A Generative-Predictive Framework to Capture Altered Brain Activity in fMRI and its Association with Genetic Risk: Application to Schizophrenia
A Generative-Predictive Framework to Capture Altered Brain Activity in fMRI and its Association with Genetic Risk: Application to Schizophrenia
复制标题
捕获 fMRI 中大脑活动变化的生成预测框架及其与遗传风险的关联：在精神分裂症中的应用
DOI：
10.1117/12.2511220
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Venkataraman, Archana
中科院分区：
文献类型：
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作者：
Ghosal, Sayan;Chen, Qiang;Goldman, Aaron L.;Ulrich, William;Berman, Karen F.;Weinberger, Daniel R.;Mattay, Venkata S.;Venkataraman, Archana
我们提出了一个生成预测框架，该框架捕获了神经典型队列和临床人群之间区域脑活动的差异，并以患者特异性遗传风险为指导。我们的模型假设，在神经型受试者的功能激活分布在一个人口平均值，并通过偏离这个神经型平均值的神经精神病患者的大脑活动的改变定义。我们采用组稀疏识别一组大脑区域，同时解释显着的功能差异，并指定一组基向量，跨越低维数据子空间。将患者特异性投影到该子空间上作为特征向量来识别与遗传风险的多变量关联。我们已经评估了我们的模型基于任务的功能磁共振成像数据集，从人口研究精神分裂症。我们将我们的模型与两种基线方法进行比较，使用最小绝对收缩和选择算子（LASSO）回归和随机森林（RF）回归，这两种方法建立了工作记忆任务期间大脑活动与精神分裂症多基因风险之间的直接关联。我们的模型在自举实验中表现出比机器学习基线更高的一致性和鲁棒性。此外，我们的模型所涉及的一组大脑区域是精神分裂症中有据可查的执行认知缺陷的基础。
We present a generative-predictive framework that captures the differences in regional brain activity between a neurotypical cohort and a clinical population, as guided by patient-specific genetic risk. Our model assumes that the functional activations in the neurotypical subjects are distributed around a population mean, and that the altered brain activity in neuropsychiatric patients is defined via deviations from this neurotypical mean. We employ group sparsity to identify a set of brain regions that simultaneously explain the salient functional differences and specify a set of basis vector, that span the low dimensional data subspace. The patient-specific projections onto this subspace are used as feature vectors to identify multivariate associations with genetic risk. We have evaluated our model on a task-based fMRI dataset from a population study of schizophrenia. We compare our model with two baseline methods, regression using Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) regression, which establishes direct association between the brain activity during a working memory task and schizophrenia polygenic risk. Our model demonstrates greater consistency and robustness across bootstrapping experiments than the machine learning baselines. Moreover, the set of brain regions implicated by our model underlie the well documented executive cognitive deficits in schizophrenia.