Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
School of Computer Science, Qinghai Normal University, Xining, China.
Methods. 2022 Aug;204:241-248. doi: 10.1016/j.ymeth.2022.04.015. Epub 2022 Apr 26.
Mild cognitive impairment (MCI) is usually considered the early stage of Alzheimer's disease (AD). Therefore, the accurate identification of MCI individuals with high risk in converting to AD is essential for the potential prevention and treatment of AD. Recently, the great success of deep learning has sparked interest in applying deep learning to neuroimaging field. However, deep learning techniques are prone to overfitting since available neuroimaging datasets are not sufficiently large. Therefore, we proposed a deep learning model fusing cortical features to address the issue of fusion and classification blocks. To validate the effectiveness of the proposed model, we compared seven different models on the same dataset in the literature. The results show that our proposed model outperformed the competing models in the prediction of MCI conversion with an accuracy of 83.3% in the testing dataset. Subsequently, we used deep learning to characterize the contribution of brain regions and different cortical features to MCI progression. The results revealed that the caudal anterior cingulate and pars orbitalis contributed most to the classification task, and our model pays more attention to volume features and cortical thickness features.
轻度认知障碍 (MCI) 通常被认为是阿尔茨海默病 (AD) 的早期阶段。因此，准确识别有向 AD 转化高风险的 MCI 个体对于 AD 的潜在预防和治疗至关重要。最近，深度学习的巨大成功引起了人们对将深度学习应用于神经影像学领域的兴趣。然而，由于可用的神经影像学数据集不够大，深度学习技术容易过拟合。因此，我们提出了一种融合皮质特征的深度学习模型来解决融合和分类块的问题。为了验证所提出模型的有效性，我们在文献中的同一数据集上比较了七个不同的模型。结果表明，我们提出的模型在测试数据集上的预测 MCI 转化率方面优于竞争模型，准确率为 83.3%。随后，我们使用深度学习来描述脑区和不同皮质特征对 MCI 进展的贡献。结果表明，后扣带回和眶额回对分类任务的贡献最大，我们的模型更关注体积特征和皮质厚度特征。
