Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification.
Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification.
复制标题
用于功能性神经影像生物标志物识别的多波段脑网络分析
DOI：
10.1109/tmi.2021.3099641
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发表时间：
2021-12
影响因子：
10.6
通讯作者：
Wu G
中科院分区：
文献类型：
--
作者：
Hu R;Peng Z;Zhu X;Gan J;Zhu Y;Ma J;Wu G
在许多神经疾病的计算机辅助诊断系统中，功能连接组谱是一种非侵入性成像生物标志物。然而，功能连接的诊断能力受到大脑中混合频率特异性神经元振荡的挑战，这使得单一功能连接网络（FCN）在捕捉疾病相关功能模式时往往能力不足。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，以半监督的方式进行联合特征学习和个性化疾病诊断，旨在关注功能神经成像数据中假定的多波段功能连接生物标志物。具体而言，我们首先通过离散小波变换将血氧水平相关（Blood Oxygenation Level Dependent， BOLD）信号分解为多个频段，然后将多个频段得到的所有全连接fnc的对齐方式转换为无参数的多频段融合模型。该融合模型对所有全连通FCN进行融合，得到每个个体主体的稀疏连通FCN（简称稀疏FCN），并使每个稀疏FCN与相邻稀疏FCN接近，远离其最远的稀疏FCN。此外，我们采用$\ well _{{1}}$ -SVM进行关节脑区选择和疾病诊断。最后，我们评估了我们提出的框架在各种神经疾病（即额颞叶痴呆（FTD），强迫症（OCD）和阿尔茨海默病（AD））上的有效性，实验结果表明，与目前最先进的方法相比，我们的框架在分类性能和选择的大脑区域方面显示出更合理的结果。源代码可以通过url https://github.com/reynard-hu/mbbna访问。
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 $\ell _{{1}}$ -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.