Improved Prediction of Imminent Progression to Clinically Significant Memory Decline Using Surface Multivariate Morphometry Statistics and Sparse Coding.
Improved Prediction of Imminent Progression to Clinically Significant Memory Decline Using Surface Multivariate Morphometry Statistics and Sparse Coding.
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DOI：
10.3233/jad-200821
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发表时间：
2021
影响因子：
4
通讯作者：
Wang, Yalin
中科院分区：
文献类型：
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作者：
Stonnington, Cynthia M.;Wu, Jianfeng;Zhang, Jie;Shi, Jie;Bauer, Robert J., III;Devadas, Vivek;Su, Yi;Locke, Dona E. C.;Reiman, Eric M.;Caselli, Richard J.;Chen, Kewei;Wang, Yalin
除了它们的其他作用外，脑成像和阿尔茨海默病（AD）的其他生物标志物有可能告知认知未受损（CU）的人进展为轻度认知障碍（MCI）的可能性，并在评估有希望的预防治疗时有利于受试者选择。我们之前描述过，在已知在AD临床前和临床阶段优先受影响的基线FDG-PET和MRI测量中，海马体积是2年内发生MCI的最佳预测因子（79%灵敏度/78%特异性），使用标准自动MRI体积算法程序，二元逻辑回归和留一法。通过使用不同的海马特征和机器学习方法来改善相同的预测，通过两个独立的前瞻性队列（Arizona和ADNI）进行交叉验证。将基于补丁的稀疏编码算法应用于78名CU成人的基线TI-MRI的海马表面特征，这些成人随后在大约2年内进展为遗忘型MCI（“进展者”），80名匹配的成人保持CU至少4年（“非进展者”）。非进展者和进展者在年龄、性别、教育和载脂蛋白E4等位基因剂量方面相匹配。我们在定义MCI时没有包括淀粉样蛋白或tau生物标志物。我们在亚利桑那州队列中实现了92%的预测准确率，在ADNI队列中实现了92%的预测准确率，当结合两个人口统计学上不同的队列时，预测准确率为90%，而使用海马体积的预测准确率为79%（亚利桑那州）和72%（ADNI）。应用于个体MRI的表面多变量形态测量和稀疏编码，即使在没有其他AD生物标志物的情况下，也可以准确地预测即将进展为MCI。
Besides their other roles, brain imaging and other biomarkers of Alzheimer’s disease (AD) have the potential to inform a cognitively unimpaired (CU) person’s likelihood of progression to mild cognitive impairment (MCI) and benefit subject selection when evaluating promising prevention therapies. We previously described that among baseline FDG-PET and MRI measures known to be preferentially affected in the preclinical and clinical stages of AD, hippocampal volume was the best predictor of incident MCI within 2 years (79% sensitivity/78% specificity), using standard automated MRI volumetric algorithmic programs, binary logistic regression and leave-one-out procedures. To improve the same prediction by using different hippocampal features and machine learning methods, cross-validated via two independent and prospective cohorts (Arizona and ADNI). Patch-based sparse coding algorithms were applied to hippocampal surface features of baseline TI-MRIs from 78 CU adults who subsequently progressed to amnestic MCI in approximately 2 years (“progessors”) and 80 matched adults who remained CU for at least 4 years (“nonprogressors”). Nonprogressors and progressors were matched for age, sex, education, and apolipoprotein E4 allele dose. We did not include amyloid or tau biomarkers in defining MCI. We achieved 92% prediction accuracy in the Arizona cohort, 92% prediction accuracy in the ADNI cohort, and 90% prediction accuracy when combining the two demographically distinct cohorts, as compared to 79% (Arizona) and 72% (ADNI) prediction accuracy using hippocampal volume. Surface multivariate morphometry and sparse coding, applied to individual MRIs, may accurately predict imminent progression to MCI even in the absence of other AD biomarkers.