Test-retest reliability of regression dynamic causal modeling.
Test-retest reliability of regression dynamic causal modeling.
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DOI：
10.1162/netn_a_00215
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发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Stephan KE
中科院分区：
文献类型：
--
作者：
Frässle S;Stephan KE
回归动态因果模型（rDCM）是一种新颖的，计算效率高的方法，用于推断有效的连接在全脑水平。虽然面部和结构效度的rDCM已经被证明，在这里，我们评估了它的重测可靠性的测试理论属性的临床应用特别重要的连接特定的估计和一致性的全脑连接模式的会话组水平的一致性。使用人类连接组项目数据集的八个不同的范例（任务和休息）和两个不同的parcellation方案，我们发现，rDCM提供了高度一致的连接估计，在整个会话组水平。第二，虽然重测信度是有限的，当平均在所有连接（范围内的平均组内相关系数0.24-0.42的任务），可靠性增加连接强度，与较强的连接表现出良好的重测信度。第三，rDCM的全脑连接模式允许以高（在某些情况下是完美的）准确度识别个体参与者。rDCM连接估计的重测信度与功能连接的措施相比，rDCM表现不佳，特别是当专注于强连接。一般来说，对于所有的方法和指标，基于任务的连接估计显示出更大的可靠性比那些从休息状态。我们的研究结果强调了rDCM在人类连接组学和临床应用中的潜力。重测信度是连通性估计在许多情况下有效性的重要前提，特别是在临床应用中。在这里，使用来自人类连接组项目的不同数据集，我们证明了回归动态因果模型（rDCM）在关注强连接时会产生良好的重测信度。与功能连通性测量的重测信度相比，rDCM在大多数情况下表现良好。此外，我们表明，可靠性不是均匀分布的：我们确定了几个区域（主要是在额叶和颞叶），通过高度可靠的连接，无论范式。最后，我们证明了个人连接配置文件是足够独特的，参与者可以被识别出高精度。我们的研究结果强调了rDCM对来自fMRI数据的定向“连接指纹”进行稳健推断的潜力。
Regression dynamic causal modeling (rDCM) is a novel and computationally highly efficient method for inferring effective connectivity at the whole-brain level. While face and construct validity of rDCM have already been demonstrated, here we assessed its test-retest reliability—a test-theoretical property of particular importance for clinical applications—together with group-level consistency of connection-specific estimates and consistency of whole-brain connectivity patterns over sessions. Using the Human Connectome Project dataset for eight different paradigms (tasks and rest) and two different parcellation schemes, we found that rDCM provided highly consistent connectivity estimates at the group level across sessions. Second, while test-retest reliability was limited when averaging over all connections (range of mean intraclass correlation coefficient 0.24–0.42 over tasks), reliability increased with connection strength, with stronger connections showing good to excellent test-retest reliability. Third, whole-brain connectivity patterns by rDCM allowed for identifying individual participants with high (and in some cases perfect) accuracy. Comparing the test-retest reliability of rDCM connectivity estimates with measures of functional connectivity, rDCM performed favorably—particularly when focusing on strong connections. Generally, for all methods and metrics, task-based connectivity estimates showed greater reliability than those from the resting state. Our results underscore the potential of rDCM for human connectomics and clinical applications. Test-retest reliability is an important prerequisite for the validity of connectivity estimates in many situations, particularly in clinical applications. Here, using different datasets from the Human Connectome Project, we demonstrate that regression dynamic causal modeling (rDCM) yields good to excellent test-retest reliability when focusing on strong connections. Comparing this with the test-retest reliability of functional connectivity measures, rDCM performed favorably in most cases. Furthermore, we show that reliability is not homogeneously distributed: We identified several regions (primarily in frontal and temporal lobe) that were linked via highly reliable connections, regardless of the paradigm. Finally, we demonstrate that individual connectivity profiles are sufficiently unique that participants can be identified with high accuracy. Our findings emphasize the potential of rDCM for robust inference on directed “connectivity fingerprints” from fMRI data.
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影响因子：
4.8
作者：
Frässle S;Harrison SJ;Heinzle J;Clementz BA;Tamminga CA;Sweeney JA;Gershon ES;Keshavan MS;Pearlson GD;Powers A;Stephan KE
通讯作者：
Stephan KE
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