Zhang Yu, Zhang Han, Chen Xiaobo, Liu Mingxia, Zhu Xiaofeng, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Mach Learn Med Imaging. 2017 Sep;10541:168-175. doi: 10.1007/978-3-319-67389-9_20. Epub 2017 Sep 7.
Sparse representation-based brain network modeling, although popular, often results in relatively large inter-subject variability in network structures. This inevitably makes it difficult for inter-subject comparison, thus eventually deteriorating the generalization capability of personalized disease diagnosis. Accordingly, group sparse representation has been proposed to alleviate such limitation by jointly estimating connectivity weights for all subjects. However, the constructed brain networks based on this method often fail in providing satisfactory separability between the subjects from (e.g., patients . normal controls), which will also affect the performance of computer-aided disease diagnosis. Based on the hypothesis that subjects from the same group should have larger similarity in their functional connectivity (FC) patterns than subjects from other groups, we propose an "inter-subject FC similarity-guided" group sparse network modeling method. In this method, we explicitly include the inter-subject FC similarity as a constraint to conduct group-wise FC network modeling, while retaining sufficient between-group differences in the resultant FC networks. This improves the separability of brain functional networks between different groups, thus facilitating better personalized brain disease diagnosis. Specifically, the inter-subject FC similarity is roughly estimated by comparing the Pearson's correlation based FC patterns of each brain region to other regions for each pair of the subjects. Then, this is implemented as an additional weighting term to ensure the adequate inter-subject FC differences between the subjects from different groups. Of note, our method retains the group sparsity constraint to ensure the overall consistency of the resultant individual brain networks. Experimental results show that our method achieves a balanced trade-off by generating the individually consistent FC networks, effectively maintaining the necessary group difference, thereby significantly improving connectomics-based diagnosis for mild cognitive impairment (MCI).
基于稀疏表示的脑网络建模虽然很流行，但往往会导致网络结构在个体间存在相对较大的变异性。这不可避免地使得个体间比较变得困难，从而最终降低个性化疾病诊断的泛化能力。因此，已提出组稀疏表示来通过联合估计所有个体的连接权重来缓解这种限制。然而，基于此方法构建的脑网络在区分不同组的个体（例如患者和正常对照）时往往无法提供令人满意的可分离性，这也会影响计算机辅助疾病诊断的性能。基于同一组个体在功能连接（FC）模式上应比其他组个体具有更大相似性的假设，我们提出了一种“个体间FC相似性引导”的组稀疏网络建模方法。在该方法中，我们明确将个体间FC相似性作为一种约束，以进行组水平的FC网络建模，同时在所得的FC网络中保留足够的组间差异。这提高了不同组之间脑功能网络的可分离性，从而有助于更好地进行个性化脑疾病诊断。具体而言，通过比较每对个体中每个脑区与其他脑区基于皮尔逊相关性的FC模式来大致估计个体间FC相似性。然后，将其作为一个额外的加权项来确保不同组个体之间有足够的个体间FC差异。值得注意的是，我们的方法保留了组稀疏性约束，以确保所得个体脑网络的整体一致性。实验结果表明，我们的方法通过生成个体一致的FC网络实现了平衡的权衡，有效保持了必要的组间差异，从而显著提高了基于连接组学的轻度认知障碍（MCI）诊断。
