The construction of brain functional network based on resting-state functional magnetic resonance imaging (fMRI) is an effective method to reveal the mechanism of human brain operation, but the common brain functional network generally contains a lot of noise, which leads to wrong analysis results. In this paper, the least absolute shrinkage and selection operator (LASSO) model in compressed sensing is used to reconstruct the brain functional network. This model uses the sparsity of L1-norm penalty term to avoid over fitting problem. Then, it is solved by the fast iterative shrinkage-thresholding algorithm (FISTA), which updates the variables through a shrinkage threshold operation in each iteration to converge to the global optimal solution. The experimental results show that compared with other methods, this method can improve the accuracy of noise reduction and reconstruction of brain functional network to more than 98%, effectively suppress the noise, and help to better explore the function of human brain in noisy environment.
基于静息态功能磁共振成像（fMRI）构建脑功能网络是揭示人脑运作机制的有效手段，但是目前常见的脑功能网络普遍包含大量噪声从而导致错误的分析结果。本文使用压缩感知中的最小绝对值收缩和选择算子（LASSO）模型对脑功能网络进行降噪重建，该模型利用 L1 范数惩罚项的稀疏性避免过拟合问题。然后，通过快速迭代阈值收缩算法（FISTA）求解，该算法在每一次迭代中通过一个收缩阈值操作来更新变量，从而收敛到全局最优解。实验结果表明：与其他几种方法相比，该方法可以将脑功能网络降噪重建的准确率提高到 98% 以上，有效地抑制了噪声，有助于即使在噪声环境下也能很好地探索人脑的功能。.
Keywords: brain functional network; compressed sensing; fast iterative shrinkage-thresholding algorithm; least absolute shrinkage and selection operator.