Toward a statistical validation of brain signatures as robust measures of behavioral substrates.
Toward a statistical validation of brain signatures as robust measures of behavioral substrates.
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DOI：
10.1002/hbm.26265
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发表时间：
2023-06-01
影响因子：
4.8
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中科院分区：
文献类型：
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“认知的大脑特征”概念作为一种数据驱动的探索性方法引起了人们的兴趣，以更好地理解涉及特定认知功能的关键大脑区域，并有可能最大限度地表征行为结果的大脑基质。之前我们提出了一种计算情景记忆特征的方法。然而，为了成为一种稳健的大脑测量方法，签名方法需要对不同群体的模型性能进行严格验证。在这里，我们报告验证结果并提供将其扩展到第二个行为领域的示例。在两个发现数据队列中的每一个中，我们得出了两个领域的区域大脑灰质厚度关联：神经心理学和日常认知记忆。我们在每个队列中随机选择 40 个大小为 400 的发现子集，计算了与结果的区域关联。我们生成了空间重叠频率图，并将高频区域定义为“共识”签名掩模。使用单独的验证数据集，我们通过比较签名模型之间的拟合度以及与基于理论的竞争模型的拟合度，评估了基于队列的共识模型拟合的可复制性和解释力。空间复制产生了收敛的共识签名区域。共识签名模型拟合在每个验证队列的 50 个随机子集中高度相关，表明具有高可复制性。在对每个完整队列的比较中，签名模型优于其他模型。在这项验证研究中，我们生成了签名模型，该模型复制了适合结果的模型，并且优于其他常用的衡量标准。两个记忆域的特征表明它们具有强烈共享的大脑基质。因此，强大的大脑特征是可以实现的，从而为行为领域的基础建模提供可靠且有用的测量方法。我们实现了一种计算数据驱动的行为结果签名的方法，该方法在验证队列中具有鲁棒性。这表明该方法可用于在大脑关联研究中开发强大的大脑表型。
The “brain signature of cognition” concept has garnered interest as a data‐driven, exploratory approach to better understand key brain regions involved in specific cognitive functions, with the potential to maximally characterize brain substrates of behavioral outcomes. Previously we presented a method for computing signatures of episodic memory. However, to be a robust brain measure, the signature approach requires a rigorous validation of model performance across a variety of cohorts. Here we report validation results and provide an example of extending it to a second behavioral domain. In each of two discovery data cohorts, we derived regional brain gray matter thickness associations for two domains: neuropsychological and everyday cognition memory. We computed regional association to outcome in 40 randomly selected discovery subsets of size 400 in each cohort. We generated spatial overlap frequency maps and defined high‐frequency regions as “consensus” signature masks. Using separate validation datasets, we evaluated replicability of cohort‐based consensus model fits and explanatory power by comparing signature model fits with each other and with competing theory‐based models. Spatial replications produced convergent consensus signature regions. Consensus signature model fits were highly correlated in 50 random subsets of each validation cohort, indicating high replicability. In comparisons over each full cohort, signature models outperformed other models. In this validation study, we produced signature models that replicated model fits to outcome and outperformed other commonly used measures. Signatures in two memory domains suggested strongly shared brain substrates. Robust brain signatures may therefore be achievable, yielding reliable and useful measures for modeling substrates of behavioral domains. We implement a method for computing data‐driven signatures of behavior outcomes that are robust across validation cohorts. This suggests the method may be used to develop robust brain phenotypes in brainwise association studies.
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