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

描述人类大脑的功能和结构对于理解其健康和病理生理学至关重要。测量大脑活动有互补的方法：脑磁图（MEG）、脑电图（EEG）和功能磁共振成像（fMRI）。前两种方法具有时间分辨率高但空间分辨率差的特点，后者具有时间分辨率低但空间分辨率高的特点。因此，融合MEG/EEG和fMRI显然是有利的，同时提供高时间和空间分辨率。此外，通过神经束成像方法获得的弥散MRI （dMRI）结构信息可以与大脑活动相结合，从而对大脑功能进行详细建模。本博士将专注于融合MEG， fMRI和dMRI的互补信息，以前所未有的空间和时间分辨率预测人类大脑生理活动的挑战。为了实现这一目标，这位博士生将通过测量同样的参与者在核磁共振和脑磁图扫描仪上观看同样的电影时的大脑活动来收集一个独特的数据集。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。

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.