Li Meng-Ting, Sun Jia-Wei, Zhan Lin-Lin, Antwi Collins Opoku, Lv Ya-Ting, Jia Xi-Ze, Ren Jun
School of Psychology, Zhejiang Normal University, Jinhua, China.
Department of Clinical Neuroscience, Division of Neuro, Karolinska Institutet, Stockholm, Sweden.
Front Neurosci. 2023 May 31;17:1120741. doi: 10.3389/fnins.2023.1120741. eCollection 2023.
Default mode network (DMN) is the most involved network in the study of brain development and brain diseases. Resting-state functional connectivity (rsFC) is the most used method to study DMN, but different studies are inconsistent in the selection of seed. To evaluate the effect of different seed selection on rsFC, we conducted an image-based meta-analysis (IBMA).
We identified 59 coordinates of seed regions of interest (ROIs) within the default mode network (DMN) from 11 studies (retrieved from Web of Science and Pubmed) to calculate the functional connectivity; then, the uncorrected maps were obtained from the statistical analyses. The IBMA was performed with the maps.
We demonstrate that the overlap of meta-analytic maps across different seeds' ROIs within DMN is relatively low, which cautions us to be cautious with seeds' selection.
Future studies using the seed-based functional connectivity method should take the reproducibility of different seeds into account. The choice of seed may significantly affect the connectivity results.
默认模式网络（DMN）是脑发育和脑疾病研究中涉及最多的网络。静息态功能连接（rsFC）是研究DMN最常用的方法，但不同研究在种子点的选择上并不一致。为了评估不同种子点选择对rsFC的影响，我们进行了一项基于图像的荟萃分析（IBMA）。
我们从11项研究（从Web of Science和Pubmed检索）中确定了默认模式网络（DMN）内感兴趣种子区域（ROI）的59个坐标，以计算功能连接；然后，通过统计分析获得未校正的图谱。使用这些图谱进行IBMA。
我们证明，DMN内不同种子ROI的荟萃分析图谱的重叠率相对较低，这提醒我们在种子点选择时要谨慎。
未来使用基于种子点的功能连接方法的研究应考虑不同种子点的可重复性。种子点的选择可能会显著影响连接结果。
