IEEE Trans Med Imaging. 2021 Dec;40(12):3843-3855. doi: 10.1109/TMI.2021.3099641. Epub 2021 Nov 30.
The functional connectomic profile is one of the non-invasive imaging biomarkers in the computer-assisted diagnostic system for many neuro-diseases. However, the diagnostic power of functional connectivity is challenged by mixed frequency-specific neuronal oscillations in the brain, which makes the single Functional Connectivity Network (FCN) often underpowered to capture the disease-related functional patterns. To address this challenge, we propose a novel functional connectivity analysis framework to conduct joint feature learning and personalized disease diagnosis, in a semi-supervised manner, aiming at focusing on putative multi-band functional connectivity biomarkers from functional neuroimaging data. Specifically, we first decompose the Blood Oxygenation Level Dependent (BOLD) signals into multiple frequency bands by the discrete wavelet transform, and then cast the alignment of all fully-connected FCNs derived from multiple frequency bands into a parameter-free multi-band fusion model. The proposed fusion model fuses all fully-connected FCNs to obtain a sparsely-connected FCN (sparse FCN for short) for each individual subject, as well as lets each sparse FCN be close to its neighbored sparse FCNs and be far away from its furthest sparse FCNs. Furthermore, we employ the l -SVM to conduct joint brain region selection and disease diagnosis. Finally, we evaluate the effectiveness of our proposed framework on various neuro-diseases, i.e., Fronto-Temporal Dementia (FTD), Obsessive-Compulsive Disorder (OCD), and Alzheimer's Disease (AD), and the experimental results demonstrate that our framework shows more reasonable results, compared to state-of-the-art methods, in terms of classification performance and the selected brain regions. The source code can be visited by the url https://github.com/reynard-hu/mbbna.
功能连接组学图谱是计算机辅助诊断系统中用于多种神经疾病的非侵入性成像生物标志物之一。然而，脑内混合频率特定神经元振荡对功能连接的诊断能力提出了挑战，这使得单个功能连接网络（FCN）常常无法捕获与疾病相关的功能模式。为了解决这一挑战，我们提出了一种新的功能连接分析框架，旨在以半监督的方式进行联合特征学习和个性化疾病诊断，旨在从功能神经影像学数据中关注潜在的多频带功能连接生物标志物。具体来说，我们首先通过离散小波变换将血氧水平依赖（BOLD）信号分解为多个频带，然后将来自多个频带的所有全连接 FCN 的对齐纳入无参数的多频带融合模型。所提出的融合模型融合所有全连接 FCN 以获得每个个体受试者的稀疏连接 FCN（简称稀疏 FCN），并使每个稀疏 FCN靠近其相邻的稀疏 FCN 并远离其最远的稀疏 FCN。此外，我们采用 l-SVM 进行联合脑区选择和疾病诊断。最后，我们在各种神经疾病（如额颞痴呆症（FTD）、强迫症（OCD）和阿尔茨海默病（AD））上评估了我们提出的框架的有效性，实验结果表明，与最先进的方法相比，我们的框架在分类性能和所选脑区方面具有更合理的结果。源代码可以通过网址 https://github.com/reynard-hu/mbbna 访问。
