Discovery and visualization of structural biomarkers from MRI using transport-based morphometry.
Discovery and visualization of structural biomarkers from MRI using transport-based morphometry.
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DOI：
10.1016/j.neuroimage.2017.11.006
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发表时间：
2018-02-15
期刊：
影响因子：
5.7
通讯作者：
Rohde GK
中科院分区：
文献类型：
--
作者：
Kundu S;Kolouri S;Erickson KI;Kramer AF;McAuley E;Rohde GK
大脑疾病通常与细微的、空间上弥散的或复杂的组织变化相关，这些变化可能在肉眼检查层面之下，甚至在磁共振成像（MRI）上也难以察觉。不幸的是，目前检查预先设定特征的计算机辅助方法，无论是基于解剖学定义的（即丘脑体积、皮质厚度）还是基于像素比较的（即基于变形的方法），都容易遗漏大量这些指标无法很好涵盖的生理变化。在本文中，我们开发了一种自动模式分析技术，它可以在不需要任何先验特征的情况下，完全确定大脑结构与可观察表型之间的关系。我们的技术称为基于传输的形态测量法（TBM），它是一种图像变换，能将大脑图像无损地映射到一个它们变得更易区分的域。这种新方法在健康老年受试者的大脑结构图像上得到了验证，在这些图像中，即使是用于区分、回归和盲源分离的线性模型，也能使TBM独立发现衰老的特征性变化，并突出有氧健身可能在晚年调节大脑健康的潜在机制。TBM是一种生成性方法，它可以通过逆变换可视化组织分布中具有生理意义的变化。所提出的框架是一种强大的技术，有可能阐明无数疾病中的基因型 - 结构 - 行为关联。
Disease in the brain is often associated with subtle, spatially diffuse, or complex tissue changes that may lie beneath the level of gross visual inspection, even on magnetic resonance imaging (MRI). Unfortunately, current computer-assisted approaches that examine pre-specified features, whether anatomically-defined (i.e. thalamic volume, cortical thickness) or based on pixelwise comparison (i.e. deformation-based methods), are prone to missing a vast array of physical changes that are not well-encapsulated by these metrics. In this paper, we have developed a technique for automated pattern analysis that can fully determine the relationship between brain structure and observable phenotype without requiring any a priori features. Our technique, called transport-based morphometry (TBM), is an image transformation that maps brain images loss-lessly to a domain where they become much more separable. The new approach is validated on structural brain images of healthy older adult subjects where even linear models for discrimination, regression, and blind source separation enable TBM to independently discover the characteristic changes of aging and highlight potential mechanisms by which aerobic fitness may mediate brain health later in life. TBM is a generative approach that can provide visualization of physically meaningful shifts in tissue distribution through inverse transformation. The proposed framework is a powerful technique that can potentially elucidate genotype-structural-behavioral associations in myriad diseases.
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影响因子：
3.5
作者：
Erickson, Kirk I.;Prakash, Ruchika S.;Voss, Michelle W.;Chaddock, Laura;Hu, Liang;Morris, Katherine S.;White, Siobhan M.;Wojcicki, Thomas R.;McAuley, Edward;Kramer, Arthur F.
通讯作者：
Kramer, Arthur F.
DOI：
10.1073/pnas.1015950108
发表时间：
2011-02-15
影响因子：
11.1
作者：
Erickson, Kirk I.;Voss, Michelle W.;Kramer, Arthur F.
通讯作者：
Kramer, Arthur F.
DOI：
10.1137/080730238
发表时间：
2010
期刊：
SIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics
影响因子：
--
作者：
Haber E;Rehman T;Tannenbaum A
通讯作者：
Tannenbaum A
DOI：
10.1023/b:visi.0000036836.66311.97
发表时间：
2004-12-01
影响因子：
19.5
作者：
Haker, S;Zhu, L;Angenent, S
通讯作者：
Angenent, S
影响因子：
5.7
作者：
Ashburner, J;Friston, KJ
通讯作者：
Friston, KJ