3The influences of environmental factors such as weather on human brain are still largely unknown. A few 2 4 neuroimaging studies have demonstrated seasonal effects, but were limited by their cross-sectional design 2 5 or sample sizes. Most importantly, the stability of MRI scanner hasn't been taken into account, which 2 6 may also be affected by environments. In the current study, we analyzed longitudinal resting-state 2 7 functional MRI (fMRI) data from eight individuals, where the participants were scanned over months to 2 8 years. We applied machine learning regression to use different resting-state parameters, including 2 9 amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional 3 0 connectivity matrix, to predict different weather and environmental parameters. For a careful control, the 3 1 raw EPI and the anatomical images were also used in the prediction analysis. We first found that daylight 3 2 length and temperatures could be reliability predicted using cross-validation using resting-state 3 3 parameters. However, similar prediction accuracies could also achieved by using one frame of EPI image, 3 4 and even higher accuracies could be achieved by using segmented or even the raw anatomical images. 3 5Finally, we verified that the signals outside of the brain in the anatomical images and signals in phantom 3 6 scans could also achieve higher prediction accuracies, suggesting that the predictability may be due to the 3 7 baseline signals of the MRI scanner. After all, we did not identify detectable influences of weather on 3 8 brain functions other than the influences on the stability of MRI scanners. The results highlight the 3 9 difficulty of studying long term effects on brain using MRI. 4 0 4 1 Keywords: daylight length, environmental effects on brain, machine learning regression, resting-state, 4 2 scanner stability, temperature, weather. 4 3 4 4 3

During healthy brain aging, different brain regions show anatomical or functional declines at different rates, and some regions may show compensatory increases in functional activity. However, few studies have explored interregional influences of brain activity during the aging process. We proposed a causality analysis framework combining high dimensionality independent component analysis (ICA), Granger causality, and least absolute shrinkage and selection operator regression on longitudinal brain metabolic activity data measured by Fludeoxyglucose positron emission tomography (FDG-PET). We analyzed FDG-PET images from healthy old subjects, who were scanned for at least five sessions with an averaged intersession interval of 1 year. The longitudinal data were concatenated across subjects to form a time series, and the first-order autoregressive model was used to measure interregional causality among the independent sources of metabolic activity identified using ICA. Several independent sources with reduced metabolic activity in aging, including the anterior temporal lobe and orbital frontal cortex, demonstrated causal influences over many widespread brain regions. On the other hand, the influenced regions were more distributed, and had smaller age-related declines or even relatively increased metabolic activity. The current data demonstrated interregional spreads of aging on metabolic activity at the scale of a year, and have identified key brain regions in the aging process that have strong influences over other regions. K E Y W O R D Saging, anterior temporal lobe, Granger causality, LASSO regression, metabolic connectivity, orbitofrontal cortex Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at

During healthy brain aging, different brain regions show anatomical or functional declines at different rates, and some regions may show compensatory increases in functional activity. However, few studies have explored interregional influences of brain activity during the aging process. We proposed a causality analysis framework combining high dimensionality independent component analysis (ICA), Granger causality, and least absolute shrinkage and selection operator regression on longitudinal brain metabolic activity data measured by Fludeoxyglucose positron emission tomography (FDG–PET). We analyzed FDG–PET images from healthy old subjects, who were scanned for at least five sessions with an averaged intersession interval of 1 year. The longitudinal data were concatenated across subjects to form a time series, and the first‐order autoregressive model was used to measure interregional causality among the independent sources of metabolic activity identified using ICA. Several independent sources with reduced metabolic activity in aging, including the anterior temporal lobe and orbital frontal cortex, demonstrated causal influences over many widespread brain regions. On the other hand, the influenced regions were more distributed, and had smaller age‐related declines or even relatively increased metabolic activity. The current data demonstrated interregional spreads of aging on metabolic activity at the scale of a year, and have identified key brain regions in the aging process that have strong influences over other regions.