Enhancing the synergy of in-vivo multimodal brain imaging

增强体内多模式脑成像的协同作用

• 批准号: RGPIN-2019-04232
• 负责人: Sossi, Vesna
• 金额: $ 6.92万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=763104
• 关键词: Enhancing; synergy; vivo; multimodal; brain

Background. Brain illness is considered the major health challenge in the 21st century; knowledge on brain function and disease mechanisms is still limited. Non-invasive brain imaging has fundamentally contributed to the current understanding of brain function, which depicts the brain as a series of networks. Neurodegenerative diseases are hypothesized to induce pathological alterations within specific networks, considered to be mediated by molecular pathology. Multi-modality (hybrid) imaging comprising simultaneous Positron Emission Tomography (PET) and magnetic resonance imaging (MRI), is particularly well suited to explore this hypothesis by investigating neurochemical correlates (PET) of the disease-associated structural and connectivity alterations (MRI). While the hardware compatibility of PET and MRI is fairly mature, data acquisition protocols and analysis methods to maximize the synergy of hybrid imaging still need to be developed. Goal. To develop algorithms aimed at enhancing the synergy of hybrid PET/MRI for brain imaging with three inter-related aims: (i) improving PET image quality; (ii) developing accurate measurements of novel multi-modality metrics related to fundamental aspects of brain function, such as energetics and interactions between neurotransmitter activity and brain functional/structural networks (iii) developing methods to quantify interactions between metabolic, functional and structural networks and disease specific multi-parameter features that could serve as disease biomarkers. Methods. The following will be developed: (i) Data denoising to improve the PET temporal resolution to make it more commensurate with MRI; (ii.a) Simultaneous calibrated BOLD (MRI) and dynamic glucose uptake (PET) measurements to obtain simultaneous separate estimates of cerebral blood flow, oxygen extraction fraction and cerebral oxygen and glucose metabolic rates. These measurements will be combined with algorithms developed under aim 3 to yield estimates of the novel metrics listed above; (ii.b) Estimation of stimuli-induced transient neurotransmitter release (PET) coupled with its effect on brain connectivity and signaling (MRI); (iii) Canonical correlation analysis, orthogonal signal correction, consensus clustering, hypergraph learning and multilayer graph analyses to extract common and unique patterns from the multimodal data and advanced statistical or machine-learning based clustering algorithm to extract relevant features. Impact. This program reflects technological and conceptual advancements in instrumentation and brain research. While some proposed methods are not intrinsically original, the novelty and significance result from selectively adapting them to hybrid imaging; they will (i) enable exploration and quantification of fundamental aspects of brain function such as brain energetics, thus opening new research avenues and (ii) maximize the investigative power of hybrid imaging also applicable to other research fields.

背景资料。脑部疾病被认为是21世纪的主要健康挑战；关于大脑功能和疾病机制的知识仍然有限。非侵入性脑成像从根本上促进了目前对大脑功能的理解，将大脑描绘成一系列网络。神经退行性疾病被认为是在特定的网络内引起病理改变，被认为是由分子病理学介导的。多模式(混合)成像包括同步正电子发射断层扫描(PET)和磁共振成像(MRI)，通过研究疾病相关结构和连接性改变(MRI)的神经化学相关性(PET)，特别适合于探索这一假说。虽然PET和MRI的硬件兼容性已经相当成熟，但仍需开发数据采集协议和分析方法，以最大限度地发挥混合成像的协同作用。进球了。开发旨在增强混合PET/MRI用于脑成像的协同作用的算法，以实现三个相互关联的目标：(I)提高PET图像质量；(Ii)开发与大脑功能的基本方面相关的新的多模式指标的准确测量，例如能量学以及神经递质活动与脑功能/结构网络之间的相互作用；(Iii)开发方法来量化代谢、功能和结构网络与可用作疾病生物标志物的疾病特异性多参数特征之间的相互作用。方法：研究方法。将开发以下内容：(I)数据去噪，以提高PET的时间分辨率，使其与MRI更相称；(Ii)同步校准BOLD(MRI)和动态葡萄糖摄取(PET)测量，以获得对脑血流量、氧提取分数以及脑氧和葡萄糖代谢率的同时单独估计。这些测量将与AIM 3下开发的算法相结合，以产生对上述新指标的估计；(Ii)对刺激诱导的瞬时神经递质释放(PET)及其对大脑连接和信号(MRI)的影响的估计；(Iii)典型相关分析、正交信号校正、共识聚类、超图学习和多层图分析，以从多模式数据中提取共同和独特的模式，以及基于高级统计或机器学习的聚类算法，以提取相关特征。冲击力。该计划反映了仪器和脑研究方面的技术和概念上的进步。虽然一些建议的方法并不是本质上的原创性，但其新颖性和意义源于有选择地使其适用于混合成像；它们将(I)使探索和量化大脑功能的基本方面，如大脑能量学，从而开辟新的研究途径；(Ii)最大限度地发挥混合成像的调查能力，也适用于其他研究领域。