Objective

Resting-state functional connectivity reveals a promising way for the early detection of dementia. This study proposes a novel method to accurately classify Healthy Controls, Early Mild Cognitive Impairment, Late Mild Cognitive Impairment, and Alzheimer's Disease individuals.

Methods

A novel mapping function based on the B distribution has been developed to map correlation matrices to robust functional connectivity. The node2vec algorithm is applied to the functional connectivity to produce node embeddings. The concatenation of these embedding has been used to derive the patients' feature vectors for further feeding into the Support Vector Machine and Logistic Regression classifiers.

Results

The experimental results indicate promising results in the complex four-class classification problem with an accuracy rate of 97.73% and a quadratic kappa score of 96.86% for the Support Vector Machine. These values are 97.32% and 96.74% for Logistic Regression.

Conclusion

This study presents an accurate automated method for dementia classification. Default Mode Network and Dorsal Attention Network have been found to demonstrate a significant role in the classification method.

Significance

A new mapping function is proposed in this study, the mapping function improves accuracy by 10–11% in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database.

中文翻译：

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随机游走 B 分布静息状态功能连接以识别阿尔茨海默病和轻度认知障碍

客观的

静息状态功能连接揭示了早期发现痴呆症的一种有希望的方法。本研究提出了一种新方法来准确分类健康对照、早期轻度认知障碍、晚期轻度认知障碍和阿尔茨海默病个体。

方法

已经开发了一种基于 B 分布的新型映射函数，用于将相关矩阵映射到稳健的功能连接。node2vec 算法应用于功能连接以生成节点嵌入。这些嵌入的串联已被用于导出患者的特征向量，以进一步输入支持向量机和逻辑回归分类器。

结果

实验结果表明，在复杂的四类分类问题中，支持向量机的准确率为 97.73%，二次 kappa 得分为 96.86%。对于 Logistic 回归，这些值分别为 97.32% 和 96.74%。

结论

这项研究提出了一种准确的痴呆分类自动化方法。已发现默认模式网络和背侧注意网络在分类方法中发挥着重要作用。

意义

本研究提出了一种新的映射函数，该映射函数将阿尔茨海默病神经影像学倡议 (ADNI) 数据库中的准确度提高了 10-11%。