A joint network optimization framework to predict clinical severity from resting state functional MRI data.
A joint network optimization framework to predict clinical severity from resting state functional MRI data.
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DOI：
10.1016/j.neuroimage.2019.116314
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发表时间：
2020-02-01
期刊：
影响因子：
5.7
通讯作者：
Venkataraman A
中科院分区：
文献类型：
--
作者：
D'Souza NS;Nebel MB;Wymbs N;Mostofsky SH;Venkataraman A
我们提出了一种新的优化框架，预测临床严重程度从静息状态功能磁共振成像（rs-fMRI）数据。我们的模型由两个耦合项组成。第一项将相关矩阵分解为定义网络流形的代表性子网络的稀疏集合。这些子网络被建模为对应于整个大脑的共激活的基本模式的秩一外产品;通过患者特定的非负系数组合子网络。第二项是线性回归模型，其使用患者特异性系数来预测临床严重程度的量度。我们在十倍交叉验证设置中在两个独立的数据集上验证我们的框架。第一组是58名诊断为自闭症谱系障碍（ASD）的患者。第二个数据集由来自公开可用的ASD数据库的63名患者组成。我们的方法优于标准的半监督框架，它采用传统的图论和统计表示学习技术，将rs-fMRI相关性与行为。相比之下，我们的联合网络优化框架利用rs-fMRI相关矩阵的结构，同时捕获组水平的影响和患者的异质性。最后，我们证明了我们提出的框架鲁棒地识别ASD的临床相关网络特征。
We propose a novel optimization framework to predict clinical severity from resting state fMRI (rs-fMRI) data. Our model consists of two coupled terms. The first term decomposes the correlation matrices into a sparse set of representative subnetworks that define a network manifold. These subnetworks are modeled as rank-one outer-products which correspond to the elemental patterns of co-activation across the brain; the subnetworks are combined via patient-specific non-negative coefficients. The second term is a linear regression model that uses the patient-specific coefficients to predict a measure of clinical severity. We validate our framework on two separate datasets in a ten fold cross validation setting. The first is a cohort of fifty-eight patients diagnosed with Autism Spectrum Disorder (ASD). The second dataset consists of sixty three patients from a publicly available ASD database. Our method outperforms standard semi-supervised frameworks, which employ conventional graph theoretic and statistical representation learning techniques to relate the rs-fMRI correlations to behavior. In contrast, our joint network optimization framework exploits the structure of the rs-fMRI correlation matrices to simultaneously capture group level effects and patient heterogeneity. Finally, we demonstrate that our proposed framework robustly identifies clinically relevant networks characteristic of ASD.
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DOI：
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发表时间：
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期刊：
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DOI：
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发表时间：
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期刊：
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影响因子：
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影响因子：
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