「Computational Neurology研究会」第2回 開催のご案内（『行動変容生物学』共催）
「Computational Neurology研究会」第2回
テーマ： The brain as a complex dynamical system
日時：2023年6月23日（金）15:00-16:30
開催場所：オンライン（zoom）
講演者：Ben D. Fulcher（シドニー大学）
https://boatneck-weeder-7b7.notion.site/Computational-Neurology-a945537f7cb54db3b20fa9c4e65c1e72
先日開かれた第１回のComputational Neurology研究会ですが、100名を超える参加者で大変盛況な研究会となりました。積極的なご参加ありがとうございます。さて、直近ではありますが6月23日（金）15:00から、zoomと広島大学でのハイブリッド形式で第２回目を開催することになりました。最近Natureに掲載されたGeometric constraints on human brain functionなど脳のダイナミクスの解析法を中心に活躍されているBen D. Fulcherさんに講演していただく予定です。学生を含めどなたでもご参加いただけます。奮ってご参加ください。
概要：Like many systems in the world around us, the brain’s is a physical system with complex activity patterns that evolve through time and can be measured in the form of multivariate time series. We now have unprecedented data on brain structure, including gene-expression atlas data with high spatial resolution and whole-brain coverage, as well as intricate recordings of the brain’s activity dynamics. What representations of the brain allow us to find informative patterns in these data that clarify how the brain works in health and disease? In this talk I will introduce different ways of treating the brain as a complex dynamical system, including a discrete network representation (a connectome) and a physical representation (in terms of spatially embedded gradients and distributed modes). I will also provide an overview of related methods that we have developed for quantifying brain dynamics, including time-series patterns of specific brain areas, as well as pairwise, and distributed coupling patterns (implemented in our hctsa and pyspi software packages). I will make reference to some specific recent applications, including inferring biomarkers of psychiatric disease, extracting data-driven representations of sleep dynamics, quantifying the effects of brain stimulation, and characterizing resting-state EEG and fMRI data.
Key Refs:
· Fulcher & Jones (2017). hctsa: A computational framework for automated time-series phenotyping using massive feature extraction. Cell Systems. http://www.cell.com/article/S2405471217304386/fulltext
· Cliff et al. (2022). Unifying Pairwise Interactions in Complex Dynamics. arXiv. http://arxiv.org/abs/2201.11941
· Fulcher et al. (2019). Multimodal gradients across mouse cortex. PNAS. http://www.pnas.org/lookup/doi/10.1073/pnas.1814144116
· Pang et al. (2023). Geometric constraints on human brain function. Nature. https://www.nature.com/articles/s41586-023-06098-1