Welcome notes

• Alan C Evans (Montreal Neurological Institute McGill University Montreal)

Room: Lecture Hall Zoom webinar: [https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09](https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09) Kenncode: 749254

BigBrain Project Educational Lecture

• Maryann Martone (University of California)

Room: Lecture Hall Zoom webinar: [https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09](https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09) Kenncode: 749254 Are we FAIR yet? Perspectives on the state of global neuroscience data sharing Neuroscience has made tremendous strides in moving towards open neuroscience over the past decade. Spurring by large investments into neuroscience infrastructure and open datasets, we are close to taking our place alongside fields such as genomics and astronomy, the poster children for open science, in providing large pools of public data. Given the rapid pace of technological development, the multimodal nature of our data and the plethora of model systems, establishing these pools was no small feat. But are we FAIR yet? We’ve made progress in making data findable and accessible, but interoperable and reusable remain a challenge. We know that large prospective data sharing projects like HBP, SPARC, ABCD, Human Connectome, US BRAIN Initiative Cell Census Network and Cell Atlasing project, succeeded in moving large amounts of data to the public domain, but is creating routine and effective data sharing according to the FAIR data principles within the reach of individual scientists across all disciplines? In this presentation, I will consider the state of global neuroscience infrastructure and challenges and benefits of routine data sharing for both big and small science. I will argue that as a whole, the global neuroscience community has taken significant steps towards FAIR, even as work remains to be done. Dr Maryann Martone is a Professor Emerita at University of California, San Diego, and Chair of the Governing Board for the International Neuroinformatics Coordinating Facility (INCF). She is past Chair of INCF’s Council on Training, Science and Infrastructure, and past President of FORCE11 where she promoted the advancement of scholarly communication and e-scholarship. Pr Martone is also the founder of SciCrunch, a tech startup based on a uniform resource description framework for neuroscience (first developed under her lead at the Neuroscience Information Framework - NIF) and featuring a portal for connecting researchers to resources (built under her leadership at the NIDDK Information Network - dkNET). She started her career as a neuroanatomist, specialising in light and electron microscopy, but her current research interests are focusing on neuroinformatics with her team at the FAIR Data Informatics Lab and consist in building ontologies for neuroscience data integration and supporting data sharing through several large consortia, including the Stimulating Peripheral Activity to Relieve Conditions (SPARC) project, the BRAIN Initiative Cell Atlas Network (BICAN) and Preclinical Interagency Research Resource for Traumatic Brain Injury (PRECISE).

AI-driven brain modelling

• Blake Richards (McGill University; MILA)
• Pouya Bashivan (McGill University; MILA)

Room: Lecture Hall Zoom webinar: [https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09](https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09) Kenncode: 749254 Artificial Neural Networks (ANNs) are the workhorse of modern AI, but they were originally invented as models of the brain. In recent years computational neuroscientists have used ANNs to model the brain to great effect, achieving state-of-the-art matches to the representations found in the brain. In this session we will outline two of the major uses for ANNs in neuroscience research: 1) as a tool to answer "why" questions and 2) as a tool for discovery. We will discuss how anatomical, connectomics, and functional data, including anatomical by the BigBrain, can facilitate progress in modeling the brain. We will end with a discussion session on best tips for using ANNs in neuroscience.

Panel discussion: Launching your Lab and Steering your Career Room: Lecture Hall Zoom webinar: [https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09](https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09) Kenncode: 749254 Moderation: Jane Roskams Panel: Pouya Bashivan, Boris Bernhardt, Lotta María Ellingsen, Sofie Valk

Welcome and Introduction

• Jón Atli Benediktsson (Rector of University of Iceland)
• Alan C Evans (Montreal Neurological Institute McGill University Montreal)
• Katrin Amunts (Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich)

Zoom webinar: [https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09](https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09) Kenncode: 749254

HIBALL Lecture in Brain Analytics and Learning

• Kári Stefánsson (deCODE Genetics, Iceland)

Zoom webinar: [https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09](https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09) Kenncode: 749254 Research into human diversity in the multiomic era Dr Kári Stefánsson is a founder and CEO of deCODE Genetics in Iceland. He is an International Member of the US National Academy of Sciences and a member of the European Molecular Biology Organization (EMBO). Presently Professor Emeritus at University of Iceland, he was previously a Professor of Neurology, Neuropathology and Neuroscience at Harvard University and Director of Neuropathology at Beth Israel Hospital in Boston, Massachussetts. Professor Stefánsson has received many accolades from the international genetics community, including the European Society of Human Genetics Award, and the prestigious William Allan Award from the American Society of Human Genetics for his lifetime achievements and substantial research contributions to human genetics. He is currently actively engaged in leading the work at deCODE genetics for the discovery of genetic variations associated with common diseases, pointing to potential drug targets.

Panel discussion: Challenges of big data integration Zoom webinar: [https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09](https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09) Kenncode: 749254

Leveraging the BigBrain for Electrophysiology

• Pedro. A. Valdes-Sosa (Joint China Cuba Lab for Neuroscience and Neurotechnology, University of Electronic Science and Technology of China, Chengdu China)

Zoom webinar: [https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09](https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09) Kenncode: 749254 Electrophysiology is an imaging technology with a high temporal resolution that is cost-effective and widely deployable in any economic setting. It is therefore of great interest to global public health. Electrophysiological Source Imaging (ESI) estimates the neural primary current densities that produce the observed EEG/MEG. While ESI is closer to the underlying neural processes of interest, there remain some critical underdeveloped research areas to fully interpret the data. In this presentation, we facilitate the use of BigBrain project to address some of these issues with the following developments: - Development of workflow to obtain ESI projected to the BigBrain atlas space. - A toolbox, CITFITSTORM that produces BigBrain compatible ESI obtained with a diversity of availability of MRI data to compute the lead yield. These are HCP-compatible MRI collections, Legacy datasets, with only T1, as well as when there is no MRI available [1]. - We have developed methods and a toolbox to integrate external high-dimensional neural mass models with great accuracy and computational efficiency. A main feature of this approach is to allow a general model of disturbed conduction delays between neural masses via a Connectome Tensor formulation [2]. With this work, we aim to facilitate biophysical models for principled multimodal data fusion in the framework of the BigBrain Project. References: [1] Areces Gonzalez, Ariosky; Paz-Linares, Deirel; Riaz, Usama; Li, Min; Wang, Ying; Kpiebaareh, Michael Y.; et al. (2023). Multimodal pipeline for HCP-compatible processing and registration of legacy datasets (MRI, MEG, and EEG). TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.22276549.v1 [2] Mitjans, A. G., Linares, D. P., Naranjo, C. L., Gonzalez, A. A., Li, M., Wang, Y., ... & Valdes-Sosa, P. A. (2023). Accurate and Efficient Simulation of Very High-Dimensional Neural Mass Models with Distributed-Delay Connectome Tensors. NeuroImage, 274, 120137. https://doi.org/10.1016/j.neuroimage.2023.120137

The Virtual Big Brain in clinical applications

• Viktor Jirsa (Institut de Neurosciences des Systèmes, Aix-Marseille Université)

Zoom webinar: [https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09](https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09) Kenncode: 749254 Over the past decade we have demonstrated that the fusion of subject-specific structural information of the human brain with mathematical dynamic models allows building biologically realistic brain network models, which have a predictive value, beyond the explanatory power of each approach independently. The network nodes hold neural population models, which are derived using mean field techniques from statistical physics expressing ensemble activity via collective variables. Our hybrid approach fuses data-driven with forward-modeling-based techniques and has been successfully applied to explain healthy brain function and clinical translation including aging, stroke and epilepsy. Here we illustrate the workflow along the example of epilepsy: we reconstruct personalized connectivity matrices of human epileptic patients using Diffusion Tensor weighted Imaging (DTI). Subsets of brain regions generating seizures in patients with refractory partial epilepsy are referred to as the epileptogenic zone (EZ). During a seizure, paroxysmal activity is not restricted to the EZ, but may recruit other healthy brain regions and propagate activity through large brain networks. The identification of the EZ is crucial for the success of neurosurgery and presents one of the historically difficult questions in clinical neuroscience. The application of latest techniques in Bayesian inference and model inversion, in particular Hamiltonian Monte Carlo, allows the estimation of the EZ, including estimates of confidence and diagnostics of performance of the inference. The example of epilepsy nicely underwrites the predictive value of personalized large-scale brain network models. The workflow of end-to-end modeling is an integral part of the European neuroinformatics platform EBRAINS and enables neuroscientists worldwide to build and estimate personalized virtual brains. Originally trained in Theoretical Physics and Philosophy in the 1990s, Dr. Jirsa has made contributions to the understanding of how network structure constrains the emergence of functional dynamics using methods from nonlinear dynamic system theory and computational neuroscience. Dr. Jirsa has been awarded several international and national awards for his research including the Francois Erbsmann Prize in 2001, NASPSPA Early Career Distinguished Scholar Award in 2004, and Grand Prix de Recherche de Provence in 2018. He serves on various Editorial Boards and has published more than 150 scientific articles and book chapters, as well as co-edited several books including the Handbook of Brain Connectivity. Dr. Jirsa is one of the Lead Scientists in the Human Brain Project and The Virtual Brain.

Sievers Lecture in Computational Neuroscience

• Paul M. Thompson (University of Southern California)

Zoom webinar: [https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09](https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09) Kenncode: 749254 ENIGMA, AI & the Human Brain: Worldwide Neuroimaging and Genetics of 30 Brain Diseases in 100,000 Individuals from 45 Countries Since 2009, the ENIGMA Consortium has published the largest worldwide neuroimaging studies of over 15 brain diseases and conditions, including Parkinson’s disease, epilepsy, ataxia and brain injury, PTSD, substance use disorder, bipolar disorder, and major depression, and neurodevelopmental conditions including OCD, ADHD and ASD. With over 2,000 participating scientists, ENIGMA has cooperatively analyzed data from 45 countries, and leads worldwide imaging genetics studies that discovered over 500 genomic loci that affect brain structure, disease risk, and brain synchrony and connectivity (Grasby et al., Science, 2020). In this lecture, we review ENIGMA’s major findings to date, including maps of disease effects on brain MRI, DTI, and functional MRI, as well as factors that influence them. A decade of genomic screens of worldwide multimodal brain images has discovered over 500 common and rare genomic variants influencing brain connectivity, brain function mapped using EEG, and rates of tissue loss in development and old age. We review the major factors influencing brain development and disease worldwide, highlighting novel work in populations of diverse ancestry and using geocoding to capture environmental drivers of disease. We also highlight new directions in AI for automatic diagnosis, disease subtyping, and prognosis based on worldwide brain data, and new efforts in ENIGMA that inform neuromodulation and interventional studies. Dr. Paul M. Thompson is a Professor of Neurology, Psychiatry, Radiology, Pediatrics, and Engineering, at the University of Southern California (USC) where he directs the Imaging Genetics Center, and is Associate Director for the Stevens Neuroimaging & Informatics Institute. Professor Thompson is also Director of the ENIGMA Center for Worldwide Medicine, Imaging & Genomics, and is Principal Investigator and Co-founder of the ENIGMA Consortium. ENIGMA has cooperatively analyzed data from over 45 countries to publish the largest worldwide neuroimaging studies of over 15 brain diseases and conditions, including Parkinson’s disease, epilepsy, ataxia and brain injury, PTSD, substance use disorder, bipolar disorder, and major depression, and neurodevelopmental conditions including OCD, ADHD and ASD. In parallel, the ENIGMA Consortium has led worldwide imaging genetics studies that discovered over 500 common and rare genomic variants that affect brain structure, disease risk, and brain connectivity (Grasby et al., Science, 2020).

Wrap up and closing

• Alan C Evans (Montreal Neurological Institute McGill University Montreal)
• Katrin Amunts (Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich)

Zoom webinar: [https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09](https://fz-juelich-de.zoom.us/j/67716299814?pwd=Q3dBRXZQa3NLL1Y2TVYvUlBqV0lQQT09) Kenncode: 749254