Download Brain stem representation of thermal and psychogenic sweating in

Am J Physiol Regul Integr Comp Physiol 304: R810–R817, 2013.
First published March 27, 2013; doi:10.1152/ajpregu.00041.2013.
Brain stem representation of thermal and psychogenic sweating in humans
Michael J. Farrell,1 David Trevaks,1 Nigel A. S. Taylor,3 and Robin M. McAllen1,2
1
Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia; 2Department of
Anatomy and Neuroscience, University of Melbourne, Parkville, Victoria, Australia; and 3Centre for Human and Applied
Physiology, University of Wollongong, Wollongong, New South Wales, Australia
Submitted 23 January 2013; accepted in final form 19 March 2013
HUMAN ECCRINE SWEAT GLANDS respond to both thermal and
nonthermal drives (26, 27), and their secretions serve a wide
range of secondary functions, such as facilitating tactile and
thermal sensitivity, increasing contact friction (grip), and reducing the risk of tissue damage (55). The main nonthermal
drive to sweating in resting individuals is mental stress or
arousal, referred to here as psychogenic sweating (32). Despite
earlier doubts (39, 48), it now seems clear that cholinergic
sympathetic nerves are the final motor pathways for both
thermal and psychogenic sweating (31) and that both types of
sweating occur over the whole body rather than being restricted
to certain skin areas (32, 33). It is probable (though not yet
formally proven) that the same individual sympathetic neurons
mediate these responses via common sudomotor units during
both thermal and nonthermal sudomotor drives.
The central neural pathways that control sweating remain
poorly delineated. One can assume that the central neurons
controlling thermal and psychogenic sweating are not identical,
although they might share common output pathways to the
Address for reprint requests and other correspondence: R. McAllen, Florey
Institute, Univ. of Melbourne, Parkville Victoria, Australia (e-mail: rmca
@florey.edu.au).
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sympathetic sudomotor nerves. The prevailing view, based on
animal studies, is that thermal sweating depends ultimately on
the anterior hypothalamus/preoptic area (18, 34, 50, 51), while
psychogenic sweating is believed to be driven from the forebrain (21, 44).
Clues to the regions of the human brain involved in psychogenic sweating can be found in imaging studies that have
related regional brain activity to sweating, usually measured as
electrodermal responses. Two elegant brain imaging studies on
psychogenic sweating by Critchley and colleagues showed, for
example, that the intensity of sweating responses to both
mental arithmetic and physical work (handgrip) was associated
most clearly with activation of a region in the dorsal midcingulate cortex (5, 7). Other measures of autonomic arousal
were also associated with activations in this region, consistent
with the view that cortical neurons in that region may ultimately drive psychogenic sweating, perhaps as part of a more
general autonomic activation (7). A separate functional magnetic resonance imaging (fMRI) study by the same group
showed that regions in the prefrontal and orbitofrontal cortex,
as well as extrastriate visual cortex and cerebellum, were
activated just before discrete sweating events, implicating
neurons in those regions as potential drivers of psychogenic
sweating (6).
Fechir and colleagues also used fMRI to identify brain
regions activated proportionally with sweating in response
to a graded mental task (12). Interestingly, while this study
showed proportional activation in “task-related” regions,
such as the visual, motor, and premotor cortices, as well as
part of the cerebellum, it failed to show proportional activation of the mid-cingulate cortex (12).
A different approach—EEG dipole analysis—was used by
Homma and colleagues to localize cerebral activation associated with sweating events during cognitive processing (21).
These experiments indicated involvement of the inferior frontal
gyrus, hippocampus, and amygdala (20, 21).
The cerebral regions with activity associated with thermal
sweating were investigated as part of a separate study by Fechir
and colleagues (13), who mapped brain regional metabolic
responses with fluorodeoxyglucose positron emission tomography during whole body warming and cooling. These workers
identified a small midline region of the posterior cingulate
cortex whose metabolic activity was inversely related to sweat
rate, but found no cerebral areas with a positive correlation.
That posterior cingulate area could represent a locus of inhibitory control of thermal sweating. Alternatively, it might reflect
this region’s known role as part of the “default mode circuit”
that is inactivated during arousal (47).
For the brain stem, limited data from human pathophysiological studies indicate that the descending fiber tracts that
supply drive to the spinal preganglionic sudomotor neurons
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Farrell MJ, Trevaks D, Taylor NAS, McAllen RM. Brain stem
representation of thermal and psychogenic sweating in humans. Am J
Physiol Regul Integr Comp Physiol 304: R810 –R817, 2013. First
published March 27, 2013; doi:10.1152/ajpregu.00041.2013.—Functional MRI was used to identify regions in the human brain stem
activated during thermal and psychogenic sweating. Two groups of
healthy participants aged 34.4 ⫾ 10.2 and 35.3 ⫾ 11.8 years (both
groups comprising 1 woman and 10 men) were either heated by a
water-perfused tube suit or subjected to a Stroop test, while they lay
supine with their head in a 3-T MRI scanner. Sweating events were
recorded as electrodermal responses (increases in AC conductance)
from the palmar surfaces of fingers. Each experimental session consisted of two 7.9-min runs, during which a mean of 7.3 ⫾ 2.1 and
10.2 ⫾ 2.5 irregular sweating events occurred during psychogenic
(Stroop test) and thermal sweating, respectively. The electrodermal
waveform was used as the regressor in each subject and run to identify
brain stem clusters with significantly correlated blood oxygen leveldependent signals in the group mean data. Clusters of significant
activation were found with both psychogenic and thermal sweating,
but a voxelwise comparison revealed no brain stem cluster whose
signal differed significantly between the two conditions. Bilaterally
symmetric regions that were activated by both psychogenic and
thermal sweating were identified in the rostral lateral midbrain and in
the rostral lateral medulla. The latter site, between the facial nuclei
and pyramidal tracts, corresponds to a neuron group found to drive
sweating in animals. These studies have identified the brain stem
regions that are activated with sweating in humans and indicate that
common descending pathways may mediate both thermal and psychogenic sweating.
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METHODS
Participants. Approval for the study was obtained from the Melbourne Health Human Research Ethics Committee (approval no.
2008.147). Two groups of 11 healthy participants were recruited for
the two parts of the study: thermal stimulation and mental task. The
thermal stimulation group had a mean age of 34.4 ( ⫾ SD 10.2) years,
and the mental task group were 35.3 ( ⫾ SD 11.8) years old. Both
groups consisted of 10 males and 1 female, with four males participating in both trials.
Physiological recordings. Galvanic skin responses (GSR) were
used to record sweating events, and these were measured using a pair
of Ag-AgCl electrodes (TSD203 electrodes; Biopac Systems, CA,
USA) that were positioned on the palmar surfaces of the participants’
right index and middle fingers. The electrodes were connected via a
cable (MECMRI-3 MRI cable; Biopac Systems, Goleta, CA, USA)
and filter (MRIRFIF interference filter set; Biopac Systems) to a
constant voltage amplifier (GSR100C Galvanic Skin Response Amplifier; Biopac Systems). The signal was digitized at 1 kHz (Power
1401; Cambridge Electronic Design, Cambridge, UK) and recorded to
computer (Spike2, ver. 7; Cambridge Electronic Design). Artifacts
incurred from magnetic field switching during scanning runs were
excised from the GSR recording. The electrodermal signal was recorded as an AC signal (0.5-Hz high pass filter) to identify discrete
electrodermal events independently of any shift in mean signal level
(28). Signals from the scanner control panel were recorded to computer and used to match the timing of electrodermal events to
functional brain stem images.
Thermal stimulation. Participants wore a body suit (Med-Eng
BCS4 Body Cooling System; Allen Vanguard, Ottawa, ON, Canada),
incorporating a network of small-diameter (4 mm) plastic tubes
through which temperature-controlled water was circulated. Lower
and upper garments extended from ankles to the neck, covering both
arms to the wrists. Additionally, a woolen blanket was placed over the
suited participants, while they were warmed and scanned. The suit
was connected to a water reservoir and a pump located in the scanner
operating room via 10 m of 15-mm diameter plastic tubing. The water
in the reservoir was adjusted to between 40°C and 50°C and was
adjusted once sweating commenced (usually after 20 –30 min) to
maintain a low mean rate of sweating events.
Mental task. Participants performed a color, word Stroop task
during scanning. Visual stimuli were projected onto a screen that was
visible to participants via a mirror mounted on the head coil. Two
different color words written with congruent or incongruent colored
letters were presented sequentially in random order for 2-min blocks,
interspersed with 30-s rest intervals, during the 7.9-min functional
brain-scanning runs. The task was to count the number of nominated
events (e.g., count the words “RED” written with yellow letters) in a
2-min block, but the subjects’ responses to the task were not analyzed
and did not form part of the experiment. The sequence of events in
thermal and psychogenic scanning runs is illustrated in Fig. 1.
Functional brain images. The magnetic resonance images used for
the study were acquired with a Siemens 3-T Trio system and 32channel head coil located at the Murdoch Children’s Research Institute (Melbourne, Australia). Functional brain stem images were acquired with blood oxygen level-dependent (BOLD) contrast that
measures hemodynamic responses subsequent to neural activity (43).
The slices of functional brain stem images (echo planar images) were
positioned in an oblique, coronal orientation aligned with the dorsal
surface of the medulla. Functional brain stem images had 21 slices of
2.5-mm thickness, and covered the entire brain stem upward from the
cervical cord. Slices were divided into a 128 ⫻ 128 matrix with a
spatial resolution of 1.88 mm ⫻ 1.88 mm. Functional images were
acquired every 1.79 s [time to repetition (TR) ⫽ 1790 ms] with a time
to echo (TE) of 30 ms and a flip angle of 70°. Scanning runs lasted 7.9
min and incorporated 265 sequential functional brain stem images.
Two scanning runs were acquired from all participants during the
thermal stimuli and mental tasks. A third scanning run was acquired
from three participants during thermal stimulation to ensure that two
runs contained sufficient instances of sweating events.
Anatomical images acquired for registration. To allow data from
different participants to be grouped, each participant’s brain images
were warped to the Montreal Neurological Institute (MNI) standard
template at a spatial resolution of 1 ⫻ 1 ⫻ 1 mm (4, 11). Two types
of anatomical image were acquired to control the warping. First, an
echo planar image (EPI) of the whole brain (whole brain EPI) was
obtained with similar parameters to the functional brain stem images
(2.5-mm oblique coronial slices aligned with the dorsal surface of
medulla, 128 ⫻ 128 matrix, in slice resolution 1.88 mm ⫻ 1.88 mm, TE
A
B
Fig. 1. Experimental timelines for thermal (A) and psychogenic (B) scanning
runs.
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follow a course through the lateral parts of the medulla oblongata. This is based on the finding that when those areas are
damaged, sweating of any cause is abolished on the ipsilateral
side of the head and neck (40). The finding that the deficit does
not extend below the neck is attributed to spinal sudomotor
pathways crossing the midline sufficiently to compensate lower
levels of the sympathetic outflow (41). Where those descending fiber tracts originate and the locations of their cell bodies
are unknown, however.
The present paper is focused on the brain stem, which has
not yet received detailed attention in human imaging studies on
sweating. We wanted to know the answers to three principal
questions: 1) Where are the brain stem neurons that control
sweating? 2) Do they differ between thermal and psychogenic
sweating? and 3) Can we identify the human homolog of the
rostral medullary cell group that drives sweating in cats (49)?
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statistical parametric maps were warped to a standard template brain,
as described above. A mask was applied to the grouped data to
exclude voxels outside the brain stem. Statistical parametric maps
were averaged between two runs for each individual, and then group
averages were calculated for all participants in each condition (psychogenic or thermal sweating), as well as for contrasts between the
two conditions.
Standard methods were used to calculate Z-scores for each voxel in
standard brain stem space, accounting for the confounding factors noted
above (1). The average statistical parametric map in each condition was
then thresholded to include only voxels where Z ⬎ 2.3. Standard procedures were then used to determine significant clusters of activation by
applying a cluster corrected threshold of P ⬍ 0.05 (59).
To test whether any brain stem region distinguished between
thermal and psychogenic sweating events, voxelwise comparisons of
BOLD signals were made between sweating-related activity in the
two conditions. The results of these comparisons were then thresholded at a cluster-corrected threshold of P ⬍ 0.05.
To characterize the time course of sweating-related BOLD signals
in regions of interest, (but only for that purpose), a sweating event was
defined as an increase exceeding one SD above the baseline noise of
the electrodermal signal, and its peak time was used as a trigger point.
Electrodermal and preprocessed BOLD signals were averaged over a
time window covering 30 s before and after the trigger time.
RESULTS
Experimental procedure. Sweating events were measured as
abrupt increases in skin conductance and were detected as
peaks in the electrodermal trace. Representative electrodermal
traces from psychogenic and thermal sweating runs (after
down-sampling) are shown in Fig. 2. While the baseline was
smooth during psychogenic sweating runs (Fig. 2A), low-level
ongoing activity was present between the large peaks during
thermal sweating (Fig. 2B). That low ongoing activity, indicating small sweating events, would have made a minor contribution to the regressor for brain stem voxel analysis. Both
the thermal stimulus and the mental task were adjusted to keep
the frequency of large sweating events low, so as to minimize
overlap and to preserve the contrast between those events and
A
B
Fig. 2. Examples of electrodermal recordings that were used to extract
sweating event-related activity from the brain stem. These show representative
records of 7.9-min scanning periods during the mental task (upper trace) and
during heating (lower trace). Previously, scanning artifacts were removed from
the raw AC recording, and the signal was down-sampled to once per 1.79 s so
as to match the imaging sequence (see MATERIALS AND METHODS). The straight
dotted line in each trace denotes one SD greater than the mean. Further details
are given in the text.
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⫽ 30 ms, flip angle ⫽ 70°), but incorporating 59 additional slices (80 in
total) and taking 5.5 s to acquire (TR ⫽ 5,500 ms). Secondly, a
high-resolution, T1-weighted anatomical image of the whole brain was
acquired from each participant (192 ⫻ 0.9 mm sagittal slices, 256
⫻ 256 matrix, in-slice resolution 0.8 mm ⫻ 0.8 mm, TR ⫽ 1900
ms, TE ⫽ 2.59 ms, flip angle ⫽ 9°).
Image registration. The transformation of functional brain stem
images from each participant to the MNI standard brain was done in
three steps using the Functional Magnetic Resonance Imaging of the
Brain’s (FMRIB’s) linear image registration tool (FLIRT) (16, 23,
24). In the first step, the middle image of a scanning run (to which all
other images in the scanning run had been realigned) was aligned with
that participant’s whole brain EPI acquired in the same scanning
session. The second step involved coregistration of the participant’s
whole brain EPI to the high-resolution T1-weighted anatomical image. Finally, the participant’s high-resolution T1-weighted anatomical
image was warped to the MNI standard brain. Matrices representing
the transformations of the three steps were multiplied to produce the
global transformation from functional brain stem space to standard
MNI space. This transformation matrix was applied to statistical
parametric maps to perform higher levels of analyses described below.
Analysis: preprocessing. Preparation and statistical analysis of
functional brain stem images were performed with the Oxford Centre
for FMRIB Software Library [Oxford, UK; FSL version 4.1 (http://
www.fmrib.ox.ac.uk/fsl/)]. Sequential functional brain stem images
from single-scanning runs were realigned spatially to the middle
image of the run, to correct for any head movement during the
scanning run using MCFLIRT (23). Images were spatially smoothed
using a Gaussian kernel of 3-mm full width at half maximum. The
time series of each scanning run was mean-based intensity normalized
(all sequential volumes scaled by the same factor) and high pass
filtered to remove low-frequency artefacts (58).
Statistical analysis. The first level of statistical analysis was performed on individual scanning runs using general linear modeling,
including local autocorrelation correction, as instituted in the FMRI
expert analysis tool [FEAT, FMRIB’s improved linear model (FILM)]
(58). The regressor of interest for analysis was the simultaneously
recorded electrodermal signal after scanning artifacts had been removed, and the signal was down-sampled to correspond to the timing
of each functional brain stem image (once per 1.79 s). To adjust the
appropriate relative timing, two factors were taken into account. The
conduction delay between neural activity in the brain and the peak
electrodermal response at the finger has been measured as 5 to 5.5 s
(21). Therefore, the electrodermal regressors were translated backward in time by 5.37 s (TR ⫻ 3). Additionally, the hemodynamic
response as measured by the BOLD signal lags 4 to 6 s after neural
activation: compensation for this delay was factored into the standard
fMRI analysis (19).
To remove the influence of known confounding factors, other
regressors were added to the analysis of individual scanning runs. The
brain stem is susceptible to physiological noise, including respiratory
effects on local magnetic field properties and changes in the blood and
cerebrospinal fluid associated with the cardiac cycle. To reduce the
effects of physiological noise, regressors from three chosen regions
were used in the model to account for variance associated with the
cardiac and respiratory cycles, according to the procedures described
by Birn et al. (3). Movement is also prevalent in the brain stem, so in
addition to realignment of images as a preprocessing procedure (see
above), the effects of movement were taken into account by including
the six motion parameters (three translations and three rotations) into
the modeling of signal changes.
The modeling of variance in BOLD signals across a time series in
each scanning run was performed on a voxel-by-voxel basis. A
parameter estimate was calculated for the fit of each regressor to the
observed BOLD signal for each voxel in the native space of the
preprocessed functional brain stem images, making a statistical parametric map for each scanning run. For subsequent analyses, these
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Table 1. Clusters of significant activation during thermal
and psychogenic sweating
Coordinates
Region
Psychogenic
Dorsal Midbrain
Dorsal Midbrain
Lateral Pons
Rostral Lateral Medulla
Rostral Lateral Medulla
Caudal Lateral Medulla
Thermal
Dorsal Midbrain
Dorsal Midbrain
Ventral Midbrain
Ventral Midbrain
Lateral Pons
Rostral Lateral Medulla
Rostral Lateral Medulla
Caudal Ventral Medulla
Caudal Ventral Medulla
Side
x
y
z
Z score
R
L
L
R
L
R
2
⫺5
⫺9
7
⫺5
9
⫺28
⫺24
⫺29
⫺35
⫺35
⫺42
⫺3
⫺4
⫺43
⫺48
⫺48
⫺50
3.99
3.81
3.67
3.42
3.19
3.58
R
L
R
R
L
R
L
R
0
1
⫺2
3
7
⫺6
5
⫺6
3
⫺29
⫺31
⫺20
⫺24
⫺25
⫺33
⫺35
⫺40
⫺39
⫺5
⫺5
⫺19
⫺20
⫺38
⫺46
⫺48
⫺58
⫺61
4.37
3.70
3.41
3.52
3.53
3.80
3.84
3.33
3.27
The table gives the coordinates of the voxel with the highest Z-score in each
cluster and value of that Z-score. The coordinates correspond to the standard
brain template developed by the Montreal Neurological Institute (11). The
three-dimensional coordinate space has its origin located at the anterior
commissure: x values are mm left (⫺) or right (⫹) of the anterior commissure;
y values denote millimeters anterior (⫹) or posterior (⫺) to the anterior
commissure, z gives millimeters above (⫹) or below (⫺) the anterior commissure.
ored green in Fig. 3. Second, on the basis that sweating events
occur bilaterally over the body but the descending brain stem
tracts drive sweating unilaterally (see introduction), we infer
that the medullary pathways on the left and the right sides must
be activated together (by an unidentified antecedent source).
Therefore, we sought areas that were activated roughly symmetrically on either side of the brain stem. This “logical filter”
highlighted two areas: the dorsal midbrain and the rostral
lateral medulla.
Dorsal midbrain activation. The dorsal midbrain region
showing bilateral consensus activation was ventral to the superior colliculi, level with the rostral end of the cerebral
aqueduct. The locus of consensus activation appeared to be
centered lateral to the periaqueductal gray matter (PAG) but
may have involved lateral parts of the PAG. The mean BOLD
signals extracted from these voxels during thermal and psychogenic sweating events are shown in Fig. 4, A and B. Their
relative timing coincided closely, reflecting the fact that the
hemodynamic response to generate the BOLD signal and the
conduction-plus-neuroffector time to generate the electrodermal response both imposed delays of about 5 s. The peak
increase in mean BOLD signal there was 0.7 to 1.0%.
Rostral medullary activation. This bilateral region of consensus activation extended through the rostral medulla up to
the lower pons. This cluster was centered near the lateral
margin of the brain stem, ventral to the facial nucleus and
dorsal to the pyramidal tract. The mean BOLD signals from
this region are shown in Fig. 4, C and D. The peak signal
increase here was 1.2 to 1.4%.
DISCUSSION
So far as we are aware, this is the first neuroimaging study
to investigate the human brain stem regions involved in sweating, although a number of studies have investigated the forebrain structures involved (e.g., 8, 14, 37, 46, 57). In answer to
the first question that we posed, we found several regions in the
midbrain, pons, and medulla that were activated in association
with thermal and psychogenic sweating (detailed in Table 1).
In answer to our second question—Was any region associated
specifically with psychogenic or thermal sweating?—no region
showed any such preference. To our final question—Could we
identify the human homolog of the rostral medullary cell group
that drives sweating in cats (49)?—the answer is yes (discussed
below).
Sweating occurs in bursts (54), driven by the bursting
pattern of sympathetic sudomotor nerve activity (2). Each burst
generates a volume of sweat, which dilates the sweat duct and
lowers transdermal resistance, measured as the electrodermal
response. At low levels of sweating (as here) the precursor
sweat is reabsorbed and disperses over the next few seconds,
the duct collapses, and skin conductance falls again. An ACcoupled skin resistance signal gives an index of bursts of
sudomotor nerve activity (28), and we have used it here to
measure the strength and timing of sweating bursts. Because
sweating bursts show clear synchrony across body regions (17,
38, 45, 56), the electrodermal signal from the fingers of one
hand served as a suitable measure of the whole body response.
The experimental design used these sporadic sweating events,
which were unsynchronized to any experimental intervention,
as a regressor; established methods were then used to identify
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the intervening periods. The mean numbers of sweating events
during each 7.9-min scanning sequence that exceeded the mean
level by 1 SD (horizontal lines in Fig. 2) were 7.3 ⫾ 2.1 and
10.2 ⫾ 2.5 for psychogenic and thermal sweating runs, respectively.
Brain stem sites activated in association with thermal and
psychogenic sweating events. The brain stem regions that
showed significant clusters of voxels activated in association
with thermal and psychogenic sweating events are listed in
Table 1. The principal regions are illustrated in Fig. 3 and will
be described further below.
No brain stem clusters were found to show a significant
reduction in BOLD signal (“deactivation”) associated with
electrodermal events during either thermal or psychogenic
sweating.
To test whether any brain stem regions were preferentially
activated with thermal or with psychogenic sweating events, a
voxelwise comparison was made between sweating-related
BOLD signal changes in the two conditions. This revealed no
brain stem region that was activated significantly more during
thermal than psychogenic sweating events, or vice versa
(P (corrected) ⬎ 0.05).
Principal regions of activation. Although there was no
statistical difference between regions activated during thermal
and psychogenic sweating events, the thresholded activation
maps from each condition were not identical. As with other
human fMRI studies, such minor anomalies are to be expected
when detecting small signals against a noisy background (25).
Two features were used to select the activated regions that
were most likely to be significant. First, on the basis that
thermal and psychogenic sweating events were evidently
equivalent (see above) “consensus” voxels that were activated
significantly by both stimuli were identified. These were col-
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Thermal
Psychogenic
A
B
L
z= -4
x= -5
C
L
brain stem regions whose BOLD signals were correlated with
that regressor.
The generator of sweating bursts is unknown. At high rates
of sweating, they become regular and occur at ⬃80 –100
bursts/min (2, 42), suggesting that they are driven by a central
“oscillator”. While it is conceivable that left-right connections
between the bilateral regions identified here could generate that
rhythm [by analogy with the respiratory rhythm (52)], it seems
more likely that they receive periodic drive from an oscillator
located elsewhere. This inference is based on the finding that
forebrain structures also show activity related to sweating
bursts (6), and the likelihood that the pontomedullary locus of
activation represents the descending output pathway from the
brain (see below). Further work is needed to answer this.
To the best of our knowledge the midbrain site we identified
as activated with sweating has not been highlighted as such in
previous human work. In cats, Davison and Koss (10) used
electrical stimulation to map sudomotor pathways from the
hypothalamus to the caudal medulla and found that the excitatory tract passed through the midbrain ventrolaterally to the
periaqueductal gray matter. Their use of electrical stimulation
prevented them from identifying synaptic relays in the pathway, because this method does not distinguish between cell
bodies and fiber tracts. An excitatory BOLD signal, however,
suggests a synaptic relay. The anatomical correspondence
between the cat and human data is plausible, so this region
D
y= -35
L
z= -48
could represent a synaptic relay in the descending sudomotor
pathway.
By contrast, the site of strong bilateral activation in the
rostral medulla was expected on the basis of animal work.
McAllen (35) identified a region near the ventral surface of the
rostral medulla, which, when activated with microinjections of
excitant amino acid, drove sweating in the cat’s paw. That
region was anatomically distinct from, and rostromedial to, the
vasomotor premotor neurons of the rostral ventrolateral medulla (RVLM; subretrofacial nucleus) (9). A subsequent study
on cats (49) mapped the sudomotor cell group to a discrete
locus between the facial nucleus and the pyramidal tract.
Excitant amino acids activate neurons but not fiber tracts (15),
so this juxtafacial region represents a synaptic relay within the
cat sudomotor pathway. The bilateral region with sweatingrelated activity identified here in the human rostral medulla
was also centered between the facial nucleus and the pyramidal
tract (Fig. 3). The strong sweating-related BOLD signal in the
juxtafacial region of the human medulla (Fig. 4) also indicates
the presence of a synaptic relay rather than a fiber tract.
Limitations. As with all human imaging studies, the conclusions reached here are based on correlation rather than proof of
causation. In the case of psychogenic sweating, for example, it
is conceivable that each sweating event signals a “miniarousal,” to which the medullary and midbrain regions identified here respond in parallel with, but not directly linked to,
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Fig. 3. Map of sweating event-related activity
in the brain stem. Significant clusters of activation during thermal sweating are shown
in red, and those during psychogenic sweating are shown in blue. Voxels in which both
achieved significance are shown in green. A:
sagittal slice 5 mm to the left of the midline
(x ⫽ ⫺5) shows the rostrocaudal locations of
the two main regions of activation that were
common to thermal and psychogenic sweating. The yellow circle in the upper part of the
figure encloses a region of common activation in the midbrain and the lower circle
indicates a common region in the rostral
medulla. B: axial (horizontal) section through
the midbrain (z ⫽ ⫺4, corresponding to upper green line in A) shows the bilateral symmetry of the common region of activation.
C: coronal slice (y ⫽ ⫺35, corresponding
with lower vertical green line in A) shows the
left-right symmetry of the common rostral
medullary activations immediately caudal to
the pons. D: dorsoventral location of those
common rostral medullary activations can be
seen in this axial section (z ⫽ ⫺48, corresponding to lower horizontal green line in A).
Note: both axial sections are displayed dorsal
side uppermost.
Both
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A
B
D
sweating. Indeed, this could also apply during thermal sweating. However, previous work has demonstrated that transient
sweating events and “arousal” (measured by mean skin conductance) are linked to activation of distinct cerebral regions
(37). Sweating events can, thus, be used to discriminate sweating-related brain activity over and above any background
arousal. This suggests that the BOLD signals correlated with
sweating events reflect sweating per se.
The participants were unselected volunteers, with only one
female in each group. Thus, although we expect brain stem
mechanisms to apply equally to both sexes, we could not test
this from our sample.
We recorded electrodermal responses from the palmar surface of fingers on one hand, and used this as a general index on
the basis that sweating events across the body occur synchronously. However, sweating responses may differ in amplitude
over body regions (33). Therefore, if any body map exists
within brain stem sweating pathways, our view would be
biased toward the hand.
Perspectives and Significance
It is encouraging to find the human homolog of a brain stem
cell group identified in animals: it gives confidence that both
findings are correct and demonstrates the evolutionary conservation of a basic physiological mechanism. In two previous
cases, fMRI studies of the human brain stem have found a
remarkably accurate correspondence between functional maps
of autonomic control and anatomical predictions based on
animal studies. These were the demonstration of the involvement of the rostral medullary raphé in cold-defense (36) and of
the RVLM (and caudal ventrolateral medulla) in vasomotor
control (29, 30). We consider that the sweating-related juxtafacial activation identified here adds a third example. Animal
studies indicate that, like the cell groups in the rostral medullary raphé and RVLM, neurons in this region (called rostral
ventromedial medulla, RVMM, in rats) send their axons directly to sympathetic preganglionic neurons (22, 53). This
juxtafacial region in the human brain stem may, therefore,
contain the final common premotor pathway that transmits to
the spinal sympathetic neurons the descending signals that
drive both thermal and psychogenic sweating. In each case, it
remains to be determined which antecedent pathways drive
those neurons to cause sweating.
ACKNOWLEGDMENTS
We acknowledge the technical expertise provided by Michael Kean of the Children’s Magnetic Resonance Imaging
Centre, Melbourne, Australia.
GRANTS
This work was supported by project Grant 509089, and R. McAllen was
supported by Principal Research Fellowship 566667 from the National Health
and Medical Research Council. M. J. Farrell received support from the Robert
J. Kleberg, Jr. and Helen C. Kleberg Foundation and the G. Harold and Leila
Y. Mathers Charitable Foundation.
AJP-Regul Integr Comp Physiol • doi:10.1152/ajpregu.00041.2013 • www.ajpregu.org
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C
Fig. 4. The average time course of peak blood oxygen
level-dependent (BOLD) signal extracted from each side
of the midbrain and medullary areas of interest (bilateral
green regions in Fig. 3), compared with the corresponding averaged electrodermal responses.
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DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the authors.
21.
AUTHOR CONTRIBUTIONS
Author contributions: M.J.F., N.A.T., and R.M.M. conception and design of
research; M.J.F. and D.T. performed experiments; M.J.F. and D.T. analyzed
data; M.J.F., D.T., N.A.T., and R.M.M. interpreted results of experiments;
M.J.F. and R.M.M. prepared figures; M.J.F., N.A.T., and R.M.M. drafted
manuscript; M.J.F., D.T., N.A.T., and R.M.M. edited and revised manuscript;
M.J.F., D.T., N.A.T., and R.M.M. approved final version of manuscript.
22.
23.
24.
REFERENCES
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
AJP-Regul Integr Comp Physiol • doi:10.1152/ajpregu.00041.2013 • www.ajpregu.org
Downloaded from http://ajpregu.physiology.org/ by 10.220.33.2 on June 17, 2017
1. Beckmann CF, Jenkinson M, Smith SM. General multilevel linear
modeling for group analysis in FMRI. NeuroImage 20: 1052–1063, 2003.
2. Bini G, Hagbarth KE, Hynninen P, Wallin BG. Thermoregulatory and
rhythm-generating mechanisms governing the sudomotor and vasoconstrictor outflow in human cutaneous nerves. J Physiol 306: 537–552, 1980.
3. Birn RM, Smith MA, Jones TB, Bandettini PA. The respiration response function: the temporal dynamics of fMRI signal fluctuations related
to changes in respiration. NeuroImage 40: 644 –654, 2008.
4. Collins DL, Neelin P, Peters TM, Evans AC. Automatic 3D intersubject
registration of MR volumetric data in standardized Talairach space. J
Comput Assist Tomogr 18: 192–205, 1994.
5. Critchley HD, Corfield DR, Chandler MP, Mathias CJ, Dolan RJ.
Cerebral correlates of autonomic cardiovascular arousal: a functional
neuroimaging investigation in humans. J Physiol 523: 259 –270, 2000.
6. Critchley HD, Elliott R, Mathias CJ, Dolan RJ. Neural activity relating
to generation and representation of galvanic skin conductance responses:
a functional magnetic resonance imaging study. J Neurosci 20: 3033–
3040, 2000.
7. Critchley HD, Mathias CJ, Josephs O, O’Doherty J, Zanini S, Dewar
BK, Cipolotti L, Shallice T, Dolan RJ. Human cingulate cortex and
autonomic control: converging neuroimaging and clinical evidence. Brain
126: 2139 –2152, 2003.
8. Critchley HD, Melmed RN, Featherstone E, Mathias CJ, Dolan RJ.
Volitional control of autonomic arousal: a functional magnetic resonance
study. NeuroImage 16: 909 –919, 2002.
9. Dampney RA. The subretrofacial vasomotor nucleus: anatomical, chemical and pharmacological properties and role in cardiovascular regulation.
Progr Neurobiol 42: 197–227, 1994.
10. Davison MA, Koss MC. Brainstem loci for activation of electrodermal
response in the cat. Am J Physiol 229: 930 –934, 1975.
11. Evans AC, Collins DL, Mills SR, Brown ED, Kelly RL, Peters TM. 3D
statistical neuroanatomical models from 305 MRI volumes. Proc IEEE
Nucl Sci Symp Med Imag Conf 1813–1817, 1993.
12. Fechir M, Gamer M, Blasius I, Bauermann T, Breimhorst M, Schlindwein P, Schlereth T, Birklein F. Functional imaging of sympathetic
activation during mental stress. NeuroImage 50: 847–854, 2010.
13. Fechir M, Klega A, Buchholz HG, Pfeifer N, Balon S, Schlereth T,
Geber C, Breimhorst M, Maihofner C, Birklein F, Schreckenberger
M. Cortical control of thermoregulatory sympathetic activation. Eur J
Neurosci 31: 2101–2111, 2010.
14. Fredrikson M, Furmark T, Olsson MT, Fischer H, Andersson J,
Langstrom B. Functional neuroanatomical correlates of electrodermal
activity: a positron emission tomographic study. Psychophysiology 35:
179 –185, 1998.
15. Goodchild AK, Dampney RA, Bandler R. A method for evoking
physiological responses by stimulation of cell bodies, but not axons of
passage, within localized regions of the central nervous system. J Neurosci
Methods 6: 351–363, 1982.
16. Greve DN, Fischl B. Accurate and robust brain image alignment using
boundary-based registration. NeuroImage 48: 63–72, 2009.
17. Hagbarth KE, Hallin RG, Hongell A, Torebjork HE, Wallin BG.
General characteristics of sympathetic activity in human skin nerves. Acta
Physiol Scan 84: 164 –176, 1972.
18. Hardy JD, Hellon RF, Sutherland K. Temperature-sensitive neurones in
the dog’s hypothalamus. J Physiol 175: 242–253, 1964.
19. Henson RN, Friston KJ. Convolution models for fMRI. In: Statistical
Parametric Mapping: The Analysis of Functional Brain Images, edited by
Friston K, Ashburner J, Kiebel S, Nichols T, and Penny W. London:
Elsevier, 2006, p. 178 –192.
20. Homma S, Matsunami K, Han XY, Deguchi K. Hippocampus in
relation to mental sweating response evoked by memory recall and mental
calculation: a human electroencephalography study with dipole tracing.
Neurosci Lett 305: 1–4, 2001.
Homma S, Nakajima Y, Toma S, Ito T, Shibata T. Intracerebral source
localization of mental process-related potentials elicited prior to mental
sweating response in humans. Neurosci Lett 247: 25–28, 1998.
Jansen AS, Wessendorf MW, Loewy AD. Transneuronal labeling of
CNS neuropeptide and monoamine neurons after pseudorabies virus injections into the stellate ganglion. Brain Res 683: 1–24, 1995.
Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization
for the robust and accurate linear registration and motion correction of
brain images. NeuroImage 17: 825–841, 2002.
Jenkinson M, Smith S. A global optimisation method for robust affine
registration of brain images. Med Image Analysis 5: 143–156, 2001.
Jezzard P, Clare S. Sources of distortion in functional MRI data. Human
Brain Map 8: 80 –85, 1999.
Kenny GP, Journeay WS. Human thermoregulation: separating thermal
and nonthermal effects on heat loss. Front Biosci 15: 259 –290, 2010.
Kondo N, Nishiyasu T, Inoue Y, Koga S. Non-thermal modification of
heat-loss responses during exercise in humans. Eur J Appl Physiol 110:
447–458, 2010.
Kunimoto M, Kirno K, Elam M, Wallin BG. Neuroeffector characteristics of sweat glands in the human hand activated by regular neural
stimuli. J Physiol 442: 391–411, 1991.
Macefield VG, Gandevia SC, Henderson LA. Neural sites involved in
the sustained increase in muscle sympathetic nerve activity induced by
inspiratory capacity apnea: a fMRI study. J Appl Physiol 100: 266 –273,
2006.
Macefield VG, Henderson LA. Real-time imaging of the medullary
circuitry involved in the generation of spontaneous muscle sympathetic
nerve activity in awake subjects. Human Brain Map 31: 539 –549, 2010.
Machado-Moreira CA, McLennan PL, Lillioja S, van Dijk W,
Caldwell JN, Taylor NAS. The cholinergic blockade of both thermally
and non-thermally induced human eccrine sweating. Exp Physiol 97:
930 –942, 2012.
Machado-Moreira CA, Taylor NAS. Psychological sweating from glabrous and nonglabrous skin surfaces under thermoneutral conditions.
Psychophysiology 49: 369 –374, 2012.
Machado-Moreira CA, Taylor NAS. Sudomotor responses from glabrous and non-glabrous skin during cognitive and painful stimulations
following passive heating. Acta Physiol (Oxf) 204: 571–581, 2012.
Magoun HW, Harrison F, Brobeck JR, Ranson SW. Activation of heat
loss mechanisms by local heating of the brain. J Neurophysiol 1: 101–114,
1938.
McAllen RM. Action and specificity of ventral medullary vasopressor
neurones in the cat. Neuroscience 18: 51–59, 1986.
McAllen RM, Farrell M, Johnson JM, Trevaks D, Cole L, McKinley
MJ, Jackson G, Denton DA, Egan GF. Human medullary responses to
cooling and rewarming the skin: a functional MRI study. Proc Natl Acad
Sci USA 103: 809 –813, 2006.
Nagai Y, Critchley HD, Featherstone E, Trimble MR, Dolan RJ.
Activity in ventromedial prefrontal cortex covaries with sympathetic skin
conductance level: a physiological account of a “default mode” of brain
function. NeuroImage 22: 243–251, 2004.
Nakayama T, Tagaki K. Minute pattern of human perspiration observed
by a continuously recording method. Jpn J Physiol 9: 359 –364, 1959.
Nakazato Y, Tamura N, Ohkuma A, Yoshimaru K, Shimazu K.
Idiopathic pure sudomotor failure: anhidrosis due to deficits in cholinergic
transmission. Neurology 63: 1476 –1480, 2004.
Nathan PW, Smith MC. The location of descending fibres to sympathetic
neurons supplying the eye and sudomotor neurons supplying the head and
neck. J Neurol Neurosurg Psychiatry 49: 187–194, 1986.
Nathan PW, Smith MC. The location of descending fibres to sympathetic
preganglionic vasomotor and sudomotor neurons in man. J Neurol Neurosurg Psychiatry 50: 1253–1262, 1987.
Nilsson AL, Nilsson GE, Oberg PA. A note on periodic sweating. Acta
Physiol Scan 108: 189 –190, 1980.
Ogawa S, Lee TM, Kay AR, Tank DW. Brain magnetic resonance
imaging with contrast dependent on blood oxygenation. Proc Natl Acad
Sci USA 87: 9868 –9872, 1990.
Ogawa T. Thermal influence on palmar sweating and mental influence on
generalized sweating in man. Jpn J Physiol 25: 525–536, 1975.
Ogawa T, Bullard RW. Characteristics of subthreshold sudomotor neural
impulses. J Appl Physiol 33: 300 –305, 1972.
PLEASE PROVIDE WORDINGS
54.
55.
56.
57.
58.
59.
transneuronal pseudorabies viral infections. Brain Res 491: 156 –162,
1989.
Takahara K. Observations on the discharge of sweat from a single sweat
gland. J Oriental Med 24: 4, 1936.
Taylor NAS, Machado-Moreira CA. Regional variations in transepidermal water loss, eccrine sweat gland density, sweat secretion rates and
electrolyte composition in resting and exercising humans. Extreme Physiol
Med 2: 4, 2013.
van Beaumont W, Bullard RW, Banerjee MR. Observations on human
sweating by resistance hygrometry. Dermatol Digest: 75–87, 1966.
Williams LM, Phillips ML, Brammer MJ, Skerrett D, Lagopoulos J,
Rennie C, Bahramali H, Olivieri G, David AS, Peduto A, Gordon E.
Arousal dissociates amygdala and hippocampal fear responses: evidence
from simultaneous fMRI and skin conductance recording. NeuroImage 14:
1070 –1079, 2001.
Woolrich MW, Ripley BD, Brady M, Smith SM. Temporal autocorrelation in univariate linear modeling of FMRI data. NeuroImage 14:
1370 –1386, 2001.
Worsley KJ, Evans AC, Marrett S, Neelin P. A three-dimensional
statistical analysis for CBF activation studies in human brain. J Cereb
Blood Flow Metab 12: 900 –918, 1992.
AJP-Regul Integr Comp Physiol • doi:10.1152/ajpregu.00041.2013 • www.ajpregu.org
Downloaded from http://ajpregu.physiology.org/ by 10.220.33.2 on June 17, 2017
46. Patterson JC, 2nd Ungerleider LG, Bandettini PA. Task-independent
functional brain activity correlation with skin conductance changes: an
fMRI study. NeuroImage 17: 1797–1806, 2002.
47. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA,
Shulman GL. A default mode of brain function. Proc Natl Acad Sci USA
98: 676 –682, 2001.
48. Robertshaw D. Neuroendocrine control of sweat glands. J Invest Dermatol 69: 121–129, 1977.
49. Shafton AD, McAllen RM. Location of cat brain stem neurons that drive
sweating. Am J Physiol Regul Integr Comp Physiol (March 6, 2013).
doi:10.1152/ajpregu.00040.2013.
50. Shibasaki M, Crandall CG. Mechanisms and controllers of eccrine
sweating in humans. Front Biosci (Schol Ed) 2: 685–696, 2010.
51. Smiles KA, Elizondo RS, Barney CC. Sweating responses during
changes of hypothalamic temperature in the rhesus monkey. J Appl
Physiol 40: 653–657, 1976.
52. Smith JC, Abdala AP, Rybak IA, Paton JF. Structural and functional
architecture of respiratory networks in the mammalian brainstem. Philos
Trans R Soc Lond B Biol Sci 364: 2577–2587, 2009.
53. Strack AM, Sawyer WB, Hughes JH, Platt KB, Loewy AD. A general
pattern of CNS innervation of the sympathetic outflow demonstrated by
R817