Penalized functional regression analysis of white-matter tract profiles in multiple sclerosis.
Penalized functional regression analysis of white-matter tract profiles in multiple sclerosis.
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DOI：
10.1016/j.neuroimage.2011.04.044
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发表时间：
2011-07-15
期刊：
影响因子：
5.7
通讯作者：
Reich, Daniel S.
中科院分区：
文献类型：
--
作者：
Goldsmith, Jeff;Crainiceanu, Ciprian M.;Caffo, Brian S.;Reich, Daniel S.
关键词：
弥散张量成像 (DTI) 能够将大脑白质无创地分割成其组成纤维束或束。这些束通常有助于特定的功能，因此束的损伤可能导致典型的残疾形式。量化特定束损伤程度的尝试部分受到从束的一端到另一端成像特性的显着空间变化的限制，这种变化可能因疾病的影响而加剧。在这里，我们开发了一种“惩罚功能回归”程序来分析空间归一化的束剖面，它有力地表征了这种空间变化。中心思想是识别并强调与临床结果评分更相关的区域部分，例如病例状态或残疾程度。该程序还为所研究的每个束和 MRI 指数生成“束异常评分”。重要的是，此过程中使用的加权函数被限制为平滑，并且使用广义线性模型来估计统计关联。我们使用来自 115 名多发性硬化症病例和 42 名健康志愿者的横断面 MRI 和功能研究的数据测试了该方法，考虑了一系列定量 MRI 指数、白质束和临床结果评分，并使用训练和测试集来验证结果。我们表明，对空间变化的关注可使区分多发性硬化症病例与健康志愿者的能力提高高达 15%（所有束和 MRI 指数的平均值：6.4%）。我们的结果证实，随着对正常空间变化的了解和表征，白质束特定成像数据的综合分析得到改善。
Diffusion tensor imaging (DTI) enables noninvasive parcellation of cerebral white matter into its component fiber bundles or tracts. These tracts often subserve specific functions, and damage to the tracts can therefore result in characteristic forms of disability. Attempts to quantify the extent of tract-specific damage have been limited in part by substantial spatial variation of imaging properties from one end of a tract to the other, variation that can be compounded by the effects of disease. Here, we develop a “penalized functional regression” procedure to analyze spatially normalized tract profiles, which powerfully characterize such spatial variation. The central idea is to identify and emphasize portions of a tract that are more relevant to a clinical outcome score, such as case status or degree of disability. The procedure also yields a “tract abnormality score” for each tract and MRI index studied. Importantly, the weighting function used in this procedure is constrained to be smooth, and the statistical associations are estimated using generalized linear models. We test the method on data from a cross-sectional MRI and functional study of 115 multiple-sclerosis cases and 42 healthy volunteers, considering a range of quantitative MRI indices, white-matter tracts, and clinical outcome scores, and using training and testing sets to validate the results. We show that attention to spatial variation yields up to 15% (mean across all tracts and MRI indices: 6.4%) improvement in the ability to discriminate multiple sclerosis cases from healthy volunteers. Our results confirm that comprehensive analysis of white-matter tract-specific imaging data improves with knowledge and characterization of the normal spatial variation.
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DOI：
10.1177/1352458510362440
发表时间：
2010-05
期刊：
Multiple sclerosis (Houndmills, Basingstoke, England)
影响因子：
--
作者：
Gorgoraptis N;Wheeler-Kingshott CA;Jenkins TM;Altmann DR;Miller DH;Thompson AJ;Ciccarelli O
通讯作者：
Ciccarelli O
影响因子：
11
作者：
Lin, X.;Tench, C. R.;Constantinescu, C. S.
通讯作者：
Constantinescu, C. S.
影响因子：
5.7
作者：
Goodlett CB;Fletcher PT;Gilmore JH;Gerig G
通讯作者：
Gerig G
影响因子：
9.9
作者：
Roxburgh, RHSR;Seaman, SR;Compston, DAS
通讯作者：
Compston, DAS
影响因子：
5.7
作者：
Reich, Daniel S.;Smith, Seth A.;Calabresi, Peter A.
通讯作者：
Calabresi, Peter A.