School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China.
Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China.
Brain Imaging Behav. 2023 Dec;17(6):628-638. doi: 10.1007/s11682-023-00785-3. Epub 2023 Aug 9.
Quite a few studies have been performed based on movie-watching functional connectivity (FC). As compared to its resting-state counterpart, however, there is still much to know about its abilities in individual identifications and individualized predictions. To pave the way for appropriate usage of movie-watching FC, we systemically evaluated the minimum number of time points, as well as the exact functional networks, supporting individual identifications and individualized predictions of apparent traits based on it. We performed the study based on the 7T movie-watching fMRI data included in the HCP S1200 Release, and took IQ as the test case for the prediction analyses. The results indicate that movie-watching FC based on only 15 time points can support successful individual identifications (99.47%), and the connectivity contributed more to identifications were much associated with higher-order cognitive processes (the secondary visual network, the frontoparietal network and the posterior multimodal network). For individualized predictions of IQ, it was found that successful predictions necessitated 60 time points (predicted vs. actual IQ correlation significant at P < 0.05, based on 5,000 permutations), and the prediction accuracy increased logarithmically with the number of time points used for connectivity calculation. Furthermore, the connectivity that contributed more to individual identifications exhibited the strongest prediction ability. Collectively, our findings demonstrate that movie-watching FC can capture rich information about human brain function, and its ability in individualized predictions depends heavily on the length of fMRI scans.
已经有相当多的研究基于观影功能连接（FC）进行。然而，与静息态相比，我们对其在个体识别和个体化预测方面的能力还有很多需要了解的地方。为了为观影 FC 的合理应用铺平道路，我们系统地评估了支持基于观影 FC 的个体识别和个体化预测的最小时间点数量以及确切的功能网络。我们基于 HCP S1200 发布中包含的 7T 观影 fMRI 数据进行了研究，并以 IQ 作为预测分析的测试案例。结果表明，仅基于 15 个时间点的观影 FC 就可以支持成功的个体识别（99.47%），并且与高级认知过程（次级视觉网络、额顶网络和后多模态网络）更相关的连接对识别的贡献更大。对于 IQ 的个体化预测，发现成功的预测需要 60 个时间点（基于 5000 次置换的预测与实际 IQ 相关性显著，P<0.05），并且预测准确性随着用于连接计算的时间点数量的增加呈对数增长。此外，对个体识别贡献更大的连接显示出最强的预测能力。总的来说，我们的研究结果表明，观影 FC 可以捕捉到有关人脑功能的丰富信息，其在个体化预测方面的能力在很大程度上取决于 fMRI 扫描的长度。
