用于轻度认知障碍诊断的群体相似约束功能脑网络建模方法

Method of functional brain network modeling with group similarity constraint for mild cognitive impairment classification

功能脑网络为理解大脑功能激活模式及大脑信息传递结构提供了一种有效的生物标记,如何更加有效地利用先验信息构建准确的脑网络模型尤为重要。提出了一种基于群体相似性约束的功能脑网络模型,通过引入张量低秩约束,利用张量的核范数,求解一个在组内具有低秩先验的脑网络组。这种方法利用组内的相似性约束,收缩脑网络的解空间,从而有效地构建更优的脑网络模型。将所构建的脑网络用于轻度认知障碍的判别任务,实验结果表明,所提出的基于群体相似性约束的脑网络建模方法,能够构建出更具判别性的脑网络,并得到了与以往研究一致的显著性连边,进一步验证了所提出方法的有效性。

Functional brain network provides an effective biomarker for understanding brain activation patterns,the development of neurodegenerative diseases,and the structure of brain signaling.How to use priori information to build accurate brain network is particularly important in subsequent applications.A functional brain network construction model based on group similarity constraints was proposed.By introducing a tensor low rank constraint and using the nuclear norm of the tensor,a brain network group with a low rank prior in the group was solved.The group similarity constraints to shrink the solution space of the brain network was used,thus constructing a better functional brain network effectively.For evaluating the performance of the proposed method,the constructed brain network was adopted for the discriminative task of mild cognitive impairment.The experimental results show that the proposed functional brain network modeling method based on group similarity constraints can construct more discriminative brain network.In addition,based on the brain network constructed by the proposed method,the significant connections consistent with previous studies are obtained,and the effectiveness of the proposed method is further verified.