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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Hypertension. Author manuscript; available in PMC 2010 May 1.
Published in final edited form as:
PMCID: PMC2674279
NIHMSID: NIHMS108568

Heightened resting neural activity predicts exaggerated stressor-evoked blood pressure reactivity

Abstract

Individuals who express relatively large-magnitude or ‘exaggerated’ blood pressure (BP) reactions to behavioral stressors are presumably at increased risk for cardiovascular disease. As shown by recent neuroimaging studies, individuals who express exaggerated stressor-evoked BP reactivity also express heightened neural activity in corticolimbic brain areas that centrally regulate the cardiovascular system. These studies, however, have exclusively examined BP reactivity and concomitant neural activity during stressor exposure. If exaggerated BP reactivity originates in part from a centrally-regulated and dispositional cardiovascular response tendency, then heightened resting (pre-stressor) corticolimbic activity may predict the subsequent expression of exaggerated stressor-evoked BP reactivity. To test this hypothesis, perfusion magnetic resonance imaging was used to quantify resting regional cerebral blood flow (rCBF; an indirect metabolic measure of neural activity) in men (n=19) and women (n=20) aged 20-37 who subsequently performed cognitive stressor tasks to evoke BP reactivity. Individuals who expressed larger task-induced rises in systolic and diastolic BP also expressed higher resting rCBF in four functionally-related corticolimbic areas: the dorsal and perigenual anterior cingulate, medial prefrontal, and insular cortices. Specifically, resting rCBF in these areas accounted respectively for 40% and 31% of the variance in systolic (P=0.001) and diastolic (P=0.008) BP reactivity, after accounting for total resting CBF, resting BP, task performance, and task-related ratings of unpleasantness, arousal, and perceived psychological control. Heightened resting corticolimbic activity may represent a neurobiological correlate of an individual’s predisposition for exaggerated stressor-evoked BP reactivity and possibly related cardiovascular risk.

Keywords: anterior cingulate cortex, blood pressure reactivity, individual differences, insula, medial prefrontal cortex, stress

Individuals who express relatively large-magnitude or ‘exaggerated’ blood pressure (BP) reactions to behavioral stressors are at moderately increased risk for hypertension1,2, stroke3, ventricular hypertrophy4, preclinical atherosclerosis5, and myocardial infarction.6,7 Although it is uncertain whether exaggerated stressor-evoked BP reactions are involved causally in the pathophysiology of these precursors and endpoints of cardiovascular disease8, there is psychometric and genetic evidence that an individual’s tendency to express exaggerated stressor-evoked BP reactivity is a reliable9,10 and partly heritable11,12 dispositional cardiovascular stress response attribute. In parallel, there is human neuroimaging evidence that stressor-evoked BP reactions are associated with patterns of neural activity in corticolimbic brain areas that centrally regulate the cardiovascular system. More precisely, stressor-evoked BP reactions have been associated reliably with activity in the anterior cingulate cortex (ACC), medial prefrontal cortex (mPFC), and insula.13-18 Together, these corticolimbic areas can regulate stressor-evoked BP reactions by modulating autonomic and neurohormonal outflow to the myocardium and vasculature via functional neural connections with one another and with subcortical cell groups (e.g., amygdala, pontine, and medullary nuclei) that are proximally involved in cardiovascular control.19-21 Further, individuals who express exaggerated stressor-evoked BP reactivity have been found to also express heightened stressor-evoked activity in the ACC, mPFC, and insula—broadly suggesting that these particular corticolimbic areas may represent components of a neural circuitry that is functionally involved in the dispositional expression of individual differences in cardiovascular stress reactivity.17,18

To date, however, neuroimaging studies of stressor-evoked BP reactivity have exclusively examined concomitant changes in BP and corticolimbic activity during stressor exposure. Critically, if individual differences in BP reactivity originate in part from a centrally-regulated and dispositional cardiovascular stress response tendency, then relatively heightened resting (pre-stressor) corticolimbic activity may directly predict the subsequent expression of comparatively larger-magnitude (exaggerated) stressor-evoked BP reactions across individuals. To test this hypothesis, perfusion magnetic resonance imaging (MRI)22 was used to quantify resting regional cerebral blood flow (rCBF; an indirect metabolic measure of neural activity) in young adults who subsequently performed cognitive stressor tasks designed to evoke BP reactivity. First tested was whether relatively heightened resting rCBF in corticolimbic areas would predict (prospectively) comparatively exaggerated BP reactions to the stressor tasks across individuals. Next tested was whether any prospective associations between resting rCBF and BP reactivity would persist after statistically accounting for plausible confounders; namely, total resting CBF, resting BP, task-related performance, and subjective ratings of task-related unpleasantness, arousal, and perceived psychological control. Lastly tested was whether the corticolimbic areas in which resting rCBF predicted BP reactivity across individuals would exhibit inter-correlations in their time-varying oscillations in the blood-oxygen level dependent (BOLD) signal, putative indicators of coherent variation in metabolically-linked neural activity.23 Such inter-correlations would provide evidence for so-called resting state functional connectivity24 between specific corticolimbic areas that may partly comprise a neural circuitry for the dispositional expression of individual differences in stressor-evoked BP reactivity.

Methods

Participants

Participants were 20 men (M age = 24.8 ± 5.1 SD) and 20 women (M age = 23.8 ± 3.8). All were right-handed and none had (1) a history of cardiovascular disease (including hypertension, stroke, myocardial infarction, congestive heart failure, and atrial and ventricular arrhythmias); (2) prior cardiovascular surgery (including coronary bypass, carotid artery, or peripheral vascular surgery); (3) a history of cancer, a chronic kidney or liver condition, Type I or II diabetes, or any pulmonary or respiratory disease; (4) any current or prior self-reported psychiatric diagnosis of a substance abuse or mood disorder; (5) prior cerebrovascular trauma involving loss of consciousness; (6) prior neurosurgery or history of neurological conditions; (7) pregnancy (verified by urine test in females); (8) color-blindness; (9) claustrophobia; (10) metallic implants; or (11) a self-reported history of using any psychotropic, lipid lowering, or cardiovascular medications.

The participants’ average seated resting BP was 117/66 mmHg (± 10/9 SD), as determined by the mean of the last 2 of 3 BP readings obtained with an oscillometric device (Critikon Dinamap 8100, Johnson & Johnson, Tampa, FL) and taken 2 min apart after a 20 min acclimation period prior to MRI testing. Data from 1 male were excluded because of excessive neuroimaging artifacts due to head-movements. Results herein are thus for the remaining 39 participants. Participants gave informed consent to study protocols, approved by the University of Pittsburgh Institutional Review Board. Supplemental information about participant characteristics and screening methods are provided online in Table S1 (please see http://hyper.ahajournals.org).

Study protocols

Participants abstained from eating, exercising, and consuming caffeinated and tobacco products for 3 hr and drinking alcoholic beverages for 12 hr before testing. At testing, participants underwent a screening interview followed by protocols to assess anthropometric measures, demographic information, and seated BP. Participants then underwent an MRI protocol. For this protocol, participants were fitted with a BP cuff matched to arm size, inserted into the MRI scanner, and asked to rest for approximately 20 min; 12-15 min later, participants completed 2 stressor tasks while in the scanner.

Neuroimaging data acquisition

Neuroimaging data were acquired on a 3T Trio TIM whole-body scanner (Siemens, Erlangen, Germany), equipped with a 12-channel phased-array head coil. Resting perfusion images were acquired with a pulsed arterial spin-labeling (PASL) sequence. For this sequence, interleaved perfusion images with and without arterial spin labeling were obtained over a 5 min 28 sec period using gradient-echo echo-planar imaging (EPI). The PASL sequence employed a flow-sensitive alternating inversion recovery method25, specifically applying a saturation pulse 700 ms after an inversion pulse. To reduce transit artifact, a 1000 ms delay separated the end of the labeling pulse and the time of image acquisition. Resting perfusion image acquisition parameters were: field of view (FOV) = 240×240 mm, matrix size = 64×64 mm, repetition time (TR) = 4 sec, echo time (TE) = 18 ms, and flip angle (FA) = 90°. Twenty-one slices (5 mm thick, 1 mm gap) were acquired sequentially in an inferior-to-superior direction, yielding 80 total perfusion images (40 labeled, 40 unlabeled; 2 initial discarded images allowing for magnetic equilibration). Resting BOLD images used to compute functional connectivity measures were acquired over a 5 min 6 sec period with a gradient-echo EPI sequence using the following parameters: FOV = 205×205 mm, matrix size = 64×64 mm, TR = 2 sec, TE = 28 ms, and FA = 90°. Thirty-nine slices (3 mm thick, no gap) were obtained sequentially in an inferior-to-superior direction, yielding 150 BOLD images (3 initial discarded images allowing for magnetic equilibration). For spatial co-registration of resting perfusion and BOLD images, T1-weighted 3D magnetization-prepared rapid gradient echo (MPRAGE) neuroanatomical images were acquired over 7 min 17 sec by these parameters: FOV = 256×208 mm, matrix size = 256×208 mm, TR = 2100 ms, inversion time (TI) = 1100 ms, TE = 3.29 ms, and FA = 8° (192 slices, 1mm thick, no gap).

Stressor tasks

After the resting period, participants completed 2 counterbalanced stressor tasks designed to evoke BP reactivity: a modified Stroop color-word interference task18 and a modified multi-source interference task (MSIT).26 The tasks were separated by a 10-12 min recovery period, during which subjective ratings of the first task were obtained (see below). Each task lasted 9 min 20 sec and was comprised of trials defining 2 alternating conditions, a less demanding congruent condition and a more demanding incongruent condition. The congruent condition and incongruent conditions lasted 52-60 sec, and were preceded by a 10-17 sec period during which participants fixated on a crosshair. Briefly, the congruent and incongruent conditions of both tasks were matched on motor response requirements and visual stimulus characteristics. Further, the incongruent condition of each task was performance-titrated, such that task accuracy was adaptively maintained at ~50% within and between individuals. In this way, task engagement and performance were experimentally approximated across participants. Supplemental task details and trial illustrations and provided online in Figure S1.

Task accuracy and subjective ratings

Task performance (accuracy) was computed as the percentage of trials correctly completed. Post-hoc, we verified that mean accuracy during the incongruent condition of each task was titrated across participants to 55.4% (± 6.8 SD), as compared with 89.8% (± 3.9 SD) during the congruent condition, t38 = 34.8, P<0.001. In conjunction with this performance-titration, mean response times to trials delivered during the incongruent condition compared with the congruent condition of each task were slowed by 465.8 ms (± 152.72 SD), t38 = 19.1, P<0.001.

To assess ratings of valence (1-very unhappy; 9-very happy), arousal (1-very calm; 9-very aroused), and perceived control (1-very little control; 9-very much control), participants completed a modified self-assessment manikin scale27 after the resting (pre-stressor) period and each task. Supplemental summaries of task accuracy and ratings are provided online in Figure S2. For the purpose of ancillary analyses, participants also completed inventories prior to testing to assess dispositional attributes of negative emotionality (trait anxiety and hostility) and recent levels of perceived life stress, which could plausibly covary with individual differences in stressor-evoked BP reactivity; however, scores on these inventories did not correlate significantly with resting BP or with stressor-evoked BP reactivity in this sample (Table S1). Hence, scores on these inventories were omitted from further analyses.

BP measurement

In the MRI scanner, participants’ BP was measured during the resting (pre-stressor) and stressor task periods from the brachial artery of the non-dominant (left) arm, which was not used for task responding. BP measurements were taken with an oscillometric device (Multigas 9500, MedRad Inc., Warrendale, PA/Leverkusen, Germany), set to inflate every 2.5 min during the resting period and once during each condition of the Stroop task and MIST. To compute resting BP, the final 3 measurements were averaged. To compute task-related BP, measurements from the demanding incongruent condition of the Stroop task and MIST were averaged. The incongruent condition—minus—resting BP difference score was used to compute BP reactivity following prior work.17,18 In this sample, men and women did not differ in resting or reactivity measures of systolic BP (SBP) or diastolic BP (DBP) (ts ≤ 1.6, P≥ 0.11). Further, across individuals, SBP and DBP changes from the resting period to the incongruent conditions of the Stroop task and MSIT were correlated (ΔSBP r = 0.70, P<0.001; ΔDBP r = 0.73, P<0.001), indicating that the tasks evoked reliable individual differences in BP reactivity. Following prior guidelines10, task-averaged SBP and DBP reactivity scores were used for subsequent analyses.

Preprocessing of neuroimaging data

Resting perfusion images were preprocessed with computational routines implemented in Statistical Parametric Mapping software (SPM2; Wellcome Trust Centre for Neuroimaging, London, UK). For preprocessing, perfusion images were realigned to the first image of the series, co-registered to each participant’s MPRAGE image, spatially normalized to the International Consortium for Brain Mapping 152 template (Montreal Neurological Institute; MNI), and re-sliced to an isotropic voxel size of 3mm3. Images were then smoothed with a 12mm full-width at half-maximum (FWHM) isotropic Gaussian kernel, after which the 40 labeled and 40 unlabeled perfusion images were submitted to pair-wise subtraction. Subtraction images were converted to an absolute CBF image series using a validated algorithm.28 This perfusion series was then averaged, generating for each individual a single resting voxel-wise rCBF image and a total CBF value, both in units of mL/100g/min.

Resting BOLD images were preprocessed using Statistical Parametric Mapping software (SPM5; Wellcome Trust Centre for Neuroimaging, London, UK). As for perfusion images, BOLD images were realigned to the first image of the series, co-registered to each participant’s MPRAGE image, normalized to the MNI152 template, and smoothed with a 6mm FWHM isotropic Gaussian kernel. After preprocessing, a BOLD signal time-series was extracted from 4 empirically-determined regions-of-interest (ROIs) for each individual, according to methods detailed previously.18 Specifically, a time-series was extracted from the mean BOLD signal of all voxels in a 6mm sphere surrounding the MNI coordinates identified in the voxel-wise regression analyses of resting perfusion (as quantified by rCBF) and stressor-evoked BP reactivity reported below. These regions (shown in Figure 1) included the dorsal anterior cingulate cortex (dACC; x, y, z MNI coordinates in mm = -6, 27, 33), perigenual anterior cingulate cortex (pACC; -9, 54, 12), medial prefrontal cortex (mPFC; 24, 51, 18), and insula (42, - 12, 12). Each time series extracted from each region for each participant was mean-centered, drift-corrected, and inspected for outliers. Any values >3SD of the series mean were replaced by averaging 2 surrounding values. Each outlier-corrected time-series was then band-pass filtered from 0.01 to 0.1 Hz to remove non-neural sources of noise using a linear-phase finite impulse-response Hamming filter of length 51 (102 sec, based on the 2 sec BOLD signal acquisition TR). Each filtered time-series was then submitted to a cross-correlation routine described below. A supplemental description of this routine is provided online in Figure S3.

Figure 1
Individual differences in stressor-evoked systolic blood pressure (SBP) reactivity were predicted by resting (pre-stressor) regional cerebral blood flow (rCBF) in the dorsal anterior cingulate cortex (dACC), perigenual anterior cingulate cortex (pACC), ...

Data analysis

To test whether resting (pre-stressor) levels of perfusion predicted subsequent stressor-evoked BP reactions across individuals, individual rCBF images were first submitted to a voxel-wise multiple regression model in SPM5. In the model, stressor-evoked SBP reactivity (computed by the average SBP change from the pre-stressor period to the incongruent Stroop task and MSIT conditions) was entered as a regressor of interest, with resting SBP entered as a covariate to account for initial BP. In the model, voxel-wise rCBF images were scaled to each individual’s total resting CBF value. To correct for multiple voxel-wise statistical testing, we maintained a whole-brain significance threshold of P≤0.005 with a cluster [k] extent of 20 contiguous voxels (3×3×3 mm3).29

After testing whether resting perfusion predicted stressor-evoked SBP reactivity, 2 hierarchical regression models were executed outside of SPM5 using SPSS software (v16, SPSS Inc., Chicago, IL). These hierarchical models tested specifically whether the prospective associations between resting perfusion and stressor-evoked SBP (model 1) or DBP (model 2) reactivity would persist after accounting for the potential confounding influence of individual differences in total resting CBF, resting BP, task accuracy, and ratings of task-related valence, arousal, and control. For the models, we extracted the mean rCBF values from the 4 ROIs in which resting perfusion predicted SBP reactivity in the voxel-wise SPM5 regression analysis described above. We then entered these extracted rCBF values as a set of predictors in the second step of the 2 models. In step-1 of both models, we entered total CBF, resting SBP (model 1) or resting DBP (model 2), task-averaged accuracy, and task-averaged ratings of valence, arousal, and control. The dependent variable for the models was the task-averaged SBP (model 1) or DBP (model 2) reactivity value for each individual. The unique percentage of variance in BP reactivity explained by the set of extracted rCBF values was evaluated by the ΔR2 from step-1 to step-2.

To determine the inter-correlations in the resting BOLD signal time-series across the 4 ROIs, we executed a cross-correlation routine detailed online in Figure S3. Briefly, pair-wise cross-correlation coefficients (r-values) were computed across the 4 ROIs’ BOLD signal time-series for each individual. The cross-correlation coefficients were then transformed to Fisher’s z-values, averaged across individuals, and used to compute 99% confidence intervals (CIs) using a bootstrapping method. This method permitted a test of whether the averaged z-values differed from 0 across individuals, indicating so-called functional connectivity between the ROIs. To aid interpretability, the averaged z-values and 99% CIs were transformed back to r-values for illustration in Figure 2.

Figure 2
Brain areas in which resting rCBF predicted stressor-evoked systolic and diastolic blood pressure reactivity exhibited cross-correlations in their time-varying oscillations in the blood-oxygen level-dependent signal, an indirect measure of neural activity. ...

Results

Task ratings and BP reactivity

The stressor tasks elicited moderate levels of subjective distress sufficient to evoke individual differences in BP reactivity. Specifically, using 9-point rating scales participants reported that they felt less happy, more aroused, and less in control while performing the tasks, as compared with the resting period (ts for all task versus resting comparisons ≥ 3.7, P≤0.001; Figure S2). Further, both SBP and DBP increased on average while participants performed the tasks, as compared with the resting period (ts for all task versus resting comparisons ≥ 3.0, P≤0.006; Figure S4).

Resting rCBF and BP reactivity

In a voxel-wise multiple regression analysis, larger-magnitude stressor-evoked SBP reactions were predicted across individuals by relatively higher levels of resting rCBF in the dorsal and perigenual areas of the left anterior cingulate cortex (dACC and pACC), the dorsal area of the right medial prefrontal cortex (mPFC), and the posterior area of the right insula (Figure 1).

Subsequent hierarchical multiple regression analyses further demonstrated that resting rCBF values extracted from the dACC, pACC, mPFC, and insula continued to predict both stressor-evoked SBP and DBP reactivity after accounting for total resting CBF, resting BP, task accuracy, and task ratings of valence, arousal, and control. Specifically, in step 1 of a 2-step hierarchical regression analysis, total CBF, resting SBP, task accuracy, and task ratings accounted for 14% of the variance in SBP reactivity (F6,32 = 0.88, R2-adj. = -0.02, P=0.52). In another 2-step regression analysis, total CBF, resting DBP, task accuracy, and task ratings accounted for 18% of the variance in DBP reactivity (F6,32 = 1.2, R2-adj. = 0.03, P=0.33). In step 2 of these regression analyses, resting rCBF values extracted from the dACC, pACC, mPFC, and insula accounted collectively for remaining variance in both SBP (F4,28 = 6.1, ΔR2 = 0.40, P=0.001) and DBP (F4,28 = 4.2, ΔR2 = 0.31, P=0.008) reactivity.

In addition, exploratory regression analyses including the same step-1 variables as above demonstrated that resting rCBF values extracted from the dACC, pACC, mPFC, and insula accounted individually for unique variance in both SBP and DBP reactivity (Table S2). Across these exploratory analyses, however, only resting rCBF extracted from the insula did not account for unique variance in DBP reactivity after family-wise error (FWE) rate correction for multiple (post-hoc) statistical testing (PFWE=0.08). By contrast, resting rCBF from the insula did account for unique variance in SBP reactivity; and, resting rCBF in all remaining areas accounted for unique variance in both SBP and DBP reactivity after FWE correction (Table S2).

Here, we recognize that during the resting period, participants may have been apprehensive about the forthcoming stressor tasks, leading to anticipatory unpleasantness or arousal and possibly increased BP reactivity or resting rCBF. This is unlikely, however, because resting ratings of valence and arousal did not correlate significantly with SBP or DBP reactivity or with resting rCBF in the dACC, pACC, mPFC, or insula (rs ranged from -0.14 to 0.25, P≥ 0.12). In addition, resting SBP and DBP did not correlate significantly with resting rCBF in the dACC, pACC, mPFC, or insula (rs - 0.21 to 0.18, P≥ 0.20).

Functional connectivity between areas where resting rCBF predicted BP reactivity

Across individuals, the dACC, pACC, mPFC, and insula exhibited moderate and directionally positive cross-correlations in their time-varying BOLD signal fluctuations, indicating resting ‘functional connectivity’ between these areas. Figure 2 shows the aggregate cross-correlation coefficients across these areas, along with their 99% bootstrapped CIs, generated by 5000 Monte Carlo simulations.

Discussion

The central finding of this study was that relatively heightened resting (pre-stressor) rCBF in the dACC, pACC, mPFC, and insula uniquely predicted comparatively larger-magnitude (exaggerated) BP reactions to subsequently completed cognitive stressor tasks across individuals (Figure 1). Further, these corticolimbic areas exhibited resting inter-correlations in their time-varying BOLD signal oscillations, indicating that they exhibited so-called functional connectivity24 with one another (Figure 2). As such, the present study provides further evidence that the dACC, pACC, mPFC, and insula could represent components of a neural circuitry that is functionally involved in the dispositional expression of individual differences in stressor-evoked BP reactivity.

An individual’s tendency to express exaggerated stressor-evoked BP reactivity has long been viewed as a dispositional attribute that may be linked to the pathophysiology of cardiovascular disease.30 Heritability studies11,12 further support the converging view that individual differences in stressor-evoked BP reactivity arise in part from genetic factors, which may involve the familial transmission of allelic polymorphisms that modify myocardial and vascular sensitivity to centrally-regulated patterns of peripheral efferent autonomic and neurohormonal outflow.31-33 A largely unsupported view, however, is that individual differences in stressor-evoked BP reactivity are solely attributable to inter-individual variation in self-reported levels of distress or negative emotionality, as subjective ratings of stress, emotionality, and arousal are weakly (and rarely significantly) associated with BP reactivity in laboratory studies.10,34 In agreement, individual differences in stressor-evoked BP reactivity were not significantly correlated with task-related unpleasantness, arousal, or perceived control ratings in this study. Nor were the prospective associations between resting corticolimbic activity and stressor-evoked BP reactivity accounted for by such ratings. Finally, self-reported ratings of dispositional negative emotionality (trait anxiety and hostility) and recent levels of perceived life stress were also not significantly correlated with stressor-evoked BP reactivity in this sample (Table S1). Hence, one interpretation of our findings in synthesis with prior work is that a tendency to express exaggerated stressor-evoked BP reactivity could arise in part from interacting central and peripheral neurobiological factors that are subject to genetic modification and relatively independent of subjective (consciously reportable) states of stress, arousal, or emotionality. In this regard, the present findings and those from prior neuroimaging studies may specifically suggest that neurobiological measures, including rCBF and functional connectivity measures, may have advantages over subjective rating measures in understanding the origins of individual differences in stressor-evoked BP reactivity.

To elaborate, it is noteworthy that acute stressors have been demonstrated in previous neuroimaging studies to evoke patterns of neural activity in the dACC, pACC, mPFC, and insula that covary reliably with concomitant changes in BP.14,16-18 There is cumulative human and nonhuman animal evidence that these corticolimbic areas are anatomically networked and that they are instrumental for regulating autonomic, neurohormonal, and cardiovascular reactions to stressors—presumably in support of adaptive behavioral action (e.g., the canonical ‘fight-or-flight’ response).13,19,20 In previous studies, however, measures of corticolimbic activity and BP reactivity were examined concomitantly and exclusively during stressor exposure. As a result, it was unknown whether resting (pre-stressor) patterns of corticolimbic activity would prospectively predict individual differences in subsequently evoked BP reactivity. Thus extending previous work, the present findings indicate that corticolimbic activity relates to individual differences in stressor-evoked BP reactivity—even when this activity is measured prior to stressor exposure. In extension, the cross-correlational (connectivity) findings illustrated in Figure 2 suggest that activity in the dACC, pACC, mPFC, and insula is likely to be functionally integrated within a broader neural circuitry. This notion is consistent with invasive human and nonhuman animal studies that have demonstrated functional and anatomical connections between these corticolimbic areas, which are important for autonomic, neurohormonal, and cardiovascular regulation.19,21,35-40 In addition, it is notable that two recent neuroimaging studies18,41 have specifically demonstrated that (1) exaggerated stressor-evoked BP reactivity and (2) increased carotid intima-media thickness, an indicator of preclinical atherosclerosis linked to stressor-evoked BP reactivity8, are both associated with a dysregulated pattern of functional connectivity between the pACC and one of its subcortical projection sites involved in cardiovascular regulation: the amygdala. In view of these recent studies and the present study, we are currently testing whether particular patterns of resting state connectivity (e.g., time-lagged, directional, multivariate) exhibited between the pACC, the other corticolimbic areas identified here, and their subcortical targets specifically predict individual differences in stressor-evoked BP reactivity or covary with associated indicators of cardiovascular risk.

Although novel, we appreciate both inferential limitations and unresolved questions raised by the present study. First, by testing younger men and women in good cardiovascular health, extrapolations to older and more heterogeneous samples are precluded. Second, by measuring resting perfusion on just 1 occasion that involved later stress testing, conjectures that relatively heightened resting corticolimbic activity may predispose toward exaggerated stressor-evoked BP reactivity remain tenuous—particularly until individual differences in resting corticolimbic activity (e.g., perfusion and functional connectivity) are shown to be reproducible over multiple occasions that do and do not involve later stress testing. Third, by employing a study design that did not include assessments of family history of hypertension or cardiovascular disease or other familial assessments permitting inferences regarding genetic influences on our findings, predictions about whether resting corticolimbic activity predicts BP reactivity in part via heritable and risk-related factors are largely premature. Finally, by not measuring preclinical cardiovascular disease indicators or cardiovascular risk factors that have been associated with stressor-evoked BP reactivity, assumptions that our findings are relevant to clinical risk remain untested.

Perspectives

To end, individuals expressing relatively exaggerated stressor-evoked BP reactivity also expressed heightened resting rCBF in the cingulate, medial prefrontal, and insular cortices—corticolimbic brain areas that also exhibited functional connectivity with each other. Collectively, these findings could be interpreted from a neurobiological perspective which views resting measures of rCBF42 and functional connectivity23 as corresponding in part to the level of metabolic ‘preparedness’ of functionally-related brain areas to respond to future cognitive, emotional, or otherwise behaviorally-salient stimuli. In particular, resting rCBF in the cingulate, medial prefrontal, and insular cortices—along with the degree of their functional connectivity—may partially correspond to their ‘preparedness’ to (a) respond to behaviorally-salient stressor stimuli and (b) communicate with subcortical cell groups that are involved regulating BP. These notions could be tested empirically by determining whether and how measures of resting corticolimbic activity and connectivity relate to patterns of neural activity elicited directly by behaviorally-salient stressors that evoke BP reactivity. Arguably, testing such notions may aid in further explicating the neurobiological factors and neural circuitries that predispose some individuals toward exaggerated stressor-evoked BP reactivity and perhaps related cardiovascular risk.

Acknowledgements

We thank Mr. Qi Shi for his technical assistance and Dr. Jeanne McCaffery for her constructive comments on a draft of this manuscript.

Sources of Funding

This work was supported by National Institutes of Health grants K01-MH070616 and R01-HL089850, The Pittsburgh Mind-Body Center (HL076852/076858), and a Commonwealth Universal Research Enhancement Grant from the Pennsylvania Department of Health.

Footnotes

Conflict(s) of Interest/Disclosure(s)

None.

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