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Abstract：
BACKGROUND: Research involving brain imaging is important for understanding common brain diseases. Study endpoints can include features and measures derived from imaging modalities, providing a benchmark against which other phenotypical data can be assessed. In trials, imaging data provide objective evidence of beneficial and adverse outcomes. Multi-centre studies increase generalisability and statistical power. However, there is a lack of practical guidelines for the set-up and conduct of large neuroimaging studies.
METHODS: We address this deficit by describing aspects of study design and other essential practical considerations that will help researchers avoid common pitfalls and data loss.
RESULTS: The recommendations are grouped into seven categories: (1) planning, (2) defining the imaging endpoints, developing an imaging manual and managing the workflow, (3) performing a dummy run and testing the analysis methods, (4) acquiring the scans, (5) anonymising and transferring the data, (6) monitoring quality, and (7) using structured data and sharing data.
CONCLUSIONS: Implementing these steps will lead to valuable and usable data and help to avoid imaging data wastage.
METHODS: We address this deficit by describing aspects of study design and other essential practical considerations that will help researchers avoid common pitfalls and data loss.
RESULTS: The recommendations are grouped into seven categories: (1) planning, (2) defining the imaging endpoints, developing an imaging manual and managing the workflow, (3) performing a dummy run and testing the analysis methods, (4) acquiring the scans, (5) anonymising and transferring the data, (6) monitoring quality, and (7) using structured data and sharing data.
CONCLUSIONS: Implementing these steps will lead to valuable and usable data and help to avoid imaging data wastage.
摘要：
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