Automated MRI measures predict progression to Alzheimer's disease.
Automated MRI measures predict progression to Alzheimer's disease.
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DOI：
10.1016/j.neurobiolaging.2010.04.023
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发表时间：
2010-08
影响因子：
4.2
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Desikan RS;Cabral HJ;Settecase F;Hess CP;Dillon WP;Glastonbury CM;Weiner MW;Schmansky NJ;Salat DH;Fischl B;Alzheimer's Disease Neuroimaging Initiative
预测轻度认知功能障碍（MCI）患者发展为阿尔茨海默病（AD）具有越来越重要的临床意义。在这项研究中，使用来自两个队列的324名MCI个体的基线T1加权MRI扫描和自动化软件工具，我们采用因子分析和考克斯比例风险模型来确定一组最能预测从MCI进展到AD的时间的神经解剖学指标。为了进行比较，还检查了脑脊液（CSF）的细胞病理学评估和代谢活性的正电子发射断层扫描（PET）测量。到三年随访时，第一队列的60名MCI个体和第二队列的58名MCI个体已进展为AD诊断。第一个队列的考克斯模型证明了内侧颞叶因素[风险比（HR）=0.43{95%置信区间（CI），0.32-0.55}，p < 0.0001]、额顶枕叶因素[HR=0.59{95% CI，0.48-0.80}，p < 0.001]、侧颞因素[HR=0.67 {95% CI，0.52-0.87}，p < 0.01]。当应用于第二队列时，这些考克斯模型显示内侧颞因子[HR=0.44 {0.32-0.61}，p < 0.001]和外侧颞因子[HR=0.49 {0.38-0.62}，p < 0.001]的显著效应。在一个联合的考克斯模型中，包括最能预测疾病进展的单个CSF、PET和MRI测量，只有内侧颞因子[HR=0.53 {95%CI，0.34-0.81}，p < 0.001]显示出显著效果。这些发现表明，内侧颞叶皮质的自动MRI测量准确可靠地预测疾病进展的时间，优于细胞和代谢测量作为临床衰退的预测因子，并且可以潜在地作为AD的预测标志物。
The prediction of individuals with mild cognitive impairment (MCI) destined to develop Alzheimer's disease (AD) is of increasing clinical importance. In this study, using baseline T1-weighted MRI scans of 324 MCI individuals from two cohorts and automated software tools, we employed factor analyses and Cox proportional hazards models to identify a set of neuroanatomic measures that best predicted the time to progress from MCI to AD. For comparison, cerebrospinal fluid (CSF) assessments of cellular pathology and positron emission tomography (PET) measures of metabolic activity were additionally examined. By three years follow-up, 60 MCI individuals from the first cohort and 58 MCI individuals from the second cohort had progressed to a diagnosis of AD. Cox models on the first cohort demonstrated significant effects for the medial temporal factor [Hazards Ratio (HR) =0.43{95% Confidence Interval (CI), 0.32-0.55}, p < 0.0001], the fronto-parietoccipital factor [HR=0.59{95% CI, 0.48-0.80}, p < 0.001], and the lateral temporal factor [HR=0.67 {95% CI, 0.52-0.87}, p < 0.01]. When applied to the second cohort, these Cox models showed significant effects for the medial temporal factor [HR=0.44 {0.32-0.61}, p < 0.001] and lateral temporal factor [HR=0.49 {0.38-0.62}, p < 0.001]. In a combined Cox model, consisting of individual CSF, PET, and MRI measures that best predicted disease progression, only the medial temporal factor [HR=0.53 {95% CI, 0.34-0.81}, p < 0.001] demonstrated a significant effect. These findings illustrate that automated MRI measures of the medial temporal cortex accurately and reliably predict time to disease progression, outperform cellular and metabolic measures as predictors of clinical decline, and can potentially serve as a predictive marker for AD.
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影响因子：
5.7
作者：
Jovicich J;Czanner S;Han X;Salat D;van der Kouwe A;Quinn B;Pacheco J;Albert M;Killiany R;Blacker D;Maguire P;Rosas D;Makris N;Gollub R;Dale A;Dickerson BC;Fischl B
通讯作者：
Fischl B
影响因子：
5.7
作者：
Fischl, B;Sereno, MI;Dale, AM
通讯作者：
Dale, AM
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
影响因子：
3.7
作者：
Arnold, Steven E.;Hyman, Bradley T.;Van Hoesen, Gary W.
通讯作者：
Van Hoesen, Gary W.
影响因子：
9.9
作者：
Jagust, W. J.;Landau, S. M.;Mathis, C. A.
通讯作者：
Mathis, C. A.