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Effects of Weather and Season on Human Brain Volume

July 2020

DOI:10.1101/2020.07.07.191239

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Authors:

Gregory A Book

Hartford Hospital

Shashwath A Meda

Hartford Hospital

Ronald J Janssen

Hartford Hospital

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We present an exploratory cross-sectional analysis of the effect of season and weather on Freesurfer-derived brain volumes from a sample of 3,279 healthy individuals collected on two MRI scanners in Hartford, CT, USA over a 15 year period. Weather and seasonal effects were analyzed using a single linear regression model with age, sex, motion, scan sequence, time-of-day, month of the year, and the deviation from average barometric pressure, air temperature, and humidity, as covariates. FDR correction for multiple comparisons was applied to groups of non-overlapping ROIs. Significant negative relationships were found between the left- and right- cerebellum cortex and pressure (t = -2.25, p = 0.049; t = -2.771, p = 0.017). Significant positive relationships were found between left- and right- cerebellum cortex and white matter between the comparisons of January/June and January/September. Significant negative relationships were found between several subcortical ROIs for the summer months compared to January. An opposing effect was observed between the supra- and infra-tentorium, with opposite effect directions in winter and summer. Cohen’s d effect sizes from monthly comparisons were similar to those reported in recent psychiatric big-data publications, raising the possibility that seasonal changes and weather may be confounds in large cohort studies. Additionally, changes in brain volume due to natural environmental variation have not been reported before and may have implications for weather-related and seasonal ailments.

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Effects of Weather and Season on Human Brain Volume

Gregory A. Book MS1, Shashwath A. Meda MS1, Ronald Janssen PhD1, Alecia D. Dager PhD1,2, Andrew

Poppe PhD1, Michael C. Stevens PhD1,2, Michal Assaf MD1,2, David Glahn PhD1,2,3, Godfrey D. Pearlson

MD1,2

1 – Olin Neuropsychiatry Research Center, Hartford Hospital, Hartford, CT

2 – Yale University, Department of Psychiatry, New Haven, CT

3 – Boston Children’s Hospital, Department of Psychiatry, Boston, MA

Corresponding Address:

Olin Neuropsychiatry Research Center

Institute of Living, Hartford Hospital

400 Retreat Avenue

Hartford, CT 06106

The authors have no conflicts of interest or financial support to declare.

.CC-BY 4.0 International licensewas not certified by peer review) is the author/funder. It is made available under a The copyright holder for this preprint (whichthis version posted July 7, 2020. . https://doi.org/10.1101/2020.07.07.191239doi: bioRxiv preprint

Abstract

We present an exploratory cross-sectional analysis of the effect of season and weather on Freesurfer-

derived brain volumes from a sample of 3,279 healthy individuals collected on two MRI scanners in

Hartford, CT, USA over a 15 year period. Weather and seasonal effects were analyzed using a single

linear regression model with age, sex, motion, scan sequence, time-of-day, month of the year, and the

deviation from average barometric pressure, air temperature, and humidity, as covariates. FDR

correction for multiple comparisons was applied to groups of non-overlapping ROIs. Significant negative

relationships were found between the left- and right- cerebellum cortex and pressure (t = -2.25, p =

0.049; t = -2.771, p = 0.017). Significant positive relationships were found between left- and right-

cerebellum cortex and white matter between the comparisons of January/June and January/September.

Significant negative relationships were found between several subcortical ROIs for the summer months

compared to January. An opposing effect was observed between the supra- and infra-tentorium, with

opposite effect directions in winter and summer. Cohen’s d effect sizes from monthly comparisons were

similar to those reported in recent psychiatric big-data publications, raising the possibility that seasonal

changes and weather may be confounds in large cohort studies. Additionally, changes in brain volume

due to natural environmental variation have not been reported before and may have implications for

weather-related and seasonal ailments.

Introduction

Studies testing the effects of weather and season on the human body have found relationships between

these environmental factors and incidence or severity of disease. Sales of headache medicines increase

when barometric pressure decreases, and spontaneous delivery rates increase when barometric

pressure drops [1, 2]. Environmental effects on specific diseases have been studied, including multiple

sclerosis (MS), schizophrenia, and Alzheimer’s. A significant relationship exists between winter

conditions and a higher incidence of onset or recurrence of multiple sclerosis [3]. A similar relationship

exists between MS relapse rates and latitude, with rates increasing further from the equator [4]. An

association has been observed between season and first-break schizophrenia and psychosis, with a

stronger effect in males [5, 6]. Seasonal rhythms in gene expression have been found to be interrupted

by Alzheimer’s disease [7]. Season and weather associations with symptoms of particular disorders have

been investigated from a public health perspective, but the underlying biological response to

environmental factors has not been as extensively studied. A study showed an association between

hippocampal volume and photoperiod [8]. One study examined the effect of time-of-day on a

longitudinal sample of 755 MS and 834 Alzheimer’s patients [9], while a second study examined a

controlled sample of 19 healthy young adults [10]. Both studies found that total brain volume decreases

throughout the day. Studies of cognition have found a seasonal periodicity associated with task

performance [11, 12].

Human response to environmental rhythms may be similar to that of other animals, as animals alter

their physiology to adapt to changing seasonal energy needs. Animal studies have found seasonal

structural changes to the hippocampus, total brain volume, and cranium size in mammal, amphibian,

and avian species [13-17]. A study of seasonal changes in the brain volume of the common shrew found

the cerebellum increasing in volume by 8.0% from summer to winter and the rest of the brain

decreasing in volume by 11.5% [13]. The tentorium appeared to act as a divider between effect

directions in this study, and much larger effects were observed in males.

Change in daylight is a significant factor in seasonal studies, but few studies have taken into account

weather conditions, and no studies have examined the effects of weather on brain volume. Weather is

often described as temperature, precipitation, and wind speed, but the most significant driver of

weather is barometric pressure. Air moves from areas of high pressure to low bringing with it wind, and

changes in temperature and precipitation. Unlike temperature and humidity which are well-controlled in

MRI scanning environments, pressure is ubiquitous and thus a good weather variable to explore. A

phenomenon similar to changes in barometric pressure that has been studied is the effect of high-

altitude exposure on brain volume. High-altitude (HA) exposure has been studied in humans and

measurements of brain volume have been conducted. A three month HA exposure caused an increase in

brain volume in one study [18]. At sea-level pressure, but in zero-gravity, cosmonauts were found to

experience brain volume changes after 189 days in space [19].

Exploring seasonal and weather changes in brain volume is best analyzed using a very large dataset.

Using a sample of healthy control subjects collected at the Olin Neuropsychiatry Research Center,

located in Hartford, CT USA over a 15 year period, we explored the effects of environmental factors of

season and weather on brain volume. Hartford is an ideal location to test weather and seasonal effects

because it is near sea-level, experiences four distinct seasons, and a wide range of weather conditions.

Because weather is highly correlated with the season, we attempt to separate the effects of weather

and season. We additionally compare the effect sizes found in this study to those found in large-scale

neuroimaging studies, and attempted to replicate previous findings of a diurnal effect on brain volume

and a change in hippocampal volume based on time of year.

Materials and Methods

Imaging Data Collection & Processing

Imaging data was gathered retrospectively from approximately 12,600 structural T1-weighted MRI scans

collected between August 2003 and October 2018 at the Olin Neuropsychiatry Research Center,

Institute of Living, in Hartford, CT USA. Subjects who received MRI scans were recruited into individual

neuropsychiatric studies. Those subjects received a complete description of the studies in which they

participated, and written informed consent was obtained prior to scanning. Scans were performed on a

Siemens Allegra 3T head-only MRI and a Siemens Skyra 3T MRI scanner (Siemens Medical Solutions,

Malvern PA). Six structural T1 MRI pulse sequences were used between the two MRI scanners (table 4).

Images were analyzed automatically using Freesurfer 6.0 [20] and the recon-all command with -

all and -notal-check options. Computational analysis was performed using an instance of the

Neuroinformatics Database (NiDB) [21] and took 195,000hrs (22.5 years) of CPU time to complete on a

300-core Linux cluster. Subcortical and summary regions of interest were extracted using the default

automatic subcortical segmentation (aseg) atlas [22]. Summary ROIs used for analysis included

BrainStem, SubCortGrayVol, CortexVol, and CerebralWhiteMatterVol. Lateral ROIs used for analysis

included left- and right- amygdala, caudate, cerebellum cortex, cerebellum white matter, hippocampus,

pallidum, putamen, thalamus, cerebral white matter, and cerebral cortex. All ROIs were corrected for

estimated total intracranial volume (eTIV), to remove effects of head volume.

Recent publications indicate that head motion is associated with a decrease in Freesurfer volumes. To

account for possible subject motion, a motion metric was calculated for each subject’s dataset using the

methods described in the paper by Reuter et al [23]. Motion was estimated from any fMRI timeseries

collected in the same imaging study as the T1 with at least 100 time points collected. Timeseries data

may have been a task or resting state scan. These motion estimates were calculated by performing rigid

realignment using FSL’s MCFLIRT tool [24]. The derivative of the resulting motion correction was

calculated, giving a displacement value in mm between adjacent time points, which ignores the effect of

slow physical motion in the scanner. Root mean square (RMS) of the maximum displacement in the x, y,

and z-directions were calculated, with the largest value used as a ‘motion’ variable for later statistical

analysis.

After processing of the imaging data through Freesurfer and FSL, cleaning and quality control was

performed. Exclusion criteria included: individuals with invalid ages, invalid/unknown sex, incidental

findings (tumor, aneurism, AVM, etc), history of traumatic brain injury, enrollment in pre-surgical

mapping studies, and incomplete Freesurfer analyses and/or fMRI data. Arbitrary cutoffs, determined

from visual inspection of the data, were used to exclude analyses with outlying results; datasets with a

BrainSegVol-to-eTIV (estimated total intracranial volume) ratio of greater than 1.05 or less than 0.6 were

excluded, as well as eTIV’s less than 900,000 mm3. Due to the size of the remaining sample, hand-editing

of Freesurfer segmented surfaces was not performed. However, rendered images of pial surface maps

were reviewed and incorrectly segmented subjects were excluded. Thumbnails of raw T1 data were also

examined and subjects with visible artifacts (usually motion related) were excluded. For subjects with

more than one scan, only the most recent MRI scan was included to attempt to balance the sample

away from a younger average age. After all quality control and data cleaning, 6,139 subjects remained.

Imaging data was pooled from over 150 separate research projects that primarily studied psychiatric

disorders – each with different enrollment criteria, different definitions of healthy, control, and patient,

and differing levels of detail for diagnoses. Some individuals received a full structured clinical interview

(SCID) to determine DSM diagnosis, but most participants did not undergo formal psychiatric diagnostic

interview. Many individuals did not receive a diagnosis but were enrolled in projects that solely enrolled

“healthy” participants. Subjects with diagnosis labels of schizophrenia, bipolar, psychosis, major

depression, Alzheimer’s, traumatic brain injury, and autism were excluded. Subjects who were not

explicitly labeled as “healthy” but were enrolled in projects which also enrolled those diagnoses were

excluded from analysis. 4,039 subjects explicitly labeled, or implicitly defined as, “healthy controls”,

remained. The remaining sample included subjects ranging in age from 9-93 years. To remove possible

pediatric effects subjects younger than 18 were excluded, and to balance the mean age between

seasons, subjects older than 65 were excluded, leaving 3,279 healthy individuals for analysis.

MRI Quality Control Data

MRI quality control (QC) data was collected semi-regularly over the course of the analysis period. QC

MRI scans were collected on the Allegra MRI using an MPRAGE (multiplanar rapid acquisition gradient

echo) pulse sequence (256x240x260 voxels, 1.3x1x1mm voxel size, 2300ms TR, 2.91ms TE, 9° flip angle)

on an ADNI phantom (The Phantom Laboratory, https://www.phantomlab.com/magphan-adni). QC

scans were collected on the Skyra MRI using an MPRAGE sequence (176x240x256 voxels,

1.1x1.1x1.2mm voxel size, 2300ms TR, 2.95ms TE, 9° flip angle) on an ACR small phantom (Newmatic

Medical, Caledonia, MI). Signal-to-noise ratio (SNR) was calculated by dividing the signal (mean intensity

of non-noise areas) by the noise (mean intensity of the corners of the image volume).

Environmental Data

Weather data was obtained using the National Oceanic and Atmospheric Administration’s (NOAA) Local

Climatological Data (LCD) search tool for the period of August 4, 2003 to October 30, 2018, from Bradley

International Airport, which is the closest weather station with contiguous data for the time period

(https://www.ncdc.noaa.gov/cdo-web/datatools/lcd). Bradley Airport is located 12 miles (20km) from

the MRI collection site and has an elevation of 170ft (51.8m). The Olin Center’s elevation is

approximately 110ft (33.5m). The LCD dataset contained hourly weather variables used in the analysis:

Dry Bulb Temp (temperature in C), Relative Humidity (humidity in %), Station Pressure (barometric

pressure in inHg). The nearest hourly measurement to the start time of the T1 scan was used in analysis.

Köppen climate classification identifies Hartford, CT, USA as a humid continental climate (Dfa)

characterized by hot summer, cold winter, and well distributed year-round precipitation, with four

distinct seasons [25]. For simplicity, astronomical season was defined as starting on the 21st day of

March, June, September, and December, so that days of the year 80-171 were labeled spring, days 172-

263 labeled summer, days 264-354 labeled fall, and all other days labeled winter. Scan time-of-day was

obtained from the DICOM header for the T1 series. Because time-of-year, temperature, and humidity

are highly correlated, we attempted to separate the effects of time of year and weather by using the

deviation of weather variables from monthly mean. Mean monthly temperature, pressure, and humidity

were calculated over the 15-year period, from which the deviation from the monthly averages of

weather at individual scan time-points was calculated. This deviation from monthly mean was then used

in analysis and referred to as pressure, temperature, and humidity. This method distinguishes the effects

of time-of-year from the effects of the departure from normal weather conditions; ie, is an effect of

temperature because temperature is hottest in July or because of warmer than average temperatures

on any given day of the year.

Statistical Analysis

We wanted to determine if effects seen were due to weather or time-of-year, so a linear model was

used where each FreeSurfer ROI served as a dependent measure, and age, motion, sex, time-of-day, and

deviation from pressure/temperature/humidity were independent continuous variables, and scan

sequence and month were categorical variables. ROIs were selected because they were whole-brain or

summary regions (total gray matter, cortex volume, etc) or defined structures (amygdala, putamen, etc)

that have been implicated in the limited prior literature. ICV may be change with age, but was not

included in the model because of its strong correlation with age. Because of previous evidence of sex

differences in brain volume, similar analyses were performed for only males and only females. Analyses

were performed using the R statistical software package (http://r-project.org) and significant results

with p < 0.05 were noted, using FDR correction for multiple comparisons across non-overlapping groups

of ROIs. Additional post-hoc t-tests were performed for each ROI for a month-month comparison.

Uncorrected p-values less than 0.05 were noted. Percent difference in volume from mean, and the

Cohen’s d effect size, of the factor of interest, between months were calculated. For graphical purposes,

monthly percent different from annual mean were calculated for each ROI.

Body-mass index data was only available on 517 of the 3279 subjects included in the main analysis. A

separate analysis of that subset, using BMI as a covariate was performed, and the results included in

supplement tables 2 and 3.

Results

Subjects

Subjects ranged in age from 18 to 65, with a mean age of 32.4 (+/- 13.5) years; 1,779 female (33.4 +/-

14.2 years), and 1,500 male (31.3 +/- 12.6 years). Pairwise t-tests by month, FDR corrected for multiple

comparisons, showed no significant differences in scan sequence or motion. Significant differences were

found in one month-month comparison for sex (supplement table 1b), and eight month-month

comparisons for age (supplement table 1a), and no significant differences for motion or scantype by

month. MRI quality control data did not indicate an association between phantom SNR and time of year.

Weather

Weather data was available within +/- two hours for 91.4% of the scans in the dataset. For the

remaining datasets, the nearest weather measurements within six hours were used. Minimum and

maximum measurements of pressure, temperature, and humidity during MRI scanning ranged from -

16.1C to 38.3C, 10% to 100%, 28.82 inHg to 30.51 inHg respectively.

Weather and seasonal effects

Pressure was negatively associated with supra-tentorial and caudate volumes, while cerebellum cortex

and white matter volumes were positively associated with pressure (table 1). Temperature and humidity

were not associated with changes in any brain regions. Several ROIs showed significant associations with

January-June and January-August comparisons (table 1). Seasonal percent-different-from-mean in males

and females were different (figure 1), particularly that cerebellum volume peaks in females in June, and

peaks in males in September (figure 1A). Effects of pressure were only found in females, and only in the

supra-tentorial, left/right cerebellum, and right cerebellum white matter. More subcortical ROIs were

significantly different than January for the month of July in males and August in females (tables 2, 3).

Post-hoc uncorrected t-tests by month showed subcortical gray matter volume decreased between

January and August (p = .003, Cohen’s d = -.228) and increased between August and December (p =

0.013, Cohen’s d = 0.203). Left- and right- cerebellum cortex increased in volume between January and

June (p = .003, Cohen’s d = .221; p = 0.011, Cohen’s d = 0.202) and decreased between July and

December (p < .001, Cohen’s d = -0.262; p = 0.007, Cohen’s d = -0.211) decreased during the same

period. The tentorium acted as a divider between effect direction, with changes from summer to winter

months being positive for supra-tentorial ROIs and negative for infra-tentorial ROIs (table 1).

Discussion

Season and weather have a known and appreciable effect on the human body, and we have found

evidence of previously unmeasured changes in brain volumes. We were unable to replicate a significant

time-of-day effect on any brain volume ROI but were able to replicate a seasonal effect on hippocampal

volume, though only in females. Time-of-day changes reported in other studies were attributed to

hydration status, and we hypothesized that extremes of weather such as a hot dry day, or cool humid

day may be reflected in brain volume. No association was observed between time-of-day and brain

volume. When controlling for other factors, changes in humidity and temperature from normal had no

effect on brain volumes. Results from other studies found mixed results on whether hydration status

significantly changes the brain volumes measured from MRI images [26, 27].

Environmental Factors as Confounds

There is increasing interest in aggregating large samples of psychiatric patients to look for evidence that

specific psychiatric diagnoses might have different brain structure than non-patient samples [28]. Brain

volume changes observed in this study reveal a possible confound in this approach to big data analysis.

Effect sizes observed from the seasonal changes in brain volume in this study were in some cases larger

than the effect sizes of patient/control comparisons in recent big-data analyses. This potentially

represents a considerable confound when drawing conclusions about patient/control population if these

other sources of variation are not controlled. Changes in environmental temperature and barometric

pressure are known to affect blood pressure and oxygen saturation and are considered confounds to

accurate vital sign measurement in clinical environments [29, 30]. Such confounds from barometric

pressure and season may also exist in neuroimaging studies.

A comparison of effect sizes can be made between this study and those of previously published large-

scale studies, using the ENIGMA consortium as example. ENIGMA has published several large-scale

studies comparing Freesurfer derived ROI volumes between controls and patients for various disorders.

An analysis of 2,028 schizophrenia patients and 2,540 controls found significant differences in

hippocampus, amygdala, thalamus, and lateral ventricles [31]. The effect size of the differences in

thalamus volume between populations in the ENIGMA analysis was 0.31 (2.74% difference), compared

to a Cohen’s d effect size between March and August in the left- and right- thalamus in the Olin sample

of 0.213 (2.98%) and 0.216 (2.94%). A comparison of 1,728 major depressive disorder (MDD) patients

and 7,199 controls showed a significant difference between populations in the hippocampus (1.25%,

Cohen’s d = 0.144) [32]. Differences were described in the amygdala, hippocampus, and thalamus in

another paper comparing 1,026 epileptics and 1,727 controls [33]. A comparison of 2,140 substance

users and 1,100 controls found differences in the amygdala, hippocampus, putamen, thalamus [34].

Results from these four papers are listed adjacent to the largest effects found in this study (table 5).

When performing large sample analyses, many unknown factors may influence results. The current

results suggest that data collection should be uniform across season, but also that it should be standard

practice to statistically model for variation due to season in geographical areas where seasons are

distinct and widely variable. It is likely important to include barometric pressure as a statistical covariate

when using data gathered from climatologically diverse data collection sites. It is entirely possible that a

case-control research study recruits most of the patients at the start of the project in the winter and fills

in the controls the following summer. Scanning more subjects of one group in a season might represent

the effect seen in a case-control analysis, especially as the effects observed in these analyses are already

somewhat small.

Biological Significance

Many health effects and diseases are associated with season or weather. Approximately 5% of the US

population experiences seasonal affective disorder in a given year [35]. Headache may have a weather-

related trigger as indicated by significantly higher sales of over-the-counter headache medications when

barometric pressure dropped the previous day [36]. Studies of the influence of weather on migraine

have shown mixed results [1, 37, 38]. Though not neurologically related, drops in barometric pressure

cause an increased risk of spontaneous cephalic delivery [2]. Our findings indicate that changes in

barometric pressure have a larger effect on the brain volume of females than males, so barometric

pressure changes may affect females in multiple ways.

A surprising finding from this study was that supra-tentorial regions gain volume when ‘bad’ weather

approaches - either when barometric pressure drops or when winter is coming - but cerebellum and

brain stem volumes change in the opposite direction. While these countervailing effects on different

parts of the brain defy easy explanation, they are not without precedent in mammals. Such seasonal

brain volume changes are similar to those of the common shrew, with the tentorium acting as a divider

between effect directions and with males having larger changes than females. Volume change directions

in the shrew are opposite that humans based on time-of-year, however that may be dependent on the

average one-and-a-half year lifespan of the shrew. Blood to infra- and supra-tentorial regions are

supplied by different vasculature, which may be responsible for opposite changes in volumes. A strong

seasonality effect raises a possible explanation of changing vitamin-D levels. Previous studies have found

negative association between vitamin-D levels and intracranial volume, and vitamin-D and season.

Subjects with lower levels of vitamin-D showed larger intracranial and white-matter volumes [39], and

lower levels of vitamin-D are found in winter [40]. Though this hypothesis is speculative, it is testable in

subjects who are prescribed light exposure during winter months for various conditions.

A possible explanation of brain volume changes is from a change in blood flow as previous studies have

found a seasonal effect on ambulatory blood pressure [41, 42]. Blood flow associated with barometric

pressure may also offer an explanation. The decrease in barometric pressure associated with an increase

in infra-tentorial volumes found in this study may be explained by a vascular response to available

oxygen levels. Oxygen concentration in the atmosphere in a low-pressure weather system (28.5inHg at

sea level) is similar to the oxygen levels (97% of normal) found at an elevation of 400m above sea level.

Lower blood oxygen concentration (SpO2) is associated with lower barometric pressure [29]. An imaging

study of mice subjected to low levels of O2 found that macro-vasculature decreased in volume, while

microvasculature blood flow increased [43]. Tissue requires more blood to deliver the same amount of

oxygen in a low-O2 environment and may thus cause a small and temporary change in brain volume.

However, this does not explain why the cerebellum follows a different pattern from the rest of the

brain.

Since brain volumes have been assumed to be static except for the effects of aging, few if any studies

have examined temporally fine-grained (daily or weekly) MRI scans for extended periods of time.

Replication of the changes found in this analysis would be best tested using a single subject, or set of

subjects, scanned daily throughout an entire year. Such data would confirm whether the effects seen in

this study are a biological effect or are only found at a group-level in a heterogeneous group. Either

finding would be important to the interpretation of large-scale heterogeneous studies.

Investigating the biological cause of such large volume changes may be clinically relevant, including why

volume changes are observed in opposite directions in the supratentorium vs infratentorium.

Investigating these changes further may be informative for seasonal disorders or discover previously

unknown seasonal effects on other diseases. From a purely statistical standpoint, adding season and

weather variables to big data analyses may improve accuracy, especially if the analysis includes

geographic sites that experience wide variation in these variables.

Limitations

Many demographic and phenotypic variables were not collected for all subjects used in this analysis.

Variables such as race, ethnicity, education, BMI, medication, menstrual cycle, recreational drug use,

and smoking status were only collected on a small subset of subjects, and those subsets often did not

overlap, were inconsistently recorded between projects, or were mostly just not available because of

inaccessibility to paper records.

Data Availability Statement

The data that support the findings of the study are available on request from the corresponding author

(GB). The data are not publicly available due them containing information that could compromise

research participant privacy/consent.

Acknowledgements

We would like to acknowledge the entire past and present staff of the Olin Neuropsychiatry Research

Center for 15 years’ worth of MRI data collection and the individual projects under which the data was

collected.

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Figures

Figure 1 – Monthly volumes – difference from yearly mean [A] Cerebellum cortex and cerebellum white

matter in males and females. [B] Total subcortical gray matter volume for male and female represented

by thick lines, and individual subcortical ROI volumes represented by thin lines. [C] Whole brain volume

for males and females. [D] Summary volumes for all subjects.

A systematic review of previous MRI studies demonstrating the relationship between season and brain volume

Preprint

Full-text available

Feb 2022

Introduction Specific diseases such as Alzheimer’s, schizophrenia and multiple sclerosis have been associated with environmental changes, including changes in season. The relationship between changes in season or photoperiod and brain volume has been widely studied in animals. However, the relationship between changes in season and brain volume in humans is not yet well established. Here, we aim to provide a comprehensive and systematic review of magnetic resonance imaging (MRI) studies examining the effects of changes in season or photoperiod on brain volume. Methods We used a systematic review approach to study reports on the relationship between changes in season or photoperiod and brain volume using MRI. PubMed database and Google Scholar search engines were used to locate appropriate. PRISMA was used in selection of appropriate studies on season or photoperiod and brain volume using MRI. Results Five studies were included in the current review: three examined the relationship between changes in photoperiod and brain volumes while two studies examined the relationship between changes in season and brain volume. Conclusion When studying other variables like changes in temperature and humidity on how they affect brain volume, no effect was observed. It was, however, observed from studies that hydration status changed brain volumes when measured using MRI. Overall, we found evidence demonstrating differences in human brain volumes across different seasons. We suggest future longitudinal research to prove changes in brain volume across different seasons.

Brain Tissue-Volume Changes in Cosmonauts

Article

Full-text available

Oct 2018
NEW ENGL J MED

Ten cosmonauts, who spent an average of 189 days in space, had changes in brain volumes — mainly decreased cortical volume and increased CSF subarachnoid and ventricular volume — with some changes persisting up to an average of 7 months after return to Earth.

Seasonal plasticity of cognition and related biological measures in adults with and without Alzheimer disease: Analysis of multiple cohorts

Article

Full-text available

Sep 2018
PLOS MED

Background There are few data concerning the association between season and cognition and its neurobiological correlates in older persons—effects with important translational and therapeutic implications for the diagnosis and treatment of Alzheimer disease (AD). We aimed to measure these effects. Methods and findings We analyzed data from 3,353 participants from 3 observational community-based cohort studies of older persons (the Rush Memory and Aging Project [MAP], the Religious Orders Study [ROS], and the Minority Aging Research Study [MARS]) and 2 observational memory-clinic-based cohort studies (Centre de Neurologie Cognitive [CNC] study at Lariboisière Hospital and the Sunnybrook Dementia Study [SDS]). We performed neuropsychological testing and, in subsets of participants, evaluated cerebrospinal fluid AD biomarkers, standardized structured autopsy measures, and/or prefrontal cortex gene expression by RNA sequencing. We examined the association between season and these variables using nested multiple linear and logistic regression models. There was a robust association between season and cognition that was replicated in multiple cohorts (amplitude = 0.14 SD [a measure of the magnitude of seasonal variation relative to overall variability; 95% CI 0.07–0.23], p = 0.007, in the combined MAP, ROS, and MARS cohorts; amplitude = 0.50 SD [95% CI 0.07–0.66], p = 0.017, in the SDS cohort). Average composite global cognitive function was higher in the summer and fall compared to winter and spring, with the difference equivalent in cognitive effect to 4.8 years’ difference in age (95% CI 2.1–8.4, p = 0.002). Further, the odds of meeting criteria for mild cognitive impairment or dementia were higher in the winter and spring (odds ratio 1.31 [95% CI 1.10–1.57], p = 0.003). These results were robust against multiple potential confounders including depressive symptoms, sleep, physical activity, and thyroid status and persisted in cases with AD pathology. Moreover, season had a marked effect on cerebrospinal fluid Aβ 42 level (amplitude 0.30 SD [95% CI 0.10–0.64], p = 0.003), which peaked in the summer, and on the brain expression of 4 cognition-associated modules of co-expressed genes (m6: amplitude = 0.44 SD [95% CI 0.21–0.65], p = 0.0021; m13: amplitude = 0.46 SD [95% CI 0.27–0.76], p = 0.0009; m109: amplitude = 0.43 SD [95% CI 0.24–0.67], p = 0.0021; and m122: amplitude 0.46 SD [95% CI 0.20–0.71], p = 0.0012), which were in phase or anti-phase to the rhythms of cognition and which were in turn associated with binding sites for several seasonally rhythmic transcription factors including BCL11A, CTCF, EGR1, MEF2C, and THAP1. Limitations include the evaluation of each participant or sample once per annual cycle, reliance on self-report for measurement of environmental and behavioral factors, and potentially limited generalizability to individuals in equatorial regions or in the southern hemisphere. Conclusions Season has a clinically significant association with cognition and its neurobiological correlates in older adults with and without AD pathology. There may be value in increasing dementia-related clinical resources in the winter and early spring, when symptoms are likely to be most pronounced. Moreover, the persistence of robust seasonal plasticity in cognition and its neurobiological correlates, even in the context of concomitant AD pathology, suggests that targeting environmental or behavioral drivers of seasonal cognitive plasticity, or the key transcription factors and genes identified in this study as potentially mediating these effects, may allow us to substantially improve cognition in adults with and without AD.

Profound seasonal changes in brain size and architecture in the common shrew

Article

Full-text available

Jul 2018
BRAIN STRUCT FUNCT

The seasonal changes in brain size of some shrews represent the most drastic reversible transformation in the mammalian central nervous system known to date. Brain mass decreases 10–26% from summer to winter and regrows 9–16% in spring, but the underlying structural changes at the cellular level are not yet understood. Here, we describe the volumetric differences in brain structures between seasons and sexes of the common shrew (Sorex araneus) in detail, confirming that changes in different brain regions vary in the magnitude of change. Notably, shrews show a decrease in hypothalamus, thalamus, and hippocampal volume and later regrowth in spring, whereas neocortex and striatum volumes decrease in winter and do not recover in size. For some regions, males and females showed different patterns of seasonal change from each other. We also analyzed the underlying changes in neuron morphology. We observed a general decrease in soma size and total dendrite volume in the caudoputamen and anterior cingulate cortex. This neuronal retraction may partially explain the overall tissue shrinkage in winter. While not sufficient to explain the entire seasonal process, it represents a first step toward understanding the mechanisms beneath this remarkable phenomenon.

Structural Brain Abnormalities in The Common Epilepsies Assessed in A Worldwide ENIGMA Study

Article

Full-text available

Jan 2018

Progressive functional decline in the epilepsies is largely unexplained. We formed the ENIGMA-Epilepsy consortium to understand factors that influence brain measures in epilepsy, pooling data from 24 research centres in 14 countries across Europe, North and South America, Asia, and Australia. Structural brain measures were extracted from MRI brain scans across 2149 individuals with epilepsy, divided into four epilepsy subgroups including idiopathic generalized epilepsies (n =367), mesial temporal lobe epilepsies with hippocampal sclerosis (MTLE; left, n = 415; right, n = 339), and all other epilepsies in aggregate (n = 1026), and compared to 1727 matched healthy controls. We ranked brain structures in order of greatest differences between patients and controls, by meta-analysing effect sizes across 16 subcortical and 68 cortical brain regions. We also tested effects of duration of disease, age at onset, and age-by-diagnosis interactions on structural measures. We observed widespread patterns of altered subcortical volume and reduced cortical grey matter thickness. Compared to controls, all epilepsy groups showed lower volume in the right thalamus (Cohen's d = -0.24 to -0.73; P < 1.49 × 10-4), and lower thickness in the precentral gyri bilaterally (d = -0.34 to -0.52; P < 4.31 × 10-6). Both MTLE subgroups showed profound volume reduction in the ipsilateral hippocampus (d = -1.73 to -1.91, P < 1.4 × 10-19), and lower thickness in extrahippocampal cortical regions, including the precentral and paracentral gyri, compared to controls (d = -0.36 to -0.52; P < 1.49 × 10-4). Thickness differences of the ipsilateral temporopolar, parahippocampal, entorhinal, and fusiform gyri, contralateral pars triangularis, and bilateral precuneus, superior frontal and caudal middle frontal gyri were observed in left, but not right, MTLE (d = -0.29 to -0.54; P < 1.49 × 10-4). Contrastingly, thickness differences of the ipsilateral pars opercularis, and contralateral transverse temporal gyrus, were observed in right, but not left, MTLE (d = -0.27 to -0.51; P < 1.49 × 10-4). Lower subcortical volume and cortical thickness associated with a longer duration of epilepsy in the all-epilepsies, all-other-epilepsies, and right MTLE groups (beta, b < -0.0018; P < 1.49 × 10-4). In the largest neuroimaging study of epilepsy to date, we provide information on the common epilepsies that could not be realistically acquired in any other way. Our study provides a robust ranking of brain measures that can be further targeted for study in genetic and neuropathological studies. This worldwide initiative identifies patterns of shared grey matter reduction across epilepsy syndromes, and distinctive abnormalities between epilepsy syndromes, which inform our understanding of epilepsy as a network disorder, and indicate that certain epilepsy syndromes involve more widespread structural compromise than previously assumed.

Seasonality and brain size are negatively associated in frogs: Evidence for the expensive brain framework

Article

Full-text available

Nov 2017

The challenges of seasonal environments are thought to contribute to brain evolution, but in which way is debated. According to the Cognitive Buffer Hypothesis (CBH) brain size should increase with seasonality, as the cognitive benefits of a larger brain should help overcoming periods of food scarcity via, for instance, increased behavioral flexibility. However, in line with the Expensive Brain Framework (EBF) brain size should decrease with seasonality because a smaller brain confers energetic benefits in periods of food scarcity. Empirical evidence is inconclusive and mostly limited to homoeothermic animals. Here we used phylogenetic comparative analyses to test the impact of seasonality on brain evolution across 30 species of anurans (frogs) experiencing a wide range of temperature and precipitation. Our results support the EBF because relative brain size and the size of the optic tectum were negatively correlated with variability in temperature. In contrast, we found no association between the variability in precipitation and the length of the dry season with either brain size or the sizes of other major brain regions. We suggest that seasonality-induced food scarcity resulting from higher variability in temperature constrains brain size evolution in anurans. Less seasonal environments may therefore facilitate the evolution of larger brains in poikilothermic animals.

Diurnal and seasonal molecular rhythms in human neocortex and their relation to Alzheimer’s disease

Article

Full-text available

Apr 2017

Circadian and seasonal rhythms are seen in many species, modulate several aspects of human physiology, including brain functions such as mood and cognition, and influence many neurological and psychiatric illnesses. However, there are few data regarding the genome-scale molecular correlates underlying these rhythms, especially in the human brain. Here, we report widespread, site-specific and interrelated diurnal and seasonal rhythms of gene expression in the human brain, and show their relationship with parallel rhythms of epigenetic modification including histone acetylation, and DNA methylation. We also identify transcription factor-binding sites that may drive these effects. Further, we demonstrate that Alzheimer’s disease pathology disrupts these rhythms. These data suggest that interrelated diurnal and seasonal epigenetic and transcriptional rhythms may be an important feature of human brain biology, and perhaps human biology more broadly, and that changes in such rhythms may be consequences of, or contributors to, diseases such as Alzheimer’s disease.

Reversible Brain Abnormalities in People Without Signs of Mountain Sickness During High-Altitude Exposure

Article

Full-text available

Sep 2016

A large proportion of lowlanders ascending to high-altitude (HA) show no signs of mountain sickness. Whether their brains have indeed suffered from HA environment and the persistent sequelae after return to lowland remain unknown. Thirty-one sea-level college students, who had a 30-day teaching on Qinghai-Tibet plateau underwent MRI scans before, during, and two months after HA exposure. Brain volume, cortical structures, and white matter microstructure were measured. Besides, serum neuron-specific enolase (NSE), C-reactive protein, and interleukin-6 and neuropsychiatric behaviors were tested. After 30-day HA exposure, the gray and white matter volumes and cortical surface areas significantly increased, with cortical thicknesses and curvatures changed in a wide spread regions; Anisotropy decreased with diffusivities increased in multiple sites of white matter tracts. Two months after HA exposure, cortical measurements returned to basal level. However, increased anisotropy with decreased diffusivities was observed. Behaviors and serum inflammatory factor did not significant changed during three time-point tests. NSE significantly decreased during HA but increased after HA exposure. Results suggest brain swelling occurred in people without neurological signs at HA, but no negative sequelae in cortical structures and neuropsychiatric functions were left after the return to lowlands. Reoxygenation changed white matter microstructure.

Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group

Article

Jan 2015

The pattern of structural brain alterations associated with major depressive disorder (MDD) remains unresolved. This is in part due to small sample sizes of neuroimaging studies resulting in limited statistical power, disease heterogeneity and the complex interactions between clinical characteristics and brain morphology. To address this, we meta-analyzed three-dimensional brain magnetic resonance imaging data from 1728 MDD patients and 7199 controls from 15 research samples worldwide, to identify subcortical brain volumes that robustly discriminate MDD patients from healthy controls. Relative to controls, patients had significantly lower hippocampal volumes (Cohen’s d=−0.14, % difference=−1.24). This effect was driven by patients with recurrent MDD (Cohen’s d=−0.17, % difference=−1.44), and we detected no differences between first episode patients and controls. Age of onset ⩽21 was associated with a smaller hippocampus (Cohen’s d=−0.20, % difference=−1.85) and a trend toward smaller amygdala (Cohen’s d=−0.11, % difference=−1.23) and larger lateral ventricles (Cohen’s d=0.12, % difference=5.11). Symptom severity at study inclusion was not associated with any regional brain volumes. Sample characteristics such as mean age, proportion of antidepressant users and proportion of remitted patients, and methodological characteristics did not significantly moderate alterations in brain volumes in MDD. Samples with a higher proportion of antipsychotic medication users showed larger caudate volumes in MDD patients compared with controls. This currently largest worldwide effort to identify subcortical brain alterations showed robust smaller hippocampal volumes in MDD patients, moderated by age of onset and first episode versus recurrent episode status.

Mega-Analysis of Gray Matter Volume in Substance Dependence: General and Substance-Specific Regional Effects

Article

Oct 2018
AM J PSYCHIAT

Objective:: Although lower brain volume has been routinely observed in individuals with substance dependence compared with nondependent control subjects, the brain regions exhibiting lower volume have not been consistent across studies. In addition, it is not clear whether a common set of regions are involved in substance dependence regardless of the substance used or whether some brain volume effects are substance specific. Resolution of these issues may contribute to the identification of clinically relevant imaging biomarkers. Using pooled data from 14 countries, the authors sought to identify general and substance-specific associations between dependence and regional brain volumes. Method:: Brain structure was examined in a mega-analysis of previously published data pooled from 23 laboratories, including 3,240 individuals, 2,140 of whom had substance dependence on one of five substances: alcohol, nicotine, cocaine, methamphetamine, or cannabis. Subcortical volume and cortical thickness in regions defined by FreeSurfer were compared with nondependent control subjects when all sampled substance categories were combined, as well as separately, while controlling for age, sex, imaging site, and total intracranial volume. Because of extensive associations with alcohol dependence, a secondary contrast was also performed for dependence on all substances except alcohol. An optimized split-half strategy was used to assess the reliability of the findings. Results:: Lower volume or thickness was observed in many brain regions in individuals with substance dependence. The greatest effects were associated with alcohol use disorder. A set of affected regions related to dependence in general, regardless of the substance, included the insula and the medial orbitofrontal cortex. Furthermore, a support vector machine multivariate classification of regional brain volumes successfully classified individuals with substance dependence on alcohol or nicotine relative to nondependent control subjects. Conclusions:: The results indicate that dependence on a range of different substances shares a common neural substrate and that differential patterns of regional volume could serve as useful biomarkers of dependence on alcohol and nicotine.

Effect of weather on temporal pain patterns in patients with temporomandibular disorders and migraine

Article

Feb 2017
J ORAL REHABIL

Background: Patients with masticatory muscle pain and migraine typically report that the intensity of pain fluctuates over time and is affected by weather changes. Weather variables, such as ambient temperature and humidity, may vary significantly depending on whether the individual is outdoor or indoor. It is, therefore, important to assess these variables at the individual level by using portable monitors, during everyday life. Objectives: To determine the temporal pattern of pain intensity in individuals affected with facial and head pain, and to investigate its relation with weather changes. Methods: Eleven patients (27.3±7.4 years) with chronic masticatory muscle pain (MP) and twenty (33.1±8.7 years) with migraine headache (MH) were asked to report their current pain level on a visual analogue scale (VAS) every hour over fourteen consecutive days. The VAS scores were collected using portable data-loggers, which were also used to record temperature, atmospheric pressure, and relative humidity. Results: VAS scores varied markedly over time in both groups. Pain VAS scores fluctuate less in the MP group than in the MH group, but their mean, minimum and maximum values were higher than those of migraine patients (all p<0.05). Pain scores<2 cm were more common in the MH than in the MP group (p<0.001). Perceived intensity of pain was negatively associated with atmospheric pressure in the MP group and positively associated with temperature and atmospheric in the MH group. Conclusion: Patients with masticatory muscle pain and patients with migraine present typical temporal pain patterns that are influenced in a different way by weather changes. This article is protected by copyright. All rights reserved.