Multimodal integration of functional neuroimaging data

越来越多的非侵入性神经成像技术现在可以用来研究正常的大脑功能，但也可以研究大脑疾病。然而，它不存在任何理想的探测技术，能够精确地测量大脑中活动发生的“何时”(毫秒尺度)和“何处”(毫米尺度)。每种神经成像技术都通过不同的机制(神经元电流、氧气消耗)测量大脑活动的一部分，有时更专注于“在哪里？”这个问题。(良好的空间分辨率)还是“什么时候？”(良好的时间分辨率)。每一种技术都有其特定的优点和局限性，单独分析每一种技术只能提供有关潜在大脑活动的部分信息。我的研究项目的目标是开发适当地组合这些多模式数据的方法，即数据融合方法，以便检测通过单独考虑每种模式而可能遗漏的额外信息。一个典型的挑战是将直接测量具有高时间分辨率的神经元活动的模式与以高空间分辨率间接测量相同功能的其他模式相结合，例如通过血流动力学过程。

More and more non-invasive neuroimaging techniques are now available to study normal brain functions but also brain disorders. However, it does not exist any ideal exploration technique able to measure precisely "when" (at a ms scale) and "where" (at a mm scale) an activity occurred in the brain. Each neuroimaging technique measures through different mechanisms (neuronal electricity, oxygen consumption) part of brain activity, being sometimes more dedicated to the question "where?" (good spatial resolution) or to the question "when?" (good temporal resolution). Each of these techniques having its specific advantages and limitations, analyzing each of them individually only provides partial information on underlying brain activity. The objective of my research project is to develop methods to appropriately combine these multimodal data, i.e., data fusion methods, in order to detect additional information that could be missed by considering each modality individually. A typical challenge is to combine modalities measuring directly neuronal activity with high temporal resolution with other modalities measuring indirectly the same function with high spatial resolution, through hemodynamic processes for instance.