Estimations of the weather effects on brain functions using functional MRI – a cautionary tale

Abstract

The influences of environmental factors such as weather on 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 MRI scanner hasn’t 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 the participants were scanned over months to years. We applied machine learning regression to use different resting-state parameters, including amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity matrix, to predict different weather and environmental parameters. For a careful control, the raw EPI and the anatomical images were also used in the prediction analysis. We first found that daylight length and temperatures could be reliability predicted using cross-validation using resting-state parameters. However, similar prediction accuracies could also achieved by using one frame of EPI image, and even higher accuracies could be achieved by using segmented or even the raw anatomical images. Finally, we verified that 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 stability of MRI scanners. The results highlight the difficulty of studying long term effects on brain using MRI.