Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA.
School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Hum Brain Mapp. 2022 Aug 1;43(11):3346-3356. doi: 10.1002/hbm.25576. Epub 2022 May 19.
The influences of environmental factors such as weather on the human brain are still largely unknown. A few neuroimaging studies have demonstrated seasonal effects, but were limited by their cross-sectional design or sample sizes. Most importantly, the stability of the MRI scanner has not been taken into account, which may also be affected by environments. In the current study, we analyzed longitudinal resting-state functional MRI (fMRI) data from eight individuals, where they were scanned over months to years. We applied machine learning regression to use different resting-state parameters, including the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity matrix, to predict different weather and environmental parameters. For careful control, the raw EPI and the anatomical images were also used for predictions. We first found that daylight length and air temperatures could be reliably predicted with cross-validation using the resting-state parameters. However, similar prediction accuracies could also be achieved by using one frame of EPI image, and even higher accuracies could be achieved by using the segmented or raw anatomical images. Finally, the signals outside of the brain in the anatomical images and signals in phantom scans could also achieve higher prediction accuracies, suggesting that the predictability may be due to the baseline signals of the MRI scanner. After all, we did not identify detectable influences of weather on brain functions other than the influences on the baseline signals of MRI scanners. The results highlight the difficulty of studying long-term effects using MRI.
环境因素（如天气）对大脑的影响在很大程度上仍然未知。一些神经影像学研究已经证明了季节效应，但受到其横断面设计或样本量的限制。最重要的是，MRI 扫描仪的稳定性尚未被考虑在内，它也可能受到环境的影响。在当前的研究中，我们分析了 8 个人的纵向静息态功能磁共振成像（fMRI）数据，他们在数月至数年内接受了扫描。我们应用机器学习回归，使用不同的静息态参数，包括低频波动幅度（ALFF）、局部一致性（ReHo）和功能连接矩阵，来预测不同的天气和环境参数。为了进行仔细的控制，还使用原始 EPI 和解剖图像进行了预测。我们首先发现，使用静息态参数进行交叉验证，可以可靠地预测日光长度和空气温度。然而，使用一帧 EPI 图像也可以实现类似的预测精度，甚至使用分割或原始解剖图像可以实现更高的预测精度。最后，解剖图像中的脑外信号和幻影扫描中的信号也可以实现更高的预测精度，这表明可预测性可能是由于 MRI 扫描仪的基线信号。毕竟，我们没有发现天气对大脑功能的可检测影响，除了对 MRI 扫描仪基线信号的影响。研究结果强调了使用 MRI 研究长期影响的困难。
