Investigating Structural Brain Changes of Dehydration
Using Voxel-Based Morphometry
Daniel-Paolo Streitbu ¨rger1*, Harald E. Mo ¨ller1, Marc Tittgemeyer2, Margret Hund-Georgiadis1¤,
Matthias L. Schroeter1,3, Karsten Mueller1
1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 2Max Planck Institute for Neurological Research, Cologne, Germany, 3Clinic for
Cognitive Neurology, University of Leipzig, Leipzig, Germany
Dehydration can affect the volume of brain structures, which might imply a confound in volumetric and morphometric
studies of normal or diseased brain. Six young, healthy volunteers were repeatedly investigated using three-dimensional T1-
weighted magnetic resonance imaging during states of normal hydration, hyperhydration, and dehydration to assess
volume changes in gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The datasets were analyzed using
voxel-based morphometry (VBM), a widely used voxel-wise statistical analysis tool, FreeSurfer, a fully automated volumetric
segmentation measure, and SIENAr a longitudinal brain-change detection algorithm. A significant decrease of GM and WM
volume associated with dehydration was found in various brain regions, most prominently, in temporal and sub-gyral
parietal areas, in the left inferior orbito-frontal region, and in the extra-nuclear region. Moreover, we found consistent
increases in CSF, that is, an expansion of the ventricular system affecting both lateral ventricles, the third, and the fourth
ventricle. Similar degrees of shrinkage in WM volume and increase of the ventricular system have been reported in studies
of mild cognitive impairment or Alzheimer `s disease during disease progression. Based on these findings, a potential
confound in GM and WM or ventricular volume studies due to the subjects’ hydration state cannot be excluded and should
be appropriately addressed in morphometric studies of the brain.
Citation: Streitbu ¨rger D-P, Mo ¨ller HE, Tittgemeyer M, Hund-Georgiadis M, Schroeter ML, et al. (2012) Investigating Structural Brain Changes of Dehydration Using
Voxel-Based Morphometry. PLoS ONE 7(8): e44195. doi:10.1371/journal.pone.0044195
Editor: Mark W. Greenlee, University of Regensburg, Germany
Received March 27, 2012; Accepted July 30, 2012; Published August 29, 2012
Copyright: ? 2012 Streitbu ¨rger et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors have no support or funding to report.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org
¤ Current address: Center of Neurorehabilitation, Faltigberg-Wald, Switzerland
A number of recent studies employing structural magnetic
resonance imaging (MRI) have aimed to investigate gray matter
(GM), white matter (WM), and the cerebrospinal fluid (CSF)
system and its explanatory power for neurodegenerative disorders
[1–3]. Regarding Alzheimer’s disease (AD), decreased GM and
WM volumes were consistently found [4,5], which are assumed to
be related to the loss of neurons and synapses. This, in turn, could
also be a plausible explanation for an accompanied increase of
CSF as several studies showed an enlarged size of the ventricles
due to brain atrophy compared to healthy controls [2,6,7]. Annual
expansion of the ventricles in healthy elderly and AD patients is
around 1.5–3.0% and 5–16%, respectively .
Such changes in brain tissue and fluids led to the idea of disease
progression measures based on different approaches and tissue
types . Nestor et al.  proposed ventricular enlargement as
a valid and sensitive short-term marker of disease progression in
subjects with AD and mild cognitive impairment (MCI) for multi-
Besides investigations of CSF in dementia, several other studies
showed CSF volume changes using VBM in non-neurodegener-
ative diseases. For example, Bendel et al.  demonstrated
a correlation of neuropsychological outcome after subarachnoid
hemorrhages with an enlargement of CSF volume. They claimed
that ventricular enlargement combined with GM loss may indicate
general brain atrophy rather than hydrocephalus. Enlarged CSF
and decreased GM volumes have also been found in women with
schizotypal personality disorder . Furthermore, Hagemann
et al.  presented a strong correlation of progesterone and CSF
volume change during the menstrual cycle. Consequently, they
recommended considering such short-term hormone-dependent
structural brain changes in VBM studies.
However, CSF can also be influenced intentionally by massive
fluid intake  and nonintentionally by external factors, such as
high ambient temperature or acute physical stress due to illness,
infections, or fever . Recent studies have thus pointed to
a potential confound in morphometric MRI studies due to
a continuous and severe lack of hydration [12,14–16]. For
example, dehydration might produce an additive effect in AD
studies because increases of CSF volume, in particular in the
ventricles, might be a result of long-term dehydration instead of
degeneration of brain tissue.
Considering intentional influence of fluid balance by water
intake, Kempton et al.  observed significant ventricular
changes in acute dehydration with structural MRI. In particular,
they found enlarged lateral ventricles but no changes in the fourth
ventricle, although the whole ventricular system is predicted to be
affected by variation in hydration status. Similarly, Duning et al.
 showed structural effects due to rehydration after de-
PLOS ONE | www.plosone.org1August 2012 | Volume 7 | Issue 8 | e44195
hydration, using a water intake of 1.5 l. This effect might be
helpful to ‘‘normalize’’ subjects’ fluid balance by changing from an
often poorly controlled (and, hence, more variable) ‘normal
hydration’ state to a more consistent ‘hyperhydration’ state.
Dickson et al. , Duning et al. , and Kempton et al. 
used the fully automatic method SIENA  as well as the manual
segmentation tools MEASURE  and Analyze in their studies.
SIENA is specifically designed to detect small morphological brain
changes in longitudinal MRI studies. It gains its sensitivity by edge
motion detection and maps changes onto surrounding edges. This
approach is not accurate on spatial information and does not
specify locations of detected volume changes. Another often-used
method to detect subtle structural changes in cross-sectional
studies is voxel-based morphometry (VBM) . It permits
a longitudinal preprocessing approach that is not limited to edge
detections and allows to assess structural changes for the entire
In our present study, we investigate if dehydration effects can be
measured in a longitudinal VBM study. Based on previous studies,
we hypothesized that dehydration leads to an enlargement of the
ventricular system. As currently detailed investigations of a poten-
tial influence on GM and WM are lacking, we additionally
hypothesize a decrease of GM and/or WM volumes due to
2.1 Subjects and Imaging Procedures
Six healthy young adults (3 female; mean 24.763.0 y, all right
handed) participated in a long-term hydration experiment. All
participants gave written consent after being informed about the
possible risks and discomforts of the experimental procedure.
Subjects also completed a health history questionnaire to assess
their suitability for undergoing MRI scanning. Imaging was
performed on a 3-T MAGNETOM Trio scanner (Siemens
Medical Solutions, Erlangen, Germany) with a birdcage trans-
mit/receive head coil. T1-weighted images were acquired with
a three-dimensional MP-RAGE sequence using the following
parameters: inversion time 650 ms; repetition time, TR =1.3 s;
TR of the gradient-echo kernel 10 ms; echo time 3.93 ms, flip
angle 10u, bandwidth 130 Hz/pixel, acquisition matrix 2566240,
field of view 2566240 mm2, slab thickness 192 mm (sagittal
orientation), 128 partitions, 95% slice resolution, 2 averages. After
zero filling, reconstructed images were obtained with a nominal
voxel size of 16161 mm3.
2.2 Study Protocol
Subjects had to follow a strict hydrating/thirsting protocol, in
particular, they were instructed not to participate in strenuous
activity and to avoid alcohol consumption during the three days of
the study. On subjects’ arrival (between 8:00 and 9:00 in the
morning), image data were acquired in a ‘normal hydration’ state.
The time of this scan is subsequently referred to as t =0. We note
that there were no restrictions or specific requirements regarding
fluid or food intake prior to this scan, which hence reflects the
natural variability of water balance in subjects recruited for typical
MRI studies. Afterwards, all subjects were instructed to drink at
least 3–4 l of water and were scanned again at t < 10 h
(subsequently referred to as ‘hyperhydration state’). A summary of
individual water intake between the first two scans is given in
Table 1. For comparison, the mean daily fluid intake in healthy
male adults is approximately 2.1 l . For the next two days (i.e.
days 2 and 3), subjects were allowed to drink 150 ml of water per
day and had to avoid meals with a high fluid content. Gullans and
Verbalis  described a steady decline of the dehydration effect
with time in a rat study. Therefore we decided to acquire three
scans on day 3 to allow additional investigation of dynamic
changes during dehydration. These three scans (subsequently
referred to as ‘dehydration scans’) were acquired at times t < 48 h,
53 h, and 58 h after the ‘normal hydration’ scan (i.e. 38 h, 43 h,
and 48 h after the ‘hyperhydration’ scan. Throughout the entire
study, body weight, daily urine flow, and meal consumption
(restricted to bread, rice, and potatoes on days 2 and 3) were
monitored to ensure participants had adhered to the protocol. A
summary of individual variations in urinary excretion and body
weight is given in Table 2. On average, subjects lost approximately
2.3% of their body weight between the first and last scan. On day
3, they had an average urinary excretion of 908 ml as compared to
an average value of approximately 1.3 l in healthy subjects under
normal conditions .
2.3 Voxel-Based Morphometry
Images were processed using the longitudinal processing
pipeline as offered in the VBM8 toolbox (Gaser, C., http://
dbm.neuro.uni-jena.de/vbm/, last accessed 07.09.2011). Seg-
mented GM, WM, and CSF images were smoothed with 8 mm3
full width at half maximum and fed into a flexible factorial design
with two factors (subject and hydration state). Assignments to the
different levels of hydration state were: ‘0’ for normal hydration at
t=0, ‘–3’ for hyperhydration at t=10 h, and ‘+1’ for dehydration
at t=48 h, 53 h, and 58 h. Additionally, statistical computations
with assignments ‘09 for normal hydration, ‘–6’ for hyperhydra-
tion, and ‘+3, +2, and +1’ for the scans during dehydration at
t=48 h, 53 h, and 58 h, respectively, were calculated in order to
investigate dynamic changes during dehydration.
No potentially confounding variables were included. Smoothed
GM, WM, and CSF images were thresholded excluding voxels
containing a probability density below 10%. Finally, non-
stationary cluster extent corrections [22,23] were applied to the
The longitudinal voxel-wise statistical edge motion detection
approach SIENAr implemented, published and provided by FSL,
was applied to our data. Default parameters as described on the
software website (, http://www.fmrib.ox.ac.uk, last accessed
08.01.2012) were chosen and comparisons of hyperhydration and
the first dehydration state were statistically assessed using one-
sample t-tests with the randomise software of FSL .
Table 1. Subjects’ water intake, Vw, between the scans
performed at normal hydration (t=0) and the hyperhydration
(t=10 h) on day 1.
Dehydration & Voxel-Based Morphometry
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Both lateral ventricles as well as the third and fourth ventricle
were segmented using the FreeSurfer image analysis suite (version.
4.5)  with default parameters on a Debian 5.0 system.
Summarized segmentation results were fed into a repeated
measurements ANOVA using SPSS version 19.0 (IBM SPSS
Inc., Chicago, IL, USA). Based on prior knowledge, one-tailed
paired t-tests were computed comparing normal hydration,
hyperhydration, and each of the dehydration datasets. Further-
more, cortical thickness data, a result of the FreeSurfer image
processing pipeline, were smoothed with a 20-mm Gaussian kernel
and statistically assessed in a similar fashion as the VBM-processed
data. In particular, a mixed-effects model using the SurfStat
software  was modeled assuming thinning in dehydration and
thickening in hyperhydration.
Gray matter analysis shows significant volume decrease due to
dehydration in the left caudate nucleus and right-cerebellar
posterior lobe, as presented in Figure 1. Figure 2 shows clusters
with significant expansion of the WM during hyperhydration as
compared to dehydration. Table 3 shows the cluster corrected p-,
t- and z-values, cluster extent, and additionally the MNI
coordinates of significant clusters, as shown in Figures 1 and 2.
Clusters are located bilaterally in the temporal lobes and sub-gyral
parietal areas, the left inferior orbito-frontal region, and the extra-
nuclear region. In addition, Figure 3 shows the extent of both
lateral, the third, and the fourth ventricle during dehydration as
compared to hyperhydration on segmented images of CSF. All
VBM results show clusters (color-coded in yellow), which
remained significant after family-wise error correction (p,0.05)
and correction for non-stationarity.
Figure 4 and Table 4 shows the summed volumes of the third,
fourth and both lateral ventricles segmented with FreeSurfer for all
hydration states. Due to technical issues, the second measurement
during dehydration had to be skipped for the fifth subject. The
segmentation results were therefore analyzed in two different ways
using repeated measurements ANOVA. In the first approach,
statistical analysis was performed without subject 5, whereas in the
second approach, the ANOVA was performed without the second
measurement during dehydration. The results show a statistically
significant effect with both approaches: F(4,16)=4.54, p=0.012
and F(3,15)=7.37, p=0.003, respectively. Percentage increase of
the ventricular volume in comparison to the baseline scan under
normal hydration is shown in Figure 5.
One-tailed paired t-tests comparing the individual measure-
ments during dehydration against hyperhydration (set to 100%)
revealed significant differences for all tests (dehydr.1: average
volume change, Dv=6.261.8%, t=8.67, p,0.0002; dehydr.2:
Dv=4.463.6%, t=2.73, p,0.027; dehydr.3 Dv=5.462.7%,
t=4.83, p,0.004). In a real-life scenario, it is highly improbable
that intentional hyperhydration is followed by unintentional
dehydration. Rather, the latter, unintentional, bias is the more
relevant. To assess hydration effects more realistically, we
accordingly compared dehydration to ‘‘normal hydration’’ (first
scan) status. While the ventricular volume consistently increased
from hyperhydration to dehydration in the first analysis, changes
between normal hydration (set to 100%) and dehydration were
more subtle, showing more variation between subjects. For this
comparison (i.e., when setting the baseline result to 100%),
changes during dehydration did not reach significance in one-
tailed paired t-tests (dehydr.1: Dv=2.664.6%, t=1.37, p,0.115 |
Dv=1.763.1%, t=1.31, p,0.124). The significant average
volume change during hyperhydration compared to normal
hydration was –3.563.6% (t=–2.35, p,0.034). We note that
inclusion of images with different contrast, for example T2-
weighted scans, might improve the sensitivity due to an improved
contrast between CSF and surrounding tissues and, hence more
accurate CSF segmentation results. Analysis of dynamic changes
during dehydration to investigate the effect described by Gullans
and Verbalis  in rats did not yield additional significant effects
(not shown). Furthermore, consideration of the subjects’ individual
fluid intake in the statistical analysis did not improve the results.
Table 2. Subjects’ cumulative urinary excretion between successive time points and body weight during the complete study.
Day 1 Day 2Day 3
t=0t=5 ht=10 ht=24 ht=29 ht=34 ht=48 ht=53 ht=58 h
Urine excretion, DVu[ml]
1 1500 3500 8000 1003000 150
21300 30000 230400 700 190370
3 10003250 700130 300330 180280
4 1100 310000 300 9000 250
5 1500 3000550 100250 550 100 150
Body weight, M [kg]
680.281.0 80.678.178.077.4 76.376.275.7
The time points of the five MRI scans are indicated in bold.
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Figure 1. Segmented clusters of the gray matter with significant extension during hyperhydration compared to dehydration in
caudate nucleus and cerebellar regions (indicated by the color code) obtained with VBM. The upper row shows the results as grayscale
Maximum Intensity Projection onto the standard SPM glass brain in coronal, axial and sagittal view.
Figure 2. Segmented clusters of the white matter with significant extension during hyperhydration compared to dehydration in
parietal and tempo-parietal regions (indicated by the color code) obtained using VBM. The upper row shows the results as grayscale
Maximum Intensity Projection onto the standard SPM glass brain in coronal, axial and sagittal view.
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SIENAr results did not show significant edge motions due to
dehydration if statistics were corrected for multiple comparisons.
When using an uncorrected voxel threshold (Figure 6), de-
hydration effects were observed in similar regions as described
by Kempton et al. . In our cohort, main effects were obtained
around the 3rdand 4thventricle and the brain stem, and minor
effects were visible around the right lateral ventricle. Additional
edge motions were detected in occipital areas.
Statistical computation of the mixed-effects model for detecting
cortical thickness changes with FreeSurfer did not yield significant
results for hyperhydration (assuming thickening of the cortical
surface) compared to dehydration (assuming thinning of the
With the current study, we demonstrate structural brain
changes due to dehydration separately for all major brain
compartments. Consistent changes upon dehydration were
revealed in GM, WM, and in the ventricular system employing
VBM and its GLM approach. Additionally, we assessed the
reliability of VBM in investigating CSF by using FreeSurfer as
a reference method. SINEAr results agreed with previously
published findings, when using an uncrorrected voxel threshold.
FreeSurfer measurements of cortical thickness did not detect
significant changes in hyperhydration compared to dehydration.
Gray matter volume reductions due to dehydration compared
to hyperhydration were found using VBM in the left caudate
nucleus and right-cerebellar posterior lobe. Although the left
caudate nucleus is also one of the regions that are affected in AD
[4,5], we are not aware of any study suggesting a particular usage
Figure 3. Segmented clusters of the cerebrospinal fluid system with significant extension during dehydration compared to
hyperhydration in the third, fourth, and both lateral ventricles (indicated by the color code) obtained with VBM. The upper row
shows the results as grayscale Maximum Intensity Projection onto the standard SPM glass brain in coronal, axial and sagittal view.
Table 3. Significant results of the VBM gray and white matter
analysis using an 8-mm3smoothing kernel and non-
stationarity correction, as it is shown in Figures 1 and 2.
x [mm]y [mm]z [mm]
0.005 6386.21 4.53
2 214 240
0.018 1845.62 4.26
0.025 378 5.534.226
0.001 7655.684.29 50
0.000 2630 5.504.20 20
0.001 10105.224.06 39
Cluster-corrected p-, t- and z-values, cluster extent, ke(in voxels) and MNI
coordinates of significant clusters found in GM and WM in hyperhydration
compared to dehydration. GM results are presented in bold.
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of this region in the diagnosis of AD or assessment of disease
Cortical GM changes resulting in thinning or thickening due to,
respectively, dehydration or hyperhydration were not detected
using FreeSurfer. This is most probably due to the small sample
size combined with the rather weak effect of thinning and
thickening in different hydration states. Reducing the voxel size
and thereby reducing partial-volume effects and improving cortex
segmentation might further help to detect cortical thickness
changes . White matter volumetric results showed larger and
more widespread results compared to GM. We found a significant
decrease of tissue volume during dehydration compared to
hyperhydration. Moreover, affected regions largely overlap with
areas of WM loss also reported in AD studies , such as the
temporal lobe, corpus callosum, inferior longitudinal fasciculus,
inferior frontal gyrus, and sub-gyral parietal lobe. Consequently,
Figure 4. Volume of the ventricular system in mm3(sum = lateral + +3rd+ +4thventricle) obtained with FreeSurfer for each hydration
state. Red lines indicate standard error.
Figure 5. Ventricular volume change, obtained using FreeSurfer segmentation results, in percent in comparison to normal
hydration (set to 100%). Red lines indicate standard error.
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VBM studies investigating WM changes should consider control-
ling for the subjects’ hydration state to avoid potential confounds
The observed shrinkage of WM volume during dehydration is
consistent with the report of a decreased apparent diffusion
coefficient during dehydration . This supports the assumption
of a loss of WM tissue water, which reduces the available space for
The observation of substantial differences in the degree of
hydration-related changes between GM and WM might be due to
partial volume effects . In particular, in folded cortical regions,
a nominal spatial resolution of 1 mm as employed in our study is
insufficient for a clear distinction of GM from WM/CSF, which
decreases the statistical power due to high classification variability.
In contrast, segmentation stability across subjects is better in many
areas of WM.
Our CSF results obtained with VBM showing an increased
volume of the lateral ventricles upon dehydration are consistent
with previous studies based on different methodological ap-
proaches [12,14,16]. Furthermore, our study extends previous
results by detecting volume changes in the entire ventricular
system including the fourth ventricle. This observation is consistent
with the expectation that a global effect like dehydration would
cause cell shrinkage and osmolality changes throughout the brain
and should, hence, affect the entire ventricular system .
Furthermore, our SIENAr results are in line with the literature
. One has to keep in mind that they were only based on an
uncorrected voxel-wise threshold. In this context, the limited
number of subjects is even more important due to the two time-
point estimation approach of SIENAr: Statistical tests with
SIENAr can assess only two time points, whereas our VBM study
design benefits form the repeated measurements (n=3) during
dehydration, which increases the degrees of freedom and thereby
the statistical power. This assumption is corroborated by the
observation that significance was also not reached employing
VBM, when the analysis was restricted to a paired t-test comparing
the measurement during hyperhydration to the first measurement
A possible explanation of the–at first sight counterintuitive–
finding of ventricular expansion in dehydration is discussed by
Gullans and Verbalis . Dehydration is accompanied by
decreased blood volume (hypovolaemia) . This might contrib-
ute to reduced brain volume and an associated increase of the
volume of the ventricular system . Acute dehydration also
increases serum osmolality (hypernatremia), which generates an
osmotic gradient and therefore results in an increased diffusion of
water from intracellular stores into extracellular space. This
process causes cell shrinkage, in particular of astrocytes, which
play an important role in water transport, and thereby leads to an
expansion of the ventricular system .
The change in ventricular volume depending on the hydration
status may now be compared with observations in diseases.
Dehydration would only have a negligible effect in schizophrenia
and multiple sclerosis in view of reported average ventricular
increases by 26%  or 20–26% [32,33], respectively. However,
one year follow-up studies showed volume increases by 5–16% in
AD patients and by 3.5% in MCI patients . Such changes are of
the same order as the increase of 6.261.8% between hyperhydra-
tion and dehydration observed in our study. Hence, dehydration
might be misclassified as a consequence of AD or MCI.
In a recent study with different subgroups of Parkinson’s disease,
ventricular enlargement ranged between 7 and 25%, depending
on subgroups . Although these changes exceeded those
observed in our study, an influence from different fluid balances
on misclassification cannot be excluded.
Finally, volume changes due to dehydration as obtained by
VBM were consistent with simultaneously obtained FreeSurfer
results. We may thus conclude that VBM reliably permits
detection of subtle changes in the CSF system. It may thus be
Table 4. Volume of the ventricular system in ml obtained with FreeSurfer for each subject.
‘Normally hydrated’ Hyper-hydrated Dehydrated
t=0t=10 ht=48 ht=53 ht=58 h
1 11.79711.49211.915 11.568 11.682
2 18.450 17.80219.096 18.93419.379
317.22817.18518.114 17.963 17.856
4 10.88410.884 11.82011.011 11.335
5 12.57111.995 12.852* 12.661
6 16.645 14.998 15.800 16.38816.208
Mean 6 SD14.663.214.163.014.963.2 15.263.7 14.963.4
SD is standard deviation. Missing volume data are indicated by an asterisk.
Figure 6. Blue color shows effects due to dehydration in
regions of the third, fourth, and right lateral ventricle
computed with SIENAr (voxel level, uncorrected). Additionally,
effects in the region of the occipital cortex were detected.
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applied not only for GM and WM segmentation but additionally Download full-text
in studies of CSF.
Based on the above discussion, control of the hydration status is
indicated when investigating GM and WM volume or the
ventricular system. However, volume change due to hyperhydra-
tion followed by long-term dehydration is not a realistic scenario
for disease studies. Involuntary dehydration starting from a normal
hydration state seems more likely, as summarized in Figure 4.
Thus, a ventricular expansion by approximately 2.6% after 40
hours of thirsting may be assumed to be a realistic amount of
uncertainty in studies investigating the ventricular system without
correcting for the hydration status. Similar results can be expected
for GM and WM volume changes.
Comparable reductions of changes were described in animal
studies  demonstrating a steady decline of the dehydration
effect on the ventricular system in rats. After 21 days of thirsting,
differences to baseline could no longer be observed. Such
a regulation might be the same for the whole human brain and
therefore long-lasting dehydration might result in a smaller impact
compared to the maximum increase from hyperhydration to
dehydration revealed in this study. A simple possibility to correct
for the acute form of the dehydration confound is to ensure
a certain amount of fluid intake of every subject in advance of
scanning. Instructions describing guidelines for sufficient fluid
intake approximately 24 hours before scanning should diminish
dehydration effects and establish improved comparability between
Conceived and designed the experiments: MT MHG HEM. Performed the
experiments: MT MHG HEM. Analyzed the data: DPS HEM MLS KM.
Contributed reagents/materials/analysis tools: DPS HEM MLS KM.
Wrote the paper: DPS HEM MT MLS KM.
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Dehydration & Voxel-Based Morphometry
PLOS ONE | www.plosone.org8August 2012 | Volume 7 | Issue 8 | e44195