NeuroImage 44 (2009) 213–222
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NeuroImage
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / y n i m g
Neural correlates of heart rate variability during emotion
Richard D. Lane a,b,⁎, Kateri McRae a,d, Eric M. Reiman a,e,f,g, Kewei Chen e,f,
Geoffrey L. Ahern a,b,c, Julian F. Thayer h,i
a
Department of Psychiatry, University of Arizona, Tucson, AZ, USA
Department of Psychology, University of Arizona, Tucson, AZ, USA
Department of Neurology, University of Arizona, Tucson, AZ, USA
d
Department of Psychology, Stanford University, Palo Alto, CA, USA
e
Banner Positron Emission Tomography Center, Banner Good Samaritan Medical Center, Phoenix, AZ, USA
f
Banner Alzheimer's Institute, Phoenix, AZ, USA
g
Translational Genomics Research Institute, Phoenix, AZ, USA
h
Department of Psychology, Ohio State University, Columbus, OH, USA
i
The Mannheim Institute of Public Health, Heidelberg University, Heidelberg, Germany
b
c
a r t i c l e
i n f o
Article history:
Received 26 May 2007
Revised 25 July 2008
Accepted 28 July 2008
Available online 9 August 2008
a b s t r a c t
The vagal (high frequency [HF]) component of heart rate variability (HRV) predicts survival in postmyocardial infarction patients and is considered to reflect vagal antagonism of sympathetic influences.
Previous studies of the neural correlates of vagal tone involved mental stress tasks that included cognitive
and emotional elements. To differentiate the neural substrates of vagal tone due to emotion, we correlated
HF-HRV with measures of regional cerebral blood flow (rCBF) derived from positron emission tomography
(PET) and 15O-water in 12 healthy women during different emotional states. Happiness, sadness, disgust and
three neutral conditions were each induced by film clips and recall of personal experiences (12 conditions).
Inter-beat intervals derived from electrocardiographic recordings during the 60-second scans were
spectrally-analyzed, generating 12 separate measures of HF-HRV in each subject. The six emotion and six
neutral conditions were grouped together and contrasted. We observed substantial overlap between
emotion-specific rCBF and the correlation between emotion-specific rCBF and HF-HRV, particularly in the
medial prefrontal cortex. Emotion-specific rCBF also correlated with HF-HRV in the caudate nucleus,
periacqueductal gray and left mid-insula. We also observed that the elements of cognitive control inherent in
this experiment (that involved focusing on the target mental state) had definable neural substrates that
correlated with HF-HRV and to a large extent differed from the emotion-specific correlates of HF-HRV. No
statistically significant asymmetries were observed. Our findings are consistent with the view that the medial
visceromotor network is a final common pathway by which emotional and cognitive functions recruit
autonomic support.
© 2008 Published by Elsevier Inc.
Introduction
The importance of the vagus nerve in the two-way communication
between the brain and the heart during emotion has been known for
over 100 years. Darwin, commenting on the work of the French
physiologist Claude Bernard wrote,
“Claude Bernard also repeatedly insists, and this deserves especial
notice, that when the heart is affected it reacts on the brain; and
the state of the brain again reacts through the pneumo-gastric
(vagus) nerve on the heart; so that under any excitement there
⁎ Corresponding author. 1501 N. Campbell Ave. Tucson, AZ 85724-5002, USA. Fax:
+1 520 626 6050.
E-mail address: [email protected] (R.D. Lane).
1053-8119/$ – see front matter © 2008 Published by Elsevier Inc.
doi:10.1016/j.neuroimage.2008.07.056
will be much mutual action and reaction between these, the two
most important organs of the body.” (Darwin, 1999, pp. 71–72,
originally published in 1872).
One way to index the central control of the heart via the vagus
nerve is the use of heart rate variability (Task Force, 1996; Thayer and
Brosschot, 2005). The heart is dually innervated by the autonomic
nervous system such that relative increases in sympathetic activity are
associated with heart rate increases and relative increases in
parasympathetic activity are associated with heart rate decreases.
Thus, relative sympathetic increases cause the time between heart
beats (the inter-beat interval) to become shorter and relative
parasympathetic increases cause the inter-beat interval to become
longer. The parasympathetic (primarily vagal) influences are pervasive
over the frequency range of the heart rate power spectrum whereas
the sympathetic influences ‘roll-off’ at about 0.15 Hz (Saul, 1990). Thus,
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R.D. Lane et al. / NeuroImage 44 (2009) 213–222
high frequency HRV (HF-HRV) represents primarily parasympathetic
influences with lower frequencies (below about 0.15 Hz) having a
mixture of sympathetic and parasympathetic autonomic influences.
The sympathetic effects are on the time scale of seconds whereas the
parasympathetic effects are on the time scale of milliseconds.
Therefore, the parasympathetic influences are the only ones capable
of producing rapid changes in the beat-to-beat timing of the heart.
This rapid modulation of heart rate is associated with both the
mechanical and neural gating of vagal outflow during respiration.
Specifically, during inspiration vagal outflow is reduced and heart rate
increases whereas during expiration vagal outflow is restored and
heart rate decreases. Consequently, heart rate variability largely
reflects the respiratory gating of the output of the vagus nerve on
the sinoatrial node of the heart (Saul, 1990).
To date only two studies have been conducted in which HRV has
been measured during functional brain imaging experiments in
healthy individuals. Critchley et al. (2003) examined HRV during an
fMRI study of mental (mental arithmetic) and physical (isometric
handgrip) stress and observed that dorsal anterior cingulate cortex
(ACC) was associated with a putative sympathetic component of HRV.
Gianaros et al. (2004) used PET and observed a positive correlation
between HF-HRV and ventral ACC activity during the n-back memory
task. Both studies involved cognitive tasks that were emotionally
stressful, and the correlations with HRV implicated different subsectors of the ACC.
Four additional studies have examined the relationship between
HRV measured outside the scanner (perhaps due to technical
challenges in measuring HRV in the fMRI environment) and BOLD
activity during fMRI. Two of these studies involved cognitive tasks.
Matthews et al. (2004) observed a positive correlation between HFHRV during the Counting Stroop task outside the scanner and ventral
ACC activity during the execution of the Counting Stroop Task in the
scanner. Neumann et al. (2006) found an inverse association between
baseline HRV as indexed by the low frequency to high frequency ratio
(LF/HF) and dorsal ACC activity during a go/no-go task. Two additional
studies examined the relationship between HRV measured outside the
scanner and functional brain imaging during emotional tasks.
O'Connor et al. (2007) observed in bereaved individuals that HFHRV assessed outside the scanner was associated with greater ventral
posterior cingulate cortex activity in response to grief stimuli. In a
region-of-interest analysis, Mujica-Parodi et al. (in press) observed
that individuals with greater levels of trait anxiety showed greater
uncoupling, or dysregulation, of their limbic responses to neutral,
fearful, and happy faces, and this uncoupling was correlated with
decreases in two indices of heart rate variability. In the latter study
BOLD activity in ACC and HF-HRV were not specifically evaluated.
These studies provide useful data indicating the relevance of vagal
tone to neural processing of cognitive and emotional stimuli. They
highlight participation of structures on the medial surface of the brain,
particularly the anterior and posterior cingulate cortices, in vagal
regulation. However, the neural correlates of HRV in relation to
cognitive and emotional stimuli have not yet been differentiated, and
the neural regulation of vagal tone during emotion in real time has not
yet been studied.
Examining the covariation of HRV and neural activity during
emotion is important for several reasons. First, the imaging studies to
date that included simultaneous HRV measurements have involved
stressful cognitive tasks that have cognitive demands in the foreground with accompanying affective components. Examining HRV
during induced emotion permits a more direct examination of the
neural regulation of HRV in relation to emotion. Second, autonomic
activity is an intrinsic part of emotional responses. A growing body of
evidence indicates that a network of structures in the brain mediate
emotional responses (Phan et al., 2002; Wager et al., 2003). Emotional
responses are themselves complex, with experiential, cognitive,
behavioral, somatomotor and visceromotor components. An examination of the covariation of HRV and brain activity during emotion
will shed light on the neural regulation of the visceromotor
component of emotional responses. Third, doing so permits a direct
test of Claude Bernard's hypothesis using modern methods.
Vagal tone has been hypothesized to play an important role in
emotion regulation (Appelhans and Luecken, 2006). Emotion regulation has been defined as consisting of automatic and intentional
processes that influence what emotions a person has, when they have
them and how they experience and express them (Gross, 1998).
Emotion regulation may therefore consist of selecting an optimal
response and inhibiting less functional responses from a broad
behavioral repertoire. Relatedly, HRV may be considered a resource
that can be drawn upon in support of this regulatory function (Thayer
and Lane, in press). From this perspective it is not surprising that greater
HRV is associated with enhanced cognitive performance (Johnsen et al.,
2003). In a study of 53 male sailors, those with higher HRV showed
more correct responses than the low HRV group on a working memory
test. In addition, the high HRV group showed faster mean reaction time,
more correct responses and fewer errors than the low HRV group on a
continuous performance task (CPT), particularly when executive
functions were involved (Hansen et al., 2003). In another study of
healthy volunteers, performance on executive tasks suffered when HRV
levels were decreased by aerobic de-training (Hansen et al., 2004).
These results raise the possibility that HRV, as an index of inhibitory
function, participates in regulating both cognitive and emotional
performance. A critical question that has not yet been addressed is
the extent to which the neural substrates of vagal regulation differ as a
function of cognitive or emotional contexts.
To examine the relationship between HF-HRV and brain activity
in the context of emotion conditions that would be broadly
generalizable, we induced both positive and negative emotions
(happiness, sadness, disgust) with two different emotion induction
techniques (film and recall) in a random effects analysis. By using
PET the typical time needed to measure HF-HRV reliably matched
the 60 second scans with 15O-water and avoided any potential
complications in HF-HRV measurement associated with the electrically hostile fMRI environment. We also included emotionally
neutral control conditions to help disentangle rCBF due to emotion
from emotion-independent effects. Based on the findings from
previous studies cited above, we hypothesized that structures in the
frontal lobe, particularly in the ACC, would be associated with HFHRV during induced emotion. We also sought to determine whether
Table 1a
Emotion-minus-Neutral rCBF
Region
X
Y
Z
Z value
Voxel p (uncorrected)
Extent (voxels)
Cluster p corrected
Cluster p (uncorrected)
Superior temporal gyrus (BA 22)
Brainstem
Middle temporal gyrus (BA 21)
Superior temporal gyrus (BA 38)
Cerebellum
62
−16
58
−42
44
− 52
− 22
6
18
− 58
10
−4
− 24
−34
−32
5.21
5.03
4.98
4.26
3.83
p < 9.3 × 10− 8
p < 2.46 × 10− 7
p < 3.23 × 10− 7
p < 6.90 × 10− 5
p < 3.30 × 10− 6
1291
3553
2516
2099
3174
p < 0.009
p < 0.018
p < 0.022
p < 0.217
p < 0.569
p < 0.012
p < 1.23 × 10− 6
p < 2.01 × 10− 5
p < 1.02 × 10− 5
p < 6.31 × 10− 5
Brain areas where there is a significant increase in rCBF during the 6 emotion conditions relative to the 6 neutral conditions. In this and all other tables a voxel threshold of p < .005
uncorrected and an extent threshold of p < .05 uncorrected was used. XYZ coordinates are in MNI space. Model 1 in Methods was used for this analysis.
R.D. Lane et al. / NeuroImage 44 (2009) 213–222
215
Table 1b
Neutral-minus-Emotion rCBF
Region
X
Y
Z
Z value
Voxel p (uncorrected)
Extent (voxels)
Cluster p corrected
Cluster p (uncorrected)
Middle Frontal Gyrus (BA 10)
Precuneus (BA31)
Middle Temporal Gyrus (BA21)
46
−24
64
48
−80
−42
16
26
−14
4.97
4.07
3.95
p < 3.36 × 10− 7
p < 2.34 × 10− 5
p < 3.85 × 10− 5
14,401
3270
408
p < 0.022
p < 0.179
p < 0.35
p < 9.5 × 10− 16
p < 2.56 × 10− 6
p < 0.04
Brain areas where there is a significant decrease in rCBF during the 6 emotion conditions relative to the 6 neutral conditions. Model 1 in Methods was used for this analysis.
A screening procedure was used to identify 12 right-handed,
neurologically and psychiatrically healthy, unmedicated female volunteers who were likely to have intense emotional responses in the PET
laboratory. The sample was restricted to females to maximize the
homogeneity of emotion-dependent changes in CBF and the likelihood
of intense self-reported emotional experiences (Shields, 1991). An
advertisement was used to recruit female volunteers between the ages
of 18 and 30 who were “able to accurately describe [their] emotional
reactions to daily events.” Psychiatric and medical histories, the
Structured Clinical Interview for DSM III-R — Non-Patient Version
(SCID-NP) (Spitzer et al., 1990), the Edinburgh Handedness Inventory
(Oldfield, 1971) and a complete neurological examination were used to
identify subjects for further evaluation. Prospective subjects were
included in the PET study if they reported separate experiences during
the previous six months of happiness, sadness, and disgust that were
each rated at least 6 on a 0 to 8 visual analog scale (8 representing the
most intense experience of that kind in their lives) and if they rated
each of an alternate screening set of three films targeting happiness,
sadness, and disgust, respectively, at least 5 on an 8 point scale. After a
complete description of the study was given to the subjects, written
consent was obtained. Subjects received compensation for their
participation in the PET study. The 12 subjects who completed the
PET protocol had a mean age of 23.3 years (SD = 3.2).
emotion film that in the judgment of the investigators evoked the target
emotion most intensely was selected for viewing during the 1-minute
scan. Three additional, emotionally “neutral” silent film clips were used
to control for potentially confounding features of the emotiongenerating film task, such as emotionally irrelevant visual stimulation
and eye movement. These clips were culled from nature films (scenes of
a beach, woods, etc) and did not include people.
In addition, autobiographical scripts of three recent experiences
were used during the PET session for the internal generation of the
same three target emotions. Three additional, emotionally “neutral”
autobiographical scripts of recent experiences were used to control for
emotionally irrelevant recall memory and visual imagery. Subjects
were instructed to focus during recall on a previously identified
moment during which the target emotions were experienced very
intensely and other emotions were experienced much less intensely.
Immediately prior to each PET scan, the subject listened to either a
brief synopsis of the film clip or the autobiographical script. During
the emotion-generating film and recall tasks, subjects were asked to
feel the relevant target emotion. For the control film and recall tasks,
subjects were asked to feel emotionally “neutral.” During the film
tasks the subjects' eyes were open and fixed on the center of a ceilingmounted 27-inch video monitor. During the recall tasks, the subjects'
eyes were closed and directed forward.
Twelve scans were performed in blocks of six for film and recall,
respectively. The order of the blocks was counterbalanced. Emotiongenerating and control tasks were performed in an alternating
sequence within each block. Whether each block began with an
elicitor or control was counterbalanced across subjects. Within these
constraints, the order of the three elicitors and three controls in each
block was randomized.
Experimental design
Self-report ratings of stimuli
During the PET session, three empirically validated silent, color,
feature film clips (Tomarken et al., 1990) were used for the external
generation of three subjectively, facially and electrophysiologically
well-characterized target emotions: happiness, sadness, and disgust.
The film clips activate relatively pure emotion and are each approximately 2 min in duration. It is notable that all of these clips include
actors displaying facial expressions. The 1-minute segment from each
Immediately following each scan subjects rated their experience of
seven emotions (interest, amusement, happiness, sadness, fear,
disgust, anger) on separate 0–8 visual analog scales. These individual
ratings were used to validate the success of the emotion induction
methods. To derive a Composite Emotion Self-Report Index to
correspond to the Emotion-minus-Neutral rCBF contrast for each
subject, we used the rating of the target emotion for each emotion
the neural correlates of vagal tone varied as a function of emotion
and emotion-independent contexts.
Methods
Subjects
Table 1c
Emotion-minus-Neutral rCBF independent of HF-HRV
Region
X
Y
Z
Z value
Voxel p uncorrected
Extent (voxels)
Cluster p corrected
Cluster p uncorrected
Thalamus
Inferior temporal gyrus (BA21)
Medial prefrontal cortex (BA 10)
Superior temporal gyrus (BA 22)
20
64
2
62
−22
−4
52
−52
2
−18
26
12
3.96
3.9
4.03
4.76
p < 3.81 × 10− 5
p < 4.81 × 10− 5
p < 2.8 × 10− 5
p < 9.91 × 10− 7
2771
1247
921
371
p < 1.90 × 10− 4
p < 0.018
p < 0.056
p < 0.469
p < 1.70 × 10− 5
p < 0.002
p < 0.005
p < 0.056
Same as Table 1a, except the Emotion-minus-Neutral rCBF contrast was generated from a model that included HF-HRV (see Model 3 in Methods). This analysis identifies rCBF unique
to the Emotion-minus-Neutral contrast and thus excludes variance in rCBF in this contrast that overlaps with variance due to HF-HRV.
Table 1d
Neutral-minus-Emotion rCBF independent of HF-HRV
Region
X
Y
Z
Z value
Voxel p uncorrected
Extent (voxels)
Cluster p corrected
Cluster p uncorrected
Precuneus (BA 31)
Ventrolateral prefrontal cortex (BA 10)
14
26
−70
66
20
−2
8.11
4.69
p < 2.86 × 10− 6
p < 3.33 × 10− 4
6753
392
p < 2.58 × 10− 8
p < 0.433
p < 2.3 × 10− 9
p < 0.051
Same as Table 1b, except the Neutral-minus-Emotion rCBF contrast was generated from a model that included HF-HRV (see Model 3 in Methods).
216
R.D. Lane et al. / NeuroImage 44 (2009) 213–222
Fig. 1. Mean Ratings (N=12) of Happiness, Sadness, and Disgust for Each Type of Film
and Recall Stimulus (Ratings on a 0–8 visual analog scale were obtained on multiple
emotions immediately after each scan. The values for film and recall controls each
represent the mean values for three scans.)
condition and averaged across the film and recall stimuli and then
used the average of the happiness, sadness and disgust ratings for
each neutral condition and averaged across the film and recall stimuli.
An Emotion-minus-Neutral self-report contrast was then generated
for each subject for analysis in relation to rCBF or HF-HRV.
HRV collection and analysis
The electrocardiogram (ECG) was recorded on polygraph paper
during each 1-minute scan. Consecutive RR intervals were measured by
hand using calipers positioned at the beginning of each R wave. Artifact
in the recordings (e.g. noise in the isoelectric line) was minimal
(estimated to affect 3 beats per thousand) and did not interfere with RR
interval measurement. Subjects were young, medically healthy and
unmedicated so that artifact due to missed beats, ectopic beats or other
abnormalities were not detected in the 144 min of ECG data evaluated.
A trained rater hand-scored all data and a second rater independently
hand-scored all inter-beat intervals for 3 randomly selected 1-minute
scans from 3 subjects (approximately 70 measurements for each
subject), and these R–R values were correlated with those used in this
study. The aggregate inter-rater reliability was r = .95, supporting the
reliability of our data. Spectral analysis using a fast Fourier transform
was used to generate the heart period power spectrum (Task Force,
1996). The high frequency (HF) component (0.15–0.40 Hz) was used as
our estimate of vagally mediated HRV. Twelve separate measures of HFHRV were obtained for each subject.
Respiratory rate measurement
We derived a respiratory rate measurement by applying an
autoregressive (AR) algorithm to each 1-minute ECG tracing corresponding to each PET scan (Thayer et al., 2002). AR estimates of
respiratory rate are less susceptible to artifacts such as movement and
have been found to be more reliable than strain gauge measurements.
Imaging parameters and imaging analysis
T1-weighted volumetric magnetic resonance images (MRIs) of the
head were acquired prior to the PET session to ensure the structural
normality of the brain, facilitate head positioning in the PET scanner,
and permit co-registration between PET and MRI images (for more
accurate normalization of PET scans and anatomical localization of PET
findings). Subject preparation for PET included the insertion of a
catheter in the left antecubital vein to permit tracer administration,
head immobilization using tape rather than a fast-hardening foam
mold to permit quantitative EEG measurement during the PET session,
Fig. 2. (a) Emotion-minus-Neutral rCBF. Brain areas where there is a significant increase
in rCBF during the 6 emotion conditions relative to the 6 neutral conditions. See Table
1a. Axial, coronal and sagittal statistical parametric maps are presented. The
significance level in z scores is color-coded. Cross-hairs are in the identical location
(coordinates = 2, 52, 26) as in panel b to facilitate comparison. The local maximum rCBF
increase in medial prefrontal cortex in this contrast is somewhat more anterior and
inferior (coordinates = 2, 64, 20). (b) Emotion-minus-Neutral rCBF independent of HFHRV. Same as in panel a, except the Emotion-minus-Neutral rCBF contrast was
generated from a model that included HF-HRV. See Table 1c. Axial, coronal and sagittal
statistical parametric maps are presented. The significance level in z scores is colorcoded. Cross-hairs pinpoint the local maximum rCBF increase in medial prefrontal
cortex in this analysis (coordinates = 2, 52, 26).
R.D. Lane et al. / NeuroImage 44 (2009) 213–222
Frith, 2006) was applied using the MarsBar plug-in in SPM to further
interrogate the findings in Table 1a as described below.
Table 2
HF-HRV values by condition
Film
Recall
217
Happiness
Sadness
Disgust
Neutral
970.6 (473.7)
2666.4 (3367.2)
1272.0 (785.4)
2654.1 (2921.9)
1637.3 (1921.0)
2040.0 (2333.8)
1905.9 (1346.8)
2828.3 (2616.6)
Mean and standard deviation of HF-HRV power values (ms2) for each condition
aggregated across 12 subjects. For the neutral conditions the value used for each subject
was the mean of three separate measurements.
and the performance of a transmission scan using a germanium/
gallium ring source to correct subsequent emission images for
radiation attenuation. During each scan, the subjects rested quietly
in the supine position without movement. Twelve 31-slice PET images
of rCBF were obtained in each subject as she alternated between
emotion-eliciting and control tasks using the ECAT 951/31 scanner
(Siemens, Knoxville, TN), 40 mCi intravenous bolus injections of [15O]water, 1-minute scans (Frith et al., 1991; Reiman et al., 1989a, 1989b).
The radiotracer was administered at predetermined times shortly
after the onset of the film and recall tasks. PET images were
reconstructed with an in-plane resolution of 10 mm full width half
maximum (FWHM) and a slice thickness of 5 mm FWHM.
The data were analyzed with statistical parametric mapping using
SPM2 (http://www.fil.ion.ucl.ac.uk/spm/). For each subject the 12 PET
images were realigned to each other, co-registered to that subject's
structural MRI, spatial normalization parameters were derived from
MRI images for each subject and then applied to the corresponding
PET images. A 12 mm FWHM Gaussian kernel was used to smooth the
images. Regional CBF equivalents were adjusted to a global mean of
50 ml/dl/min by proportional scaling.
The PET data were analyzed using random effects models. At the
first level of analysis for each individual subject, four types of
individual-level models were used to fully investigate the relationship
between rCBF, the emotion conditions and HF-HRV. Model 1 was
conditions-only and coded only for condition (Emotion and Neutral).
This model was used to examine the main effect of emotion
(Tables 1a, 1b). Model 2 was a single-subject covariate-only model
that examined the effects of HF-HRV across emotional and neutral
conditions (Table 3). Model 3 included both condition coding and HFHRV to examine the effects of emotion or HF-HRV while considering
the effects of the other (Tables 1c, 1d, 5). Model 4 was an interaction
model that examined the relationship between rCBF and HF-HRV
separately for the emotional and neutral conditions, so that these
relationships could be directly compared at each voxel (Table 4). In all
cases, the relationship between condition or HF-HRV or both and rCBF
was evaluated at the individual subject level, and maps representing
the strength of this relationship were entered into random effects,
group-level t-tests. A threshold peak of t > 3.11 (p < .005) uncorrected
at the voxel level, extent threshold 5 voxels, was used to generate
results, and among those findings those that also met or exceeded the
p < .05 uncorrected threshold at the cluster level are presented in the
tables and results. Moreover, a small volume correction (SVC) for
bilateral anterior rostral medial frontal cortex (arMFC) (Amodio and
Results
Effects of emotion inductions on self report and rCBF
Previous reports from this dataset focused on the separate neural
substrates of happiness, sadness and disgust (Lane et al., 1997a) and the
neural correlates of emotion induced by film vs. recall (Reiman et al.,
1997). As shown in Fig. 1, the emotion induction methods were
successful in inducing the target emotions as indicated by self-reported
ratings immediately following each scan.
Table 1a and Fig. 2a displays the findings for the main effect of the
rCBF contrast of Emotion-minus-Neutral (E − N) in a model with
conditions only (Model 1). Table 1c and Fig. 2b display the unique
variance attributable to the main effect of the rCBF contrast of
Emotion-minus-Neutral (E − N) in a model that included HF-HRV
(Model 3). A comparison of Figs. 2a and b reveals considerable overlap.
The latter (Fig. 2b, Table 1c), which excludes variance shared with HFHRV, shows significant activation in the thalamus, medial prefrontal
cortex and right superior and inferior temporal cortices. Based on
evidence that medial prefrontal cortex is commonly activated during
emotion (Amodio and Frith, 2006), we applied a SVC for this region to
the analysis that generated Table 1a and obtained a significant result
for bilateral anterior rostral medial frontal cortex (p < 0.041 [voxel level
FWE corrected] and p < 0.044 [cluster level corrected p]).
Tables 1b (from Model 1) and 1d (from Model 3) display the
findings from the Neutral-minus-Emotion (N − E) contrast. The latter,
which excludes variance shared with HF-HRV, shows significant rCBF
decreases during emotion in the right precuneus and the right
ventrolateral prefrontal cortex.
To evaluate whether self-reported emotional experiences were
associated with the Emotion-minus-Neutral rCBF findings, we used
the Composite Emotion Self-Report Index for the E − N contrast (see
Methods) using a group-level one-sample t-test in SPM and observed
no significant associations with emotion-specific rCBF from Model 3
(Table 1c), even at a reduced threshold (p < 0.05). This same Composite
Emotion Self-Report Index was evaluated in relation to HF-HRV using
SPSS and again no significant association was observed (p = .64).
Effects of emotion inductions on HRV
The means and standard deviations for HF-HRV in each emotion
and neutral condition for both film and recall are listed in Table 2.
Consistent with previous research, HF-HRV was lower during the
emotion conditions compared to the neutral conditions [t(11) = 1.54,
p = 0.07, one tailed].
A comparison of respiratory rate (breaths per minute) between the
Emotion (mean = 14.96 (2.6)] and Neutral (mean = 14.83 (2.7)] conditions derived from AR spectral analysis revealed no significant
difference [t(11) = 0.169, p = 0.869]. This indicates that HF-HV comparisons between the Emotion and Neutral conditions are not confounded by respiratory rate.
Table 3
Correlation of HF-HRV with rCBF across all conditions
Region
X
Y
Z
Z value
Voxel p uncorrected
Extent (voxels)
Cluster p corrected
Cluster p uncorrected
Right superior prefrontal cortex (BA 8, 9)
Left rostral anterior cingulate cortex (BA 24)
Right dorsolateral prefrontal cortex (BA 46)
26
−6
50
2
44
30
40
42
48
48
58
− 32
52
− 38
40
8
8
2
50
28
42
3.3
2.96
3.25
3.2
3.05
3.04
2.97
p < 4.91 × 10− 4
p < 0.002
p < 5.74 × 10− 4
p < 0.001
p < 0.001
p < 0.001
p < 0.001
1015
983
862
p < 0.012
p < 0.014
p < 0.025
p < 7.74 × 10− 4
p < 9.00 × 10− 4
p < .002
753
p < 0.043
p < .003
Right parietal cortex (BA 40)
Brain areas where there is a significant correlation of HF-HRV with rCBF across all conditions. Model 2 in Methods was used for this analysis.
218
R.D. Lane et al. / NeuroImage 44 (2009) 213–222
rCBF correlates of HF-HRV across all conditions
Images from all 12 conditions (film and recall, emotion and
neutral) for each subject were entered into a single-subject covariateonly design, using mean-centered HRV values for each scan. A contrast
that weighted the intra-subject covariance positively was then
entered into a one-sample t-test for a random effects analysis. Four
areas exceeded the a priori threshold. Results are listed in Table 3 and
Fig. 3 and demonstrate positive correlations in the right superior
prefrontal cortex (BA 8,9), the left rostral anterior cingulate cortex
(ACC) (BA 24), the right dorsolateral prefrontal cortex (BA46) and the
right parietal cortex (BA40). These findings reflect the brain areas that
covary with HF-HRV when the contributions of emotion and cognition
are not disentangled.
Emotion-specific neural correlates of HF-HRV
We next evaluated the positive emotion-specific neural correlates of
HF-HRV by regressing HF-HRV with rCBF under Emotion and Neutral
scans and contrasting the degree of association of HF-HRV/rCBF
between the two conditions. These contrasts were computed for each
subject and then aggregated across subjects using a random effects
one-sample t-test analysis. As depicted in Table 4 and Fig. 4, the positive
emotion-specific neural correlates of HF-HRV included a broad bilateral
Fig. 3. Correlation of HF-HRV with rCBF across all conditions. Depiction in three dimensions of the four brain areas where there is a significant correlation between HF-HRV and rCBF
across all conditions. Cross-hairs are located at the local maximum in each cluster.
R.D. Lane et al. / NeuroImage 44 (2009) 213–222
219
Table 4
Correlation of HF-HRV with emotion-specific rCBF
Region
X
Y
Z
Z value
Voxel p uncorrected
Extent (voxels)
Cluster p corrected
Cluster p uncorrected
Caudate (head)
Midbrain (including periaqueductal gray)
Left insula
Medial prefrontal cortex BA 10
−4
18
−30
2
6
−20
10
52
6
−2
−10
26
3.78
3.42
3.28
3.56
p < 7.77 × 10− 5
p < 3.17 × 10− 4
p < 5.20 × 10− 4
p < 1.84 × 10− 4
463
339
229
208
p < 0.102
p < 0.252
p < 0.539
p < 0.613
p < 0.005
p < 0.014
p < 0.037
p < 0.046
Brain areas where there is a significant correlation of HF-HRV with rCBF associated with the Emotion-minus-Neutral contrast. Model 4 in Methods was used for this analysis.
swath of the ventral striatum with a peak local maximum at the head of
the caudate nucleus, the medial prefrontal cortex (BA10), the midbrain
in a region that includes the periaqueductal gray (PAG) and the left midinsula. It should be noted that the medial prefrontal cortex area
identified in Table 4 is identical to that in Table 1c, and the midbrain area
identified in Table 4 is only a few mm. from the local maximum in the
thalamus identified in Table 1c.
The parameter estimates for each of the four areas listed in Table 4
was determined next: the slope for each regression line was
determined, the E − N slope difference was derived for each subject
and these E − N slope differences were aggregated across the 12 subjects.
Fig. 5 demonstrates that the slope was greater for E than N (i.e. there is
greater change in rCBF per unit of change in HF-HRV) in all four areas.
Asymmetry effects were tested by identifying homologous regions
on the opposite hemisphere based upon the peak activations reported in
Tables 3 and 4. Whereas the peak activations suggested some lateralized
effects, all asymmetry analyses failed to reach statistical significance.
Inverse correlation
The inverse or negative correlation of HF-HRV with E − N rCBF
revealed just one area, the cuneus (BA 19) [coordinates = −22, −92, 30;
z = 4.07, cluster size = 2102, voxels, p < .001 corrected] that met a priori
statistical criteria.
Emotion-independent neural correlates of HF-HRV
We next evaluated the positive correlates of HF-HRV independent
of emotion. To do so we generated the positive correlation of HF-HRV
Fig. 4. Correlation of HF-HRV with emotion-specific rCBF. Brain areas where there is a
significant correlation of HF-HRV with rCBF associated with the Emotion-minusNeutral contrast. See Table 4. Sagittal and axial views of the correlation with HF-HRV in
medial prefrontal cortex and coronal and axial views of the correlation with HF-HRV in
the left insula are depicted.
with rCBF across all 12 scans within each subject, removed variance
due to the E − N rCBF main effect in that subject and then aggregated
across subjects in a random effects analysis. Table 5 demonstrates
significant associations in pregenual medial prefrontal cortex (BA10)/
anterior cingulate cortex (BA32), right dorsolateral prefrontal cortex
(BA10/45) and bilateral parietal cortices.
Discussion
This is the third known study in which HF-HRV has been assessed
during functional neuroimaging and the first involving emotion. By
identifying rCBF specific to emotion, we were able examine how the
main effect due to emotion corresponded to the covariation of
emotion-specific rCBF with HF-HRV. By removing variance due to the
main effect of emotion-specific rCBF we were able to examine how
rCBF independent of emotion covaried with HF-HRV. In addition, we
were also able to compare each of these patterns to the covariation of
HF-HRV with rCBF across the entire experiment. This permitted a
dissection of the relative contributions of emotion and cognitive
conditions to the neural correlates of HF-HRV in a context analogous
to the two previous studies, in which both cognitive and emotional
elements were simultaneously operative.
In our emotion-specific analyses we aggregated conditions in order
to have sufficient statistical power and subtracted the neutral from the
emotion conditions in order to identify the neural correlates of vagal
tone that were specific to emotion. The comparison of the main effect of
emotion-specific rCBF independent of HF-HRV (Table 1c, Fig. 2b) with
the correlation between emotion-specific rCBF and HF-HRV (Table 4,
Fig. 4) revealed the striking finding that the coordinates of the medial
prefrontal cortex (BA10) were identical in the two analyses. This is an
area that participates in establishing a representation of one's own
emotions or mental states as well as those of others (Lane et al., 1997b;
Ochsner et al., 2004). The covariation of activity of this structure with
HF-HRV suggests that this particular cognitive function is simultaneously linked to activation of the medial visceromotor network (Ongur
Fig. 5. Parameter estimates from the correlation of HF-HRV with emotion-specific rCBF.
Parameter estimates from the correlation of HF-HRV with emotion-specific rCBF for the
four areas shown in Table 4. Positive values for each parameter estimate indicate that
there is a greater change in rCBF per unit of change in HF-HRV in the emotion conditions
relative to the neutral conditions in that area. Units on the ordinate are arbitrary.
220
R.D. Lane et al. / NeuroImage 44 (2009) 213–222
Table 5
Correlation of HF-HRV with rCBF excluding variance due to emotion-specific rCBF
Region
X
Y
Z
Z value
Voxel p uncorrected
Extent (voxels)
Cluster p corrected
Cluster p uncorrected
Pregenual medial prefrontal cortex (BA 32/10)
Right superior frontal gyrus (BA 10/46)
Left parietal cortex (BA 40)
Right parietal cortex (BA 40, 39)
−8
28
−44
46
48
56
−34
−52
4
10
38
44
3.47
3.53
3.34
3.89
p < 2.64 × 10− 4
p < 2.10 × 10− 4
p < 4.19 × 10− 4
p < 5.02 × 10− 5
1333
1235
235
229
p < 0.001
p < 0.002
p < 0.584
p < 0.603
p < 6.63 × 10− 5
p < 0.001
p < .049
p < .051
Brain areas where there is a significant correlation between HF-HRV and rCBF across all conditions excluding variance in rCBF due to the Emotion-minus-Neutral contrast (see Model
1 in Methods and Table 1a).
et al., 1998; Price, 1999). Since most studies that explore the function of
this region do not include measures of HRV, it will be important in
future studies to examine whether main effect and covariate analyses
yield the same result as they did in this study.
While it is tempting to speculate about the possible contribution of
afferent autonomic input to the representation of one's own
emotional state, our experimental design did not enable us to
disentangle whether the correlations that we observed constituted
afferent, bottom-up vs. efferent, top-down mechanisms. However, as
noted above, PET imaging offers the unique advantage of capturing
sustained brain activity concurrently with accurately measured HFHRV. Given that HF-HRV can change on the order of milliseconds, and
that subjects maintained the same emotional state for 1 min, it is our
contention that the observed findings represent the activity associated
with an equilibration of afferent and efferent mechanisms — not
unlike that described by Claude Bernard over one hundred years ago.
Disentangling afferent from efferent mechanisms will require experimental manipulations or the study of special populations (e.g. primary
autonomic dysfunction as studied by Critchley et al., 2001).
A second region identified in the correlation between HF-HRV and
emotion-specific rCBF was a broad area that included the thalamus, an
area activated in the emotion-specific rCBF main effect, and the
periaqueductal gray (PAG). Although PET lacks sufficient resolution to
be certain that a correlation with PAG (or any other brainstem
nucleus) is present, the images in Figs. 2 and 4 are suggestive. The PAG
is responsible for coordinating visceral and behavioral responses to
stress and threat (Price, 1999). The cortical projections to the PAG arise
almost exclusively from the medial visceromotor network or
structures closely related to it. Inspiratory neurons in the nucleus
tractus solitarius (a vagal nucleus in the brainstem) are activated by
stimulation of the PAG (Huang et al., 2000), further supporting its
relevance to cardiorespiratory function as measured by HF-HRV.
A third area identified in the correlation between HF-HRV and
emotion-specific rCBF is the caudate nucleus. Emotions are clearly
associated with automatic action patterns including gestures and
facial expressions as well as approach and avoidance behavioral
tendencies. In fact, some theorists think that emotion is fundamentally an action tendency (Frijda, 1986). Obrist (1981) pointed out that
the autonomic changes associated with a mental state covary with the
expected motor output that accompanies that state. In this PET
experiment, subjects were told that they should not move. This
instruction required that actions, which were more likely with
emotion than neutral conditions, be inhibited. We speculate that
such inhibition was associated with activation of inhibitory corticalbasal ganglia circuits (from dorsolateral prefrontal cortex or anterior
cingulate cortex) (Alexander et al., 1986) that resulted in greater
synaptic activity at the caudate nucleus. Somatomotor metabolic
demands were therefore likely greater during emotion than neutral
conditions, consistent with the trend for HF-HRV to be lower (i.e.
arousal to be higher) during emotion. Our finding that HF-HRV
correlates with neural activity in the caudate nucleus, as well as the
PAG, may reflect the close linkage between visceromotor and
somatomotor activity in the context of emotional arousal.
The fourth area identified in the correlation between HF-HRV and
emotion-specific rCBF is the left mid-insula. The posterior insular
cortex is the primary projection area for visceral sensation, while the
anterior insula, particularly on the right side (Craig, 2003), is a higher
association area for these bodily signals (Rolls, 1992) and is involved in
remapping these signals into conscious bodily feelings (Critchley et al.,
2001). Electrical stimulation of the insula produces changes in arterial
pressure, heart rate, respiration, piloerection, and other autonomic
effects in laboratory animals and humans (Cheung and Hachinski,
2000). A study of seven patients with strokes in the left insula
demonstrated decreased HRV and other indices of electrical instability
compared to control subjects (Oppenheimer et al., 1996), a finding that
corresponds well to our observations in this study.
The positive correlation between activity in these structures and
HF-HRV means that as brain activity INCREASES the braking action on
the heart INCREASES. While this may at first appear counterintuitive,
this phenomenon constitutes the core of the neurovisceral integration
model that two of us have discussed at some length (Thayer and Lane,
2000, in press). The frontal lobe areas identified in this study have
been implicated in different aspects of conscious processing of
emotional responses. The neurovisceral integration model holds that
conscious experience of emotion requires the transmission of
subcortical affective information to the cerebral cortex and new
evidence by Williams et al. (2006) indicates that top-down feedback
from cortical to subcortical structures is necessary for conscious
emotional experience to occur. In addition, our model holds that the
top-down inhibitory influence has a modulatory effect on the
subcortical centers that shapes the nature of subjective experience.
This is consistent with the more general principle that inhibition
serves to ‘sculpt’ excitatory neural action at all levels of the neuraxis to
produce context appropriate responses to environmental demands
(Knight et al., 1999; Thayer, 2006; Thayer and Lane, 2005).
Two brain areas were observed to be associated with decreases in
emotion-specific rCBF independent of HF-HRV: right ventrolateral cortex
and right precuneus. Growing evidence indicates that the right
ventrolateral prefrontal cortex participates in inhibiting emotional
responses (Lieberman et al., 2007; Aron et al., 2004; Colcombe et al.,
2005; Garavan et al.,1999; Konishi et al.,1999). Thus, greater activity in the
context of decreased emotion (relative to neutral) would be expected. The
precuneus (posteromedial parietal lobe) has one of the highest resting
metabolic rates and is thought to be a component of the default network
(participating in self-related mental representations at rest) (Cavanna and
Trimble, 2006). Emotion-specific rCBF decreases may be explained by
greater activity in the precuneus in the neutral state, which is comparable
to rest, whereas activity related to spontaneous self-generated mental
states was likely reduced during the emotion conditions in response to the
prescriptive instructions of the experimenter.
For the sake of completeness we also examined the inverse
correlation between emotion-specific rCBF and HF-HRV. This analysis
revealed a single large cluster in extra-striate visual cortex. Perhaps
this is best understood by considering that HF-HRV is low when
arousal is high. The finding that rCBF is increased in extra-striate
visual cortex during conditions of high emotional arousal is well
established (Lane et al., 1999) and may be related at least in part to
feedback from the amygdala to all levels of the visual processing
stream (Morris et al., 1998).
Our task instructions to subjects also required that they sustain in
working memory the goal of maintaining the target emotion or neutral
state for the entire duration of each scan. Maintenance of that target
R.D. Lane et al. / NeuroImage 44 (2009) 213–222
state also involved some degree of regulation in order to maintain the
target content at a sufficiently intense level. While these processes
applied to both emotion and neutral conditions and are reflected in the
findings in Table 3, the effects of task maintenance and selection
independent of emotion are listed in Table 5. Dosenbach et al. (2006,
2007) have shown that across many tasks bilateral inferior parietal
cortex participates in maintaining task set (along with dorsal anterior
cingulate cortex and bilateral anterior insula/frontal operculum).
Dorsolateral prefrontal cortex (DLPFC) (BA46) is well known to play a
key role in working memory (Goldman-Rakic, 1996). BA46 has also
been implicated in several imaging studies involving emotion regulation, including a reappraisal task (Ochsner et al., 2002), the regulation of
anticipatory anxiety (Kalisch et al., 2005) and suppression of negative
affect (Phan et al., 2005). Kuhl et al. (2007) have shown that DLPFC
participates in selection and control in the presence of mnemonic
competition. The right superior frontal cortex is an area involved in
executive control of attentional shifting (Nagahama et al., 1999),
particularly in relation to working memory (Milham et al., 2001), and
monitoring the contextual significance of information retrieved from
episodic memory (Henson et al., 1999). Given that all of these cognitive
functions were likely operative in this study, and that all involve mental
effort and some degree of inhibition of mental content that is not
maintained or selected, the positive correlation with HF-HRV suggests
that the cognitive functions instantiated in these brain areas were
supported with increases in vagal tone.
Another striking finding was that rostral ACC correlated with HFHRV across all experimental conditions (Table 3) as well as when the
main effect of emotion-specific rCBF was removed (Table 5). This
finding was unexpected given that this locus is considered part of the
“affective division” of the ACC (Bush et al., 2000). We have previously
demonstrated that the so-called “cognitive division” of the ACC is not
exclusively cognitive (McRae et al., 2008), and here we believe that we
have demonstrated that the “affective division” of the ACC is not
exclusively affective. Our current results suggest that rostral ACC is
involved in coordinating the autonomic adjustments associated with
maintaining a self-focused mental state that may but need not include
emotion (Amodio and Frith, 2006). The rostral ACC is a key component
of the medial visceromotor network. Price et al. (1996) have
demonstrated that medial prefrontal structures are highly interconnected, including the ventral and dorsal ACC areas observed in the two
previous studies involving concurrent functional brain imaging and
HF-HRV assessments. It is therefore possible that all three studies
activated a similar final common pathway from the frontal lobe to the
vagal nuclei in the brainstem.
Several of us (Ahern et al., 2001) previously observed in epilepsy
patients undergoing intracarotid injection of sodium amytal that
larger and faster heart rate increases and greater vagally mediated
HRV decreases were observed with right than left-sided injections.
These findings are consistent with known asymmetries in the
autonomic innervation of the heart such that the sinoatrial node,
from which normal sinus rhythm emanates, is innervated almost
exclusively by right-sided sympathetic and parasympathetic fibers
(Schwartz, 1984). In addition, neural tract tracing studies originating
in rat myocardium suggest that predominantly right-sided brain
structures contribute to cardiac vagal control (Ter Horst and Postema,
1997). Although three of the four significant correlations across all
conditions (Table 3) were right-sided, asymmetry analyses for those
findings as well for the emotion-specific covariate analysis (Table 4),
which produced results involving brain structures on both the right
and left sides, failed to meet our a priori threshold for statistical
significance. These results are not consistent with our own expectations of right-sided predominance in the regulation of vagal tone or
Craig's (2005) suggestion that cortical regulation of vagal tone is
predominantly left-sided. Further research with a variety of tasks,
larger samples of men and women, and effective connectivity
analyses, as well as additional lesion studies, will be needed to sort
221
out in what contexts and in what brain areas right vs. left-sided
structures predominate in vagal regulation.
There were several limitations to this study. First, only female
subjects were used. Whereas this had the distinct advantage of selecting
subjects known for their superior emotion-related abilities, it limits our
ability to generalize these results to males. Clearly future research will
need to replicate these findings in males. Second, the sample size was
relatively small, thus exposing us to an increased risk of a Type II error.
For example, this may have significantly hampered the asymmetry
analyses, as noted above. In addition, future studies with larger sample
sizes might well find additional neural structures associated with HRV
during emotion. Third, the nature of the emotion tasks involved multiple
emotions, an emotionally neutral state, two different emotion induction
techniques and the explicit instruction to the subjects to attend to and
maintain the target mental state. It will be important to characterize the
correlates of HF-HRV during simpler emotion tasks and compare them
to emotionally neutral control tasks as well.
In conclusion, this study examined the neural correlates of HF-HRV
during emotion in real time. We observed substantial overlap between
the rCBF main effects of emotion and their correlation with HF-HRV,
particularly in the medial prefrontal cortex. The associations that
we observed between HF-HRV and emotion-specific rCBF in the
caudate nucleus and the PAG are consistent with the close linkage
between visceromotor and somatomotor activity in the context of
emotion, and the association between HF-HRV and activity in the left
insula is consistent with its well-known role in emotion and
autonomic regulation. Our experimental design also enabled us to
demonstrate that the elements of cognitive control inherent in this
experiment had definable neural substrates that correlated with HFHRV and to a large extent differed from the emotion-specific
correlates of HF-HRV. Nevertheless, our findings are consistent with
the view that the medial visceromotor network is a final common
pathway by which emotional and cognitive functions recruit autonomic support.
Acknowledgments
This work was supported by MH00972 and MH59964 to RDL. The
authors thank Beatrice Axelrod, Daniel J. Bandy, Nissa Blocker, YuKuang Chang, Carolyn Fort, Bradley W. Holmgren, Siobhan O'Neill and
Lang-Sheng Yun for their technical support.
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