Shanghai Center for Mathematical Sciences, Fudan University, 220 Handan Road, Shanghai, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, China.
School of Mathematical Sciences, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui 230026, China.
Neuroimage. 2022 Jul 15;255:119166. doi: 10.1016/j.neuroimage.2022.119166. Epub 2022 Apr 6.
Magnetic Resonance Imaging (MRI) technology has been increasingly used in neuroscience studies. Reproducibility of statistically significant findings generated by MRI-based studies, especially association studies (phenotype vs. MRI metric) and task-induced brain activation, has been recently heavily debated. However, most currently available reproducibility measures depend on thresholds for the test statistics and cannot be use to evaluate overall study reproducibility. It is also crucial to elucidate the relationship between overall study reproducibility and sample size in an experimental design. In this study, we proposed a model-based reproducibility index to quantify reproducibility which could be used in large-scale high-throughput MRI-based studies including both association studies and task-induced brain activation. We performed the model-based reproducibility assessments for a few association studies and task-induced brain activation by using several recent large sMRI/fMRI databases. For large sample size association studies between brain structure/function features and some basic physiological phenotypes (i.e. Sex, BMI), we demonstrated that the model-based reproducibility of these studies is more than 0.99. For MID task activation, similar results could be observed. Furthermore, we proposed a model-based analytical tool to evaluate minimal sample size for the purpose of achieving a desirable model-based reproducibility. Additionally, we evaluated the model-based reproducibility of gray matter volume (GMV) changes for UK Biobank (UKB) vs. Parkinson Progression Marker Initiative (PPMI) and UK Biobank (UKB) vs. Human Connectome Project (HCP). We demonstrated that both sample size and study-specific experimental factors play important roles in the model-based reproducibility assessments for different experiments. In summary, a systematic assessment of reproducibility is fundamental and important in the current large-scale high-throughput MRI-based studies.
磁共振成像（MRI）技术在神经科学研究中得到了越来越多的应用。MRI 研究，特别是基于关联研究（表型与 MRI 指标）和任务诱发脑激活的研究，其统计上显著发现的可重复性最近受到了广泛的争论。然而，大多数现有的可重复性测量方法都依赖于测试统计量的阈值，不能用于评估研究的整体可重复性。在实验设计中，阐明研究整体可重复性与样本量之间的关系也至关重要。在这项研究中，我们提出了一种基于模型的可重复性指标来量化可重复性，可用于包括关联研究和任务诱发脑激活在内的大规模高通量 MRI 研究。我们使用几个最近的大型 sMRI/fMRI 数据库，对一些关联研究和任务诱发脑激活进行了基于模型的可重复性评估。对于大脑结构/功能特征与某些基本生理表型（如性别、BMI）之间的大型样本量关联研究，我们证明了这些研究的基于模型的可重复性超过 0.99。对于 MID 任务激活，也可以观察到类似的结果。此外，我们提出了一种基于模型的分析工具，用于评估达到理想基于模型的可重复性所需的最小样本量。此外，我们还评估了 UKB 与 PPMI 之间以及 UKB 与 HCP 之间灰质体积（GMV）变化的基于模型的可重复性。我们证明，样本量和研究特定的实验因素在不同实验的基于模型的可重复性评估中都起着重要作用。总之，在当前大规模高通量 MRI 研究中，系统的可重复性评估是基础和重要的。
