Invited speakers
Marco Marino
Principal Investigator at the Department of General Psychology, University of Padua
Movement Control and Neuroplasticity Research Group, KU Leuven, Belgium​
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Mapping the brain in space: how microgravity can be used to model ageing
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In recent decades, our understanding of microgravity's effects on the human body has advanced significantly, spurred by renewed space exploration efforts such as the upcoming ARTEMIS mission, which aims to establish a human presence on the Moon for extended periods. While the impact of microgravity on muscle atrophy and skeletal reshaping - key features of ageing - is well documented, the implications for the neural system remain largely unexplored. Unlike the gradual physiological changes that we experience on Earth, these effects appear in a matter of weeks or months in space, suggesting a potential acceleration of ageing processes. Most importantly, these changes induced by a microgravity condition are reversible and body physiology can be restored following an appropriate training, once the exposure to microgravity is over. In this context, microgravity could be used as a model to induce accelerated ageing, and telescope the detrimental effects of ageing. In this talk, starting from state-of-the-art research about the brain in space, I will introduce a novel framework based on high-density electroencephalography to map brain networks, and investigate the neural mechanisms induced by microgravity to test the hypothesis of accelerated ageing in the human brain.
TALKS
Torge Worbs
PhD Student at Magnetic Resonance Section, Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
Personalized Electric Field Simulations in TMS and TES using SimNIBS: Applications in Research and Clinical Practice
Transcranial Magnetic Stimulation (TMS) and Transcranial Electric Stimulation (TES) are powerful tools for noninvasive brain stimulation, widely used in both research and clinical applications including functional mapping, and the treatment of conditions such as depression and PTSD. Despite their effectiveness, both TMS and TES exhibit significant interindividual variability in physiological stimulation effects, leading to variability in stimulation outcomes. By leveraging advanced simulation tools like SimNIBS, it is possible to create personalized models that predict electric field (E-field) distribution in the brain, enhancing the clinical efficacy of both TMS and TES protocols. In this talk, I will outline the principles behind E-field simulations in TMS and TES, demonstrating how these simulations can be applied for optimizing stimulation protocols in clinical applications and for conducting basic research on neuronal activation.
Aaron Miller
MSc Student at the Faculty of Physics, Universität Leipzig, Germany
Research assistant at Brain Networks Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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Bridging Worlds: How does Computational Modeling at Multiple Scales Help us Understand Brain Stimulation and Function?
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Computational modeling and simulation is a vast landscape of techniques that intersect computer science, mathematics, (bio)physics, neuroscience, and more. These techniques approach understanding the brain’s behavior and dynamics ‘in silico’, implementing computer simulation methods at multiple length and time scales. From the subcellular level we can explore the activation and signal propagation along individual neurons, as well as the interaction between electromagnetic brain stimulation methods and brain tissue. At higher levels we can motivate an understanding of dynamics by studying the behavior of large populations of neurons interconnected by cortical circuits, called ‘neural mass modeling.’ I take us on a tour through some of the computational techniques and models used to study brain stimulation in multiple dimensions. My masters thesis work in modeling the activation of the primary motor cortex by transcranial magnetic stimulation (TMS) provides a framework to dive into these simulation methods from the subcellular to the neural mass scale both within and beyond to the brain. By leveraging experimental data in concert with simulations, we can begin to uncover the mechanisms that drive the ‘black box’ box system of the brain and ultimately explore impactful research and clinical applications.
Semantic cognition is the ability to extract knowledge from the environment and use it to guide behavior. Computational models resulted in the conceptualisation of the anterior temporal lobe (ATL) as a graded semantic center where cross-modal information is embedded into a unified representation. Likewise, social cognition, i.e., the knowledge about other individuals and social constructs, also relies on the ATL, shaping the behavior, and may be associated with psychological affectations. In this talk we will cover the computational research on the brain organization of semantic cognition and the proposal of the ATL as a semantic hub. Then we will focus on social cognition and discuss its relevance to a variety of mental health problems, such as mood and anxiety disorders.
Iván González García
PhD Student in Neuropsychology and Functional Neuroimaging Group, Department of Basic and Clinical Psychology and Psychobiology, Jaume I University, Castellon de la Plana, Spain
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Computerized approaches to mimic the semantic neural network:
Novel paths to understanding mental pathologies
Carlo Andrea Sartori
PhD Student at the Department of Electronics, Information and Bioengineering (DEIB)
Politecnico di Milano, Italy
In silico modelling, from neurons to functional networks: Simulations of cerebellar learning
This talk will explore the use of biologically inspired in silico models to study cerebellar learning, focusing on models built from cellular and synaptic dynamics. These models are based on spiking neural networks (SNNs), which are designed to closely mimic biological neuron behavior, capturing spatiotemporal patterns of activity. SNNs allow the incorporation of both short- and long-term synaptic plasticity mechanisms, which are essential for learning. In the context of cerebellar learning, the Eye-Blink Classical Conditioning (EBCC) paradigm serves as a critical framework and has already been implemented in SNNs of the cortico-cerebellar microcircuit, where motor learning is believed to be learned, stored, and modified. From a molecular perspective, numerous studies in the literature present strategies that target cerebellar synapses to regulate plasticity. One key neuromodulator is nitric oxide (NO), which plays a central role in modulating plasticity at the synaptic level, acting as a necessary—but not sufficient—condition. Due to its chemical properties, NO can diffuse freely through membranes, influencing multiple synapses within its diffusion area. Functionally, NO could act as an activator of plasticity in specific active areas of the cerebellum. After modeling NO diffusion and production using the NO Diffusion Simulator (NODS), our work focused on the EBCC protocol to assess the impact of NO modulation on long-term potentiation and depression within a cortico-cerebellar microcircuit SNN.
Doris Pischedda
Assistant Professor at the Department of Brain and Behavioral Sciences
University of Pavia, Italy
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Multiscale Brain Modeling: Toward Digital Brain Twins
The brain is a multiscale system that needs to be studied at different levels of organization as brain activity occurs across different spatial and temporal scales. Models of neural dynamics targeting these diverse scales (from the microlevel of neurons, to the mesoscale of neuronal populations, to the macroscale of large-scale brain networks) and facilitating their integration not only advance our understanding of brain function and dysfunction but also provide a basis for testing therapeutic interventions in the clinical practice. Virtual brain models use neurophysiological data on brain structure and function to simulate brain dynamics and predict network parameters. Using subject-specific data, these models can be personalized towards effective digital brain twins that enable simulations mimicking brain functions in health and disease. This approach supports the vision of precision medicine, where brain models tailored to individual patients can predict disease progression and optimize treatment strategies. In this talk, I will introduce virtual brain modeling and discuss its development into virtual brain twins with an example of application.
WORKSHOPS
Arianna Menardi
Padova Neuroscience Center, University of Padua, Italy
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Graph Theory Connectivity applied to the Human Brain
This workshop will cover some basic concepts of graph theory, specifically focusing on measures of segregation and integration to understand the basic organizational principles that make a system efficient. We will apply the use of graph theory analysis on real neuroimaging data (functional resonance imaging). Practical hands-on sessions will be carried out to familiarize with the data, the extrapolation of measures, as well as getting acquainted with several different tools to plot and visually represent brain graphs. At the end of the workshop, students will have gained a basic understanding on the strengths and limitations of this technique applied to neuroimaging data.
Torge Worbs
DTU, Kongens Lyngby, Denmark
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SimNIBS Introduction and Tutorial
Accurate electric field (E-field) simulation is crucial for optimizing non-invasive brain stimulation techniques, such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS). These simulations allow researchers and clinicians to visualize and predict the distribution of E-fields in the brain, ensuring targeted and effective stimulation for both research and clinical applications such as the treatment of various psychiatric conditions. This workshop provides an introduction to SimNIBS, a free and open-source software package for the Simulation of Non-invasive Brain Stimulation. It allows for realistic calculations of the E-field induced by TMS and transcranial electric stimulation (TES). The workshop will include: 1.Key concepts of E-field simulations 2.Overview of the SimNIBS pipeline 3.Creation of head models using CHARM 4.Simulations using the SimNIBS GUI 5.SimNIBS scripting and advanced simulations 6.Creating group results using SimNIBS simulations
This course delves into computational neuroscience, the intersection of engineering and neuroscience, exploring how advanced technologies can be used to simulate the human brain. Implementing bioinspired neural networks in silico is indeed a powerful tool for studying brain processes. These networks grant access to the real-time behavior of individual neurons within a complex circuitry. Students will have the opportunity to participate in hands-on experiments involving the creation of simple brain simulations using available tools and software.
Francesco De Santis
PhD Student at the Department of Electronics, Information and Bioengineering (DEIB)
Politecnico di Milano, Italy
Spiking Neural Networks: graphical and coding approaches for modeling neurons and networks
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Juan Lupiáñez
Professor of Experimental Psychology and Cognitive Neuroscience,
Director of Cognitive Neuroscience Group at the University of Granada, Spain
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Attention and (arousal / executive) Vigilance decrement as measured with the ANTI-Vea task: Vigilance, Attention control, and mind-wandering
Human attention is a complex concept encompassing multiple components. In this seminar I will present an integrative model in which the three attentional functions of selection (selection of information, in the input, or attentional orienting, selection in time, or alertness, and selection in the output, or cognitive control), are carried out either automatically under stimulus-driven control or rather voluntarily under top-down control. Then, I will introduce the ANTI-Vea task, which constitutes a validated tool for the measurement of the three attentional functions and their interactions, together with two differentiated vigilance components, Executive Vigilance ─the ability to maintain attention over time to detect infrequent events─ and Arousal Vigilance ─ the ability to maintain the needed activation levels throughout the sleep-wake cycle─. Data from different experiments with the ANTI-Vea task will be presented showing its usefulness to dissociate the two vigilance components, to characterize attentional performance in different populations, and to investigate how attention and vigilance can be improved or hindered by different conditions. Furthermore, the ANTI-Vea free platform (anti-vea.ugr.es) for online data collection and analysis will be introduced with practical exercises.