Image Display, Enhancement, and Analysis Laboratory, Biomedical Research Imaging Center and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
PLoS One. 2012;7(5):e37828. doi: 10.1371/journal.pone.0037828. Epub 2012 May 30.
In this paper, a high-dimensional pattern classification framework, based on functional associations between brain regions during resting-state, is proposed to accurately identify MCI individuals from subjects who experience normal aging. The proposed technique employs multi-spectrum networks to characterize the complex yet subtle blood oxygenation level dependent (BOLD) signal changes caused by pathological attacks. The utilization of multi-spectrum networks in identifying MCI individuals is motivated by the inherent frequency-specific properties of BOLD spectrum. It is believed that frequency specific information extracted from different spectra may delineate the complex yet subtle variations of BOLD signals more effectively. In the proposed technique, regional mean time series of each region-of-interest (ROI) is band-pass filtered (0.025 ≤ ƒ ≤ 0.100 Hz) before it is decomposed into five frequency sub-bands. Five connectivity networks are constructed, one from each frequency sub-band. Clustering coefficient of each ROI in relation to the other ROIs are extracted as features for classification. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy obtained by this approach is 86.5%, which is an increase of at least 18.9% from the conventional full-spectrum methods. A cross-validation estimation of the generalization performance shows an area of 0.863 under the receiver operating characteristic (ROC) curve, indicating good diagnostic power. It was also found that, based on the selected features, portions of the prefrontal cortex, orbitofrontal cortex, temporal lobe, and parietal lobe regions provided the most discriminant information for classification, in line with results reported in previous studies. Analysis on individual frequency sub-bands demonstrated that different sub-bands contribute differently to classification, providing extra evidence regarding frequency-specific distribution of BOLD signals. Our MCI classification framework, which allows accurate early detection of functional brain abnormalities, makes an important positive contribution to the treatment management of potential AD patients.
本文提出了一种基于静息态脑区间功能关联的高维模式分类框架，旨在从经历正常衰老的个体中准确识别 MCI 患者。该方法采用多谱网络来描述由病理攻击引起的复杂而微妙的血氧水平依赖（BOLD）信号变化。在识别 MCI 患者时使用多谱网络，是因为 BOLD 光谱具有固有频率特异性。人们认为，从不同光谱中提取的频率特异性信息可能更有效地描绘 BOLD 信号的复杂而微妙变化。在提出的技术中，在将每个感兴趣区域（ROI）的区域平均时间序列分解为五个频带子带之前，对其进行带通滤波（0.025 ≤ ƒ ≤ 0.100 Hz）。构建了五个连接网络，每个网络来自一个频带子带。提取每个 ROI 与其他 ROI 的相关聚类系数作为分类特征。通过留一交叉验证评估分类准确性，以确保性能的泛化。该方法的分类准确率为 86.5%，比传统的全谱方法至少提高了 18.9%。通过交叉验证对泛化性能的估计显示，在接收器操作特性（ROC）曲线下的面积为 0.863，表明具有良好的诊断能力。还发现，基于所选特征，前额叶皮层、眶额皮层、颞叶和顶叶区域的部分区域为分类提供了最具鉴别力的信息，这与之前研究报告的结果一致。对个体频带子带的分析表明，不同的子带对分类的贡献不同，为 BOLD 信号的频率特异性分布提供了额外的证据。我们的 MCI 分类框架允许对功能脑异常进行准确的早期检测，为潜在 AD 患者的治疗管理做出了重要的积极贡献。
