Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry.
Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry.
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DOI：
10.1002/brb3.733
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发表时间：
2017-07
影响因子：
3.1
通讯作者：
Leporé N
中科院分区：
文献类型：
--
作者：
Tsao S;Gajawelli N;Zhou J;Shi J;Ye J;Wang Y;Leporé N
基于基线测量的阿尔茨海默病（AD）进展预测使我们能够了解疾病进展，并对有关治疗策略的决策具有影响。为此，我们将一种预测性多任务机器学习方法（cFSGL）与一种新的基于MR的海马多变量形态测量表面图（mTBM）相结合，以预测患者未来的认知评分。先前的研究表明，同时执行所有未来时间点预测的多任务学习框架（cFSGL）可用于编码稀疏性和时间平滑性。作者表明，该方法能够使用基于FreeSurfer的基线MRI特征、MMSE评分、人口统计信息和ApoE状态来预测ADNI受试者的认知结果。虽然体积信息可能包含大脑状态的一般信息，但我们假设海马特异性信息可能在AD的预测建模中更有用。为此，我们采用一种基于多变量张量的参数表面分析方法（mTBM）从海马表面提取特征。我们将mTBM特征与传统的表面特征（如中轴距离、雅可比行列式以及雅可比主特征值中的2个）相结合，得到7个归一化的海马表面图，每个图有300个点。通过将这些7 × 300 = 2100个特征与之前的约350个特征结合起来，我们说明了这种类型的稀疏化方法如何应用于海马体的整个表面地图，从而产生比之前尝试的大2个数量级的特征空间。通过将cFSGL多任务机器学习框架的功能与海马体表面的AD敏感mTBM特征图相结合，我们能够提高ADAS认知评分6、12、24、36和48个月的预测性能。
Prediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi‐task machine learning method (cFSGL) with a novel MR‐based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients. Previous work has shown that a multi‐task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able to predict cognitive outcomes of ADNI subjects using FreeSurfer‐based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied a multivariate tensor‐based parametric surface analysis method (mTBM) to extract features from the hippocampal surfaces. We combined mTBM features with traditional surface features such as middle axis distance, the Jacobian determinant as well as 2 of the Jacobian principal eigenvalues to yield 7 normalized hippocampal surface maps of 300 points each. By combining these 7 × 300 = 2100 features together with the previous ~350 features, we illustrate how this type of sparsifying method can be applied to an entire surface map of the hippocampus that yields a feature space that is 2 orders of magnitude larger than what was previously attempted. By combining the power of the cFSGL multi‐task machine learning framework with the addition of AD sensitive mTBM feature maps of the hippocampus surface, we are able to improve the predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.
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影响因子：
9.9
作者：
Jack, CR;Shiung, MM;Petersen, RC
通讯作者：
Petersen, RC
与阿尔茨海默氏病和脑脊液生物标志物相关的遗传基因座在芬兰病例控制队列中。
影响因子：
3.7
作者：
Elias-Sonnenschein LS;Helisalmi S;Natunen T;Hall A;Paajanen T;Herukka SK;Laitinen M;Remes AM;Koivisto AM;Mattila KM;Lehtimäki T;Verhey FR;Visser PJ;Soininen H;Hiltunen M
通讯作者：
Hiltunen M
影响因子：
38.1
作者：
Frisoni, Giovanni B.;Fox, Nick C.;Jack, Clifford R., Jr.;Scheltens, Philip;Thompson, Paul M.
通讯作者：
Thompson, Paul M.
影响因子：
5.7
作者：
Fischl, Bruce
通讯作者：
Fischl, Bruce
影响因子：
5.7
作者：
Hua X;Leow AD;Parikshak N;Lee S;Chiang MC;Toga AW;Jack CR Jr;Weiner MW;Thompson PM;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative