Multimodal integration of functional neuroimaging data

功能神经影像数据的多模态整合

• 批准号: 356610-2013
• 负责人: Grova, Christophe
• 金额: $ 1.68万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=549955
• 关键词: Multimodal; integration; functional; neuroimaging; data

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.

越来越多的非侵入性神经成像技术现在可用于研究正常的大脑功能，但也脑疾病。然而，不存在任何能够精确测量大脑中活动发生的“时间”（以ms尺度）和“位置”（以mm尺度）的理想探索技术。每一种神经成像技术都通过不同的机制（神经元电、耗氧量）来测量大脑活动的一部分，有时更致力于解决“在哪里？（良好的空间分辨率）或问题“什么时候？”（时间分辨率高）。这些技术中的每一种都有其特定的优点和局限性，单独分析它们中的每一种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的方法来联合收割机这些多模态数据，即，数据融合方法，以检测单独考虑每种模态可能遗漏的额外信息。一个典型的挑战是将以高时间分辨率直接测量神经元活动的联合收割机模态与以高空间分辨率间接测量相同功能的其他模态（例如通过血液动力学过程）相结合。