Okinawa Institute of Science and Technology (OIST) Graduate University, Japan

TITLE: Brain/MINDS 2.0 and the Digital Brain Project

Abstract: Following the conclusion of the Brain/MINDS project (2014-2024), a new six-year program Multidisciplinary Frontier Brain and Neuroscience Discoveries (Brain/MINDS 2.0) has started. A remarkable feature of this program is that the Digital Brain plays a central role in integrating structural and dynamic brain data from multiple species for understanding brain functions and tackling neuropsychiatric disorders. This talk will present what is the Digital Brain of Brain/MINDS 2.0, how we can build that, and how we can use that. The primary aims of the Digital Brain Project are to develop open-source software tools for data-driven model building by integrating anatomical, genetic, physiological, and behavioral data from mice, marmosets, macaques and humans and to provide cloud-based platform for cross-species data search, data-driven model building, and simulation analyses. By utilizing those tools and platforms, we aim to build models that realize brain functions like reinforcement learning and Bayesian inference and reproduce neurodegenerative disorders like Parkinson’s disease and psychiatric disorders like schizophrenia to help early diagnosis and exploration of therapeutic and preventive strategies. This ambitious project requires fresh talents from math, computation, AI and brain sciences, as well as broad international collaborations. Through this conference, we hope to extend our network with researchers and research projects with overlapping interests and technologies.

SEU-ALLEN Joint Center, Southeast University, China

TITLE: Toward Building a Whole Brain Connectome at Single Neuron Resolution

Abstract: In this talk I will discuss our work of a large-scale study of whole-brain morphometry, analyzing 3.7 peta-voxels of mouse brain images at the single-cell resolution, producing one of the largest multi-morphometry databases of mammalian brains to date. We annotated 3D locations of cell bodies of 182,497 neurons, modeled 15,441 dendritic microenvironments, characterized the full morphology of 1,876 neurons along with their axonal motifs, and detected 2.63 million axonal varicosities that indicate potential synaptic sites. Our analysis covers six levels of information related to neuronal populations, dendritic microenvironments, single-cell full morphology, sub-neuronal dendritic and axonal arborization, axonal varicosities , and sub-neuronal structural motifs, along with a quantification of the diversity and stereotypy of patterns at each level. Overall, our study provides an integrative description of key anatomical structures of neurons and their types, covering a wide range of scales and features, and contributes a large-scale resource to understanding neuronal diversity in the mammalian brain. With this dataset, we start to formulate a possible whole brain scale connectome at the single neuron resolution for mouse brains.

Baycrest Health Sciences, McMaster University & University of Toronto, Canada

TITLE: Predictive Neuroscience for Precision Aging: Dementia Prevention, Detection, Treatment, and Care

Abstract: The world is aging faster now than ever before, and as we age the risk of dementia is growing. Brain changes linked to Alzheimer’s Disease (AD) and related dementias begin years before the onset of clinical symptoms. However, we lack sufficient tools to accurately identify individuals during preclinical stages of dementia, which limits our ability to implement interventions that could prevent or slow disease progression. Although memory loss is one of the most common symptoms of dementia, visual perceptual and attention can also be impacted at the early stages of disease, but can be difficult to assess throughout disease progression. I will describe some of the ways in aging affects visual perception (including behavioural assessments and electrophysiological markers of face processing and contour integration), how those changes differ in healthy aging and neurodegeneration, and how tools from vision science can probe function in individuals living with dementia and beyond. The results of the work I will discuss are important for developing rapid, non-verbal assessments of visual function that could be used as early screening tools for dementia and assessment throughout disease progression. I also will share other examples of Baycrest’s approach to predictive neuroscience for precision aging, including a range of collaborative opportunities spanning dementia prevention, detection, treatment, and care.

University of California, Irvine, USA

TITLE: Graphical Modeling of Brain Networks

Abstract: Much of our understanding of human brain function is developed from the analysis of statistical relationships between brain signals and behavior. Graphical models of brain signals are generative models that potentially provide causal insight into brain signals and their relationship to behavior and disease. I will discuss different studies in graphical modeling that we have used to (1) model structure-function relationships, (2) model the relationship between brain injury and function, (3) develop new approaches to hyperscanning based on symbolic dynamics, (4) model joint latent space to link cognitive parameters to both neural signals and behavioral measures. To study structure-function relationships we incorporate anatomical knowledge of brain networks to build a graphical model of brain signals and demonstrate in fMRI data that we can predict the effects of disconnection due to injury in stroke (Wodeyar et al., 2021). These graphical models capture the dynamic effects of injury in a manner not apparent in anatomy or in the raw signals. Measures of network properties in structurally informed graphical models of EEG reflect how efficient signal routing is essential to maintain motor functional status after stroke(Zhou et al., 2024). Graphical modeling also provides an entirely new approach to hyperscanning in coordination and other forms of social cognitive neuroscience. We modeled the joint state of two individuals performing coordinated motor tasks with simultaneous EEG recordings, as a transition network in a symbol space defined by the graphical models, i.e., a graph of graphs. The symbolic dynamics over this graphical model capture the different coordination modes in a manner not possible by statistical analysis of correlations between brain signals. Graphical modeling can also be useful for formulating the link between brain activity and latent cognitive processes. Behavioral measures, such as accuracy and speed of motor responses, reflect latent cognitive processes underlying decision making. We have developed a novel approach that allows a theoretical account of the cognitive process of decision-making, and artificial neural networks to estimate a joint latent space to link cognitive parameters to both neural signals and behavioral measures (Vo et al., 2024). This joint latent space model is a valuable new framework for computational cognitive neuroscience, allowing for new forms of inference and hypothesis generation. The power of graphical modeling can allow for a more comprehensive understanding of the triplet relationship between behavior, brain activity, and cognitive processes.

University of California Los Angeles, USA

TITLE: Neuroinformatics and Cognitive Ontologies

Abstract: Decades of cognitive neuroimaging work has identified distinct patterns of brain activation that occur during performance of different tasks, as well as revealed patterns of task-general activation and deactivation. These data can in principle be used to provide the basis for constructing biologically informed, data-driven taxonomies of psychological processes. This talk will highlight some of the progress and challenges associated with the construction of cognitive ontologies based on functional neuroimaging data.