A model-based approach to assess reproducibility for large-scale high-throughput MRI-based studies

NeuroImage

Yinglei Lai¹  Shitong Xiang¹  Wei Cheng²  Jujiao Kang²  Weikang Gong³  Andreas Heinz³  Sylvane Desrivières⁴  Liang Ma⁴  Gunter Schumann⁵  Jianfeng Feng⁶  Fengzhu Sun⁷  Tianye Jia⁸  Zeyu Jiao⁹  Chao Xie⁹ 

[1] 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;Center for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Welcome Center for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom;Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China;Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China;School of Mathematical Sciences, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui 230026, China;Shanghai Center for Mathematical Sciences, Fudan University, 220 Handan Road, Shanghai, China;

关键词: Reproducibility;    Association studies;    MRI (magnetic resonance imaging);    Sample size;    Heterogeneity;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown