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: 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=714708
• 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。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，它将大脑描绘成一系列的网络。神经退行性疾病被假设在特定网络中诱导病理改变，被认为是由分子病理学介导的。同时包括正电子发射断层扫描（PET）和磁共振成像（MRI）的多模态（混合）成像，特别适合通过研究疾病相关结构和连通性改变（MRI）的神经化学相关性（PET）来探索这一假设。虽然PET和MRI的硬件兼容性已经相当成熟，但仍需要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。