Benefits of multi-modal fusion analysis on a large-scale dataset: Life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure
Benefits of multi-modal fusion analysis on a large-scale dataset: Life-span patterns of inter-subject variability in cortical morphometry and white matter microstructure
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DOI：
10.1016/j.neuroimage.2012.06.038
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发表时间：
2012-10-15
期刊：
影响因子：
5.7
通讯作者：
Westlye, Lars T.
中科院分区：
文献类型：
--
作者：
Groves, Adrian R.;Smith, Stephen M.;Westlye, Lars T.
近年来，神经成像研究变得越来越多模式，研究人员通常会获取几种不同类型的MRI数据，并沿着不同的管道处理它们，这些管道为每个受试者的大脑提供了一组互补的窗口。然而，很少有人尝试在同一分析中综合各种方式。链接ICA是一个健壮的数据融合模型，它采用多模态数据，并根据一组多模态组件来表征主体间的可变性。本文研究了在大型磁共振成像（MRI）形态测量和扩散张量成像（DTI）数据集上运行链接ICA时发现的组件类型，该数据集包括484名年龄从8岁到85岁的健康受试者。我们发现了几个与年龄、性别和颅内容积相关的强全局特征；特别是，有一个成分预测年龄的准确率很高（r = 0.95）。大多数剩余成分描述了白质或灰质的空间局部变异性模式，许多成分包括两种组织类型。多模态成分往往位于与解剖学相关的大脑区域，这表明它们之间存在形态和可能的功能关系。局部分量显示了基于表面的皮质厚度与区域化、基于体素的形态测量（VBM）之间的关系，以及三种不同的DTI测量之间的关系。此外，我们报告了与工件相关的组件（例如扫描仪软件升级），这些组件在这种规模的数据集中是预期的。提取的100个成分中，大多数具有可解释的空间模式，并且使用劈半验证被发现是可靠的。这项工作提供了关于大脑结构正常主体间变异性的新信息，并展示了链接ICA作为跨模式特征提取数据融合方法的潜力。这种探索性方法自动生成模型来解释数据中的结构，并且对于大规模研究来说可能特别强大，在大规模研究中可以更详细地探索种群变异。(C) 2012爱思唯尔公司版权所有。
Neuroimaging studies have become increasingly multimodal in recent years, with researchers typically acquiring several different types of MRI data and processing them along separate pipelines that provide a set of complementary windows into each subject's brain. However, few attempts have been made to integrate the various modalities in the same analysis. Linked ICA is a robust data fusion model that takes multi-modal data and characterizes inter-subject variability in terms of a set of multi-modal components. This paper examines the types of components found when running Linked ICA on a large magnetic resonance imaging (MRI) morphometric and diffusion tensor imaging (DTI) data set comprising 484 healthy subjects ranging from 8 to 85 years of age. We find several strong global features related to age, sex, and intracranial volume; in particular, one component predicts age to a high accuracy (r = 0.95). Most of the remaining components describe spatially localized modes of variability in white or gray matter, with many components including both tissue types. The multimodal components tend to be located in anatomically-related brain areas, suggesting a morphological and possibly functional relationship. The local components show relationships between surface-based cortical thickness and arealization, voxel-based morphometry (VBM), and between three different DTI measures. Further, we report components related to artifacts (e.g. scanner software upgrades) which would be expected in a dataset of this size. Most of the 100 extracted components showed interpretable spatial patterns and were found to be reliable using split-half validation. This work provides novel information about normal inter-subject variability in brain structure, and demonstrates the potential of Linked ICA as a feature-extracting data fusion approach across modalities. This exploratory approach automatically generates models to explain structure in the data, and may prove especially powerful for large-scale studies, where the population variability can be explored in increased detail. (C) 2012 Elsevier Inc. All rights reserved.