用小波与图论分析脑部影像，区分阿尔茨海默病、轻度痴呆和健康人。
Multi-Scale Graph Theoretical Analysis of Resting-State fMRI for Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Healthy Controls
- 结合小波变换与图论，捕捉脑网络在不同频段的动态变化。
- 在ADNI数据集上实现三类人群分类，关键区域连接模式被识别。
- 适合神经科学与医学影像分析研究者，助力早期诊断。
阿尔茨海默病（AD）是一种以记忆衰退和认知下降为特征的神经退行性疾病，早期检测对及时干预至关重要。然而，症状表现多样，早期诊断困难。静息态功能磁共振成像（rs-fMRI）可捕捉自发脑活动与功能连接，这些在AD和轻度认知障碍（MCI）中已被证实异常。传统方法如皮尔逊相关系数虽用于构建关联矩阵，但常忽略脑活动的动态与非平稳特性。本研究提出一种新方法，融合离散小波变换（DWT）与图论，建模脑网络的动态行为，通过时间-频率表示实现更精细的网络动力学分析。利用机器学习自动识别不同疾病阶段的脑网络模式。该方法应用于阿尔茨海默病神经影像倡议（ADNI）数据库的rs-fMRI数据，验证了其作为早期诊断工具及疾病进展监测的潜力。统计分析揭示了不同频段下受累的特定脑区与连接，深化了对疾病影响脑功能机制的理解。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) is a neurodegenerative disorder marked by memory loss and cognitive decline, making early detection vital for timely intervention. However, early diagnosis is challenging due to the heterogeneous presentation of symptoms. Resting-state functional magnetic resonance imaging (rs-fMRI) captures spontaneous brain activity and functional connectivity, which are known to be disrupted in AD and mild cognitive impairment (MCI). Traditional methods, such as Pearson's correlation, have been used to calculate association matrices, but these approaches often overlook the dynamic and non-stationary nature of brain activity. In this study, we introduce a novel method that integrates discrete wavelet transform (DWT) and graph theory to model the dynamic behavior of brain networks. Our approach captures the time-frequency representation of brain activity, allowing for a more nuanced analysis of the underlying network dynamics. Machine learning was employed to automate the discrimination of different stages of AD based on learned patterns from brain network at different frequency bands. We applied our method to a dataset of rs-fMRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, demonstrating its potential as an early diagnostic tool for AD and for monitoring disease progression. Our statistical analysis identifies specific brain regions and connections that are affected in AD and MCI, at different frequency bands, offering deeper insights into the disease's impact on brain function.
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