Yu Renping, Zhang Han, An Le, Chen Xiaobo, Wei Zhihui, Shen Dinggang
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC, USA.
Med Image Comput Comput Assist Interv. 2016 Oct;9900:37-45. doi: 10.1007/978-3-319-46720-7_5. Epub 2016 Oct 2.
Analysis of brain functional connectivity network (BFCN) has shown great potential in understanding brain functions and identifying biomarkers for neurological and psychiatric disorders, such as Alzheimer's disease and its early stage, mild cognitive impairment (MCI). In all these applications, the accurate construction of biologically meaningful brain network is critical. Due to the sparse nature of the brain network, sparse learning has been widely used for complex BFCN construction. However, the conventional -norm penalty in the sparse learning equally penalizes each edge (or link) of the brain network, which ignores the link strength and could remove strong links in the brain network. Besides, the conventional sparse regularization often overlooks group structure in the brain network, ., a set of links (or connections) sharing similar attribute. To address these issues, we propose to construct BFCN by integrating both and . Specifically, a novel correlation-weighted sparse group constraint is devised to account for and balance among (1) sparsity, (2) link strength, and (3) group structure, in a unified framework. The proposed method is applied to MCI classification using the resting-state fMRI from ADNI-2 dataset. Experimental results show that our method is effective in modeling human brain connectomics, as demonstrated by superior MCI classification accuracy of 81.8%. Moreover, our method is promising for its capability in modeling more biologically meaningful sparse brain networks, which will benefit both basic and clinical neuroscience studies.
脑功能连接网络（BFCN）分析在理解脑功能以及识别神经和精神疾病（如阿尔茨海默病及其早期阶段轻度认知障碍（MCI））的生物标志物方面已显示出巨大潜力。在所有这些应用中，准确构建具有生物学意义的脑网络至关重要。由于脑网络的稀疏性，稀疏学习已被广泛用于复杂的BFCN构建。然而，稀疏学习中的传统 -范数惩罚对脑网络的每条边（或链接）进行同等惩罚，这忽略了链接强度，可能会去除脑网络中的强链接。此外，传统的稀疏正则化常常忽略脑网络中的组结构，即一组具有相似属性的链接（或连接）。为了解决这些问题，我们建议通过整合 和 来构建BFCN。具体而言，设计了一种新颖的相关加权稀疏组约束，以便在一个统一框架中兼顾并平衡（1）稀疏性、（2）链接强度和（3）组结构。所提出的方法应用于使用ADNI - 2数据集的静息态功能磁共振成像进行MCI分类。实验结果表明，我们的方法在对人类脑连接组进行建模方面是有效的，MCI分类准确率高达81.8%，证明了其优越性。此外，我们的方法有望能够对更具生物学意义的松颅脑网络进行建模，这将使基础和临床神经科学研究都受益。
