Association of Neuroimaging Data with Behavioral Variables: A Class of Multivariate Methods and Their Comparison Using Multi-Task FMRI Data.
Association of Neuroimaging Data with Behavioral Variables: A Class of Multivariate Methods and Their Comparison Using Multi-Task FMRI Data.
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DOI：
10.3390/s22031224
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发表时间：
2022-02-05
期刊：
影响因子：
--
通讯作者：
Adali T
中科院分区：
文献类型：
--
作者：
Akhonda MABS;Levin-Schwartz Y;Calhoun VD;Adali T
从不同的模式或不同的任务和条件中收集多个相关的神经成像的数据，我们具有非成像的数据，例如认知或行为变量，并且是通过这两组数据的关联（NEUROOMIGIGION），即我们可以理解和不使用Neuronon和Sockoccon和cockoce但是，存在多个神经成像数据集或模态的方法；共同分析成像数据集和行为变量，使成像数据和行为特征的多元关系可以识别出模拟结果表明，我们提出的方法在识别成像和行为组件的相关性比当前的方法与功能性磁性成像（FMRI）call sche colle colless colle colless colle colle colle colle colle colle colless colless colless colless colless colless colless colless colless colless colle colless colless colles colless colles colless colless colle colles colless colles colles colle colle colless consection。揭示在多个数据集中估计的中央执行网络（CEN）与衡量工作记忆的行为变量有很强的相关性，这是传统方法未识别的结果。
It is becoming increasingly common to collect multiple related neuroimaging datasets either from different modalities or from different tasks and conditions. In addition, we have non-imaging data such as cognitive or behavioral variables, and it is through the association of these two sets of data—neuroimaging and non-neuroimaging—that we can understand and explain the evolution of neural and cognitive processes, and predict outcomes for intervention and treatment. Multiple methods for the joint analysis or fusion of multiple neuroimaging datasets or modalities exist; however, methods for the joint analysis of imaging and non-imaging data are still in their infancy. Current approaches for identifying brain networks related to cognitive assessments are still largely based on simple one-to-one correlation analyses and do not use the cross information available across multiple datasets. This work proposes two approaches based on independent vector analysis (IVA) to jointly analyze the imaging datasets and behavioral variables such that multivariate relationships across imaging data and behavioral features can be identified. The simulation results show that our proposed methods provide better accuracy in identifying associations across imaging and behavioral components than current approaches. With functional magnetic resonance imaging (fMRI) task data collected from 138 healthy controls and 109 patients with schizophrenia, results reveal that the central executive network (CEN) estimated in multiple datasets shows a strong correlation with the behavioral variable that measures working memory, a result that is not identified by traditional approaches. Most of the identified fMRI maps also show significant differences in activations across healthy controls and patients potentially providing a useful signature of mental disorders.
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