Hierarchical Structured Sparse Learning for Schizophrenia Identification
Hierarchical Structured Sparse Learning for Schizophrenia Identification
复制标题
用于精神分裂症识别的分层结构化稀疏学习
DOI：
10.1007/s12021-019-09423-0
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发表时间：
2020
期刊：
影响因子：
3
通讯作者：
Mingxia Liu
中科院分区：
文献类型：
--
作者：
Mingliang Wang;Xiaoke Hao;Jiashuang Huang;Kangcheng Wang;Li Shen;Xijia Xu;Daoqiang Zhang;Mingxia Liu
低频波动分数幅值（fALFF）已被广泛应用于静息状态功能磁共振成像（rs-fMRI）诊断精神分裂症（SZ）。然而，以往的研究通常测量低频波动范围内（0.01 ~ 0.08Hz）的fALFF，不能完全覆盖静息状态下大脑复杂的神经活动模式。此外，现有的研究通常忽略了这样一个事实，即每个特定的频段都可以描述大脑中神经活动的独特自发波动。因此，本文提出了一种新的分层结构稀疏学习方法，以充分利用四个不同频段（0.01Hz至0.25Hz）的特异性和互补性结构信息进行SZ诊断。该方法既保留了多个频带间的部分群结构，又保留了每个频带内的特定特征。我们进一步开发了一种高效的优化算法来求解所提出的目标函数。我们在一个真实的SZ数据集上验证了我们提出的方法的有效性。此外，为了证明该方法的通用性，我们将我们提出的方法应用于阿尔茨海默病神经影像学倡议（ADNI）数据库的一个子集。在这两个数据集上的实验结果表明，与几种最先进的方法相比，我们提出的方法在脑疾病分类方面取得了很好的效果。
Fractional amplitude of low-frequency fluctuation (fALFF) has been widely used for resting-state functional magnetic resonance imaging (rs-fMRI) based schizophrenia (SZ) diagnosis. However, previous studies usually measure the fALFF within low-frequency fluctuation (from 0.01 to 0.08Hz), which cannot fully cover the complex neural activity pattern in the resting-state brain. In addition, existing studies usually ignore the fact that each specific frequency band can delineate the unique spontaneous fluctuations of neural activities in the brain. Accordingly, in this paper, we propose a novel hierarchical structured sparse learning method to sufficiently utilize the specificity and complementary structure information across four different frequency bands (from 0.01Hz to 0.25Hz) for SZ diagnosis. The proposed method can help preserve the partial group structures among multiple frequency bands and the specific characters in each frequency band. We further develop an efficient optimization algorithm to solve the proposed objective function. We validate the efficacy of our proposed method on a real SZ dataset. Also, to demonstrate the generality of the method, we apply our proposed method on a subset of Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results on both datasets demonstrate that our proposed method achieves promising performance in brain disease classification, compared with several state-of-the-art methods.