Alzheimer's disease is a common progressive neurodegenerative disorder in the elderly. Diffusion tensor imaging (DTI) has been widely used to explore structural integrity and to describe white matter degeneration in Alzheimer's disease. Previous research has indicated that the change of connections between white matter fiber tracts is very important for investigating the brain function of Alzheimer's disease patients. However, whether white matter features can be used as potential biomarkers for predicting Alzheimer's disease tendency requires more in-depth research. In this study, we investigated the relationship between the damage in white matter tracts and the decline of cognitive function in Alzheimer's disease. DTI data were collected from 38 Alzheimer's disease patients and 30 normal controls. Fiber assignment by continuous tracking approach was used to establish connections between different brain regions of the whole brain, network-based statistical analysis and support vector machine classification analysis were used to explore the connection of whole-brain fiber bundles between the two groups. Most importantly, our results showed that the connections between brain regions of Alzheimer's disease patients were damaged, and the damage were mainly located in the right hemisphere, there was a certain degree of lateralization effect. Using whole-brain fiber bundle connection network as a feature for classification, we found it helped to improve the classification accuracy in Alzheimer's disease patients, which is useful for early clinical diagnosis of Alzheimer's disease. These findings further suggested that we can use the whole-brain fiber bundle connection network of Alzheimer's disease patients as a potential diagnostic indicator of Alzheimer's disease in the future.

中文翻译：

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阿尔茨海默病患者全脑纤维束连接网络的变化有偏侧化的倾向。

阿尔茨海默病是老年人常见的进行性神经退行性疾病。扩散张量成像（DTI）已被广泛用于探索结构完整性和描述阿尔茨海默病中的白质变性。此前的研究表明，白质纤维束之间连接的变化对于研究阿尔茨海默病患者的大脑功能非常重要。然而，白质特征是否可以作为预测阿尔茨海默病倾向的潜在生物标志物，还需要更深入的研究。在这项研究中，我们研究了阿尔茨海默病中白质束损伤与认知功能下降之间的关系。DTI 数据收集自 38 名阿尔茨海默病患者和 30 名正常对照。采用连续跟踪方法进行纤维分配，建立全脑不同脑区之间的连接，采用基于网络的统计分析和支持向量机分类分析来探讨两组之间全脑纤维束的连接。最重要的是，我们的研究结果表明，阿尔茨海默病患者大脑区域之间的连接受到损害，而且损害主要位于右半球，存在一定程度的偏侧化效应。利用全脑纤维束连接网络作为分类特征，我们发现它有助于提高阿尔茨海默病患者的分类准确性，这对于阿尔茨海默病的早期临床诊断很有用。这些发现进一步表明，我们可以利用阿尔茨海默病患者的全脑纤维束连接网络作为未来阿尔茨海默病的潜在诊断指标。