Multimodal integration of functional neuroimaging data

功能神经影像数据的多模态整合

• 批准号: RGPIN-2018-06707
• 负责人: Grova, Christophe
• 金额: $ 2.4万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714089
• 关键词: Multimodal; integration; functional; neuroimaging; data

Based on different physical and physiological principles, several non-invasive neuroimaging techniques are now available to study the brain in function. However, the ideal technique that would explore brain neuronal activity at a millimeter scale and at a millisecond scale does not exist. Therefore, it is necessary to integrate multimodal data, to overcome the intrinsic limitations of each individual modality and to potentiate their combined use. We propose to use multimodal integration to elucidate the fundamental bases of two important processes driving brain activity: (i) Neurovascular coupling (NVC) describes the relationship between neuronal bioelectric activity and hemodynamic response of the brain bringing the required nutrients (oxygen, glucose) to the active region. Most popular functional imaging techniques are monitoring only these indirect hemodynamic responses, occurring several seconds after the neuronal discharge. (ii) Functional connectivity patterns measure slow fluctuations of hemodynamic signals (<0.05Hz) to characterize the underlying resting-state networks (RSNs) structure of brain activity. These patterns are organized in space and highly reproducible from subject to subject. Functional connectivity offers a unique perspective to identify RSNs-based biomarkers from only few minutes of resting state functional Magnetic Resonance Imaging (fMRI) data. Objective 1: Taking advantage from direct measurements of neuronal activity in multimodal studies. Having significantly contributed to the domain of Electro-/Magneto-EncephaloGraphy (EEG/MEG) source localization, we propose to optimize and carefully validate EEG/MEG source localization during resting state, to disentangle the neuronal origins of RSNs ongoing hemodynamic fluctuations. Objective 2: Establishing new sensitive and specific biomarkers from multimodal resting state data. Using a new method we developed to estimate connector hubs from resting state fMRI data (Lee et al Neuroimage 2016), we will assess if connector hubs exhibit specific dynamic (EEG/MEG source imaging) and metabolism (oxygenation and glucose baseline metabolism) signatures, providing a unique framework to characterize the driving role of connector hubs in RSN fluctuations. Objective 3: Developing personalized/portable imaging modalities to study brain activity in everyday life. Exploiting absorption properties of infrared light within brain tissues, we developed personalized EEG/Near InfraRed Spectroscopy (NIRS) investigations to monitor cortical fluctuations of oxy- and deoxy-hemoglobin. We propose to develop personalized EEG/NIRS during the whole night, to map for the first time, hemodynamic fluctuations during normal sleep, sleep disorders and interactions between sleep and epileptic activity. All proposed methods will be evaluated on healthy controls and patients with focal epilepsy.

基于不同的物理和生理原理，现在有几种非侵入性神经成像技术可用于研究大脑的功能。然而，在毫米尺度和毫秒尺度上探索大脑神经元活动的理想技术并不存在。因此，有必要整合多模态数据，克服每一种单独模态的内在限制，并加强它们的组合使用。