Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China.
Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2023 Feb;8(2):171-180. doi: 10.1016/j.bpsc.2020.12.007. Epub 2020 Dec 29.
Individualized and reliable biomarkers are crucial for diagnosing Alzheimer's disease (AD). However, lack of accessibility and neurobiological correlation are the main obstacles to their clinical application. Machine learning algorithms can effectively identify personalized biomarkers based on the prominent symptoms of AD.
Episodic memory-related magnetic resonance imaging (MRI) features of 143 patients with amnesic mild cognitive impairment (MCI) were identified using a multivariate relevance vector regression algorithm. The support vector machine classification model was constructed using these MRI features and verified in 2 independent datasets (N = 994). The neurobiological basis was also investigated based on cognitive assessments, neuropathologic biomarkers of cerebrospinal fluid, and positron emission tomography images of amyloid-β plaques.
The combination of gray matter volume and amplitude of low-frequency fluctuation MRI features accurately predicted episodic memory impairment in individual patients with amnesic MCI (r = 0.638) when measured using an episodic memory assessment panel. The MRI features that contributed to episodic memory prediction were primarily distributed across the default mode network and limbic network. The classification model based on these features distinguished patients with AD from normal control subjects with more than 86% accuracy. Furthermore, most identified episodic memory-related regions showed significantly different amyloid-β positron emission tomography measurements among the AD, MCI, and normal control groups. Moreover, the classification outputs significantly correlated with cognitive assessment scores and cerebrospinal fluid pathological biomarkers' levels in the MCI and AD groups.
Neuroimaging features can reflect individual episodic memory function and serve as potential diagnostic biomarkers of AD.
个体化且可靠的生物标志物对于阿尔茨海默病（AD）的诊断至关重要。然而，缺乏可及性和神经生物学相关性是其临床应用的主要障碍。机器学习算法可以根据 AD 的突出症状有效地识别个性化生物标志物。
使用多变量关联向量回归算法识别 143 例遗忘型轻度认知障碍（MCI）患者的与情景记忆相关的磁共振成像（MRI）特征。使用这些 MRI 特征构建支持向量机分类模型，并在 2 个独立数据集（N=994）中进行验证。还基于认知评估、脑脊液神经病理生物标志物和淀粉样蛋白-β斑块的正电子发射断层扫描图像来研究神经生物学基础。
当使用情景记忆评估小组进行测量时，灰质体积和低频波动 MRI 特征的组合可准确预测遗忘型 MCI 个体患者的情景记忆障碍（r=0.638）。有助于情景记忆预测的 MRI 特征主要分布在默认模式网络和边缘网络中。基于这些特征的分类模型能够以超过 86%的准确率将 AD 患者与正常对照组区分开来。此外，在 AD、MCI 和正常对照组中，大多数确定的与情景记忆相关的区域在淀粉样蛋白-β正电子发射断层扫描测量中表现出显著差异。此外，分类输出与 MCI 和 AD 组中认知评估评分和脑脊液病理生物标志物水平显著相关。
神经影像学特征可以反映个体情景记忆功能，并作为 AD 的潜在诊断生物标志物。
