NeuroImage 44 (2009) 213–222

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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, USAb Department of Psychology, University of Arizona, Tucson, AZ, USAc Department of Neurology, University of Arizona, Tucson, AZ, USAd Department of Psychology, Stanford University, Palo Alto, CA, USAe Banner Positron Emission Tomography Center, Banner Good Samaritan Medical Center, Phoenix, AZ, USAf Banner Alzheimer's Institute, Phoenix, AZ, USAg Translational Genomics Research Institute, Phoenix, AZ, USAh Department of Psychology, Ohio State University, Columbus, OH, USAi The Mannheim Institute of Public Health, Heidelberg University, Heidelberg, Germany

⁎ Corresponding author. 1501 N. Campbell Ave. Tucs+1 520 626 6050.

E-mail address: lane@email.arizona.edu (R.D. Lane).

1053-8119/$ – see front matter © 2008 Published by Eldoi:10.1016/j.neuroimage.2008.07.056

a b s t r a c t

a r t i c l e i n f o

Article history:

The vagal (high frequency Received 26 May 2007Revised 25 July 2008Accepted 28 July 2008Available online 9 August 2008

[HF]) component of heart rate variability (HRV) predicts survival in post-myocardial 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 cognitiveand emotional elements. To differentiate the neural substrates of vagal tone due to emotion, we correlatedHF-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 andthree 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 werespectrally-analyzed, generating 12 separate measures of HF-HRV in each subject. The six emotion and sixneutral conditions were grouped together and contrasted. We observed substantial overlap betweenemotion-specific rCBF and the correlation between emotion-specific rCBF and HF-HRV, particularly in themedial 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 inthis experiment (that involved focusing on the target mental state) had definable neural substrates thatcorrelated with HF-HRV and to a large extent differed from the emotion-specific correlates of HF-HRV. Nostatistically significant asymmetries were observed. Our findings are consistent with the view that the medialvisceromotor network is a final common pathway by which emotional and cognitive functions recruitautonomic support.

© 2008 Published by Elsevier Inc.

Introduction

The importance of the vagus nerve in the two-way communicationbetween the brain and the heart during emotion has been known forover 100 years. Darwin, commenting on the work of the Frenchphysiologist Claude Bernard wrote,

“Claude Bernard also repeatedly insists, and this deserves especialnotice, that when the heart is affected it reacts on the brain; andthe state of the brain again reacts through the pneumo-gastric(vagus) nerve on the heart; so that under any excitement there

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will be much mutual action and reaction between these, the twomost 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 vagusnerve is the use of heart rate variability (Task Force, 1996; Thayer andBrosschot, 2005). The heart is dually innervated by the autonomicnervous system such that relative increases in sympathetic activity areassociated with heart rate increases and relative increases inparasympathetic activity are associated with heart rate decreases.Thus, relative sympathetic increases cause the time between heartbeats (the inter-beat interval) to become shorter and relativeparasympathetic increases cause the inter-beat interval to becomelonger. The parasympathetic (primarily vagal) influences are pervasiveover the frequency range of the heart rate power spectrum whereasthe sympathetic influences ‘roll-off’ at about 0.15 Hz (Saul,1990). Thus,

214 R.D. Lane et al. / NeuroImage 44 (2009) 213–222

high frequency HRV (HF-HRV) represents primarily parasympatheticinfluences with lower frequencies (below about 0.15 Hz) having amixture of sympathetic and parasympathetic autonomic influences.The sympathetic effects are on the time scale of seconds whereas theparasympathetic effects are on the time scale of milliseconds.Therefore, the parasympathetic influences are the only ones capableof producing rapid changes in the beat-to-beat timing of the heart.This rapid modulation of heart rate is associated with both themechanical and neural gating of vagal outflow during respiration.Specifically, during inspiration vagal outflow is reduced and heart rateincreases whereas during expiration vagal outflow is restored andheart rate decreases. Consequently, heart rate variability largelyreflects the respiratory gating of the output of the vagus nerve onthe sinoatrial node of the heart (Saul, 1990).

To date only two studies have been conducted in which HRV hasbeen measured during functional brain imaging experiments inhealthy individuals. Critchley et al. (2003) examined HRV during anfMRI study of mental (mental arithmetic) and physical (isometrichandgrip) 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 correlationbetween HF-HRV and ventral ACC activity during the n-back memorytask. Both studies involved cognitive tasks that were emotionallystressful, and the correlations with HRV implicated different sub-sectors of the ACC.

Four additional studies have examined the relationship betweenHRV measured outside the scanner (perhaps due to technicalchallenges in measuring HRV in the fMRI environment) and BOLDactivity during fMRI. Two of these studies involved cognitive tasks.Matthews et al. (2004) observed a positive correlation between HF-HRV during the Counting Stroop task outside the scanner and ventralACC activity during the execution of the Counting Stroop Task in thescanner. Neumann et al. (2006) found an inverse association betweenbaseline HRV as indexed by the low frequency to high frequency ratio(LF/HF) and dorsal ACC activity during a go/no-go task. Two additionalstudies examined the relationship between HRVmeasured outside thescanner and functional brain imaging during emotional tasks.O'Connor et al. (2007) observed in bereaved individuals that HF-HRV assessed outside the scanner was associated with greater ventralposterior cingulate cortex activity in response to grief stimuli. In aregion-of-interest analysis, Mujica-Parodi et al. (in press) observedthat individuals with greater levels of trait anxiety showed greateruncoupling, or dysregulation, of their limbic responses to neutral,fearful, and happy faces, and this uncoupling was correlated withdecreases in two indices of heart rate variability. In the latter studyBOLD activity in ACC and HF-HRV were not specifically evaluated.

These studies provide useful data indicating the relevance of vagaltone to neural processing of cognitive and emotional stimuli. Theyhighlight participation of structures on themedial surface of the brain,particularly the anterior and posterior cingulate cortices, in vagalregulation. However, the neural correlates of HRV in relation tocognitive and emotional stimuli have not yet been differentiated, andthe neural regulation of vagal tone during emotion in real time has notyet been studied.

Table 1aEmotion-minus-Neutral rCBF

Region X Y Z Z value Voxel p (u

Superior temporal gyrus (BA 22) 62 −52 10 5.21 p<9.3×10Brainstem −16 −22 −4 5.03 p<2.46×1Middle temporal gyrus (BA 21) 58 6 −24 4.98 p<3.23×1Superior temporal gyrus (BA 38) −42 18 −34 4.26 p<6.90×1Cerebellum 44 −58 −32 3.83 p<3.30×1

Brain areas where there is a significant increase in rCBF during the 6 emotion conditions reluncorrected and an extent threshold of p< .05 uncorrected was used. XYZ coordinates are in

Examining the covariation of HRV and neural activity duringemotion is important for several reasons. First, the imaging studies todate that included simultaneous HRV measurements have involvedstressful cognitive tasks that have cognitive demands in the fore-ground with accompanying affective components. Examining HRVduring induced emotion permits a more direct examination of theneural regulation of HRV in relation to emotion. Second, autonomicactivity is an intrinsic part of emotional responses. A growing body ofevidence indicates that a network of structures in the brain mediateemotional responses (Phan et al., 2002; Wager et al., 2003). Emotionalresponses are themselves complex, with experiential, cognitive,behavioral, somatomotor and visceromotor components. An exam-ination of the covariation of HRV and brain activity during emotionwill shed light on the neural regulation of the visceromotorcomponent of emotional responses. Third, doing so permits a directtest of Claude Bernard's hypothesis using modern methods.

Vagal tone has been hypothesized to play an important role inemotion regulation (Appelhans and Luecken, 2006). Emotion regula-tion has been defined as consisting of automatic and intentionalprocesses that influence what emotions a person has, when they havethem and how they experience and express them (Gross, 1998).Emotion regulation may therefore consist of selecting an optimalresponse and inhibiting less functional responses from a broadbehavioral repertoire. Relatedly, HRV may be considered a resourcethat can be drawn upon in support of this regulatory function (ThayerandLane, inpress). Fromthis perspective it is not surprising that greaterHRV is associatedwith enhanced cognitive performance (Johnsen et al.,2003). In a study of 53 male sailors, those with higher HRV showedmore correct responses than the low HRV group on aworking memorytest. In addition, the highHRV group showed fastermean reaction time,more correct responses and fewer errors than the low HRV group on acontinuous performance task (CPT), particularly when executivefunctions were involved (Hansen et al., 2003). In another study ofhealthy volunteers, performance on executive tasks sufferedwhenHRVlevels were decreased by aerobic de-training (Hansen et al., 2004).These results raise the possibility that HRV, as an index of inhibitoryfunction, participates in regulating both cognitive and emotionalperformance. A critical question that has not yet been addressed isthe extent to which the neural substrates of vagal regulation differ as afunction of cognitive or emotional contexts.

To examine the relationship between HF-HRV and brain activityin the context of emotion conditions that would be broadlygeneralizable, we induced both positive and negative emotions(happiness, sadness, disgust) with two different emotion inductiontechniques (film and recall) in a random effects analysis. By usingPET the typical time needed to measure HF-HRV reliably matchedthe 60 second scans with 15O-water and avoided any potentialcomplications in HF-HRV measurement associated with the elec-trically hostile fMRI environment. We also included emotionallyneutral control conditions to help disentangle rCBF due to emotionfrom emotion-independent effects. Based on the findings fromprevious studies cited above, we hypothesized that structures in thefrontal lobe, particularly in the ACC, would be associated with HF-HRV during induced emotion. We also sought to determine whether

ncorrected) Extent (voxels) Cluster p corrected Cluster p (uncorrected)−8 1291 p<0.009 p<0.0120−7 3553 p<0.018 p<1.23×10−6

0−7 2516 p<0.022 p<2.01×10−5

0−5 2099 p<0.217 p<1.02×10−5

0−6 3174 p<0.569 p<6.31×10−5

ative to the 6 neutral conditions. In this and all other tables a voxel threshold of p< .005MNI space. Model 1 in Methods was used for this analysis.

Table 1bNeutral-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) 46 48 16 4.97 p<3.36×10−7 14,401 p<0.022 p<9.5×10−16

Precuneus (BA31) −24 −80 26 4.07 p<2.34×10−5 3270 p<0.179 p<2.56×10−6

Middle Temporal Gyrus (BA21) 64 −42 −14 3.95 p<3.85×10−5 408 p<0.35 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.

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the neural correlates of vagal tone varied as a function of emotionand emotion-independent contexts.

Methods

Subjects

A screening procedure was used to identify 12 right-handed,neurologically and psychiatrically healthy, unmedicated female volun-teers who were likely to have intense emotional responses in the PETlaboratory. The sample was restricted to females to maximize thehomogeneity of emotion-dependent changes in CBF and the likelihoodof intense self-reported emotional experiences (Shields, 1991). Anadvertisement was used to recruit female volunteers between the agesof 18 and 30 who were “able to accurately describe [their] emotionalreactions to daily events.” Psychiatric and medical histories, theStructured 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 examinationwere used toidentify subjects for further evaluation. Prospective subjects wereincluded in the PET study if they reported separate experiences duringthe previous six months of happiness, sadness, and disgust that wereeach rated at least 6 on a 0 to 8 visual analog scale (8 representing themost intense experience of that kind in their lives) and if they ratedeach of an alternate screening set of three films targeting happiness,sadness, and disgust, respectively, at least 5 on an 8 point scale. After acomplete description of the study was given to the subjects, writtenconsent was obtained. Subjects received compensation for theirparticipation in the PET study. The 12 subjects who completed thePET protocol had a mean age of 23.3 years (SD=3.2).

Experimental design

During the PET session, three empirically validated silent, color,feature film clips (Tomarken et al., 1990) were used for the externalgeneration of three subjectively, facially and electrophysiologicallywell-characterized target emotions: happiness, sadness, and disgust.The film clips activate relatively pure emotion and are each approxi-mately 2 min in duration. It is notable that all of these clips includeactors displaying facial expressions. The 1-minute segment from each

Table 1cEmotion-minus-Neutral rCBF independent of HF-HRV

Region X Y Z Z value Voxel p u

Thalamus 20 −22 2 3.96 p<3.81×Inferior temporal gyrus (BA21) 64 −4 −18 3.9 p<4.81×Medial prefrontal cortex (BA 10) 2 52 26 4.03 p<2.8×1Superior temporal gyrus (BA 22) 62 −52 12 4.76 p<9.91×

Same as Table 1a, except the Emotion-minus-Neutral rCBF contrast was generated from amoto the Emotion-minus-Neutral contrast and thus excludes variance in rCBF in this contrast

Table 1dNeutral-minus-Emotion rCBF independent of HF-HRV

Region X Y Z Z value Voxel p

Precuneus (BA 31) 14 −70 20 8.11 p<2.86Ventrolateral prefrontal cortex (BA 10) 26 66 −2 4.69 p<3.33

Same as Table 1b, except the Neutral-minus-Emotion rCBF contrast was generated from a m

emotionfilm that in the judgmentof the investigators evoked the targetemotion most intensely was selected for viewing during the 1-minutescan. Three additional, emotionally “neutral” silent film clipswere usedto control for potentially confounding features of the emotion-generating film task, such as emotionally irrelevant visual stimulationandeyemovement. These clipswere culled fromnaturefilms (scenes ofa beach, woods, etc) and did not include people.

In addition, autobiographical scripts of three recent experienceswere used during the PET session for the internal generation of thesame three target emotions. Three additional, emotionally “neutral”autobiographical scripts of recent experienceswere used to control foremotionally irrelevant recall memory and visual imagery. Subjectswere instructed to focus during recall on a previously identifiedmoment during which the target emotions were experienced veryintensely and other emotions were experienced much less intensely.

Immediately prior to each PET scan, the subject listened to either abrief synopsis of the film clip or the autobiographical script. Duringthe emotion-generating film and recall tasks, subjects were asked tofeel the relevant target emotion. For the control film and recall tasks,subjects were asked to feel emotionally “neutral.” During the filmtasks the subjects' eyes were open and fixed on the center of a ceiling-mounted 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. Emotion-generating and control tasks were performed in an alternatingsequence within each block. Whether each block began with anelicitor or control was counterbalanced across subjects. Within theseconstraints, the order of the three elicitors and three controls in eachblock was randomized.

Self-report ratings of stimuli

Immediately following each scan subjects rated their experience ofseven emotions (interest, amusement, happiness, sadness, fear,disgust, anger) on separate 0–8 visual analog scales. These individualratings were used to validate the success of the emotion inductionmethods. To derive a Composite Emotion Self-Report Index tocorrespond to the Emotion-minus-Neutral rCBF contrast for eachsubject, we used the rating of the target emotion for each emotion

ncorrected Extent (voxels) Cluster p corrected Cluster p uncorrected

10−5 2771 p<1.90×10−4 p<1.70×10−5

10−5 1247 p<0.018 p<0.0020−5 921 p<0.056 p<0.00510−7 371 p<0.469 p<0.056

del that included HF-HRV (see Model 3 in Methods). This analysis identifies rCBF uniquethat overlaps with variance due to HF-HRV.

uncorrected Extent (voxels) Cluster p corrected Cluster p uncorrected

×10−6 6753 p<2.58×10−8 p<2.3×10−9

×10−4 392 p<0.433 p<0.051

odel that included HF-HRV (see Model 3 in Methods).

Fig. 1. Mean Ratings (N=12) of Happiness, Sadness, and Disgust for Each Type of Filmand Recall Stimulus (Ratings on a 0–8 visual analog scale were obtained on multipleemotions immediately after each scan. The values for film and recall controls eachrepresent the mean values for three scans.)

Fig. 2. (a) Emotion-minus-Neutral rCBF. Brain areas where there is a significant increasein rCBF during the 6 emotion conditions relative to the 6 neutral conditions. See Table1a. Axial, coronal and sagittal statistical parametric maps are presented. Thesignificance 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 rCBFincrease in medial prefrontal cortex in this contrast is somewhat more anterior andinferior (coordinates=2, 64, 20). (b) Emotion-minus-Neutral rCBF independent of HF-HRV. Same as in panel a, except the Emotion-minus-Neutral rCBF contrast wasgenerated from a model that included HF-HRV. See Table 1c. Axial, coronal and sagittalstatistical parametric maps are presented. The significance level in z scores is color-coded. Cross-hairs pinpoint the local maximum rCBF increase in medial prefrontalcortex in this analysis (coordinates=2, 52, 26).

216 R.D. Lane et al. / NeuroImage 44 (2009) 213–222

condition and averaged across the film and recall stimuli and thenused the average of the happiness, sadness and disgust ratings foreach neutral condition and averaged across the film and recall stimuli.An Emotion-minus-Neutral self-report contrast was then generatedfor each subject for analysis in relation to rCBF or HF-HRV.

HRV collection and analysis

The electrocardiogram (ECG) was recorded on polygraph paperduring each 1-minute scan. Consecutive RR intervalsweremeasured byhand using calipers positioned at the beginning of each Rwave. Artifactin the recordings (e.g. noise in the isoelectric line) was minimal(estimated to affect 3 beats per thousand) and did not interferewith RRinterval measurement. Subjects were young, medically healthy andunmedicated so that artifact due tomissed beats, ectopic beats or otherabnormalities were not detected in the 144 min of ECG data evaluated.A trained rater hand-scored all data and a second rater independentlyhand-scored all inter-beat intervals for 3 randomly selected 1-minutescans from 3 subjects (approximately 70 measurements for eachsubject), and these R–R values were correlated with those used in thisstudy. The aggregate inter-rater reliability was r=.95, supporting thereliability of our data. Spectral analysis using a fast Fourier transformwas used to generate the heart period power spectrum (Task Force,1996). The high frequency (HF) component (0.15–0.40 Hz) was used asour estimate of vagallymediatedHRV. Twelve separatemeasures of HF-HRV were obtained for each subject.

Respiratory rate measurement

We derived a respiratory rate measurement by applying anautoregressive (AR) algorithm to each 1-minute ECG tracing corre-sponding to each PET scan (Thayer et al., 2002). AR estimates ofrespiratory rate are less susceptible to artifacts such as movement andhave been found to be more reliable than strain gauge measurements.

Imaging parameters and imaging analysis

T1-weighted volumetric magnetic resonance images (MRIs) of thehead were acquired prior to the PET session to ensure the structuralnormality of the brain, facilitate head positioning in the PET scanner,and permit co-registration between PET and MRI images (for moreaccurate normalization of PET scans and anatomical localization of PETfindings). Subject preparation for PET included the insertion of acatheter in the left antecubital vein to permit tracer administration,head immobilization using tape rather than a fast-hardening foammold to permit quantitative EEGmeasurement during the PET session,

Table 2HF-HRV values by condition

Happiness Sadness Disgust Neutral

Film 970.6 (473.7) 1272.0 (785.4) 1637.3 (1921.0) 1905.9 (1346.8)Recall 2666.4 (3367.2) 2654.1 (2921.9) 2040.0 (2333.8) 2828.3 (2616.6)

Mean and standard deviation of HF-HRV power values (ms2) for each conditionaggregated across 12 subjects. For the neutral conditions the value used for each subjectwas the mean of three separate measurements.

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and the performance of a transmission scan using a germanium/gallium ring source to correct subsequent emission images forradiation attenuation. During each scan, the subjects rested quietlyin the supine positionwithout movement. Twelve 31-slice PET imagesof rCBF were obtained in each subject as she alternated betweenemotion-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 shortlyafter the onset of the film and recall tasks. PET images werereconstructed with an in-plane resolution of 10 mm full width halfmaximum (FWHM) and a slice thickness of 5 mm FWHM.

The data were analyzed with statistical parametric mapping usingSPM2 (http://www.fil.ion.ucl.ac.uk/spm/). For each subject the 12 PETimages were realigned to each other, co-registered to that subject'sstructural MRI, spatial normalization parameters were derived fromMRI images for each subject and then applied to the correspondingPET images. A 12 mm FWHM Gaussian kernel was used to smooth theimages. Regional CBF equivalents were adjusted to a global mean of50 ml/dl/min by proportional scaling.

The PET data were analyzed using random effects models. At thefirst level of analysis for each individual subject, four types ofindividual-level models were used to fully investigate the relationshipbetween rCBF, the emotion conditions and HF-HRV. Model 1 wasconditions-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 modelthat examined the effects of HF-HRV across emotional and neutralconditions (Table 3). Model 3 included both condition coding and HF-HRV to examine the effects of emotion or HF-HRV while consideringthe effects of the other (Tables 1c, 1d, 5). Model 4 was an interactionmodel that examined the relationship between rCBF and HF-HRVseparately for the emotional and neutral conditions, so that theserelationships could be directly compared at each voxel (Table 4). In allcases, the relationship between condition or HF-HRV or both and rCBFwas evaluated at the individual subject level, and maps representingthe strength of this relationship were entered into random effects,group-level t-tests. A threshold peak of t>3.11 (p< .005) uncorrectedat the voxel level, extent threshold 5 voxels, was used to generateresults, and among those findings those that also met or exceeded thep< .05 uncorrected threshold at the cluster level are presented in thetables and results. Moreover, a small volume correction (SVC) forbilateral anterior rostral medial frontal cortex (arMFC) (Amodio and

Table 3Correlation of HF-HRV with rCBF across all conditions

Region X Y Z Z value Voxe

Right superior prefrontal cortex (BA 8, 9) 26 42 40 3.3 p<4.Left rostral anterior cingulate cortex (BA 24) −6 48 8 2.96 p<0.Right dorsolateral prefrontal cortex (BA 46) 50 48 8 3.25 p<5.

2 58 2 3.2 p<0.Right parietal cortex (BA 40) 44 −32 50 3.05 p<0.

30 52 28 3.04 p<0.40 −38 42 2.97 p<0.

Brain areas where there is a significant correlation of HF-HRV with rCBF across all condition

Frith, 2006) was applied using the MarsBar plug-in in SPM to furtherinterrogate the findings in Table 1a as described below.

Results

Effects of emotion inductions on self report and rCBF

Previous reports from this dataset focused on the separate neuralsubstrates of happiness, sadness and disgust (Lane et al.,1997a) and theneural correlates of emotion induced by film vs. recall (Reiman et al.,1997). As shown in Fig. 1, the emotion induction methods weresuccessful in inducing the target emotions as indicated by self-reportedratings immediately following each scan.

Table 1a and Fig. 2a displays the findings for the main effect of therCBF contrast of Emotion-minus-Neutral (E−N) in a model withconditions only (Model 1). Table 1c and Fig. 2b display the uniquevariance attributable to the main effect of the rCBF contrast ofEmotion-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 HF-HRV, shows significant activation in the thalamus, medial prefrontalcortex and right superior and inferior temporal cortices. Based onevidence that medial prefrontal cortex is commonly activated duringemotion (Amodio and Frith, 2006), we applied a SVC for this region tothe analysis that generated Table 1a and obtained a significant resultfor bilateral anterior rostral medial frontal cortex (p<0.041 [voxel levelFWE corrected] and p<0.044 [cluster level corrected p]).

Tables 1b (from Model 1) and 1d (from Model 3) display thefindings from the Neutral-minus-Emotion (N−E) contrast. The latter,which excludes variance shared with HF-HRV, shows significant rCBFdecreases during emotion in the right precuneus and the rightventrolateral prefrontal cortex.

To evaluate whether self-reported emotional experiences wereassociated with the Emotion-minus-Neutral rCBF findings, we usedthe Composite Emotion Self-Report Index for the E−N contrast (seeMethods) using a group-level one-sample t-test in SPM and observedno significant associations with emotion-specific rCBF from Model 3(Table 1c), even at a reduced threshold (p<0.05). This same CompositeEmotion Self-Report Index was evaluated in relation to HF-HRV usingSPSS 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 emotionand neutral condition for both film and recall are listed in Table 2.Consistent with previous research, HF-HRV was lower during theemotion conditions compared to the neutral conditions [t(11)=1.54,p=0.07, one tailed].

A comparison of respiratory rate (breaths per minute) between theEmotion (mean=14.96 (2.6)] and Neutral (mean=14.83 (2.7)] condi-tions derived from AR spectral analysis revealed no significantdifference [t(11)=0.169, p=0.869]. This indicates that HF-HV compar-isons between the Emotion and Neutral conditions are not con-founded by respiratory rate.

l p uncorrected Extent (voxels) Cluster p corrected Cluster p uncorrected

91×10−4 1015 p<0.012 p<7.74×10−4

002 983 p<0.014 p<9.00×10−4

74×10−4 862 p<0.025 p< .002001001 753 p<0.043 p< .003001001

s. 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 andneutral) for each subject were entered into a single-subject covariate-only design, usingmean-centered HRV values for each scan. A contrastthat weighted the intra-subject covariance positively was thenentered into a one-sample t-test for a random effects analysis. Fourareas exceeded the a priori threshold. Results are listed in Table 3 andFig. 3 and demonstrate positive correlations in the right superiorprefrontal cortex (BA 8,9), the left rostral anterior cingulate cortex(ACC) (BA 24), the right dorsolateral prefrontal cortex (BA46) and theright parietal cortex (BA40). These findings reflect the brain areas that

Fig. 3. Correlation of HF-HRV with rCBF across all conditions. Depiction in three dimensions oacross all conditions. Cross-hairs are located at the local maximum in each cluster.

covary with HF-HRVwhen the contributions of emotion and cognitionare not disentangled.

Emotion-specific neural correlates of HF-HRV

Wenext evaluated the positive emotion-specific neural correlates ofHF-HRV by regressing HF-HRV with rCBF under Emotion and Neutralscans and contrasting the degree of association of HF-HRV/rCBFbetween the two conditions. These contrasts were computed for eachsubject and then aggregated across subjects using a random effectsone-sample t-test analysis. As depicted inTable 4 and Fig. 4, the positiveemotion-specific neural correlates of HF-HRV included a broad bilateral

f the four brain areas where there is a significant correlation between HF-HRV and rCBF

Table 4Correlation 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) −4 6 6 3.78 p<7.77×10−5 463 p<0.102 p<0.005Midbrain (including periaqueductal gray) 18 −20 −2 3.42 p<3.17×10−4 339 p<0.252 p<0.014Left insula −30 10 −10 3.28 p<5.20×10−4 229 p<0.539 p<0.037Medial prefrontal cortex BA 10 2 52 26 3.56 p<1.84×10−4 208 p<0.613 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.

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swath of the ventral striatumwith a peak localmaximumat the head ofthe caudate nucleus, the medial prefrontal cortex (BA10), the midbrainin a region that includes the periaqueductal gray (PAG) and the leftmid-insula. It should be noted that the medial prefrontal cortex areaidentified inTable 4 is identical to that inTable 1c, and themidbrain areaidentified in Table 4 is only a few mm. from the local maximum in thethalamus identified in Table 1c.

The parameter estimates for each of the four areas listed in Table 4was determined next: the slope for each regression line wasdetermined, the E−N slope difference was derived for each subjectand these E−Nslopedifferenceswere aggregated across the 12 subjects.Fig. 5 demonstrates that the slope was greater for E than N (i.e. there isgreater change in rCBF per unit of change in HF-HRV) in all four areas.

Asymmetry effects were tested by identifying homologous regionson the opposite hemisphere based upon the peak activations reported inTables 3 and 4.Whereas the peak activations suggested some lateralizedeffects, all asymmetry analyses failed to reach statistical significance.

Inverse correlation

The inverse or negative correlation of HF-HRV with E−N rCBFrevealed just one area, the cuneus (BA 19) [coordinates=−22, −92, 30;z=4.07, cluster size=2102, voxels, p<.001 corrected] that met a prioristatistical criteria.

Emotion-independent neural correlates of HF-HRV

We next evaluated the positive correlates of HF-HRV independentof 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 asignificant correlation of HF-HRV with rCBF associated with the Emotion-minus-Neutral contrast. See Table 4. Sagittal and axial views of the correlationwith HF-HRV inmedial prefrontal cortex and coronal and axial views of the correlation with HF-HRV inthe left insula are depicted.

with rCBF across all 12 scans within each subject, removed variancedue to the E−N rCBF main effect in that subject and then aggregatedacross subjects in a random effects analysis. Table 5 demonstratessignificant 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 assessedduring functional neuroimaging and the first involving emotion. Byidentifying rCBF specific to emotion, we were able examine how themain effect due to emotion corresponded to the covariation ofemotion-specific rCBF with HF-HRV. By removing variance due to themain effect of emotion-specific rCBF we were able to examine howrCBF independent of emotion covaried with HF-HRV. In addition, wewere also able to compare each of these patterns to the covariation ofHF-HRV with rCBF across the entire experiment. This permitted adissection of the relative contributions of emotion and cognitiveconditions to the neural correlates of HF-HRV in a context analogousto the two previous studies, in which both cognitive and emotionalelements were simultaneously operative.

In our emotion-specific analyses we aggregated conditions in orderto have sufficient statistical power and subtracted the neutral from theemotion conditions in order to identify the neural correlates of vagaltone thatwere specific to emotion. The comparison of themain effect ofemotion-specific rCBF independent of HF-HRV (Table 1c, Fig. 2b) withthe correlation between emotion-specific rCBF and HF-HRV (Table 4,Fig. 4) revealed the striking finding that the coordinates of the medialprefrontal cortex (BA10) were identical in the two analyses. This is anarea that participates in establishing a representation of one's ownemotions or mental states as well as those of others (Lane et al., 1997b;Ochsner et al., 2004). The covariation of activity of this structure withHF-HRV suggests that this particular cognitive function is simulta-neously linked to activationof themedial 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 thefour areas shown in Table 4. Positive values for each parameter estimate indicate thatthere is a greater change in rCBF per unit of change in HF-HRV in the emotion conditionsrelative to the neutral conditions in that area. Units on the ordinate are arbitrary.

Table 5Correlation 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) −8 48 4 3.47 p<2.64×10−4 1333 p<0.001 p<6.63×10−5

Right superior frontal gyrus (BA 10/46) 28 56 10 3.53 p<2.10×10−4 1235 p<0.002 p<0.001Left parietal cortex (BA 40) −44 −34 38 3.34 p<4.19×10−4 235 p<0.584 p< .049Right parietal cortex (BA 40, 39) 46 −52 44 3.89 p<5.02×10−5 229 p<0.603 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 Model1 in Methods and Table 1a).

220 R.D. Lane et al. / NeuroImage 44 (2009) 213–222

et al., 1998; Price, 1999). Sincemost studies that explore the function ofthis region do not include measures of HRV, it will be important infuture studies to examine whether main effect and covariate analysesyield the same result as they did in this study.

While it is tempting to speculate about the possible contribution ofafferent autonomic input to the representation of one's ownemotional state, our experimental design did not enable us todisentangle whether the correlations that we observed constitutedafferent, bottom-up vs. efferent, top-down mechanisms. However, asnoted above, PET imaging offers the unique advantage of capturingsustained brain activity concurrently with accurately measured HF-HRV. Given that HF-HRV can change on the order of milliseconds, andthat subjects maintained the same emotional state for 1 min, it is ourcontention that the observed findings represent the activity associatedwith an equilibration of afferent and efferent mechanisms — notunlike that described by Claude Bernard over one hundred years ago.Disentangling afferent from efferent mechanisms will require experi-mental manipulations or the study of special populations (e.g. primaryautonomic dysfunction as studied by Critchley et al., 2001).

A second region identified in the correlation between HF-HRV andemotion-specific rCBFwas a broad area that included the thalamus, anarea activated in the emotion-specific rCBF main effect, and theperiaqueductal gray (PAG). Although PET lacks sufficient resolution tobe certain that a correlation with PAG (or any other brainstemnucleus) is present, the images in Figs. 2 and 4 are suggestive. The PAGis responsible for coordinating visceral and behavioral responses tostress and threat (Price, 1999). The cortical projections to the PAG arisealmost exclusively from the medial visceromotor network orstructures closely related to it. Inspiratory neurons in the nucleustractus solitarius (a vagal nucleus in the brainstem) are activated bystimulation of the PAG (Huang et al., 2000), further supporting itsrelevance to cardiorespiratory function as measured by HF-HRV.

A third area identified in the correlation between HF-HRV andemotion-specific rCBF is the caudate nucleus. Emotions are clearlyassociated with automatic action patterns including gestures andfacial expressions as well as approach and avoidance behavioraltendencies. In fact, some theorists think that emotion is fundamen-tally an action tendency (Frijda, 1986). Obrist (1981) pointed out thatthe autonomic changes associated with a mental state covary with theexpected motor output that accompanies that state. In this PETexperiment, subjects were told that they should not move. Thisinstruction required that actions, which were more likely withemotion than neutral conditions, be inhibited. We speculate thatsuch inhibition was associated with activation of inhibitory cortical-basal ganglia circuits (from dorsolateral prefrontal cortex or anteriorcingulate cortex) (Alexander et al., 1986) that resulted in greatersynaptic activity at the caudate nucleus. Somatomotor metabolicdemands were therefore likely greater during emotion than neutralconditions, consistent with the trend for HF-HRV to be lower (i.e.arousal to be higher) during emotion. Our finding that HF-HRVcorrelates with neural activity in the caudate nucleus, as well as thePAG, may reflect the close linkage between visceromotor andsomatomotor activity in the context of emotional arousal.

The fourth area identified in the correlation between HF-HRV andemotion-specific rCBF is the left mid-insula. The posterior insularcortex is the primary projection area for visceral sensation, while the

anterior insula, particularly on the right side (Craig, 2003), is a higherassociation area for these bodily signals (Rolls, 1992) and is involved inremapping these signals into conscious bodily feelings (Critchley et al.,2001). Electrical stimulation of the insula produces changes in arterialpressure, heart rate, respiration, piloerection, and other autonomiceffects in laboratory animals and humans (Cheung and Hachinski,2000). A study of seven patients with strokes in the left insulademonstrated decreased HRV and other indices of electrical instabilitycompared to control subjects (Oppenheimer et al., 1996), a finding thatcorresponds well to our observations in this study.

The positive correlation between activity in these structures andHF-HRV means that as brain activity INCREASES the braking action onthe heart INCREASES. While this may at first appear counterintuitive,this phenomenon constitutes the core of the neurovisceral integrationmodel that two of us have discussed at some length (Thayer and Lane,2000, in press). The frontal lobe areas identified in this study havebeen implicated in different aspects of conscious processing ofemotional responses. The neurovisceral integration model holds thatconscious experience of emotion requires the transmission ofsubcortical affective information to the cerebral cortex and newevidence by Williams et al. (2006) indicates that top-down feedbackfrom cortical to subcortical structures is necessary for consciousemotional experience to occur. In addition, our model holds that thetop-down inhibitory influence has a modulatory effect on thesubcortical centers that shapes the nature of subjective experience.This is consistent with the more general principle that inhibitionserves to ‘sculpt’ excitatory neural action at all levels of the neuraxis toproduce 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 inemotion-specific rCBF independent of HF-HRV: right ventrolateral cortexand right precuneus. Growing evidence indicates that the rightventrolateral prefrontal cortex participates in inhibiting emotionalresponses (Lieberman et al., 2007; Aron et al., 2004; Colcombe et al.,2005;Garavanet al.,1999;Konishi et al.,1999). Thus, greateractivity in thecontext of decreased emotion (relative to neutral)would be expected. Theprecuneus (posteromedial parietal lobe) has one of the highest restingmetabolic rates and is thought to be a component of the default network(participating in self-relatedmental representations at rest) (Cavanna andTrimble, 2006). Emotion-specific rCBF decreases may be explained bygreater activity in the precuneus in the neutral state, which is comparableto rest, whereas activity related to spontaneous self-generated mentalstateswas likely reducedduring theemotion conditions in response to theprescriptive instructions of the experimenter.

For the sake of completeness we also examined the inversecorrelation between emotion-specific rCBF and HF-HRV. This analysisrevealed a single large cluster in extra-striate visual cortex. Perhapsthis is best understood by considering that HF-HRV is low whenarousal is high. The finding that rCBF is increased in extra-striatevisual cortex during conditions of high emotional arousal is wellestablished (Lane et al., 1999) and may be related at least in part tofeedback from the amygdala to all levels of the visual processingstream (Morris et al., 1998).

Our task instructions to subjects also required that they sustain inworkingmemory the goal of maintaining the target emotion or neutralstate for the entire duration of each scan. Maintenance of that target

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state also involved some degree of regulation in order to maintain thetarget content at a sufficiently intense level. While these processesapplied to both emotion and neutral conditions and are reflected in thefindings in Table 3, the effects of task maintenance and selectionindependent of emotion are listed in Table 5. Dosenbach et al. (2006,2007) have shown that across many tasks bilateral inferior parietalcortex participates in maintaining task set (along with dorsal anteriorcingulate cortex and bilateral anterior insula/frontal operculum).Dorsolateral prefrontal cortex (DLPFC) (BA46) is well known to play akey role in working memory (Goldman-Rakic, 1996). BA46 has alsobeen implicated in several imaging studies involving emotion regula-tion, including a reappraisal task (Ochsner et al., 2002), the regulation ofanticipatory anxiety (Kalisch et al., 2005) and suppression of negativeaffect (Phan et al., 2005). Kuhl et al. (2007) have shown that DLPFCparticipates in selection and control in the presence of mnemoniccompetition. The right superior frontal cortex is an area involved inexecutive control of attentional shifting (Nagahama et al., 1999),particularly in relation to working memory (Milham et al., 2001), andmonitoring the contextual significance of information retrieved fromepisodic memory (Henson et al., 1999). Given that all of these cognitivefunctionswere likely operative in this study, and that all involvementaleffort and some degree of inhibition of mental content that is notmaintained or selected, the positive correlation with HF-HRV suggeststhat the cognitive functions instantiated in these brain areas weresupported with increases in vagal tone.

Another striking finding was that rostral ACC correlated with HF-HRV across all experimental conditions (Table 3) as well as when themain effect of emotion-specific rCBF was removed (Table 5). Thisfinding was unexpected given that this locus is considered part of the“affective division” of the ACC (Bush et al., 2000). We have previouslydemonstrated that the so-called “cognitive division” of the ACC is notexclusively cognitive (McRae et al., 2008), and here we believe that wehave demonstrated that the “affective division” of the ACC is notexclusively affective. Our current results suggest that rostral ACC isinvolved in coordinating the autonomic adjustments associated withmaintaining a self-focused mental state that may but need not includeemotion (Amodio and Frith, 2006). The rostral ACC is a key componentof the medial visceromotor network. Price et al. (1996) havedemonstrated that medial prefrontal structures are highly intercon-nected, including the ventral and dorsal ACC areas observed in the twoprevious studies involving concurrent functional brain imaging andHF-HRV assessments. It is therefore possible that all three studiesactivated a similar final common pathway from the frontal lobe to thevagal nuclei in the brainstem.

Several of us (Ahern et al., 2001) previously observed in epilepsypatients undergoing intracarotid injection of sodium amytal thatlarger and faster heart rate increases and greater vagally mediatedHRV decreases were observed with right than left-sided injections.These findings are consistent with known asymmetries in theautonomic innervation of the heart such that the sinoatrial node,from which normal sinus rhythm emanates, is innervated almostexclusively by right-sided sympathetic and parasympathetic fibers(Schwartz, 1984). In addition, neural tract tracing studies originatingin rat myocardium suggest that predominantly right-sided brainstructures contribute to cardiac vagal control (Ter Horst and Postema,1997). Although three of the four significant correlations across allconditions (Table 3) were right-sided, asymmetry analyses for thosefindings as well for the emotion-specific covariate analysis (Table 4),which produced results involving brain structures on both the rightand left sides, failed to meet our a priori threshold for statisticalsignificance. These results are not consistent with our own expecta-tions of right-sided predominance in the regulation of vagal tone orCraig's (2005) suggestion that cortical regulation of vagal tone ispredominantly left-sided. Further research with a variety of tasks,larger samples of men and women, and effective connectivityanalyses, as well as additional lesion studies, will be needed to sort

out in what contexts and in what brain areas right vs. left-sidedstructures predominate in vagal regulation.

There were several limitations to this study. First, only femalesubjectswere used.Whereas this had the distinct advantage of selectingsubjects known for their superior emotion-related abilities, it limits ourability to generalize these results to males. Clearly future research willneed to replicate these findings in males. Second, the sample size wasrelatively small, thus exposing us to an increased risk of a Type II error.For example, this may have significantly hampered the asymmetryanalyses, as noted above. In addition, future studies with larger samplesizes might well find additional neural structures associated with HRVduringemotion. Third, thenature of the emotion tasks involvedmultipleemotions, an emotionally neutral state, two different emotion inductiontechniques and the explicit instruction to the subjects to attend to andmaintain the targetmental state. It will be important to characterize thecorrelates of HF-HRV during simpler emotion tasks and compare themto emotionally neutral control tasks as well.

In conclusion, this study examined the neural correlates of HF-HRVduring emotion in real time.We observed substantial overlap betweenthe rCBF main effects of emotion and their correlation with HF-HRV,particularly in the medial prefrontal cortex. The associations thatwe observed between HF-HRV and emotion-specific rCBF in thecaudate nucleus and the PAG are consistent with the close linkagebetween visceromotor and somatomotor activity in the context ofemotion, and the association between HF-HRV and activity in the leftinsula is consistent with its well-known role in emotion andautonomic regulation. Our experimental design also enabled us todemonstrate that the elements of cognitive control inherent in thisexperiment had definable neural substrates that correlated with HF-HRV and to a large extent differed from the emotion-specificcorrelates of HF-HRV. Nevertheless, our findings are consistent withthe view that the medial visceromotor network is a final commonpathway by which emotional and cognitive functions recruit auto-nomic support.

Acknowledgments

This work was supported by MH00972 and MH59964 to RDL. Theauthors thank Beatrice Axelrod, Daniel J. Bandy, Nissa Blocker, Yu-Kuang Chang, Carolyn Fort, Bradley W. Holmgren, Siobhan O'Neill andLang-Sheng Yun for their technical support.

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