New methods for enhanced brain activity mapping through multi-modal data-fusion and deep learning

通过多模态数据融合和深度学习增强大脑活动映射的新方法

• 批准号: 2830309
• 金额: --
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2830309
• 关键词: New; methods; enhanced; brain; activity

Characterizing the human brain's function and structure is crucial to understand its physiology in health and pathology. There are complementary ways of measuring brain activity: magnetoencephalography (MEG), electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). The first two are characterised by high temporal but poor spatial resolution, the latter by low temporal but high spatial resolution. Thus, fusing MEG/EEG and fMRI would clearly be advantageous, providing both high temporal and spatial resolution. Furthermore, structural information derived from diffusion MRI (dMRI) via tractography methods can be coupled with brain activity to allow detailed modelling of brain function.This PhD will focus on the challenge of fusing the complementary information from MEG, fMRI and dMRI to predict the human brain physiological activity with unprecedented spatial and temporal resolution. Towards this goal, the PhD student will collect a unique dataset by measuring brain activity while the same participants watch the same movies in both MRI and MEG scanners. When observers view film clips their brains show correlated responses in specific areas, making the information-fusion easier.

描述人类大脑的功能和结构对于了解其健康和病理生理学至关重要。测量大脑活动的方法有很多：脑磁图（MEG）、脑电图（EEG）和功能性磁共振成像（fMRI）。前两个的特点是高的时间，但空间分辨率差，后者的时间低，但高的空间分辨率。因此，融合MEG/EEG和fMRI显然是有利的，提供了高的时间和空间分辨率。此外，通过纤维束成像方法从弥散MRI（dMRI）中获得的结构信息可以与大脑活动相结合，以实现大脑功能的详细建模。该博士将专注于融合MEG，fMRI和dMRI的互补信息的挑战，以前所未有的空间和时间分辨率预测人脑生理活动。为了实现这一目标，博士生将通过测量大脑活动来收集一个独特的数据集，同时相同的参与者在MRI和MEG扫描仪中观看相同的电影。当观察者观看电影片段时，他们的大脑在特定区域显示出相关的反应，使信息融合更容易。