA; Anna Zilverstand, University of Minnesota; Torsten Wüstenberg, Charité Medical University Berlin, Germany; Falk Kiefer, University of Heidelberg, Germany; Amy Janes, Harvard Medical School, USA) How to Add fMRI Drug Cue Reactivity to the ENIGMA Consortium: Road Ahead, Hugh Garavan, University of Vermont)

How brain evolutionary mechanisms could inspire AI structural designs

Across evolution and, in particular, in brain evolutionary development we can observe how diverse adaptive biological mechanisms are displayed as a solution to environmental demands. In this talk, I will discuss some examples of emerging evolutionary developmental strategies allowing to increase brain computational capacities and how neurodevelopmental conservation, divergence, and convergence would inspire AI systems optimization.

Predictive processing in cortical circuits

“Changing Memory on the Fly, re-evaluation of learned behaviour I n Drosophila” “Metabolic Regulation of Neural Stem Cells” “The answer is in the sauce”

Watching single molecules in action: How this can be used in neurodegeneration

This talk aims to show how new physical methods can advance biological and biomedical research. A major advance in physical chemistry in the last two decades has been the development of quantitative methods to directly observe individual molecules in solution, attached to surfaces, in the membrane of live cells or more recently inside live cells. These single-molecule fluorescence studies have now reached a stage where they can provide new insights into important biological problems. After presenting the principles of these methods, I will give some examples from our current research to probe the molecular basis of neurodegeneration. Here we have used single-molecule fluorescence to detect and analyse the low concentrations of soluble protein aggregates thought to be responsible for Alzheimer’s disease and determine the mechanisms by which they damage neurons. Lastly, I will describe how fundamental science aimed at watching single molecules incorporating nucleotides into DNA gave rise to a new rapid method to sequence DNA that is now widely used.