第2回 Computational Neurology研究会

開催日

2023年6月23日（金）15:00-16:30

開催場所

ハイブリッド（zoom＋広島大学）

詳細

◆◆◆ 第2回『Computational Neurology研究会』開催のご案内 ◆◆◆

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さんに広島大学とzoomのハイブリッドでの講演をしていただく予定です。

講演者：Ben D. Fulcher（シドニー大学）
開催場所：ハイブリッド（zoom＋広島大学）
日時：2023年6月23日（金）15:00-16:30

タイトル：The brain as a complex dynamical system

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

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