Effects of weather and season on human brain volume.
Ontology highlight
ABSTRACT:
SUBMITTER: Book GA
PROVIDER: S-EPMC7990212 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:This study examines the effects of season and weather on mood (valence and activation) and travel satisfaction (measured by the Satisfaction with Travel Scale). Analyses are presented of 562 time-sampled morning commutes to work made by 363 randomly sampled people in three different Swedish cities asking them to use smartphones to report their mood in their home before and directly after the commutes. These reports as well as satisfaction with the commute obtained in summer and winter are linked to weather data and analyzed by means of fixed-effects regression analyses. The results reveal main effects of weather (temperature and precipitation) on mood and travel satisfaction (temperature, sunshine, precipitation, and wind speed). The effects of weather on mood and travel satisfaction differ depending on travel mode. Temperature leads to a more positive mood, wind leads to higher activation for public transport users, and sunshine leads to a more negative mood for cyclists and pedestrians. Sunshine and higher temperatures make travel more relaxed although not for cycling and walking, and rain and snow lead to a higher cognitive assessed quality of travel.
| S-EPMC5292578 | biostudies-literature
Project description:BackgroundCycling has the potential to provide health, environmental and economic benefits but the level of cycling is very low in New Zealand and many other countries. Adverse weather is often cited as a reason why people do not cycle. This study investigated temporal and seasonal variability in cycle volume and its association with weather in Auckland, New Zealand's largest city.MethodsTwo datasets were used: automated cycle count data collected on Tamaki Drive in Auckland by using ZELT Inductive Loop Eco-counters and weather data (gust speed, rain, temperature, sunshine duration) available online from the National Climate Database. Analyses were undertaken using data collected over one year (1 January to 31 December 2009). Normalised cycle volumes were used in correlation and regression analyses to accommodate differences by hour of the day and day of the week and holiday.ResultsIn 2009, 220,043 bicycles were recorded at the site. There were significant differences in mean hourly cycle volumes by hour of the day, day type and month of the year (p < 0.0001). All weather variables significantly influenced hourly and daily cycle volumes (p < 0.0001). The cycle volume increased by 3.2% (hourly) and 2.6% (daily) for 1°C increase in temperature but decreased by 10.6% (hourly) and 1.5% (daily) for 1 mm increase in rainfall and by 1.4% (hourly) and 0.9% (daily) for 1 km/h increase in gust speed. The volume was 26.2% higher in an hour with sunshine compared with no sunshine, and increased by 2.5% for one hour increase in sunshine each day.ConclusionsThere are temporal and seasonal variations in cycle volume in Auckland and weather significantly influences hour-to-hour and day-to-day variations in cycle volume. Our findings will help inform future cycling promotion activities in Auckland.
| S-EPMC3368741 | biostudies-literature
Project description:Winter tourism is an important economic factor in the European Alps, which could be exposed to severely changing meteorological conditions due to climate change in the future. The extent to which meteorology influences winter tourism figures has so far been analyzed mainly based on monthly or seasonal data and in relation to skier numbers. Therefore, we record for the first time daily visitor numbers at five Bavarian winter tourism destinations based on 1518 webcam images using object detection and link them to meteorological and time-related variables. Our results show that parameters such as temperature, cloud cover or sunshine duration, precipitation, snow depth, wind speed, and relative humidity play a role especially at locations that include other forms of winter tourism in addition to skiing. In the ski resorts studied, on the other hand, skiing is mostly independent of current weather conditions, which can be attributed mainly to artificial snowmaking. Moreover, at the webcam sites studied, weekends and vacation periods had an equal or even stronger influence on daily visitor numbers than the current weather conditions. The extent to which weather impacts the (future) visitor numbers of a winter tourism destination must therefore be investigated individually and with the inclusion of non-meteorological variables influencing human behavior.
| S-EPMC10794479 | biostudies-literature
Project description:OBJECTIVE While there is a long history of interest in measuring brain growth, as of yet there is no definitive model for normative human brain volume growth. The goal of this study was to analyze a variety of candidate models for such growth and select the model that provides the most statistically applicable fit. The authors sought to optimize clinically applicable growth charts that would facilitate improved treatment and predictive management for conditions such as hydrocephalus. METHODS The Weibull, two-term power law, West ontogenic, and Gompertz models were chosen as potential models. Normative brain volume data were compiled from the NIH MRI repository, and the data were fit using a nonlinear least squares regression algorithm. Appropriate statistical measures were analyzed for each model, and the best model was characterized with prediction bound curves to provide percentile estimates for clinical use. RESULTS Each model curve fit and the corresponding statistics were presented and analyzed. The Weibull fit had the best statistical results for both males and females, while the two-term power law generated the worst scores. The statistical measures and goodness of fit parameters for each model were provided to assure reproducibility. CONCLUSIONS The authors identified the Weibull model as the most effective growth curve fit for both males and females. Clinically usable growth charts were developed and provided to facilitate further clinical study of brain volume growth in conditions such as hydrocephalus. The authors note that the homogenous population from which the normative MRI data were compiled limits the study. Gaining a better understanding of the dynamics that underlie childhood brain growth would yield more predictive growth curves and improved neurosurgical management of hydrocephalus.
| S-EPMC6212293 | biostudies-literature
Project description:Specific anatomical patterns are seen in various diseases affecting the brain. Clinical studies on the topography of pathologies are often limited by the absence of a normalization of the prevalence of pathologies to the relative volume of the affected anatomical structures. A comprehensive reference on the relative volumes of clinically relevant anatomical structures serving for such a normalization, is currently lacking. The analyses are based on anatomical high-resolution three-dimensional T1-weighted magnetic resonance imaging data of 30 healthy Caucasian volunteers, including 14 females (mean age 37.79 years, SD 13.04) and 16 males (mean age 38.31 years, SD 16.91). Semi-automated anatomical segmentation was used, guided by a neuroanatomical parcellation algorithm differentiating 96 structures. Relative volumes were derived by normalizing parenchymal structures to the total individual encephalic volume and ventricular segments to the total individual ventricular volume. The present investigation provides the absolute and relative volumes of 96 anatomical parcellation units of the human encephalon. A larger absolute volume in males than in females is found for almost all parcellation units. While parenchymal structures display a trend towards decreasing volumes with increasing age, a significant inverse effect is seen with the ventricular system. The variances in volumes as well as the effects of gender and age are given for each structure before and after normalization. The provided atlas constitutes an anatomically detailed and comprehensive analysis of the absolute and relative volumes of the human encephalic structures using a clinically oriented parcellation algorithm. It is intended to serve as a reference for volume-standardization in clinical studies on the topographic prevalence of pathologies.
| S-EPMC8203509 | biostudies-literature
Project description: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.
| S-EPMC9248317 | biostudies-literature
Project description:Spatial transformation of MR brain images is a standard tool used in automated anatomical parcellation and other quantitative and qualitative methods to assess brain tissue volume, composition, and distribution. Despite widespread use, the quantitative effects of spatial transformation on regional brain volume estimates have been little studied. We report on the effects of transformation on regional brain volumes of 38 (17M, 21F) manually parcellated brains. After tracing in native space, regions of interest were transformed using a classic piecewise-linear Talairach transformation (Tal) or a nonlinear registration (AIR 5th order nonlinear algorithm, 158 parameters) to one of three Talairach-based templates: 1) Tal50, constructed from 50 Talairach-transformed normal brains, 2) the MNI 305 atlas, 3) IA38, constructed from MNI305-transformed scans of the 38 subjects used in this study. Native volumes were compared to the transformed volumes. We found that: 1) significant group-level differences can be obtained in transformed data sets that are in the opposite direction of effects obtained in native space; 2) the effects of transformation are heterogeneous across brain regions, even after covarying for total brain volume and age; 3) volumetric intra-class correlations between native and transformed brains differ by registration method and template choice, region, and tissue type; and 4) transformed brains produced hippocampus and corpus callosum volume proportions that were significantly different from those obtained in native space. Our results suggest that region-based volumetric differences uncovered by spatial-transformation-based methods should be replicated in native-space brains, and that meta-analyses should take into account whether volumes are determined using spatially-transformed images and/or specific automated methods.
| S-EPMC2581899 | biostudies-other
Project description:The novel coronavirus (SARS-CoV-2) has spread globally and has been declared a pandemic by the World Health Organization. While influenza virus shows seasonality, it is unknown if COVID-19 has any weather-related affect. In this work, we analyze the patterns in local weather of all the regions affected by COVID-19 globally. Our results indicate that approximately 85% of the COVID-19 reported cases until 1 May 2020, making approximately 3 million reported cases (out of approximately 29 million tests performed) have occurred in regions with temperature between 3 and 17 °C and absolute humidity between 1 and 9 g/m3. Similarly, hot and humid regions outside these ranges have only reported around 15% or approximately 0.5 million cases (out of approximately 7 million tests performed). This suggests that weather might be playing a role in COVID-19 spread across the world. However, this role could be limited in US and European cities (above 45 N), as mean temperature and absolute humidity levels do not reach these ranges even during the peak summer months. For hot and humid countries, most of them have already been experiencing temperatures >35 °C and absolute humidity >9 g/m3 since the beginning of March, and therefore the effect of weather, however little it is, has already been accounted for in the COVID-19 spread in those regions, and they must take strict social distancing measures to stop the further spread of COVID-19. Our analysis showed that the effect of weather may have only resulted in comparatively slower spread of COVID-19, but not halted it. We found that cases in warm and humid countries have consistently increased, accounting for approximately 500,000 cases in regions with absolute humidity >9 g/m3, therefore effective public health interventions must be implemented to stop the spread of COVID-19. This also means that 'summer' would not alone stop the spread of COVID-19 in any part of the world.
| S-EPMC7432279 | biostudies-literature
Project description:Neural testosterone metabolism, particularly the synthesis of oestradiol (E(2)) via the aromatase enzyme, is important for sexual behaviours in many vertebrates. In green anole lizards, E(2) metabolised from testosterone facilitates female receptivity and increases sexual motivation in males. Testosterone treatment increases aromatase activity in the whole brain homogenates of gonadectomised male, but not female, anoles, which is an effect limited to the breeding season (BS). To investigate the potential for local effects of this enzyme in reproductive behaviour, we used in situ hybridisation for aromatase mRNA to examine expression during the BS and nonbreeding season (NBS) in areas of the brain that control male sexual behaviours [preoptic area (POA) and amygdala (AMY)], as well as one regulating female reproductive behaviours ventromedial hypothalamus (VMH). Males had a greater total number of aromatase-expressing cells in the POA than females, and the density of aromatase-expressing cells (number per unit volume) was greater in the VMH and AMY of females. This density was also higher during the BS than NBS in the POA. Expression of aromatase in the AMY appeared to be lateralised because trends were detected for the left side to have more total cells and more cells per unit volume than the right. These results suggest that, similar to other vertebrates, regional aromatisation of testosterone may be important for the control of sex-specific reproductive behaviours.
| S-EPMC3042525 | biostudies-literature
Project description:Due to human-induced climate and landscape changes, distribution and abundance of many ungulate species have increased worldwide. Especially in areas where natural predators are absent, hunting is the essential management tool for regulating ungulate populations. Therefore, understanding the factors associated with harvest rates is the first step toward an adaptive management approach. Weather influences hunter and ungulate behavior and thus presumably harvest, but how and which meteorological parameters are linked to harvest numbers have rarely been evaluated. We used nearly 65,000 "sit and wait" and driven hunt harvests of roe deer (Capreolus capreolus) in Bavaria, Germany, and weather data from 2008 to 2017 to test for factors affecting roe deer harvests (i.e., temperature, rain hours, wind speed, sunshine duration, snow depth, workdays vs. weekends, month) using zero-inflated negative binomial mixed-effect models. Our results reveal that, besides workdays, high temperatures and prolonged rain resulted in fewer harvested animals, whereas sunshine duration in summer and snow height in snow-rich areas partially favored harvests during sitting hunts in summer and winter, respectively. The influence of wind speed varied over the course of the year. In summer and autumn, wind speed commonly had a negative effect, positively affecting harvests in winter in some regions. Daily harvest numbers decreased during the summer and autumn hunting periods (May till mid-October), while they increased during the winter period (mid-October till mid-January). Interestingly, harvest success during driven hunts, which are planned well in advance and therefore take place largely independent of weather conditions, was similarly affected by the weather. This result suggests that the inferred weather influence is not only due to the hunters' decisions but also due to deer behavior. Since many ungulate populations may further benefit from climate change, building an understanding of the relationship between hunting success and weather will aid adaptive ungulate management.
| S-EPMC8328461 | biostudies-literature