GCS-fMRI group, Koelliker Hospital and University of Turin, Turin, Italy; Functional neuroimaging and complex neural systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
GCS-fMRI group, Koelliker Hospital and University of Turin, Turin, Italy; Functional neuroimaging and complex neural systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy.
Neurosci Biobehav Rev. 2022 Jun;137:104659. doi: 10.1016/j.neubiorev.2022.104659. Epub 2022 Apr 9.
Coordinate-based meta-analysis (CBMA) is a research strategy widely used in the field of human brain imaging. Although dedicated tools as BrainMap or Neurosynth had been developed in past years, some of the crucial steps necessary to identify and compose the dataset are still user-based, resulting in a not standardized approach to literature search, as well as in time-consuming and prone to errors procedures. In particular, this concern involves the assessment of voxel-wise whole brain analyses in contrast to ROI-based ones, and the identification of available lists of peaks of effect (i.e., x,y,z coordinates of the foci). Here, we propose six simple actions that can be undertaken by any researcher and by the publishing system, allowing to limit the risk of erroneous decisions on the inclusion of experimental data in the meta-analytic dataset. This straightforward and useful strategy would reduce possible bias in CBMA, therefore allowing to obtain more reliable results.
基于坐标的荟萃分析（CBMA）是人类大脑成像领域广泛使用的研究策略。尽管过去几年已经开发了专门的工具，如 BrainMap 或 Neurosynth，但仍需要一些关键步骤来识别和组合数据集，这些步骤仍然是基于用户的，导致文献搜索方法不标准化，并且耗时且容易出错。特别是，这涉及到对体素全脑分析与基于 ROI 的分析进行评估，以及识别可用的效应峰列表（即，焦点的 x、y、z 坐标）。在这里，我们提出了六个简单的操作步骤，任何研究人员和出版系统都可以采用这些操作步骤，从而可以限制在将实验数据纳入荟萃分析数据集中时做出错误决策的风险。这种简单而有用的策略可以减少 CBMA 中的可能偏差，从而获得更可靠的结果。
