Hypertension is a leading risk factor for cardiovascular disease and a major contributor to healthcare costs worldwide [
The central nervous system (CNS) has also been implicated in the aetiology and maintenance of some forms of EH. The CNS is a target of the disease which, if untreated, progresses to blood pressure (BP) levels threatening the integrity of cerebral vessels, potentially inducing stroke [
Considerable evidence supports the connection between pain perception and BP regulation. It has been proposed that acute BP increases may reduce pain, thus establishing hypertension through instrumental learning [
The layman concept that stress can cause hypertension still lacks strong empirical support. A review of studies examining the relationship of stress and hypertension [
Pain sensations, consisting of sensory, affective, and cognitive experiences, modulate EEG oscillations across a wide range of frequency bands, presumably reflecting the mechanisms involved in cortical activation and inhibition [
Taking into consideration the issues presented above, the current study attempted an integrative approach to the factors interacting in the setting of hypertension. The objective was to compare well-characterised, untreated hypertensives and matched normotensive controls in terms of (i) arterial blood pressure variables (24-hour ambulatory blood pressure monitoring (ABPM)), (ii) CNS electrophysiological responsiveness (EEG), and (iii) behavioural responsiveness (pain perception and tolerance) under exposure to sympathoexcitatory stress and pain induced by the CPT.
Although the impact of experimental pain on EEG has attracted experimental interest, the existing available data do not warrant the formulation of specific hypotheses regarding the relationship between brain oscillations and newly diagnosed, untreated hypertension. Therefore this axis of our design has an exploratory character.
The hypertensive (HT) group consisted of 22 newly diagnosed untreated hypertensives (11 men, 11 women; mean age =
Participants were instructed to abstain from alcohol, cigarette smoking, coffee/tea, and exercise for at least 30 minutes prior to testing. The study flow diagram can be seen in Table
Outline of experimental measurements.
Task | Measurements | Duration | |
---|---|---|---|
Day 1 | Habituation to laboratory environment and CPT conditions | 1 hour approximately | |
Onset of 24 hr ABPM procedure (Section |
24 hours | ||
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Day 2 | Cold pressor test rest baseline | EEG recording period 1 | 3 min |
Hand immersion in 2°C water bath | EEG recording period 2 (unit of stress) | 1 min | |
Continuation of immersion (2°C ) until spontaneous withdrawal | EEG recording period 3 (pain tolerance) |
|
|
Cold pressor test recovery (Section |
EEG recording period 4 | 3 min |
24-hour ABPM was conducted on all subjects on a usual working day by means of the Spacelabs 90217 ambulatory blood pressure monitor (Spacelabs Inc., Redmond, Wash). The appropriate sized cuff was placed around the nondominant arm and 3 consecutive blood pressure determinations were recorded along with sphygmomanometric measurements to verify that there was no difference greater than 5 mmHg on the average of the 2 sets of values. Throughout the 24-hour monitoring readings were obtained automatically at 15-minute intervals and all subjects had at least 3 valid readings per hour. The resulting 80 to 96 pairs of systolic and diastolic BP readings per recording with the corresponding recording time were used to calculate blood pressure derivatives. All subjects were instructed to maintain their usual daytime activities between 6:00 AM and 10:00 PM and rest-sleep between 10:00 PM and 6:00 AM [
The CPT is a method commonly used to evoke a sympathoexcitatory stress response [
EEG data collection and analysis: for acquiring the EEG data, the EMOTIV Epoc EEG system was used (EMOTIV, 20141). This device has a wireless amplifier, and 14 wet saline electrodes, corresponding at the positions AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, and AF4 according to the international 10–20 system (see Figure
The device has also an embedded 16-bit ADC which was used to digitize the data with 128 Hz sampling frequency per channel. The data were sent via Bluetooth to a computer with the EMOTIV Control Panel software installed, allowing the visual monitoring of the impedance of the electrode contact to the scalp. The EMOTIV Epoc EEG device is part of a number of low-cost EEG systems, which have been recently applied for research aims. However, recent research assessing their reliability provides converging evidence indicating their capacity to measure consistently EEG signals [
Two electrodes located just above the subject’s ears (P3, P4) were used as reference. Electrode resistance was kept constantly below 5 kΩ. The EEG signals were band-passed filtered with Butterworth 0.5–8 Hz, 8–12 Hz, 12–28 Hz, and 28–45.5 Hz filters.
In order to analyse the data from the experimental setup, a wavelet-based analysis was performed using EEGLAB 13.5.4b [
Furthermore, certain coefficients will be generated corresponding to the noise affected zones and some other coefficients will be generated in the areas corresponding to the actual EEG. Although these coefficients are associated with frequency components, they are modified in the time domain, where each coefficient corresponds to a time range. An appropriate choice of wavelet coefficients would result in removing the noisy part of the EEG signal to some extent, while retaining the useful part of the signal [
For each electrode the total measurements were divided into four time segments based on the previous described experimental procedure. The wavelet coefficients were split into the following eight standardized bands: Delta (1–4 Hz), Theta1 (4–6 Hz), Theta2 (6–8 Hz), Alpha1 (8–10 Hz), Alpha2 (10–12 Hz), Beta1 (12.5–16 Hz), Beta2 (16.5–20 Hz), Beta3 (20.5–28 Hz), and Gamma (28.5–45 Hz). The wavelet cycles of the transform were dynamically increased so that the time width of the wavelet corresponding to the highest frequency of the Gamma band is to be half the time width of that related to the lowest frequency of the Delta band, thus, allowing a higher frequency resolution (resulting from 3 cycles at 1 Hz to above 67 cycles at 45 Hz). The wavelet coefficients were averaged over time and then scales contained within each frequency band were summed together to yield the absolute activity within each band [
Statistical analysis was performed with the STATISTICA 12.0 software for Windows. A first analysis involved a between-group, Repeated Measures design. Power spectrum density of EEG recordings from 14 electrodes was expressed as Delta, Theta1, Theta2, Alpha1, Alpha2, Beta1, Beta2, Beta3, and Gamma values. Each frequency was analysed as a dependent variable in separate, 1-way Repeated Measures ANOVAs. In each ANOVA, the independent variable was Group Membership ((1) normotensive controls versus (2) hypertensives); the Repeated Measures factor was phase, which included four levels corresponding to the stages of the Cold Pressor Test ((1) pretest resting phase, (2) stress unit phase, (3) tolerance phase, and (4) posttest resting phase). Special attention was given to interactions as those would provide the strongest evidence as to differential response profiles of hypertensives versus controls. The overall relationship among the clinical and the EEG variables was further investigated by Canonical Analysis; in order to ascertain the relative significance of the variables they showed the highest individual association with the two states of the subjects (i.e., healthy controls and hypertensives). On the left side of the equation we chose those EEG variables that clearly and statistically significant showed an interaction effect via the standard Repeated Measures ANOVA paradigm. Each subgroup of these variables was analysed separately so four Canonical Analyses were carried out, one for each group, that is, Gamma, Alpha1, Alpha2, Delta, and Theta1. On the right side of the equation we chose Maximum Diastolic Blood Pressure of the day (Max DBP day), Minimum Heart Rate of the day (Min HR day), Mean Blood Pressure 24 hours (Mean MBP24), and Minimum Diastolic Blood Pressure of the day (Min DBP day) by performing Discriminant Function Analysis that included all clinical variables and controls/hypertensives as the dependent variable.
We also performed the standard Student’s
There was no significant difference between normotensives and hypertensives although a tendency was revealed towards greater pain tolerance in hypertensives measured as self-determined duration (min) of exposure to the cold bath (mean tolerance, controls =
Overall the ABPM variables differed significantly between the two compared groups; however a Discriminant Function Analysis revealed the four most differentiators variables; see Table
Hypertensives | Controls |
| |||
---|---|---|---|---|---|
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Mean | SD | Mean | SD | ||
Mean MBP24 | 106.19 | 7.28 | 91.09 | 5.95 | 0.000 |
Max DBP day | 118.59 | 11.15 | 98.22 | 25.97 | 0.002 |
Min HR day | 62.18 | 8.87 | 52.27 | 16.33 | 0.019 |
Min MBP day | 85.50 | 9.92 | 68.00 | 19.00 | 0.001 |
A significant main effect of phase was noted in several electrodes (Delta: O2 and F8; Theta1: F3; Alpha1: O2 and P8; Alpha2: F3 and AF4; Beta2: F3 and F4; and, finally, in Gamma electrodes AF3 and F7). Overall, signals tended to rise during the tolerance phase and drop during the posttest resting phase.
However, the central finding was the interactions observed between Group Membership and phase. Analyses revealed interactions in Delta, Theta1, Alpha1, Alpha2, Beta2, and Gamma values. In examining this relationship we encountered four distinct response patterns which support our hypothesis that hypertensives have a differential electrophysiological response profile to environmental stimuli as those are simulated by the phases of the Cold Pressor Test.
DBA at F3 and P8 leads had higher values for hypertensives than controls overall and particularly in the tolerance and posttest phases. A similar finding was noted for electrode O2 but with a greater difference between the two groups, with controls showing a continuous value decline from pretest to posttest. In the control group, lead F4 showed a sharp value drop at the posttest phase, whereas hypertensives demonstrated a slight increase at the same phase. In the control group the AF4 lead revealed a steady drop from the beginning of the procedure to the end, similarly to the O2 lead; in contrast, the hypertensives group sustained the same level of activity in all four experimental phases (Table
|
Pretest | Stress unit | Tolerance | Posttest | Interaction | |||||
---|---|---|---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | Mean | SD | Mean | SD | |||
Delta F3 | ||||||||||
Controls | 18 | 42.21 | 4.18 | 42.32 | 3.88 | 40.76 | 3.49 | 42.13 | 3.89 |
|
Hypertensives | 22 | 44.14 | 7.32 | 43.88 | 7.27 | 45.46 | 6.39 | 44.67 | 6.76 | |
Delta O2 | ||||||||||
Controls | 18 | 38.74 | 6.83 | 37.73 | 7.29 | 36.94 | 6.55 | 36.77 | 7.34 |
|
Hypertensives | 22 | 42.59 | 8.31 | 41.90 | 8.86 | 44.13 | 7.04 | 41.89 | 7.73 | |
Delta P8 | ||||||||||
Controls | 18 | 42.05 | 8.11 | 41.95 | 8.12 | 40.58 | 6.98 | 41.58 | 7.46 |
|
Hypertensives | 22 | 44.48 | 9.63 | 44.24 | 8.93 | 46.54 | 7.16 | 44.14 | 8.44 | |
Delta F8 | ||||||||||
Controls | 18 | 47.09 | 3.87 | 47.15 | 3.76 | 47.10 | 3.64 | 44.78 | 3.18 |
|
Hypertensives | 22 | 47.62 | 7.40 | 47.77 | 7.24 | 47.97 | 7.09 | 48.57 | 6.98 | |
Delta AF4 | ||||||||||
Controls | 18 | 44.71 | 5.63 | 44.64 | 6.03 | 43.66 | 5.63 | 43.05 | 4.71 |
|
Hypertensives | 22 | 47.19 | 8.40 | 47.11 | 8.30 | 48.24 | 7.19 | 47.78 | 8.19 |
Delta brain activity (DBA).
TBA 1 at F3 and AF4 leads produced a statistically significant interaction (Table
|
Pretest | Stress unit | Tolerance | Posttest | Interaction | |||||
---|---|---|---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | Mean | SD | Mean | SD | |||
Theta1 F3 | ||||||||||
Controls | 18 | 41.33 | 3.40 | 41.52 | 3.49 | 39.88 | 2.90 | 41.25 | 3.34 |
|
Hypertensives | 22 | 42.56 | 5.54 | 42.77 | 5.42 | 43.02 | 5.25 | 42.60 | 4.95 | |
Theta1 AF4 | ||||||||||
Controls | 18 | 43.65 | 5.10 | 43.80 | 5.79 | 42.04 | 4.79 | 43.43 | 5.43 |
|
Hypertensives | 22 | 45.55 | 7.01 | 45.80 | 6.68 | 46.11 | 6.60 | 45.29 | 6.51 |
Theta1 brain activity (T1BA).
Overall, ABA 1 values at O2 and P8 leads were higher in hypertensives than controls throughout the experiment, whereas controls showed a significant drop in the posttest resting phase (Table
|
Pretest | Stress unit | Tolerance | Posttest | Interaction | |||||
---|---|---|---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | Mean | SD | Mean | SD | |||
Alpha1 O2 | ||||||||||
Controls | 18 | 36.38 | 6.28 | 35.58 | 7.26 | 35.84 | 7.46 | 34.13 | 6.46 |
|
Hypertensives | 22 | 39.34 | 6.39 | 39.07 | 7.39 | 39.56 | 7.19 | 39.20 | 6.18 | |
Alpha1 P8 | ||||||||||
Controls | 18 | 39.39 | 7.02 | 39.08 | 7.28 | 39.63 | 7.11 | 36.84 | 5.46 |
|
Hypertensives | 22 | 41.41 | 8.01 | 41.09 | 7.24 | 41.70 | 7.81 | 41.27 | 7.35 |
|
Pretest | Stress unit | Tolerance | Posttest | Interaction | |||||
---|---|---|---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | Mean | SD | Mean | SD | |||
Alpha2 AF3 | ||||||||||
Controls | 18 | 38.56 | 3.93 | 36.71 | 4.14 | 38.82 | 4.07 | 38.58 | 3.70 |
|
Hypertensives | 22 | 38.88 | 5.52 | 40.04 | 4.15 | 39.22 | 5.66 | 39.15 | 4.55 | |
Alpha2 F3 | ||||||||||
Controls | 18 | 37.13 | 3.59 | 35.76 | 3.48 | 37.28 | 3.39 | 37.20 | 3.17 |
|
Hypertensives | 22 | 38.11 | 4.44 | 38.72 | 4.23 | 38.51 | 4.22 | 38.57 | 3.93 | |
Alpha2 AF4 | ||||||||||
Controls | 18 | 38.23 | 4.16 | 36.69 | 3.73 | 38.52 | 4.43 | 38.28 | 4.77 |
|
Hypertensives | 22 | 40.01 | 5.69 | 40.73 | 4.58 | 40.43 | 5.20 | 39.97 | 4.79 |
Alpha1 brain activity (A1BA).
In the case of Alpha1 electrode P8 the statistically significant interaction was due to the within the control group drop in the Power Spectral Density value at the posttest phase compared to the pretest (
A2BA values showed significant interactions at leads AF3, F3, and AF4. The results followed a different pattern from that noted with A1BA. Both groups had similar values during the pretest, with the control group subsequently showing a marked drop during the stress unit phase. In contrast, in that phase hypertensives actually showed a small rise, which levelled off during the tolerance and the posttest phases (Table
Alpha2 brain activity (A2BA).
|
Pretest | Stress unit | Tolerance | Posttest | Interaction | |||||
---|---|---|---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | Mean | SD | Mean | SD | |||
Beta2 T8 | ||||||||||
Controls | 18 | 36.26 | 4.70 | 36.12 | 4.77 | 36.60 | 5.10 | 36.88 | 5.36 |
|
Hypertensives | 22 | 35.72 | 6.56 | 36.06 | 6.47 | 36.00 | 6.75 | 35.55 | 6.22 |
Beta2 brain activity (B2BA).
As in the case of Alpha1 electrode P8 the statistically significant interaction in Beta2 electrode T8 was due to the within the control group rise in the Power Spectral Density value at the posttest phase compared to the stress unit phase (
GBA values for leads T8, AF3, AF4, and FC6 revealed statistically significant Group X Phase interactions (Table
|
Pretest | Stress unit | Tolerance | Posttest | Interaction | |||||
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Mean | SD | Mean | SD | Mean | SD | Mean | SD | |||
Gamma AF3 | ||||||||||
Controls | 18 | 33.39 | 2.41 | 33.24 | 2.40 | 33.58 | 2.239 | 33.49 | 3.20 |
|
Hypertensives | 22 | 31.90 | 4.19 | 33.13 | 3.92 | 33.37 | 3.943 | 32.25 | 3.46 | |
Gamma T8 | ||||||||||
Controls | 18 | 33.70 | 3.44 | 33.49 | 4.24 | 31.73 | 4.16 | 33.69 | 4.96 |
|
Hypertensives | 22 | 32.46 | 4.90 | 32.83 | 5.24 | 34.70 | 3.44 | 31.77 | 4.89 | |
Gamma FC6 | ||||||||||
Controls | 18 | 32.81 | 3.32 | 32.85 | 3.83 | 34.24 | 2.02 | 33.28 | 4.21 |
|
Hypertensives | 22 | 31.57 | 3.74 | 32.10 | 4.40 | 31.47 | 3.90 | 30.97 | 3.44 |
T8 values showed the greatest difference between the two groups during the tolerance phase with hypertensives having higher values than controls.
The electrode FC6 recordings showed a general higher value range for controls especially during the tolerance and the posttest phases (Table
Gamma brain activity (GBA).
EEG oscillations based on the EMOTIV Epoc EEG apparatus; coloured sites indicate significant group differences.
Once more, the statistically significant interaction for Gamma electrode AF3 was due to statistically significant differences in the Power Spectral Density values what were noted in the hypertensives group who had a sharp rise from the pretest to the stress unit phase (
On the left side of the equation we chose those EEG variables that clearly and statistically significantly showed an interaction effect via the standard Repeated Measures ANOVA paradigm. Each subgroup of these variables was analysed separately so four Canonical Analyses were carried out, one for each group, that is, Gamma, Alpha1, Alpha2, Delta, and Theta1. On the right side of the equation we chose Max DBP day, Min HR day, Min MBP24, and Min DBP day by performing Discriminant Function Analysis that included all clinical variables and controls/hypertensives as the dependent variable. Discriminant Function Analysis is useful in deciding which set of variables is best in discriminating between groups of subjects and thus suitable for our purpose in isolating those clinical variables that most strongly predicted membership in our groups.
The obtained results revealed that Delta values where the most strongly associated with the clinical state of the subjects (Canonical
The relevant correlation matrices showing the electrodes involved with correlations higher than 0.3 as derived by the Canonical Analyses are given in Table
Correlations left set with right set | Mean MBP241 | Max DBP day2 | Min HR day3 | Min MBP day4 |
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Delta tolerance F3 |
|
0.27 | −0.11 | 0.26 |
Delta tolerance O2 |
|
0.15 | 0.26 |
|
Alpha1 pretest resting O2 | 0.22 | 0.07 |
|
|
Alpha1 stress unit O2 | 0.25 | 0.12 | 0.27 |
|
Alpha1 tolerance O2 | 0.24 | 0.09 | 0.27 |
|
Alpha1 posttest resting O2 |
|
0.17 |
|
|
Alpha2 stress unit AF3 | 0.24 | 0.11 | 0.11 |
|
Alpha2 stress unit F3 |
|
0.22 | 0.23 |
|
Alpha2 posttest resting F3 | 0.26 | 0.17 | 0.12 |
|
Beta2 posttest resting T8 | − |
− |
|
|
Gamma pretest resting FC6 | −0.27 | − |
|
|
Gamma stress unit T8 | −0.24 | − |
|
|
Gamma tolerance FC6 | − |
− |
|
|
Gamma posttest resting AF3 | −0.24 | − |
|
− |
Gamma posttest resting T8 | − |
− |
|
|
Gamma posttest resting FC6 | − |
− |
|
|
1Figure
Heliograph of the correlations between Mean Mean Blood Pressure 24 hours and the statistically significant different CPT-induced EEG oscillations depicted by concentric circles in the (−1)-(0)-(+1) continuum.
Heliograph of the correlations between Maximum Diastolic Blood Pressure of the day and the statistically significant different CPT-induced EEG oscillations depicted by concentric circles in the (−1)-(0)-(+1) continuum.
Heliograph of the correlations between Minimum Heart Rate of the day and the statistically significant different CPT-induced EEG oscillations depicted by concentric circles in the (−1)-(0)-(+1) continuum.
Heliograph of the correlations between Minimum Mean Blood Pressure of the day and the statistically significant different CPT-induced EEG oscillations depicted by concentric circles in the (−1)-(0)-(+1) continuum.
The study explored putative relationships between arterial blood pressure variables (24 hr ABPM) and electrophysiological responsiveness (EEG activity) elicited by exposure to sympathoexcitatory stress/pain induced by the CPT in untreated hypertensives and normotensive controls.
Although the two groups differed significantly in all arterial blood pressure variables, a Discriminant Function Analysis revealed that the most robust group differentiators were four: Maximum Diastolic Blood Pressure of the day (Max DBP day), Minimum Heart Rate of the day (Min HR day), Mean Blood Pressure 24 hours (Mean MBP 24), and Minimum Diastolic Blood Pressure of the day (Min DBP day).
An initial series of ANOVA analyses determined significant group differences in CPT-induced EEG oscillations, which were then covaried with the four most robust cardiovascular differentiators by means of Canonical Analyses. This revealed positive correlations between cardiovascular variables and Delta oscillations (1–4 Hz) during the tolerance phase; in high-alpha oscillations (10–12 Hz) during the stress unit and posttest phase; and in low-alpha oscillations (8–10 Hz) during all four CPT phases.
In contrast, negative correlations were noted between cardiovascular variables and Beta2 oscillations (16.5–20 Hz) during the posttest phase and Gamma oscillations (28.5–45 Hz) during all four CPT phases.
These associations were localised at several sites across the cerebral hemispheres, predominantly in the right one, and in left frontal lobe.
On the behavioural level, pain tolerance measured in terms of self-determined exposure to the CPT ice bath beyond the obligatory 1 min stress unit phase revealed a tendency towards greater tolerance in hypertensives, although this did not reach statistical significance (
Given the multiplicity of electrophysiological observations based on the initial ANOVAs analyses, for the purposes of this discussion we have focused on the instances where the Canonical Correlation Analysis revealed strong relationships between the cardiovascular and electrophysiological group differentiators of the study.
The correlations identified between cardiovascular group differentiators and Delta brain activity (DBA) appear compatible with previous human and animal studies suggesting that increased cerebral activity in the spectrum of DBA is associated with increased arterial pressure, probably mediated through suppressed baroreflex control of heart rate [
DBA correlations were noted in the left frontal and the right occipital areas. This is consistent with evidence that DBA is involved in cortical communication over long distances [
Our findings revealed positive correlations between cardiovascular variables and high (10–12 Hz) alpha oscillations during the CPT stress unit and posttest phases and in low (8–10 Hz) alpha oscillations during the 4 CPT phases overall. The high-alpha subband (A2BA) is considered an index of task-specific sensorimotor activity regulation [
The low-alpha subband (A1BA) is considered an index of general tonic alertness [
It is a reasonable assumption that the pretest brain oscillations noted in our study may be explained in terms of anticipation of pain. Such anticipation can cause mood changes and behavioural adaptations which may influence subsequent pain perception [
A negative correlation was noted between cardiovascular variables and B2BA during the posttest phase. Given that Beta brain activity plays a role in motor processing, a possible interpretation of this correlation is that pain-related B2BA modulations reflect the preparation and execution of a defensive response [
A negative correlation between GBA and cardiovascular group differentiators emerged from our study. GBA is considered to play a crucial role in cortical integration and perception [
In contrast, longer-lasting painful stimulation (perception of tonic pain) does not appear to be encoded by GBA in the somatosensory cortex, but rather in the medial prefrontal cortex, close to premotor and cingulate cortices [
Previous studies have shown that GBA is enhanced during attentional selection of sensory information [
In the service of adaptive environmental engagement low coping capacity has been associated with a more pronounced decrease in GBA [
As a whole, the correlations we noted between brain oscillations and cardiovascular parameters may be better understood in the light of reports suggesting that cognitive alterations depend upon the degree of hypertension. It is established that the systolic and diastolic blood pressures have effects on distinct cognitive domains [
Furthermore, the relative scalp locations of differences in magnitude of cerebral activation between the two hemispheres could determine the overall changes in blood pressure and heart rate. This idea is supportive to the view, which states that the two cerebral hemispheres act in concert to promote changes in cardiovascular functioning; however, the right hemisphere predominantly modulates sympathetic efferents, while the left hemisphere predominantly modulates parasympathetic efferents, of the autonomic nervous system [
In conclusion, our results add to a growing body of evidence that the brain is implicated in the initiation of high blood pressure while it is itself altered by early disease processes. Thus the brain and vasculature may be independently and concurrently targeted by the factors inducing essential hypertension [
Previous studies have thoroughly evaluated the impact of TR of BP variation on target organ damage. A cross-sectional study in 514 normotensive and uncomplicated hypertensive patients demonstrated that the 24-hour rate of systolic BP variation was greater in hypertensive than in normotensive subjects and was the only office or ambulatory BP monitoring parameter that was linearly and independently associated with carotid intima-media thickening [
Our results also demonstrate the advantage of simultaneous EEG recording under well-defined pain inducing conditions. Our approach of factoring contributions from multiple, interconnected brain processes is relevant to all studies which attempt to link evoked brain responses with behaviour and demonstrates that exploiting these interactions leads to a more complete understanding of brain response to stimulation and the psychophysiological emergence of the experience of pain.
A number of limitations must be considered when interpreting the findings of the current study. First, the study relied on a relatively small sample. Second, we employed CPT as a measure of pain. Although this approach is consistent with the literature, we cannot necessarily generalize the current results to other types of painful stimuli, such as thermal stimuli. Third, an additional limitation relates to the fact that EEG and cardiovascular measurements were noncontemporaneous. This potentially limits the findings in that the two measures were not precisely coupled. Fourth, data were collected in a single testing session. Therefore, we cannot comment on the stability of the relation between hypertension and EEG activity over time.
Nevertheless, the study adds to the understanding of the role of brain oscillations evoked by stress and/or pain stimulation in the underlying mechanisms of hypertension. Our data suggest that brain oscillations in response to stress and/or pain challenge may give greater insight into underlying systems and the mechanisms of hypertension.
EEG recorded in the course of the CPT provides a measure of cortical activity, on the basis of which untreated hypertensives may be differentiated from healthy controls. Furthermore, the method utilized in this study helps isolate pain-related features in the EEG during CPT in association with cardiovascular variables. This lends credibility to the hypothesis that top-down and bottom-up control mechanisms are implicated in the development of hypertension. Future applications of the methodology may help identify specific EEG features related to the neuronal processing of pain perception in hypertension.
The authors declare that there are no conflicts of interest.
The authors gratefully appreciate Professor Antonio T. Alexandridis and Ph.D. student Panos Papageorgiou of the Department of Electrical and Computer Engineering, University of Patras, Greece, for their valuable contribution of biosignal processing and analyzing. The authors would also like to thank Emmanouil A. Kitsonas, Electrical Engineer, Ph.D. Technical Director, Eugenides Foundation Member TCG, IEEE, IPS Council, for his technical support in the experimental procedure.