Multi-variate and multi-modal modelling of neuroimaging data to better understand brain ageing

神经影像数据的多变量和多模式建模，以更好地了解大脑衰老

• 批准号: RGPIN-2020-05448
• 负责人: Chakravarty, Mallar
• 金额: $ 3.42万
• 依托单位: McGill 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=717364
• 关键词: Multi; variate; multi; modal; modelling

Understanding variation in ageing requires sophisticated techniques that can examine brain structure and function across volumetric, microstructural, and functional dimensions. Here, we leverage recent developments from my group for the integration of multi-modal MRI data (using non negative matrix factorization [NMF]) to build models that allow for an improved understanding of how variation in brain structure and function integrate and are related to cognition, with specific focus on the hippocampus. We will use publicly available data (the Human Connectome Project) containing state-of-the-art MRI of brain microstructure related myelin content and axonal properties in addition normative brain function using resting state functional MRI (rsfMRI). We will relate cognitive and functional tasks back to MRI-derived measures. Finally, we will use these techniques in a machine learning-based prediction model for individual age in a well-characterized healthy elderly sample. The goal would be to identify patterns of covariance that best predict cognitive function and age using an integrated analysis strategy that leverages NMF to define components of variance and then to examine the association of each component across cognitive measures using partial least squares (PLS). Compared to common decomposition techniques such as principal and independent component analysis, NMF outputs are more interpretable, sparse, and more spatially contiguous and non overlapping. Research Goals: 1) Microstructural parcellation of the human hippocampus and its relationship with cognition: NMF outputs individual weightings for each subject will be estimated to assess microstructural variability across the 330 unrelated study participants in HCP. Inputs to the NMF will be indices from structural MRI thought to reflect myelin content (T1/T2 ratio) and indices from diffusion MRI (fractional anisotropy and mean diffusivity). Subject-specific component weighting will be related back to 32 cognitive tests administered by the HCP using PLS. 2) Functional parcellation of the human hippocampus and its relationship with cognition: Here we will use a hippocampus-whole-brain approach to derive a rsfMRI based parcellation. Once again the relationship between our parcellation will be related back to the 32 cognitive measures using PLS. 3) Using NMF to predict individual age in a normative population: We will use existing data from the Whitehall II Imaging Sub-study, collected in the FMRIB Centre at Oxford. This dataset includes structural and diffusion MRI, as well as resting-state fMRI scans from 800 community-dwelling adults aged 60-85 years old. The development of an accelerated ageing biomarker has potential relevance to ageing studies in the future focused on heterogeneity of ageing trajectories and developing as a method that can help to better understand the variance in normative ageing. predicting future ageing trajectory.

了解衰老的变化需要复杂的技术，这些技术可以检查体积，微结构和功能维度的大脑结构和功能。在这里，我们利用我小组的最新发展进行多模式MRI数据的整合（使用非负矩阵分解[NMF]）来构建模型，从而可以改善对大脑结构和功能的变化如何整合并与认知相关的变化，并与认知有关，并特别关注Hippocampus。 我们将使用包含与脑微结构相关的髓磷脂含量和轴突特性的最新MRI的公开数据（人类连接项目），此外，还使用静止状态功能性MRI（RSFMRI）。我们将将认知和功能性任务与MRI衍生的措施联系起来。最后，我们将在特征良好的健康老年人样本中的基于机器学习的预测模型中使用这些技术。目的是确定协方差模式，使用综合分析策略最好地预测认知功能和年龄，该策略利用NMF来定义方差的组成部分，然后使用部分最小二乘（PLS）检查每个组件在认知措施中的关联。与常见的分解技术（例如主组件和独立组件分析）相比，NMF输出更容易解释，稀疏，并且在空间上更连续和非重叠。 研究目标： 1）人类海马及其与认知的关系的微观结构分层：估计每个受试者的NMF输出个体权重评估HCP中330个不相关研究参与者的微结构可变性。 NMF的输入将是从认为反映髓磷脂含量（T1/T2比）的结构MRI的指标和扩散MRI（分数各向异性和平均扩散率）的指标。主体特定的组件加权将与使用PLS进行的HCP进行的32次认知测试有关。 2）人类海马及其与认知的关系的功能性分析：在这里，我们将使用海马 - 全脑 - 大脑方法来得出基于RSFMRI的分析。再次，我们的分析之间的关系将与使用PLS的32个认知度量有关。 3）使用NMF预测规范人群中的个体年龄：我们将使用牛津FMRIB中心收集的Whitehall II成像子研究中的现有数据。该数据集包括结构和扩散的MRI，以及从800名60-85岁的社区成年人进行的静止状态fMRI扫描。 加速衰老生物标志物的发展与未来的衰老研究具有潜在的相关性，该研究集中于衰老轨迹的异质性，并作为一种方法作为一种方法，可以帮助更好地了解规范性衰老的差异。预测未来的衰老轨迹。