Analysis of longitudinal diffusion-weighted images in healthy and pathological aging: An ADNI study

Kruggel Frithjof; Masaki Fumitaro; Solodkin Ana

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Analysis of longitudinal diffusion-weighted images in healthy and pathological aging: An ADNI study

机译：健康和病理老化中纵向扩散加权图像分析：ADNI研究

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Background & new method: The widely used framework of voxel-based morphometry for analyzing neuroimages is extended here to model longitudinal imaging data by exchanging the linear model with a linear mixed-effects model. The new approach is employed for analyzing a large longitudinal sample of 756 diffusion-weighted images acquired in 177 subjects of the Alzheimer's Disease Neuroimaging initiative (ADNI).

机译：背景与新方法：本文扩展了基于体素的神经图像形态计量学的广泛应用框架，通过将线性模型与线性混合效应模型交换，对纵向成像数据进行建模。这一新方法被用于分析一个大型纵向样本，其中756张扩散加权图像采集于177名阿尔茨海默病神经成像倡议（ADNI）受试者。

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来源
《Journal of Neuroscience Methods》 |2017年第1期|共15页
作者
Kruggel Frithjof; Masaki Fumitaro; Solodkin Ana;
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作者单位

Univ Calif Irvine Dept Biomed Engn Irvine CA USA;

Univ Calif Irvine Dept Biomed Engn Irvine CA USA;

Univ Calif Irvine Dept Anat &

Neurobiol &

Neurol Irvine CA USA;

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原文格式 PDF
正文语种 eng
中图分类人体生理学;
关键词
Voxel-based morphometry; Linear mixed-effects modeling; Aging; Biomarker; Longitudinal imaging; Alzheimer's disease;

机译：基于体素的形态计量学;线性混合效应模型;变老生物标志物;纵向成像;阿尔茨海默病;

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机译：健康和病理老化中纵向扩散加权图像分析：ADNI研究
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Analysis of longitudinal diffusion-weighted images in healthy and pathological aging: An ADNI study

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