Characterising multiscale brain-wide neural dynamics

表征多尺度全脑神经动力学

• 批准号: 2435457
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2435457
• 关键词: Characterising; multiscale; brain; wide; neural

State-of-the-art experimental techniques can record neural dynamics of human brain with finedetails. Recordings can be done across different brain areas and different scales from single cells,local filed potentials (LFPs) to intracranial electroencephalograms (iEEG). Particularly, these datacan be collected in humans, typically human patients. For instance, abnormal brain activity incertain areas of the brain causes seizures and in some cases loss of awareness and characterisedbehaviour in epileptic patients. Computational modelling has allowed us to be able to characterisethese interactions to be able to find the influence of brain regions on the activity of our brain. Thisproject is dedicated to establishing a connection between iEEG data recorded and LFP signals,which will be used in a machine learning model to be able to stablish characterizing features for acomputational model of the connection. This model will be a generalised brain dynamics model,which could be manipulated into a functional epilepsy seizure characterising model.

先进的实验技术可以精细地记录人脑的神经动力学过程。可以在不同的大脑区域和不同的尺度上进行记录，从单细胞、局部场电位（LFP）到颅内脑电图（iEEG）。特别地，这些抗体可以在人类，通常是人类患者中收集。例如，大脑某些区域的异常活动会导致癫痫发作，在某些情况下，癫痫患者会失去意识和特征行为。计算模型使我们能够描述这些相互作用，从而能够发现大脑区域对大脑活动的影响。该项目致力于在记录的iEEG数据和LFP信号之间建立连接，该连接将用于机器学习模型，以便能够为连接的计算模型建立表征特征。该模型将是一个广义的脑动力学模型，它可以被操纵成一个功能性癫痫发作表征模型。