动态功能脑网络模型的多任务融合Lasso方法

目的 传统的静息态功能性磁共振成像（fMRI）的功能脑网络（FBN）研究是基于在整个扫描过程中FBN固定不变的假设。但是，最近的研究表明FBN是动态变化的，而且其中蕴含着丰富的信息。本文提出一种多任务融合最小绝对值收缩和选择算子（Lasso）方法来构建静息态fMRI的动态FBN。 方法 提出的多任务融合Lasso方法可以在构建动态FBN时，保留网络的稀疏性及子序列的时间平滑性。具体来说，首先用滑动窗方法得到交叠的静息态fMRI子序列；然后用多任务融合Lasso方法联合地估计一个样本的所有子序列的功能连接从而构建动态FBN，用k均值聚类算法得到每类样本子序列的功能连接的聚类中心，并将所有类的聚类中心组成回归矩阵；最后根据回归矩阵求样本的回归系数，将其作为特征进行分类，验证多任务融合Lasso方法对动态FBN建模的有效性。 结果 采用公开的fMRI数据集来验证多任务融合Lasso模型构建动态FBN的分类效果。实验使用阿尔兹海默症神经影像学计划（ADNI）公开的fMRI数据集中的阿尔兹海默症患者、早期轻度认知功能障碍患者和健康被试3组数据，并用准确率、灵敏度和特异度来评估算法的分类性能。在3组二分类实验中，本文方法分别达到了92.31%、80.00%和84.00%的准确率。实验结果表明，与静态FBN模型和其他传统的动态FBN模型相比，本文方法能取得更好的分类效果。结论 本文提出的多任务融合Lasso构建动态FBN的方法，能有效地保留网络的稀疏性和子序列的时间平滑性，同时提高算法的分类效果，在一定程度上为脑部疾病的诊断提供帮助。多任务融合Lasso模型可以用于动态FBN的构建，挖掘功能连接的动态信息，同时整个算法可以用于基于fMRI数据的脑部疾病的分类研究中。

Multi-task fused Lasso method for constructing dynamic functional brain network of resting-state fMRI

Objective Functional brain network(FBN) has emerged as an effective tool in examining the functional abnormalities of the brain network in patients with brain disease. FBN is a mathematical representation of brain, in which the brain region is the node, and a functional connectivity between each pair of brain regions is an edge. The functional connectivity between the brain regions can reveal disease-related abnormalities in brain physiology. The FBN can be measured by several neuroimaging techniques. Functional magnetic resonance imaging(fMRI) is one of the most commonly used neuroimaging techniques. fMRI can detect the functional activities of the brain based on blood oxygen level dependent(BOLD) signals. Moreover, the resting-state fMRI can measure spontaneous fluctuations in BOLD signals, which is useful in exploring the abnormal brain activities in patients with brain disease. Conventional FBN studies of the resting-state fMRI assume the temporal stationarity of FBN across the duration of the scan. However, these static FBN studies ignore the existence of slightly different mental activities during the entire scan session. In addition, recent studies suggest that the FBN exhibit dynamic changes, which may contain powerful information. This paper presents a multi-task fused least absolute shrinkage and selection operation(Lasso) method to construct the dynamic FBN of a resting-state fMRI. Method The proposed multi-task fused Lasso can preserve the sparsity and temporal smoothness of the dynamic FBN. Specifically, we impose a sparsity constraint to the functional connectivity between the brain regions, which is based on some neurophysiological findings that a brain region only directly interacts with a few other brain regions in neurological processes. In addition, the adjacent fMRI sub-series are required to be similar, which is based on the temporal smoothness of the dynamic FBN. We first use the sliding window approach to generate a sequence of overlapping resting-state fMRI sub-series. Second, the proposed multi-task fused Lasso is used to construct the dynamic FBN. K-means clustering is applied to obtain cluster centroids of these FBNs from the same class. All the cluster centroids are grouped together to form a regression matrix. Finally, the FBNs of the samples are regressed against the regression matrix to obtain the regression coefficients, which serve as features for classification. The classification can further verify the effectiveness of our method for constructing the dynamic FBN. The overall framework can be used for brain disease classification based on fMRI data, in which the features are extracted from the constructed dynamic FBN. Result We use a public fMRI dataset to verify the classification performance of the dynamic FBN constructed by the multi-task fused Lasso. Three groups of patients with Alzheimer''s disease(AD), patients with early mild cognitive impairment(eMCI) and healthy controls(HCs) from the Alzheimer''s disease neuroimaging initiative(ADNI) fMRI dataset are used for the experiment. Accuracy, sensitivity, and specificity are used to assess the classification performance. For the classification of the AD patients and HCs, our method achieves 92.31% accuracy, 96.15% sensitivity, and 88.46% specificity. For the classification of the eMCI patients and HCs, our method achieves 80.00% accuracy, 83.33% sensitivity, and 76.92% specificity. For the classification of the AD and eMCI patients, our method achieves 84.00% accuracy, 84.62% sensitivity, and 83.33% specificity. Experiment results demonstrate the improved performance of our method compared with the static and the traditional dynamic FBN models. The improved classification performance of our method indicates that the features extracted by the multi-task fused Lasso have advantages over the static or the traditional dynamic FBN models for classification purposes. Conclusion This study presents a method for constructing a dynamic FBN of resting-state fMRI. The overall framework can be used for brain disease classification based on the constructed dynamic FBN. The proposed method can preserve the sparsity and temporal smoothness of the dynamic FBN and improve the classification performance simultaneously. The proposed method may contribute to the diagnosis of brain diseases to some extent. The proposed method can lead to an improved understanding of the dynamic FBN and brain diseases. The multi-task fused Lasso can be used to construct the dynamic FBN, which can explore the useful dynamic information of functional connectivity. In addition, this method can be used for the classification of brain diseases based on fMRI data.