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: 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=763655
• 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，可以在体积，微观结构和功能维度上检查大脑结构和功能。在这里，我们利用我的团队最近的发展，整合多模态MRI数据（使用非负矩阵分解[NMF]）来构建模型，从而更好地理解大脑结构和功能的变化如何整合并与认知相关，特别关注海马体。我们将使用公开可用的数据（人类连接组项目），其中包含最先进的脑微结构相关髓鞘含量和轴突特性的MRI，以及使用静息状态功能MRI（rsfMRI）的正常脑功能。我们将把认知和功能任务与MRI衍生的测量结果联系起来。最后，我们将在一个基于机器学习的预测模型中使用这些技术来预测健康老年人样本中的个体年龄。我们的目标是确定最好的预测认知功能和年龄的协方差模式，使用综合分析策略，利用NMF定义方差的组成部分，然后使用偏最小二乘法（PLS）检查每个组成部分在认知测量中的关联。与主成分分析和独立成分分析等常见分解技术相比，NMF输出更具可解释性，稀疏性，空间连续性和非重叠性。 研究目标：1）人类海马体的微结构分组及其与认知的关系：将估计每个受试者的NMF输出个体权重，以评估HCP中330名无关研究参与者的微结构变异性。NMF的输入将是被认为反映髓鞘含量的结构MRI的指数（T1/T2比）和扩散MRI的指数（各向异性分数和平均扩散率）。受试者特定组分权重将与HCP使用PLS进行的32项认知测试相关。2)人类海马体的功能分区及其与认知的关系：在这里，我们将使用一个海马体全脑的方法来获得一个基于rsfMRI的分区。再一次，我们的分组之间的关系将与使用PLS的32个认知测量相关。3)使用NMF预测正常人群中的个体年龄：我们将使用在牛津FMRIB中心收集的Whitehall II成像子研究的现有数据。该数据集包括800名年龄在60-85岁之间的社区居民的结构和弥散MRI以及静息状态fMRI扫描。 加速老化生物标志物的发展与未来的老龄化研究具有潜在的相关性，这些研究侧重于老化轨迹的异质性，并发展为一种有助于更好地了解正常老化差异的方法。预测未来的衰老轨迹。