Gao Ziwen, Zhu Wanqiu, Li Yuqing, Ye Wei, Chen Xiao, Zhou Shanshan, Li Xiaohu, Li Xiaoshu, Yu Yongqiang
Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China.
J Alzheimers Dis. 2024 Dec;102(4):1111-1120. doi: 10.1177/13872877241296130. Epub 2024 Nov 25.
Alzheimer's disease (AD) is strongly associated with slowly progressive hippocampal atrophy. Elucidating the relationships between local morphometric changes and disease status for early diagnosis could be aided by machine learning algorithms trained on neuroimaging datasets.
This study intended to propose machine learning models for the accurate identification and cognitive function prediction across the AD severity spectrum based on structural magnetic resonance imaging (sMRI) of the bilateral hippocampi.
The high-resolution sMRI data of 120 AD dementia patients, 232 amnestic mild cognitive impairment (aMCI) patients, and 206 healthy controls (HCs) were included from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The classification capacity and cognitive predict ability of hippocampal volume was evaluated by multiple pattern analysis using the support vector machine (SVM) and relevance vector regression (RVR) application of the Pattern Recognition for Neuroimaging Toolbox, separately. For validation, the analyses were performed using a biomarker-based regrouping method and another independent local dataset.
The SVM application produced a total accuracy of 94.17%, 80.85%, and 70.74% and area under receiver operating characteristic curves of 0.97, 0.87, and 0.72 between HC versus AD dementia, HC versus aMCI, and aMCI versus AD dementia classification, respectively. The RVR application significantly predicted the baseline and mean cognitive function at three years of follow-up. Qualitatively consistent results were obtained using different regrouping method and the local dataset.
The machine learning methods based on the bilateral hippocampi distinguished across the AD severity spectrum and predicted the baseline and the longitudinal cognitive function with greater accuracy.
阿尔茨海默病（AD）与缓慢进展的海马萎缩密切相关。通过在神经影像数据集上训练的机器学习算法，有助于阐明局部形态计量学变化与疾病状态之间的关系以实现早期诊断。
本研究旨在基于双侧海马的结构磁共振成像（sMRI），提出用于准确识别AD严重程度谱并预测认知功能的机器学习模型。
纳入来自阿尔茨海默病神经影像倡议（ADNI）的120例AD痴呆患者、232例遗忘型轻度认知障碍（aMCI）患者和206例健康对照（HC）的高分辨率sMRI数据。分别使用神经影像模式识别工具箱中的支持向量机（SVM）和相关向量回归（RVR）应用，通过多模式分析评估海马体积的分类能力和认知预测能力。为进行验证，使用基于生物标志物的重新分组方法和另一个独立的本地数据集进行分析。
SVM应用在HC与AD痴呆、HC与aMCI、aMCI与AD痴呆分类之间的总准确率分别为94.17%、80.85%和70.74%，受试者操作特征曲线下面积分别为0.97、0.87和0.72。RVR应用显著预测了随访三年时的基线和平均认知功能。使用不同的重新分组方法和本地数据集获得了定性一致的结果。
基于双侧海马的机器学习方法能够区分AD严重程度谱，并更准确地预测基线和纵向认知功能。
