Relating whole-brain functional connectivity to self-reported negative emotion in a large sample of young adults using group regularized canonical correlation analysis.
Relating whole-brain functional connectivity to self-reported negative emotion in a large sample of young adults using group regularized canonical correlation analysis.
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DOI：
10.1016/j.neuroimage.2021.118137
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发表时间：
2021-08-15
期刊：
影响因子：
5.7
通讯作者：
Williams LM
中科院分区：
文献类型：
--
作者：
Tozzi L;Tuzhilina E;Glasser MF;Hastie TJ;Williams LM
我们研究的目标是使用功能连接将大脑功能映射到负面情绪的自我报告。在来自人类连接组项目(N=652)的一个健康个体的大型数据集中，我们首先量化了负面面孔匹配任务中的功能连接性，以分离由情绪刺激诱导的模式。然后，我们在补充的无任务休息状态下也做了同样的实验。为了确定这两种情况下的功能连通性与负面情绪自我报告之间的关系，我们引入了组正则典型相关分析(GRCCA)，这是一种新的算法，扩展了典型相关分析，以模拟已建立的大脑网络中功能连通性的共同属性。为了最大限度地减少过拟合，我们使用交叉验证来优化GRCCA的正则化参数，并使用排列来测试我们的结果在数据集的坚持部分中的重要性。GRCCA始终优于平面正则化典型相关分析。推广到坚持测试集的唯一典型相关性是基于静止状态数据(r=0.175，排列检验p=0.021)。这种典型的相关性主要体现在愤怒-攻击性上。它显示扣带回、眶前叶、顶上、听觉和视觉皮质以及脑岛的高负荷。在皮质下，我们观察到苍白球的高负荷。对于脑网络，它主要加载在初级视觉、轨道-情感和腹侧多模式网络上。在这里，我们介绍了GRCCA的第一个神经成像应用，这是一种用于正则化典型相关分析的新算法，它考虑了正则化方案中变量的分组。使用GRCCA，我们证明了涉及视觉、轨道-情感和多通道网络的功能连接是研究主观愤怒和攻击的功能关联的有前途的目标。至关重要的是，我们的方法和发现也强调了交叉验证、正规化和对相关神经成像研究的保留数据进行测试的必要性，以避免夸大效应。
The goal of our study was to use functional connectivity to map brain function to self-reports of negative emotion. In a large dataset of healthy individuals derived from the Human Connectome Project (N = 652), first we quantified functional connectivity during a negative face-matching task to isolate patterns induced by emotional stimuli. Then, we did the same in a complementary task-free resting state condition. To identify the relationship between functional connectivity in these two conditions and self-reports of negative emotion, we introduce group regularized canonical correlation analysis (GRCCA), a novel algorithm extending canonical correlations analysis to model the shared common properties of functional connectivity within established brain networks. To minimize overfitting, we optimized the regularization parameters of GRCCA using cross-validation and tested the significance of our results in a held-out portion of the data set using permutations. GRCCA consistently outperformed plain regularized canonical correlation analysis. The only canonical correlation that generalized to the held-out test set was based on resting state data (r = 0.175, permutation test p = 0.021). This canonical correlation loaded primarily on Anger-aggression. It showed high loadings in the cingulate, orbitofrontal, superior parietal, auditory and visual cortices, as well as in the insula. Subcortically, we observed high loadings in the globus pallidus. Regarding brain networks, it loaded primarily on the primary visual, orbito-affective and ventral multimodal networks. Here, we present the first neuroimaging application of GRCCA, a novel algorithm for regularized canonical correlation analyses that takes into account grouping of the variables during the regularization scheme. Using GRCCA, we demonstrate that functional connections involving the visual, orbito-affective and multimodal networks are promising targets for investigating functional correlates of subjective anger and aggression. Crucially, our approach and findings also highlight the need of cross-validation, regularization and testing on held out data for correlational neuroimaging studies to avoid inflated effects.
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