Arousal and Vigilance: Do They Differ? Study in a Sleep Inertia Paradigm

Sleep Research Online 5(3): 83-87, 2003 http://www.sro.org/2003/tassi/83/ Printed in the USA. All rights reserved. 096-24X 2003 WebSciences Arousal and Vigilance: Do They Differ? Study in a Sleep Inertia Paradigm Patricia Tassi, Anne Bonnefond, Alain Hoeft, Roland Eschenlauer, Alain Muzetand Centre d'etudes de Physiologie Appliquée du CNRS 2, rue Becquerel 67087 Strasbourg Cedex, France The present experiment was conducted to determine whether vigilance and arousal are different functional entities, even though the two concepts are usually confounded. Our hypothesis is that vigilance is a state specifically associated with attentional availability, whereas arousal is a state independent of attention and based on neuronal activation. If this is true, differential effects on performance can be expected. Namely, we propose that arousal essentially affects speed of information processing, whereas vigilance is more linked to accuracy and/or omissions. The sleep inertia paradigm has been chosen to distinguish the effects of low arousal and hypovigilance. We compared performance in either sleep deprived or non sleepdeprived subjects in the Descending Subtraction Test (DST) presented in a complex and simple version. Speed and error indices were the dependent variables. Results showed that speed was deteriorated in both groups during the first 5 min after awakening, but only in the simple task, suggesting a slowing down of mental processing. By contrast, the error index reflecting accuracy showed, in the complex task only, increased errors during the first and last 5-min period in the sleepdeprived subjects but not in the Control Group. All together, these data are in favor of a functional difference between arousal and vigilance, and these two functions could affect performance in specific ways. CURRENT CLAIM: Vigilance and arousal are different functional entities; vigilance is specifically associated with attentional availability, whereas arousal is independent of attention and based on neuronal activation. The concept of vigilance has been used for a long time to refer independently either to central nervous activation or to attentional resources. The term vigilance was introduced for the first time by Henry Head, a British neurologist, to describe the efficiency of the organism in its whole or in one of its parts (Head, 923). Later, he suggested that, when vigilance is high, the mind and body are ready to react to any event, external or internal (Head, 926). As suggested by Broughton (994), the term however, is more widely applied to the central nervous system where it accounts for the efficiency of the nervous processes in response to a stimulus or an event. On a psychological point of view, vigilance is often measured by reaction time or the ability to detect weak and rare stimuli in monotonous tasks, whereas from an electrophysiological standpoint, it can also be measured by event-related potentials (ERP), mismatch negativity (MMN), pupillometry, and other electrical measures. This heterogeneity to measure vigilance suggests that the concept is unclear, sometimes associated with psychological-attentional processes, sometimes assimilated to an electrophysiological status subtended by neuronal activation. Whatever the type of measure, vigilance has been empirically quantified by different levels ranging from sleep to hyperarousal. It has been demonstrated in a wide variety of experiments that vigilance was impaired at both ends of this continuum, performance displaying an inverted U curve first described by Yerkes and Dodson in 908. These authors studied the time course of adaptive behavior of mice as a function of their motivation to obtain food, considering motivation as a factor likely to modulate the basal level of vigilance. Considering their X axis, performance increased from somnolence, relaxed wake to focused attention, then decreased as emotion and overexcitement was reached. All together, it suggests that the authors considered these variables as belonging to a single functional entity, which only differed on a quantitative standpoint. Later, many authors showed that the task itself could elicit intrinsic activation, and therefore determine the translation of the curve either to the left (complex tasks) or to the right (simple tasks) (Sjoberg, 977; Kumari and Corr, 996; Wolter, 999) to reach the optimal balance between task complexity and basal state of vigilance. According to the Yerkes-Dodson Law, complex tasks need a lower level of basal vigilance and simple tasks a higher level to be best conducted. However, performance was still considered as the global expression of an intrinsic state, and no attempt was made to define this state more accurately. Therefore, we speculate the existence of a functional distinction between vigilance and arousal. More precisely, we propose that vigilance would be specifically associated with attentional availability, whereas arousal would be independent of attention and based on neuronal activation. At least, two Correspondence: Patricia Tassi, Ph.D., Centre d'etudes de Physiologie Appliquée du CNRS, 2, rue Becquerel 67087 Strasbourg Cedex, Tel: 33-3-88-0-67-69, Fax: 33-3-88-0-62-45, E-mail: tassip@neurochem.u-strasbg.fr.

84 TASSI ET AL. arguments are in favor of this hypothesis. The first relates to neuroanatomy. Posner defined different attentional systems including cerebral areas that do not overlap the structures involved in the general alerting system (Posner and Petersen, 990; Posner and Dehaene, 994; Fernandez-Duque and Posner, 997). Portas et al. (998) observed changes in thalamic activity mediating specific interactions between attention and arousal. Therefore, even though there might be a tight relationship between attention and arousal, these two functions obviously do not share the same cerebral structures. The second argument relates to a number of studies showing the maintenance of relevant responses to external stimuli during all sleep stages (Nielsen-Bohlman et al., 99 ; Winter et al., 995) suggesting the existence of attentive processing even when arousal is very low. However, because of the close relationship existing between arousal and vigilance, the main difficulty to test the hypothesis of their functional specificity is to design an experimental paradigm likely to isolate vigilance from arousal. For this reason, we propose two operational hypotheses: the first one concerns the experimental paradigm likely to dissociate arousal and vigilance, the second turns on the dependent variables that could be specifically involved in arousal and/or vigilance. The first operational hypothesis states that sleep inertia per se (i.e., without prior sleep deprivation) would be a period of low arousal but normal vigilance and therefore a good candidate for this study. Sleep inertia has been defined as a transitional state between sleep and wake (for a review, see Tassi and Muzet, 2000). The transition between the two states is not an immediate and all-or-none process, but rather a progressive mechanism found to dissipate in an asymptotic manner and which could last several hours (Jewett et al., 999). Moreover, Ogilvie and Simons (992) showed that sleep inertia could be characterized on an electrophysiological standpoint by a more synchronized EEG as compared to full wake. The hypothesis according to which sleep inertia could rather be a period of low arousal without decremental vigilance is suggested by the results of Tassi et al. (992) who found, during sleep inertia, higher reaction time without changes in error rate. This effect on sleep inertia could be abolished by an intense noise likely to produce cortical activity. By contrast, subjects during sleep deprivation (Tassi et al., 993) were not only impaired on speed but also accuracy, suggesting that different mechanisms could be involved in both states. This leads to the second operational hypothesis concerning the dependent variable predominantly involved in arousal and/or vigilance. We speculate that a state of low arousal (with a more or less synchronized EEG) would essentially impair the speed of mental processing but spare accuracy given the fact that, under normal conditions (i.e., after a normal sleep night), attentional resources should be intact. By contrast, when prior sleep deprivation is associated, sleep inertia could also affect the attentional status by a lowered level of vigilance, and therefore produce a decrement in speed and accuracy. In the present experiment, we tested this hypothesis by comparing performance immediately after awakening, either with or without prior sleep deprivation, dissociating carefully between speed and accuracy in a simple and a complex version of the same task. METHODS Subjects Twenty-four male subjects volunteers (aged 25.4±2. years) participated in this experiment. All subjects were informed about the general nature of the experiment and gave their signed informed consent. They were selected on the basis of the Horne and Ostberg eveningness-morningness questionnaire (Horne and Ostberg, 976). Fourteen belonged to the intermediate class, five were rather evening types, two were definitely evening types, and three were rather morning types. They were randomized in both experimental groups. All subjects underwent a medical examination showing regular sleep-wake cycles and no significant sleep, medical or psychiatric disorders. Experimental Design Each subject came twice to the laboratory with a one week interval between the two nights. The first night was a habituation night. Subjects slept from 23:00 to 7:00; they wore electrodes and a rectal probe but no recordings were performed. One week later, each subject came for a second night, which was either a partial sleep deprivation night (Experimental Group: n=2) or a full sleep night (Control Group: n=2). In the Control Group, subjects slept from 23:00 to 7:00 without interruption, whereas the Experimental Group was kept awake until 5:00 and then slept from 5:00 to 7:00. Subjects were awakened by the voice of the experimenter through an intercom and then immediately put in front of their computer beside the bed. Both groups were submitted to a one-hour task in the evening (from 2:00 to 22:00) and again the next morning immediately after awakening (from 7:00 to 8:00). In each group, subjects were submitted either to a simple task (n=6) or to a complex task (n=6). The experiment was realized in an apartment provided with two climatic 0-m 2 chambers (one per subject) where subjects slept and performed the tasks in constant air temperature (20 C) and background noise (35 db). During the partial sleep deprivation night, subjects stayed in an adjacent living room under constant control to avoid microsleep episodes. Performance Task Training for the task was performed twice during the habituation session (evening and morning) but these data were not included in the final analysis. The task was the Descending Subtraction Test (DST) used in two different versions. The complex version was the usual form of the DST, where subjects had to subtract decrementing digits (from 0 to ) starting from a three-digit number (randomly generated by the computer) with instructions to do so as quickly and accurately as possible. This task is very demanding not only for the mental arithmetic operations, but also because of the high working memory load necessary to record the digit to subtract at each trial. In the simple version, subjects were asked to subtract the same digit (permanently displayed on the screen), which changed every 20 trials. Hence, this version required the same cognitive processes as the complex task, but without any substantial memory load. Performance was recorded every three minutes and later pooled in four 5-min periods. Two dependent variables were

AROUSAL AND VIGILANCE: DO THEY DIFFER? 85 measured: the total number of mental subtraction per three min, and the number of errors per three min. Given the large inter-individual differences in performing this task (whatever the version), we calculated a ratio between morning and evening considered as the speed and error index. When the speed index (number of operations in the morning/number of operations in the evening) was <, it meant less operations computed during the morning session, i.e., impaired speed of information processing after awakening. An error index (number of errors in the morning/number of errors in the evening) > reflected impaired accuracy after awakening with more errors in the morning than during the prior evening test session. Physiological Recordings Three EEGs were measured: F3, C3 and P3 referenced to the right mastoïd (A2), right and left EOGs from the outer canthus referenced to the left mastoïd (A), one EMG of the chin, and ECG. Sleep-stage recordings were made for every 30- s epoch of the night, following the standard procedure defined by Rechtshaffen and Kales (968). For technical reasons, we lost the polysomnographic data from three subjects: one in the Experimental Group and two in the Control Group. Temperature data were recorded every minute during the whole night by an ambulatory system (Bioblock ref. 86980). The sterile epoxy probe (ø 4 mm) was placed by the subject at a depth of approximately0 cm. Statistical Analyses A three-way ANOVA was performed on all data with two between factors (Group x Task) and one within factor (Period). The post-hoc comparisons were made by applying the Newman-Keuls test (NK). RESULTS Speed Index A three-factor analysis of variance (2x2x4: Group x Task x 5-min period) revealed a significant decrease of the speed index during the first 5-min period of the test session both in the Experimental Group and in the Control Group (F 3,60 =4.20; p=0.0). Post-hoc comparison showed significant differences between the first and second period (NK: p=0.0) and between the first and third period (NK: p=0.02) (Figure ). This effect was mainly due to the simple task. As shown in Figure 2, there is an important decrease of the speed index during the first 5-min period mainly observed in the Experimental Group. A post-hoc comparison for both groups revealed significant differences between the first and second period (NK: p=0.00) as well as a significant difference between the first and third (NK: p=0.02) and the first and last period (NK: p=0.03). Figure 2 reveals a progressive increase in the Experimental Group from the first to the last 5-min period. There was no effect of Group or Period in the complex task and the speed indices of both groups ranged around, suggesting similar performance in the morning as compared to the evening.,4,3,2, 0,9 0,8 0,7 0,6 0,5 0,4 ** Time Course of Speed in Both Tasks 2 3 4 5-min Period after Awakening Experimental Control Figure. Mean speed index (±SE) in the sleep deprived (Experimental) and non-sleep deprived (Control) subjects over the four 5-min periods of test session (both tasks pooled).,4,3,2, 0,9 0,8 0,7 0,6 0,5 0,4 Figure 2. Mean speed index (±SE) in the sleep deprived (Experimental) and non-sleep deprived (Control) subjects over the four 5-min periods of test session in the simplified version of the DST. 6 5 4 3 2 0 *** Time Course of Speed in the Simple Task 2 3 4 5-min Period after Awakening Experimental Control Time Course of Accuracy in the Complex Task 2 3 4 5-min Period after Awakening Experimental Control Figure 3. Mean error index (±SE) in the sleep deprived (Experimental) and non-sleep deprived (Control) subjects over the four 5-min periods of test session in the complex version of the DST.

86 TASSI ET AL. Error Index The analysis of variance revealed a significant Group x Period interaction (F 3,60 =3.58; p=0.02) showing that sleep inertia effect on error index was present in the Experimental Group but not in the Control Group. Moreover, the Experimental Group exhibited increased errors in the last 5-min period, which was not present in the Control Group. A post-hoc comparison revealed a significant difference between the first period on one hand and the second and third period on the other hand (respectively, NK: p=0.02; p=0.03) but not between the first and last period. The Control Group, by contrast, remained constant over the whole session with a low error index, suggesting very few errors in the morning as compared to the evening test session. The detailed analysis on the two tasks reveals that this effect was mainly due to the complex task, as illustrated in Figure 3, with a significant Group effect (F,20 =6.86; p=0.00). This decrement cannot be attributed to final sleep stage, since most awakenings that occur out of Stage 2 are known to produce very small sleep inertia. Physiological Data As shown in Table, the analysis of variance revealed a reduced latency for all sleep stages in the Experimental Group as compared to the Control Group (L2: F.7 = 8.3; p=0.0; L3: F.7 =8.95; p=0.0; L4: F.7 =7.28; p=0.02; LREM: F.7 =2.66; p=0.006). The percentage of SWS was significantly higher in the Experimental Group (F,7 =6.44; p=0.00) but the percentage of REM was lower as compared to the Control Group (F.7 =8.89; p=0.00) (Table ). All together, sleep efficiency (total time asleep/total time in bed) did not differ significantly between the Experimental and the Control Groups (F.7 =4.; p=0.06). Sleep stages at awakening were distributed as follows: Experimental Group: awakening from SWS (2), from REM (), from Stage 2 (9); Control Group: awakening from REM (), Stage 2 (). Temperature showed higher values for the Experimental Group, as long as subjects remained awake, and then a sharp decrease at 5:00 when they were allowed to sleep. There was, however, no phase delay in the minimum of temperature either in the Experimental or Control Group. Sleep Parameters Experimental Group Control Group L (min) 4.78 ±.6 9.5 ± 2.69 L2 (min) 9.78 ±.9 ** 34.25 ± 8.95 L3 (min) 9.64 ± 3.44 ** 49.58 ± 0.09 L4 (min) 28.92 ± 4.33 * 58.6 ± 0.6 LREM (min) 49.0 ± 0.65 ** 67.4 ± 7.08 % W 0.22 ± 2.2 5.5 ±.6 % S 4.82 ±.3 7. ±.36 % S2 44.32 ± 4.38 52.68 ± 2.09 % SWS 46.2 ± 4.93 ** 23.33 ±.8 % REM 4.7 ± 2.05 ** 6.86 ±.32 SE 90.87 ±.74 95.8 ±.05 **p < 0.0 *p < 0.05 Table Sleep Datas DISCUSSION Our hypothesis was that sleep inertia without prior sleep deprivation would produce performance decrement in speed of information processing only due to low arousal during this period. By contrast, prior sleep deprivation would induce hypovigilance characterized by lowered attentional resources, and this could mainly affect accuracy. A similar result was found in a somewhat different paradigm where comparisons were made between performance decrement during sleep inertia with or without partial sleep deprivation (Tassi et al., 992, 993). To this respect, we were expecting an absence of difference between deprived and non-deprived subjects for the speed measure, whereas accuracy would be detrimental only in the sleep-deprived subjects. Our results partly confirm this hypothesis. A sleep inertia effect was found on speed in both groups during the first 5-min period. However, this effect was mainly present in the simple task where subjects had to compute mental subtractions in a rather monotonous setting. The absence of decreased speed during the period of sleep inertia in the complex task, whatever the group, could account for the Yerkes-Dodson Law, which advocates that, in complex tasks, the optimal level of basal arousal should be lower than in simple tasks because of the activating process of the task itself. This could explain why only the simple task, but not the complex one, displayed a clear effect of sleep inertia on speed during the first 5-min period after awakening. The relatively short duration of sleep inertia in our experiment can be explained by the fact that sleep deprivation was very mild, and an otherwise normal 8-hour night does usually produce only small performance decrements upon awakening, even though this issue is most controversial (Dinges et al., 987; Tassi et al., 992; Sallinen et al., 998; Jewett et al., 999). Accuracy showed quite a different profile. Even though we did not obtain a clear main effect between groups, our results showed a significant Group x Period interaction reflecting decreased accuracy only in the Experimental Group. Moreover, in the complex task, there was a higher error rate in the Experimental Group in the first and last period and no effect in the Control Group. It has been observed in a number of studies that DST, in its usual version, is very sensitive to attentional lapses (Stampi et al., 990). Therefore, our results suggest impaired attention at awakening in the sleep-deprived subjects resulting in hypovigilance as defined in our Introduction. The hypovigilance hypothesis is also strengthened by the increased errors observed during the last 5-min period, which was an unexpected result, but could suggest increased attentional gaps probably due to fatigue, which does not exist in the Control Group who slept eight hours. To this respect, hypovigilance would be a critical state for accuracy, whereas low arousal alone would not produce major decrement on accuracy as suggested by the very low error index in the Control Group over all the test sessions. However, this hypothesis on dependent variables should be further studied in task-paced tests, which could favor other strategies. The polysomnographic data showed that Stage 2, SWS and REM latencies were significantly decreased and,

AROUSAL AND VIGILANCE: DO THEY DIFFER? 87 even more importantly, the percentage of SWS was considerably increased as compared to the Control Group. This suggests that the 2-hour sleep period allowed in this protocol was quite efficient and could partly compensate the prior sleep deprivation effect. Therefore, even with very mild sleep deprivation, we could obtain an interesting difference between speed and accuracy indices, unless this experimental design is precisely the sine qua non condition to obtain it. As a matter of fact, speculating a functional difference between arousal and vigilance does not mean that they do not interact very tightly. Therefore, massive sleep deprivation could possibly mask its genuine effect on performance because of the interaction between low arousal and hypovigilance. Thus, in our experimental conditions, the hypothesis of a functional difference between vigilance and arousal was partly confirmed. We propose that both states could be based on different functional mechanisms, but share enough processes to interact very strongly. We speculate that this interaction is certainly more complex than a simple monotonous function, as suggested by the Yerkes-Dodson Law and its derived products. However, one needs to remain cautious, since our results might be very dependent on the type of task used. In the next step, we plan to replicate this experiment with other tasks to strengthen our hypothesis and to record (in passive and active subjects) electrophysiological parameters during sleep inertia in order to compare the time course of delta power density with or without prior sleep deprivation. ACKNOWLEDGMENTS The authors do not wish to include any acknowledgments. REFERENCES. Broughton RJ. La vigilance et la somnolence. In: Billiard M, ed. Le sommeil normal et pathologique. Masson: Paris, 994. 2. Dinges DF, Orne MT, Whitehouse WG, Orne EC. 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