While functional neuroimaging studies typically focus on a particular paradigm to investigate network connectivity, the human brain appears to possess an intrinsic "trait" architecture that is independent of any given paradigm. We have previously proposed the use of "cross-paradigm connectivity (CPC)" to quantify shared connectivity patterns across multiple paradigms and have demonstrated the utility of such measures in clinical studies. Here, using generalizability theory and connectome fingerprinting, we examined the reliability, stability, and individual identifiability of CPC in a group of highly-sampled healthy traveling subjects who received fMRI scans with a battery of five paradigms across multiple sites and days. Compared with single-paradigm connectivity matrices, the CPC matrices showed higher reliability in connectivity diversity, lower reliability in connectivity strength, higher stability, and higher individual identification accuracy. All of these assessments increased as a function of number of paradigms included in the CPC analysis. In comparisons involving different paradigm combinations and different brain atlases, we observed significantly higher reliability, stability, and identifiability for CPC matrices constructed from task-only data (versus those from both task and rest data), and higher identifiability but lower stability for CPC matrices constructed from the Power atlas (versus those from the AAL atlas). Moreover, we showed that multi-paradigm CPC matrices likely reflect the brain's "trait" structure that cannot be fully achieved from single-paradigm data, even with multiple runs. The present results provide evidence for the feasibility and utility of CPC in the study of functional "trait" networks and offer some methodological implications for future CPC studies.

中文翻译：

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跨范式连接：可靠性、稳定性和实用性。

虽然功能性神经影像学研究通常侧重于研究网络连接的特定范式，但人类大脑似乎拥有独立于任何给定范式的内在“特征”架构。我们之前曾提议使用“跨范式连接 (CPC)”来量化跨多个范式的共享连接模式，并证明了此类措施在临床研究中的实用性。在这里，我们使用泛化性理论和连接组指纹识别，在一组高采样的健康旅行受试者中检查了 CPC 的可靠性、稳定性和个体可识别性，这些受试者在多个地点和日期接受了一组五种范式的 fMRI 扫描。与单范式连通矩阵相比，CPC矩阵表现出更高的连接多样性可靠性，更低的连接强度可靠性，更高的稳定性和更高的个体识别准确度。所有这些评估都随着 CPC 分析中包含的范式数量的增加而增加。在涉及不同范式组合和不同脑图谱的比较中，我们观察到由仅任务数据（与来自任务和休息数据的数据相比）构建的 CPC 矩阵的可靠性、稳定性和可识别性显着更高，CPC 矩阵具有更高的可识别性但稳定性更低由 Power 地图集构建而成（相对于来自 AAL 地图集的那些）。此外，我们发现多范式 CPC 矩阵可能反映了大脑的“特质”结构，而单范式数据无法完全实现这一点，即使多次运行。目前的结果为 CPC 在功能“特质”网络研究中的可行性和实用性提供了证据，并为未来的 CPC 研究提供了一些方法论意义。