Bridging Imaging, Genetics, and Diagnosis in a Coupled Low-Dimensional Framework-猫眼课题宝

DOI：

10.1007/978-3-030-32251-9_71

发表时间：

2019-10

期刊：

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中科院分区：

其他

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作者：

Sayan Ghosal;Qiang Chen;A. Goldman;William Ulrich;K. Berman;D. Weinberger;V. Mattay;A. Venkataraman

我们提出了一个联合字典学习框架，耦合成像和遗传学数据在一个低维子空间的指导下的临床诊断。我们使用图形正则化惩罚来同时捕获区域间的大脑相互作用，并识别跨越低维空间的代表性解剖基向量集。我们进一步采用组稀疏性来找到跨越相同潜在空间的遗传基向量的代表性集合。最后，潜在的投影是用来分类患者与对照。我们已经评估了我们的模型上的两个任务功能磁共振成像范例和单核苷酸多态性（SNP）数据，从精神分裂症患者和匹配的神经典型对照。我们采用了十倍交叉验证技术来显示我们的模型的预测能力。我们比较我们的模型与典型相关分析的成像和遗传学数据和随机森林分类。我们的方法在两个任务数据集上都显示出更好的预测准确性。此外，相关的大脑区域和遗传变异是精神分裂症中有据可查的缺陷的基础。

We propose a joint dictionary learning framework that couples imaging and genetics data in a low dimensional subspace as guided by clinical diagnosis. We use a graph regularization penalty to simultaneously capture inter-regional brain interactions and identify the representative set anatomical basis vectors that span the low dimensional space. We further employ group sparsity to find the representative set of genetic basis vectors that span the same latent space. Finally, the latent projection is used to classify patients versus controls. We have evaluated our model on two task fMRI paradigms and single nucleotide polymorphism (SNP) data from schizophrenic patients and matched neurotypical controls. We employ a ten fold cross validation technique to show the predictive power of our model. We compare our model with canonical correlation analysis of imaging and genetics data and random forest classification. Our approach shows better prediction accuracy on both task datasets. Moreover, the implicated brain regions and genetic variants underlie the well documented deficits in schizophrenia.

Bridging Imaging, Genetics, and Diagnosis in a Coupled Low-Dimensional Framework

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