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Original Studies |
Laboratoire des Régulations Physiologiques et des Rythmes Biologiques chez l’Homme (F.C., C.G., G.B.), Institut de Physiologie, Faculté de Médecine, Université Louis Pasteur, Strasbourg 67085, France; Centre d’Etudes de Physiologie Appliquée (C.J., A.M.), Centre National de la Recherche Scientifique, Strasbourg 67087, France; and Unité de Physiologie de la Vigilance, Département des Facteurs Humains (F.C.), Centre de Recherches du Service de Santé des Armées Emile Pardé, La Tronche 38702, France
Address all correspondence and requests for reprints to: Florian Chapotot, Unité de Physiologie de la Vigilance, 24 avenue des Maquis du Grésivaudan, CRSSA "Emile Pardé" B.P. 87, 38702 La Tronche Cedex, France.
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Abstract |
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Analysis of individual profiles demonstrated a declining tendency for EEG ß activity and cortisol secretory rate, with an overall temporal relationship indicated by positive and significant cross-correlation coefficients between the two variables in all subjects (average r = 0.565, P < 0.001). Changes in cortisol secretion lagged behind fluctuations in EEG ß activity, with an average delay of 10 min for all the subjects. On the average, 4.6 ± 0.4 cortisol secretory pulses and 4.9 ± 0.5 peaks in EEG ß activity were identified by a detection algorithm. A significant, although not systematic, association between the episodes in the two variables was found: 44% of the peaks in EEG ß activity (relative amplitude, near 125%; P < 0.001) occurred during an ascending phase of cortisol secretion, cortisol secretory rates increasing by 40% (P < 0.01) 10-min after peaks in EEG ß activity. However, no significant change in EEG ß activity was observed during the period from 50 min before to 50 min after pulses in cortisol secretion.
In conclusion, the present study describes a temporal coupling between cortisol release and central alertness, as reflected in the waking EEG ß activity. These findings suggest the existence of connections between the mechanisms involved in the control of hypothalamo-pituitary-adrenal activity and the activation processes of the brain, which undergoes varying degrees of alertness throughout daytime wakefulness.
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Introduction |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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activity between 0.5–4 Hz), has been
shown to be preceded by or concomitant with a decrease in cortisol
secretory rates (11). These results confirmed that alternations between
rapid-eye-movement (REM) sleep and non-REM sleep are associated with
changes in cortisol secretion (8, 13). Consequently, the existence of
common mechanisms influencing both the central nervous system and the
hypothalamo-pituitary-adrenal (HPA) axis should be physiologically
relevant during sleep, if not during both sleep and wakefulness. Although sleep EEG has been extensively studied (14), the time course of the waking EEG activity has been studied far less, because of artifacts contaminating EEG recordings. However, diurnal fluctuations of the human background EEG, a neurophysiological indicator of the brain’s functional state (15), have been shown to occur spontaneously, with patterns depending on the EEG spectrum frequency band (16, 17). Indeed, homeostatic wake-dependent and circadian components of the EEG have been recently identified (18). Moreover, it has been shown that daytime temporal changes in different frequency bands of the waking EEG occur periodically (19, 20), with an ultradian periodicity of 70–120 min (21), comparable with that of cortisol release (1). From a neurophysiological point of view, EEG ß activity (13–35 Hz) is observed during wakefulness and REM sleep. It is controlled by the thalamus and generated by the desynchronization of cortical neurons in response to cognitive processes and attention requirements (22, 23). This specific activity is considered to be an index of the level of brain activation, because increased alertness has been shown to be associated with EEG desynchronization (23, 24).
Therefore, the coupling of HPA axis activity and the level of central alertness was examined during wakefulness using a 70-sec gaze fixation task, performed every 10 min, with EEG and plasma cortisol measurements being taken simultaneously.
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Subjects and Methods |
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Ten healthy males, between 21 and 27 yr old, with a standard body mass index (21.7 ± 0.9 kg/m2), volunteered and gave their informed written consent after the approval of the protocol by the local ethics committee. They were selected, after a medical examination, based on their sleep schedules and quality. All evening- or morning-type people and subjects with sleep disorders or jet-lag experience, shift work, or sleep deprivation during the preceding weeks were excluded from the study. A preliminary EEG test was performed to exclude subjects exhibiting more than 10 eye blinks per minute. Subjects having low sleep efficiencies (<80%), during the night preceding the daytime measures of alertness, were excluded from the analyses.
Protocol
Experiments were performed in two soundproof air-conditioned rooms, with blood samples collected and electrophysiological signals monitored in an adjacent room. Closed-circuit television allowed constant supervision of the subjects. Special attention was given to avoid intercurrent sleep episodes. When awake, the subjects were maintained in dim light (<100 lux). They slept in the laboratory for two consecutive nights, from 2300–0700 h, each night being followed by a daytime period during which they remained supine and were submitted to a serial 70-sec gaze-fixation task. This task was repeated every 10 min, from 0900–1500 h, during the habituation period (day I) and from 0900–1800 h during the experimental period (day II). Between gaze-fixation periods, the subjects read, listened to music, watched television, or conversed with an experimenter. They received standardized meals at 0700 h, 1200 h, and 1900 h. At 1600 h on day I, a catheter was inserted under local anesthesia into an antecubital vein and was kept patent with heparinized solutions, allowing the subjects to remain supine until the end of the experiment. Electrodes for polygraphic recordings were attached 1 h before the beginning of each recording period and detached at the end of the procedure. Polygraphic sleep recordings were performed during the night, from day I to day II.
Waking-EEG recordings
Continuous waking-EEG recordings were performed with electrodes attached with collodion and placed according to the 10/20 international system (25) using both central EEG derivations (C3 and C4), referenced to the contralateral mastoid apophysis (A2 and A1, respectively). One transversal electrooculographic derivation and 1 chin electromyographic derivation were attached for artifact control (26). After high-pass (0.3 Hz) and low-pass (35 Hz) filtering, EEG signals were converted to numeric with a sampling frequency of 256 Hz and then stored on a computer hard disk. EEG data were then imported into ERA (Phitools, Grenoble, France), a Matlab 5.2 (The MathWorks, Natick, MA) software package for polysomnographic and quantitative EEG analyses. Subsequently, spectra were computed for each consecutive 2-sec epoch using a fast Fourier transform algorithm (27). Median spectra were computed over the 2-sec epochs falling in the last 60 sec of each gaze fixation task. From poorly artifacted recordings, this procedure allows data averaging, without the interference of ectopic values. The first 10 sec of each task were not taken into account, to let the subjects accommodate to the task. Missing values related to accidental electrode detachment represented less than 5% of the total recording samples and were linearly interpolated. Spectral time series were then smoothed using a weighted moving-average window of three points. The spectral parameter considered was the absolute power density, expressed in µV2, in the 13–35 Hz frequency band (EEG ß activity), because it has been shown to constitute a good index of the physiological level of alertness (24). Each subject was informed 30 sec in advance (t-30) that the task was going to start, the beginning (t0) and the end (t70) of each task being communicated orally. The task was repeated 54 times from 0900–1800 h on day II. Before the beginning of the recording procedure, the subject was asked to comply with the following instructions: Do not move and do not make any particular mental or physical effort except for keeping your eyes open, looking in front of you, and staying awake.
Blood sampling and plasma measurements
During the last 24-h period, blood was withdrawn continuously, from 1800 h (day I) to 1800 h (day II), using a peristaltic pump (Ismatec, Bioblock Scientific, Strasbourg, France), and was sampled (every 10 min) into tubes containing EDTA-K2 (1 mg/mL). A maximum of 200 mL blood per subject was removed during the experiment. Samples were immediately centrifuged at 4 C, and the plasma was stored at -25 C. Plasma cortisol concentrations were measured by RIA (Diagnostic Systems Laboratories, Inc., Webster, TX). The assay sensitivity was 0.2 pg/mL; the intraassay coefficient of variation was 10% under 6 pg/mL and 4% above 6 pg/mL. All samples from each subject were measured in the same assay.
Determination of cortisol secretory rates
Cortisol secretory rates were estimated from the corresponding cortisol plasma levels using a deconvolution procedure. A two-compartment model for hormone distribution and degradation was applied with half-lives of 5 and 65 min. The distribution vol was set at 53 dL for all subjects, and the fraction associated with the first compartment was 80%. Statistical error propagation of uncertainty in plasma level measurements was taken into account in the determination of the SD associated with each estimated secretory rate.
Regression analysis
Trends in the time series of cortisol secretory rate and EEG ß activity, throughout daytime, were assessed by a first-order regression analysis. The slopes and their corresponding significance levels were computed for all individuals.
Cross-correlation analysis
Daytime temporal relationships between cortisol secretory rate
and EEG ß activity were determined by cross-correlation analysis,
with series transformed into Z-scores [Z-score = (x-µ)/
,
where x is the original data, µ the mean value, and the
SD of the data]. Cross-correlation coefficients between
the two chronological series were computed for 10-min lags falling
between -20 and +20 min, each lag corresponding to an experimental
measurement period. For positive lags, EEG ß activity would
anticipate cortisol secretion; conversely, for negative lags, cortisol
secretion would precede EEG ß activity. A 
2 test of
homogeneity was computed with individual transformed coefficients (28).
When homogeneity was assumed for the whole group, individual
correlation coefficients were averaged, using Fisher’s transform,
yielding an average estimate of the correlation (29).
Peak detection
Significant peaks in EEG ß activity and pulses of cortisol secretion were identified using a modification of the detection algorithm ULTRA (30). Increases and decreases in cortisol secretory rates and EEG ß absolute power density were considered significant when the sum of the SDs associated with the successive time points exceeded a subject-dependent threshold. This threshold was set at a constant percentage of the variance of the time series (25% for cortisol secretory rate and 15% for EEG ß activity). The detection threshold of cortisol secretory pulses was chosen so as to always exceed the error associated with the radioimmunological measurement method. For each significant peak in EEG ß activity and pulse of cortisol secretory rate, the time of occurrence of the crest (maximal peak value), the onset of the ascending phase, and the offset of the descending phase were determined.
Mean peak analyses
After the detection of peaks in EEG ß activity and of pulses in cortisol secretory rate, the average peak in each variable was computed with regard to the level in the concurrent variable. For each subject, all peaks or pulses were aligned by their crest and averaged, point by point, from 50 min before to 50 min after the crest. To obtain an average peak for the entire group of subjects, the 10 individual mean peaks were expressed as a percentage of the individual mean peak level and were subsequently averaged among subjects. Percentages from the individual mean peaks were processed by a one-way ANOVA for repeated measures, with time as the factor. Post hoc comparisons of the different time points were performed with a Dunnett t-test in reference to a control value corresponding to the mean of the peaks in the time range under study (100%).
Coincidence analysis
When the mean peak analysis revealed significant results, the
association between the crest in one variable and the ascending phases
in the concurrent variable was tested. This was performed by a
2 test, taking into account the relative proportion of
10-min samples in the different phases throughout the duration of the
experiment.
In all statistical analyses, differences were considered to be significant when the P value was less than 0.05. All results are expressed as mean ± SEM. Only data from day II, during which electrophysiological and endocrine variables were measured simultaneously, were concerned with statistical analyses. In all analyses, EEG data were chosen from the least artifacted derivation (C3 or C4).
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Results |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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Profiles of EEG ß activity were characterized by a mean level of
6.0 ± 1.1 µV2 (showing a large interindividual
variability) and by a diurnal coefficient of variation of 19.3 ±
1.9% (which indicated noticeable variations during the daytime). For
both cortisol secretory rate and EEG ß activity, regression analysis
on individual profiles demonstrated significant linear trends
throughout daytime for almost all subjects. Table 1
gives the individual slopes with their
corresponding significance level for the two variables. A majority of
subjects exhibited negative and significant slopes (nine subjects for
cortisol secretory rates and eight for EEG ß activity). During the
9-h waking period, 4.9 ± 0.5 peaks in EEG ß activity and
4.6 ± 0.4 pulses of cortisol secretion were identified.
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Cross-correlation analysis between cortisol secretory rate and EEG
ß activity revealed significant and positive coefficients in all
individuals. For each subject, the highest coefficient, with its
corresponding time lag, is given in Table 2. All individual coefficients being
homogeneous (2 = 8.10, P > 0.5), an
average cross-correlation coefficient was computed and found to be
highly significant (average r = 0.565, P <
0.001). The individual time lags, lying between -10 and +20 min,
indicated that fluctuations in EEG ß activity tended to precede that
of cortisol secretory rate. Figure 1
illustrates four individual Z-score profiles of EEG ß activity with
regard to the corresponding cortisol secretory rate during the daytime
waking period.
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The analysis of the mean peak in EEG ß activity, Fig. 2A, showed a significant effect of time
on the corresponding cortisol secretory rates (F(11,90) =
2.28, P < 0.05). Peak related increases in EEG ß
activity occurred at -10 min, 0 min, and +10 min from the crest; and
they averaged +15%, +25%, and +15%, respectively, over the mean peak
level (P < 0.001). The corresponding increases in
cortisol secretory rate, occurring at the time of the crest in EEG ß
activity (0 min), averaged +30% over the mean pulse level
(P < 0.05) and reached a maximum 10 min later, about
+40% over the mean pulse level (P < 0.01). On the
other hand, the mean cortisol secretory pulse, as illustrated in Fig. 2B, was not accompanied by any significant increase or decrease in the
corresponding levels of EEG ß activity (F(11,90) = 1.16,
n.s.). Numerical results of these analyses are summarized in Table 3. Coincidence analysis between the crest
of EEG ß activity peaks and the different phases of cortisol
secretory pulses indicates that 44% of the crest in EEG ß activity
peaks occurred during an ascending phase of cortisol secretory rate.
Taking into account the relative proportion of ascending and
nonascending phases in cortisol secretory rate during the experimental
period (22%), this association was highly significant
(2 = 13.72, P < 0.001).
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Discussion |
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The finding that EEG ß activity showed a declining episodic pattern during the daytime does not corroborate the findings of previous studies in which a linear increase in EEG ß activity was reported as hours of wakefulness accumulated (16, 31, 32). However, these findings were based on EEG measurements performed every 1–2 h; and such infrequent sampling intervals may obviously obscure the characterization of a rapidly changing EEG activity. In contrast to protocols used in previous studies, constant bed rest prevented postural and exercise effects. Control of the preceding sleep EEG recordings also prevented sleep loss effects, by excluding subjects with poor sleep efficiencies from the study. In fact, an increase in EEG ß activity has been shown to correspond to a homeostatic effect of sleep deprivation and has been interpreted as evidence of an increasing effort to maintain wakefulness (31). The parallel decrease observed from morning to evening in both EEG ß and HPA axis activities should therefore correspond to the declining phase of a circadian rhythm. Indeed, if fatigue factors related to the consecutive fixation tasks would interfere, EEG ß activity should rather increase, to maintain a sustained wakefulness state. Concerning circadian variations in the waking EEG, conflicting results have been published. Whereas a previous study reported a pronounced circadian rhythm in EEG ß activity with lower values during the night hours (33), Aeschbach et al. (18), during a constant routine protocol, did not find a significant circadian component in this EEG activity. Discrepancies between these results may therefore be attributable either to the experimental conditions or to the different procedures used for rhythm identification.
The various analytical methods used in the present study to assess temporal relationships between cortisol secretory rate and EEG ß activity all demonstrate the existence of a significant coupling of the two physiological variables. Cross-correlation reveals an overall temporal relationship with fluctuations in EEG ß activity being followed by parallel changes in cortisol secretion, with an average delay of 10 min. This analysis estimates the overall coordinate behavior of two time series but does not adequately estimate temporal links between discrete events occurring in these series, such as pulses of cortisol secretion and peaks in EEG ß activity. Indeed, large concomitant peaks in the two chronological series may influence the correlation, masking the effects of nonsynchronized small peaks, even when they are more numerous. Taking into account the limitations of this method, the association between the two variables was further assessed by two different analyses. The mean peak analysis indicated that peaks in EEG ß activity were associated with consecutive increases in the amount of cortisol released into the bloodstream, with a delay of 10 min. In contrast, reciprocal mean peak analysis showed that pulses in cortisol secretion were not significantly associated with changes in EEG ß activity in the time range under study (±50 min). A coincidence analysis finally revealed an association between the crest of peaks in EEG ß activity and the ascending phases in cortisol secretion. This association, however, far from being systematic, indicates that peaks in EEG ß activity do not always precede pulses of cortisol secretion, so that cortisol secretory pulses may also occur by stimulation from separate origins.
The temporal dynamic of EEG ß activity and cortisol secretion,
by displaying parallel changes, emphasized the importance of
fluctuating vigilance states in the regulation of endocrine systems.
These findings, concerning variations in central alertness and HPA
activity during the waking period, have to be examined with regard to
previous sleep-endocrine analyses. Indeed, a robust inverse
relationship has been reported between cortisol secretory rates and EEG
activity, observed during non-REM sleep (11, 34). This activity is
generated by thalamocortical and corticocortical circuits, and it
corresponds to a synchronized brain state. It has been reported
to oscillate reciprocally with EEG ß activity throughout REM and
non-REM sleep (35). By inference, it may be suggested that HPA axis
activity runs parallel to the central alertness level of which EEG ß
activity is an indicator. However, the relationship between cortisol
secretion and EEG ß activity has not yet been demonstrated during
sleep.
The question now rises as to whether HPA axis activity may be related
to other EEG indices of cerebral functions, as would be the case with
the limbic system, which is implied in memory processes and from which
EEG 
activity (4–8 Hz frequency band) originates. This brain
structure has been demonstrated to process corticosteroid feedback
control over diurnal oscillations of HPA secretion (36). To respond to
such questions, further research is needed, concerning the
interrelationships between the different generators of EEG rhythmic
activities during wakefulness.
The present finding of a temporal coupling between EEG ß activity and cortisol secretion suggests the involvement of HPA hormones in the regulation of the brain activation level. It is now well known that fast EEG rhythms in the 13–35 Hz frequency range increase in sleep-deprived humans (18, 31, 32). Sleep deprivation may therefore constitute an adapted design for testing the robustness of the interaction between central nervous processes of alertness and HPA axis, because an increase in cortisol levels has been reported as a result of sleep loss (37). To a certain extent, it may be hypothesized that HPA axis activity and EEG ß activity represent two elements of an arousal system through which a psychoneuroendocrine regulation of alertness takes place within the whole brain-body.
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Acknowledgments |
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Footnotes |
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Received June 1, 1998.
Revised August 11, 1998.
Accepted August 25, 1998.
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