We investigate whether and how we can improve the cost efficiency of neuroimaging studies with well-tailored fMRI tasks. The comparative study is conducted using a novel network science-driven Bayesian connectome-based predictive method, which incorporates network theories in model building and substantially improves precision and robustness in imaging biomarker detection. The robustness of the method lays the foundation for identifying predictive power differential across fMRI task conditions if such difference exists. When applied to a clinically heterogeneous transdiagnostic cohort, we found shared and distinct functional fingerprints of neuropsychological outcomes across seven fMRI conditions. For example, emotional N-back memory task was found to be less optimal for negative emotion outcomes, and gradual-onset continuous performance task was found to have stronger links with sensitivity and sociability outcomes than with cognitive control outcomes. Together, our results show that there are unique optimal pairings of task-based fMRI conditions and neuropsychological outcomes that should not be ignored when designing well-powered neuroimaging studies.

翻译：本研究探讨了是否以及如何通过精心设计的fMRI任务来提高神经影像学研究的成本效益。我们采用一种基于网络科学驱动的贝叶斯连接组预测新方法进行比较研究，该方法将网络理论融入模型构建，显著提升了影像生物标志物检测的精度与稳健性。该方法的稳健性为识别不同fMRI任务条件间可能存在的预测效能差异奠定了基础。将此方法应用于临床异质性的跨诊断队列时，我们发现了七种fMRI条件下神经心理学结果共有的及特异性的功能指纹。例如，情感N-back记忆任务对负性情绪结果的预测效果较差，而渐变式持续操作任务与敏感度及社交性结果的关联强度高于其与认知控制结果的关联。综合而言，我们的研究结果表明：任务态fMRI条件与神经心理学结果之间存在独特的最优匹配关系，在设计高功效的神经影像学研究时不应忽视这一规律。