Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.
Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK; Radboud University Medical Centre, Department of Cognitive Neuroscience, Nijmegen, the Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands.
Med Image Anal. 2021 Jul;71:102050. doi: 10.1016/j.media.2021.102050. Epub 2021 Mar 31.
Neuroimaging allows for the non-invasive study of the brain in rich detail. Data-driven discovery of patterns of population variability in the brain has the potential to be extremely valuable for early disease diagnosis and understanding the brain. The resulting patterns can be used as imaging-derived phenotypes (IDPs), and may complement existing expert-curated IDPs. However, population datasets, comprising many different structural and functional imaging modalities from thousands of subjects, provide a computational challenge not previously addressed. Here, for the first time, a multimodal independent component analysis approach is presented that is scalable for data fusion of voxel-level neuroimaging data in the full UK Biobank (UKB) dataset, that will soon reach 100,000 imaged subjects. This new computational approach can estimate modes of population variability that enhance the ability to predict thousands of phenotypic and behavioural variables using data from UKB and the Human Connectome Project. A high-dimensional decomposition achieved improved predictive power compared with widely-used analysis strategies, single-modality decompositions and existing IDPs. In UKB data (14,503 subjects with 47 different data modalities), many interpretable associations with non-imaging phenotypes were identified, including multimodal spatial maps related to fluid intelligence, handedness and disease, in some cases where IDP-based approaches failed.
神经影像学可以对大脑进行非侵入性的详细研究。在大脑中发现人群变异性模式的数据驱动式发现对于早期疾病诊断和了解大脑具有巨大的潜在价值。由此产生的模式可以用作成像衍生表型（IDP），并可能补充现有的专家 curated IDP。然而，由数千名受试者的多种不同结构和功能成像方式组成的人群数据集，带来了前所未有的计算挑战。在此，首次提出了一种多模态独立成分分析方法，该方法可扩展用于融合全英国生物库（UKB）数据集的体素水平神经影像学数据，该数据集很快将达到 10 万名成像受试者。这种新的计算方法可以估计人群变异性模式，从而提高使用 UKB 和人类连接组计划的数据预测数千种表型和行为变量的能力。与广泛使用的分析策略、单模态分解和现有的 IDP 相比，高维分解实现了更高的预测能力。在 UKB 数据（14503 名受试者，47 种不同的数据模态）中，确定了许多与非成像表型相关的可解释关联，包括与流体智力、利手性和疾病相关的多模态空间图，在某些情况下，基于 IDP 的方法无法做到这一点。
