Estimations of the weather effects on brain functions using functional MRI: A cautionary note

Abstract

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.

Individual differences in time-varying and stationary brain connectivity during movie watching from childhood to early adulthood: Age, sex, and behavioral associations

“…The prediction accuracies of FSIQ using stationary connectivity, as measured by the correlation between actual and predicted cores, were on the order of 0.3. This level of accuracy is consistent with findings from previous studies on brain-behavior predictions ( Cui and Gong, 2018 , Di et al, 2022a , Marek et al, 2022 ). It is important to note that this effect size is reasonable considering the limitations in measurement reliability ( Zuo et al, 2019 ).…”

Prolonged Longitudinal Transcutaneous Auricular Vagus Nerve Stimulation Effect on Striatal Functional Connectivity in Patients with Major Depressive Disorder

“…In addition, we observed the phenomenon where the changes of rsFC in the HCs were opposed to those in the MDD patients, which was also observed in another resting-state fMRI study [ 33 ] and brain structural study [ 67 ] in MDD patients, respectively. We consider that the brain function changes dynamically over time [ 68 , 69 ], season or temperature [ 70 ], or lifestyle [ 67 ], and is also closely related to mental function and body metabolism [ 69 ]. Second, changes in rsFC were associated with intervention in MDD patients, but not in HCs.…”

A functional MRI pre-processing and quality control protocol based on statistical parametric mapping (SPM) and MATLAB

“…In this paper, we outline a processing pipeline for fMRI data that has been used in our lab, and detail the quality control procedure after each of the pre-processing steps. The pre-processing pipeline is suitable for all resting-state, task state, and movie watching conditions (Di and Biswal, 2019 , 2020 , 2022 ; Di et al, 2020 , 2022a , b ). The protocol is based on Statistical Parametric Mapping (SPM) ( https://www.fil.ion.ucl.ac.uk/spm/ ) under MATLAB environment.…”