The 17th International Conference on Brain Informatics (BI 2024)
—Brain Science meets Artificial Intelligence
December 13-15, 2024, Bangkok, Thailand
Conference Program --(Glance)-- Main Conference Program --(Full)-- Workshop Program --(Full)-- BI 2024 Zoom Link Table --(Full)-- Participant Registration : OPEN |
All accepted papers, including those from workshops and special sessions, will be published by Springer Nature as a volume of the LNAI Brain Informatics Book Series. The Brain Informatics conference series maintains an official collaboration with the Brain Informatics journal, an interdisciplinary Open Access journal indexed in ESCI and published by Springer Nature. Authors of the best papers will be invited to expand their work and publish it as special issue articles in the Brain Informatics journal with a complete waiver of the open-access article-processing fee. Furthermore, workshop and special session organizers may be invited to prepare a book proposal on special topics for potential publication in the Springer-Nature Brain Informatics & Health Book Series, or a special issue in the Brain Informatics Journal. |
About the Conference
The International Conference on Brain Informatics (BI) series has established itself as the world's premier research conference on Brain Informatics, which is an emerging interdisciplinary and multidisciplinary research field that combines the efforts of Cognitive Science, Neuroscience, Machine Learning, Data Science, Artificial Intelligence (AI), and Information and Communication Technology (ICT) to explore the main problems that lie in the interplay between human brain studies and informatics research.
The 17th International Conference on Brain Informatics (BI'24) provides a premier international forum to bring together researchers and practitioners from diverse fields for presentation of original research results, as well as exchange and dissemination of innovative and practical development experiences on Brain Informatics research, brain-inspired technologies and brain/mental health applications.
Topics and Areas
The key theme of the conference is "Brain Science meets Artificial Intelligence".
The BI'24 solicits high-quality original research and application papers (full paper and abstract presentation submissions). Relevant topics include but are not limited to:
Keynote Speakers
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
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.
Important Dates
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Paper Submission and Publications
1. 9-12 pages are strongly encouraged for the regular papers including figures and references in Springer LNCS Proceedings format (https://www.springer.com/us/computer-science/lncs/conference-proceedings-guidelines). Over length papers will be charged for 100$ per page. 2. All papers will be peer-reviewed and accepted based on originality, significance of contribution, technical merit, and presentation quality. 3. All papers accepted (and all workshop & special sessions' full-length papers) will be published by Springer as a volume of the Springer-Nature LNAI Brain Informatics Book Series (https://link.springer.com/conference/brain).
Research abstracts are encouraged and will be accepted for presentations in an oral presentation format and/or poster presentation format. Each abstract submission should include the title of the paper and an abstract body within 1500 words. Note: The abstract will not be included in the conference proceedings to be published by Springer.
High quality BI conference papers may be nominated to submit an extended version for a fast track review and publication at the Brain Informatics Journal (https://braininformatics.springeropen.com/) an international, peer-reviewed, interdisciplinary Open Access journal published by Springer Nature. Discount or no open access article-processing fee will be charged for BI conference paper authors.
Workshop/special session organizers and BI conference session chairs may consider and can be invited to prepare a book proposal of special topics for possible book publication in the Springer-Nature Brain Informatics & Health Book Series (https://www.springer.com/series/15148), or a special issue at the Brain Informatics Journal.
Poster-Conference Publication
1. Accepted full papers will be selected to publish in the Brain
Informatics Journal upon revision.
2. Discount or no article-processing fee will be charged for
authors of Brain Informatics conference (https://braininformatics.springeropen.com/).
3. The organizers of Workshops and Special-Sessions are invited to prepare a book proposal based on the topics of the workshop/special session for possible book publication in the Springer-Nature Brain Informatics and Health book series (http://www.springer.com/series/15148).