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.

McMaster University & University of Toronto, Canada

TITLE: To be Confirmed.

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: Brain Dynamics and Flexible Behaviors

Abstract: Executive control processes and flexible behaviors rely on the integrity of, and dynamic interactions between, large-scale functional brain networks. The right insular cortex is a critical component of a salience/midcingulo-insular network that is thought to mediate interactions between brain networks involved in externally oriented (central executive/lateral frontoparietal network) and internally oriented (default mode/medial frontoparietal network) processes. How these brain systems reconfigure with development is a critical question for cognitive neuroscience, with implications for neurodevelopmental pathologies affecting brain connectivity. I will describe studies examining how brain network dynamics support flexible behaviors in typical and atypical development, presenting evidence suggesting a unique role for the dorsal anterior insular from studies of meta-analytic connectivity modeling, dynamic functional connectivity, and structural connectivity. These findings from adults, typically developing children, and children with autism suggest that structural and functional maturation of insular pathways is a critical component of the process by which human brain networks mature to support complex, flexible cognitive processes throughout the lifespan.