Associating weather to brain and body function
Poster No:
2125
Submission Type:
Abstract Submission
Authors:
Eric Ceballos1, Jessica Royer1, Boris Bernhardt2, Bratislav Misic3
Institutions:
1McGill University, Montreal, QC, 2McGill University, Montreal, Quebec, 3Montreal Neurological Institute, Montreal, Quebec
First Author:
Co-Author(s):
Introduction:
Weather's impact on human physiology remains poorly understood, despite evidence linking meteorological factors to mood and cognition [1]. Advances in large-scale health and climate datasets now enable systematic investigation of how weather influences body health.
Our study examines the relationship between local weather factors - temperature, precipitation, pressure, wind speed, and wind direction - and health markers across multiple physiological systems, including cardiovascular, pulmonary, musculoskeletal, immune, renal, hepatic, metabolic, and nervous systems. By integrating small- and large-scale health datasets, this analysis aims to uncover how specific body systems statistically associate to weather, providing new insights into environmental determinants of health.
Our study examines the relationship between local weather factors - temperature, precipitation, pressure, wind speed, and wind direction - and health markers across multiple physiological systems, including cardiovascular, pulmonary, musculoskeletal, immune, renal, hepatic, metabolic, and nervous systems. By integrating small- and large-scale health datasets, this analysis aims to uncover how specific body systems statistically associate to weather, providing new insights into environmental determinants of health.
Methods:
Body Health Data
Body health data were drawn from the UK Biobank (n=502,150) and included measures across multiple body systems (e.g., cardiovascular, pulmonary, musculoskeletal, immune, renal, hepatic, metabolic) [2]. Physical health metrics (e.g., BMI, bone density, grip strength) and physiological measures (e.g., blood pressure, pulse, blood/urine biomarkers) were chosen according to previous studies looking at brain-body interactions [3, 4].
Brain Imaging Data
Resting-state fMRI data from the MICA-MICs dataset (n=50) were used to explore links between weather and brain function [5]. Preprocessing followed standardized micapipe protocols [6], including motion correction, spatial alignment, and independent component analysis for noise removal. Functional connectivity was calculated as the summed connectivity of each brain region to all others, enabling reduced-dimensionality analysis of brain function.
Weather Data
Weather data were sourced from the Meteostat library, which aggregates records from open-access weather services across Europe and Canada. Key weather features - temperature, precipitation, wind speed, wind direction, and air pressure - were extracted using location-based matching to the nearest weather station for each participant's data point.
Partial Least Squares Analysis
PLS was used to identify shared variance between body health/brain function (X) and weather data (Y) [7]. Both X and Y were scaled to unit variance before decomposition via singular value decomposition of their cross-covariance matrix. Latent variables capturing maximum shared variance were identified, and their significance was tested using permutation analysis, with Y shuffled relative to X. The input data was correlated with their corresponding scores to assess the contribution of individual features to the latent components.
Body health data were drawn from the UK Biobank (n=502,150) and included measures across multiple body systems (e.g., cardiovascular, pulmonary, musculoskeletal, immune, renal, hepatic, metabolic) [2]. Physical health metrics (e.g., BMI, bone density, grip strength) and physiological measures (e.g., blood pressure, pulse, blood/urine biomarkers) were chosen according to previous studies looking at brain-body interactions [3, 4].
Brain Imaging Data
Resting-state fMRI data from the MICA-MICs dataset (n=50) were used to explore links between weather and brain function [5]. Preprocessing followed standardized micapipe protocols [6], including motion correction, spatial alignment, and independent component analysis for noise removal. Functional connectivity was calculated as the summed connectivity of each brain region to all others, enabling reduced-dimensionality analysis of brain function.
Weather Data
Weather data were sourced from the Meteostat library, which aggregates records from open-access weather services across Europe and Canada. Key weather features - temperature, precipitation, wind speed, wind direction, and air pressure - were extracted using location-based matching to the nearest weather station for each participant's data point.
Partial Least Squares Analysis
PLS was used to identify shared variance between body health/brain function (X) and weather data (Y) [7]. Both X and Y were scaled to unit variance before decomposition via singular value decomposition of their cross-covariance matrix. Latent variables capturing maximum shared variance were identified, and their significance was tested using permutation analysis, with Y shuffled relative to X. The input data was correlated with their corresponding scores to assess the contribution of individual features to the latent components.
Results:
Using PLS, we associated 36 weather features to 40 body health (discovery set n=61,755; replication set n=61,945) and found one significant latent variable (p=0.001). Weather association was driven primarily by mean daily temperature, with smaller contributions from barometric pressure and wind speed. Body markers linked to this variable were, e.g., vitamin D, creatinine and cystatin C, positively, while systolic/diastolic blood pressure had negative loadings. Findings were replicated, highlighting temperature as a key driver of weather-body associations.
We explored the relationship between 36 weather and 110 brain features using functional imaging data from 50 participants in the MICA-MICs dataset. PLS revealed a significant latent variable (p=0.026), again with temperature having the highest contribution. Brain loadings were positive across all brain networks except the subcortex, suggesting that cortical functional connectivity is sensitive to weather variability. We plan to extend our analysis on the UK Biobank brain imaging subset to confirm and expand on these findings.
We explored the relationship between 36 weather and 110 brain features using functional imaging data from 50 participants in the MICA-MICs dataset. PLS revealed a significant latent variable (p=0.026), again with temperature having the highest contribution. Brain loadings were positive across all brain networks except the subcortex, suggesting that cortical functional connectivity is sensitive to weather variability. We plan to extend our analysis on the UK Biobank brain imaging subset to confirm and expand on these findings.
Conclusions:
In conclusion, our study provides novel evidence of a link between weather, particularly temperature, and brain-body function.
Modeling and Analysis Methods:
Multivariate Approaches 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Physiology, Metabolism and Neurotransmission:
Physiology, Metabolism and Neurotransmission Other 1
Keywords:
Statistical Methods
Systems
Other - Environmental influences
1|2Indicates the priority used for review
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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[2] Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., ... & Marchini, J. (2018). The UK Biobank resource with deep phenotyping and genomic data. Nature, 562(7726), 203-209.
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[4] Tian, Y. E., Cropley, V., Maier, A. B., Lautenschlager, N. T., Breakspear, M., & Zalesky, A. (2023). Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nature medicine, 29(5), 1221-1231.
[5] Royer, J., Rodríguez-Cruces, R., Tavakol, S., Larivière, S., Herholz, P., Li, Q., ... & Bernhardt, B. C. (2022). An open MRI dataset for multiscale neuroscience. Scientific data, 9(1), 569.
[6] Cruces, R. R., Royer, J., Herholz, P., Larivière, S., De Wael, R. V., Paquola, C., ... & Bernhardt, B. C. (2022). Micapipe: A pipeline for multimodal neuroimaging and connectome analysis. NeuroImage, 263, 119612.
[7] McIntosh, A. R., & Lobaugh, N. J. (2004). Partial least squares analysis of neuroimaging data: applications and advances. Neuroimage, 23, S250-S263.