Tosun Duygu, Joshi Sarang, Weiner Michael W
Department of Radiology and Biomedical Imaging, University of California - San Francisco, San Francisco, CA USA.
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA (72 S Central Campus Drive, Room 3750, Salt Lake City, UT 84112).
Ann Clin Transl Neurol. 2014 Mar;1(3):160-170. doi: 10.1002/acn3.40.
To identify brain atrophy from structural-MRI and cerebral blood flow(CBF) patterns from arterial spin labeling perfusion-MRI that are best predictors of the Aβ-burden, measured as composite F-AV45-PET uptake, in individuals with early mild cognitive impairment(MCI). Furthermore, to assess the relative importance of imaging modalities in classification of Aβ+/Aβ- early mild cognitive impairment.
Sixty-seven ADNI-GO/2 participants with early-MCI were included. Voxel-wise anatomical shape variation measures were computed by estimating the initial diffeomorphic mapping momenta from an unbiased control template. CBF measures normalized to average motor cortex CBF were mapped onto the template space. Using partial least squares regression, we identified the structural and CBF signatures of Aβ after accounting for normal cofounding effects of age, sex, and education.
F-AV45-positive early-MCIs could be identified with 83% classification accuracy, 87% positive predictive value, and 84% negative predictive value by multidisciplinary classifiers combining demographics data, ApoE ε4-genotype, and a multimodal MRI-based Aβ score.
Multimodal-MRI can be used to predict the amyloid status of early-MCI individuals. MRI is a very attractive candidate for the identification of inexpensive and non-invasive surrogate biomarkers of Aβ deposition. Our approach is expected to have value for the identification of individuals likely to be Aβ+ in circumstances where cost or logistical problems prevent Aβ detection using cerebrospinal fluid analysis or Aβ-PET. This can also be used in clinical settings and clinical trials, aiding subject recruitment and evaluation of treatment efficacy. Imputation of the Aβ-positivity status could also complement Aβ-PET by identifying individuals who would benefit the most from this assessment.
从结构磁共振成像(structural-MRI)中识别脑萎缩,并从动脉自旋标记灌注磁共振成像(arterial spin labeling perfusion-MRI)的脑血流(CBF)模式中识别出最能预测早期轻度认知障碍(MCI)个体中以复合F-AV45-PET摄取量衡量的Aβ负荷的因素。此外,评估成像方式在Aβ阳性/Aβ阴性早期轻度认知障碍分类中的相对重要性。
纳入67名ADNI-GO/2早期MCI参与者。通过从无偏对照模板估计初始微分同胚映射动量来计算体素水平的解剖形状变异测量值。将归一化至平均运动皮层CBF的CBF测量值映射到模板空间。使用偏最小二乘回归,在考虑年龄、性别和教育程度的正常混杂效应后,我们识别出Aβ的结构和CBF特征。
通过结合人口统计学数据、载脂蛋白E ε4基因型和基于多模态MRI的Aβ评分的多学科分类器,F-AV45阳性早期MCI的分类准确率为83%,阳性预测值为87%,阴性预测值为84%。
多模态MRI可用于预测早期MCI个体的淀粉样蛋白状态。MRI是识别Aβ沉积的廉价且非侵入性替代生物标志物的极具吸引力的候选方法。在成本或后勤问题阻碍使用脑脊液分析或Aβ-PET检测Aβ的情况下,我们的方法有望用于识别可能为Aβ阳性的个体。这也可用于临床环境和临床试验,辅助受试者招募和治疗疗效评估。Aβ阳性状态的推算还可通过识别最能从此评估中受益的个体来补充Aβ-PET。