Multiscale structural mapping of Alzheimer's disease neurodegeneration.
Multiscale structural mapping of Alzheimer's disease neurodegeneration.
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DOI：
10.1016/j.nicl.2022.102948
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Salat DH
中科院分区：
文献类型：
--
作者：
Jang I;Li B;Riphagen JM;Dickerson BC;Salat DH
提出了一种多尺度结构映射(MSSM)方法，用于使用单一的结构脑图像来量化阿尔茨海默病的神经退行性变。MSSM程序捕捉整个大脑皮层的宏观结构属性和组织微结构的间接指标。与皮质厚度和海马体体积等传统测量方法相比，MSSM程序提供了更强的检测阿尔茨海默病变性和轻度认知障碍的能力，因此可能提供阿尔茨海默病神经退化的敏感测量。最近描述的阿尔茨海默病(AD)的生物学框架强调了三种类型的病理来表征这种疾病，称为“淀粉样蛋白/tau/神经变性”(A-T-N)状态。神经退行性变的成分通常由结构磁共振成像(MRI)得出的萎缩测量来定义，例如海马体体积。影像上的神经退行性改变与疾病症状和预后有关。因此，基于图像的AD神经变性的灵敏定量在一系列临床和研究操作中具有重要的作用。虽然海马区体积是神经退行性变的一个敏感指标，但这一指标受到除AD之外的其他几种临床情况的影响，因此缺乏特异性。相反，被称为“AD的皮质特征”的选择性区域性皮质萎缩为AD的病理提供了更高的特异性。尽管萎缩即使在疾病的临床前阶段也是明显的，但通过将组织微结构特性包括在神经退化测量中，有可能增加对退化的敏感性。然而，为了促进临床可行性，这种信息应该从单一的、短的、非侵入性的成像方案中获得。我们提出了一种多尺度磁共振成像方法，通过从单一的脑结构图像中量化宏观结构(形态计量学)和微观结构(从多层皮质和皮质下白质获得的组织属性)的特征来推进先前的工作(称为多尺度结构映射；MSSM)。采用逐点偏最小二乘回归方法对这些多尺度结构特征进行压缩。当将AD患者与认知功能正常的匹配老年人进行对比时，MSSM程序显示出更广泛的区域组差异，包括仅使用皮质厚度时没有统计学意义的区域。此外，通过多种机器学习算法和集成程序，我们发现MSSM可以准确检测AD痴呆患者(AUROC=0.962，AUPRC=0.976)和后来发展为AD痴呆的轻度认知障碍患者(AUROC=0.908，AUPRC=0.910)。这些发现证明了通过多尺度映射提供的神经退行性变量化的关键进步。未来的工作将确定这项技术在没有损害的情况下准确检测早期损害和生物标记物阳性个体的敏感性。
A multiscale structural mapping (MSSM) procedure is proposed for the quantification of neurodegeneration in Alzheimer's disease using a single structural brain image. The MSSM procedure captures both macrostructural properties and indirect index of tissue microstructure throughout the cerebral cortex. The MSSM procedure provides enhanced ability for the detection of degeneration in Alzheimer’s disease and mild cognitive impairment compared to traditional measures such as cortical thickness and hippocampal volume and therefore may provide a sensitive measure of Alzheimer’s disease neurodegeneration. The recently described biological framework of Alzheimer’s disease (AD) emphasizes three types of pathology to characterize this disorder, referred to as the ‘amyloid/tau/neurodegeneration’ (A-T-N) status. The ‘neurodegenerative’ component is typically defined by atrophy measures derived from structural magnetic resonance imaging (MRI) such as hippocampal volume. Neurodegeneration measures from imaging are associated with disease symptoms and prognosis. Thus, sensitive image-based quantification of neurodegeneration in AD has an important role in a range of clinical and research operations. Although hippocampal volume is a sensitive metric of neurodegeneration, this measure is impacted by several clinical conditions other than AD and therefore lacks specificity. In contrast, selective regional cortical atrophy, known as the ‘cortical signature of AD’ provides greater specificity to AD pathology. Although atrophy is apparent even in the preclinical stages of the disease, it is possible that increased sensitivity to degeneration could be achieved by including tissue microstructural properties in the neurodegeneration measure. However, to facilitate clinical feasibility, such information should be obtainable from a single, short, noninvasive imaging protocol. We propose a multiscale MRI procedure that advances prior work through the quantification of features at both macrostructural (morphometry) and microstructural (tissue properties obtained from multiple layers of cortex and subcortical white matter) scales from a single structural brain image (referred to as ‘multi-scale structural mapping’; MSSM). Vertex-wise partial least squares (PLS) regression was used to compress these multi-scale structural features. When contrasting patients with AD to cognitively intact matched older adults, the MSSM procedure showed substantially broader regional group differences including areas that were not statistically significant when using cortical thickness alone. Further, with multiple machine learning algorithms and ensemble procedures, we found that MSSM provides accurate detection of individuals with AD dementia (AUROC = 0.962, AUPRC = 0.976) and individuals with mild cognitive impairment (MCI) that subsequently progressed to AD dementia (AUROC = 0.908, AUPRC = 0.910). The findings demonstrate the critical advancement of neurodegeneration quantification provided through multiscale mapping. Future work will determine the sensitivity of this technique for accurately detecting individuals with earlier impairment and biomarker positivity in the absence of impairment.
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影响因子：
5.7
作者：
Fischl, B;Sereno, MI;Dale, AM
通讯作者：
Dale, AM
DOI：
10.1016/j.jalz.2013.11.009
发表时间：
2014-11
期刊：
Alzheimer's & dementia : the journal of the Alzheimer's Association
影响因子：
--
作者：
Gomar JJ;Conejero-Goldberg C;Davies P;Goldberg TE;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
3.7
作者：
Dickerson, Bradford C.;Bakkour, Akram;Salat, David H.;Feczko, Eric;Pacheco, Jenni;Greve, Douglas N.;Grodstein, Fran;Wright, Christopher I.;Blacker, Deborah;Rosas, H. Diana;Sperling, Reisa A.;Atri, Alireza;Growdon, John H.;Hyman, Bradley T.;Morris, John C.;Fischl, Bruce;Buckner, Randy L.
通讯作者：
Buckner, Randy L.
影响因子：
5.7
作者：
Cho Y;Seong JK;Jeong Y;Shin SY;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
DOI：
10.1093/brain/awp123
发表时间：
2009-08
期刊：
Brain : a journal of neurology
影响因子：
--
作者：
Desikan RS;Cabral HJ;Hess CP;Dillon WP;Glastonbury CM;Weiner MW;Schmansky NJ;Greve DN;Salat DH;Buckner RL;Fischl B;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative