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Cyclic alternating pattern in sleep and its relationship to creativity
Sleep Medicine 12 (2011) 361–366
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Sleep Medicine journal homepage: www.elsevier.com/locate/sleep
Original Article
Cyclic alternating pattern in sleep and its relationship to creativity Valeria Drago a,e,⇑, Paul S. Foster c,d, Kenneth M. Heilman c, Debora Aricò b, John Williamson c, Pasquale Montagna e, Raffaele Ferri b a
IRCCS San Giovanni Di Dio Fatebenefratelli, Via Pilastroni, 4, 25125 Brescia, Italy Department of Neurology, Oasi Institute for Research on Mental Retardation and Brain Aging (IRCCS), Via Conte Ruggero, 73, 94018 Troina (EN), Italy c Department of Neurology, Center for Neuropsychological Studies, University of Florida College of Medicine, 100 S. Newell Drive, 32610-0015 Gainesville, FL, USA d Department of Psychology, Middle Tennessee State University, 1301 East Main Street, 37132-0001 Murfreesboro, TN, USA e Neurology Service, Department of Neurology, University of Bologna, Ugo Foscolo, 7, 40123 Bologna, Italy b
a r t i c l e
i n f o
Article history: Received 26 July 2010 Received in revised form 27 October 2010 Accepted 5 November 2010
Keywords: Sleep Creativity Cyclic alternating pattern Torrance Frontal lobe functions Arousal
a b s t r a c t Background/objectives: Sleep has been shown to enhance creativity, but the reason for this enhancement is not entirely known. There are several different physiologic states associated with sleep. In addition to rapid (REM) and non-rapid eye movement (NREM) sleep, NREM sleep can be broken down into Stages (1– 4) that are characterized by the degree of EEG slow-wave activity. In addition, during NREM sleep the cyclic alternating pattern (CAPs) of EEG activity has been described which can also be divided into three subtypes (A1–A3) according to the frequency of the EEG waves. Differences in CAP subtype ratios have been previously linked to cognitive performances. The purpose of this study was to asses the relationship between CAP activity during sleep and creativity. Methods: The participants were eight healthy young adults (four women) who underwent three consecutive nights of polysomnographic recording and took the Abbreviated Torrance Test for Adults (ATTA) on the second and third mornings after the recordings. Results: There were positive correlations between Stage 1 of NREM sleep and some measures of creativity such as fluency (R = .797; p = .029) and flexibility (R = .43; p = .002), between Stage 4 of NREM sleep and originality (R = .779; p = .034) and a global measure of figural creativity (R = .758; p = .040). There was also a negative correlation between REM sleep and originality (R = .827; p = .042). During NREM sleep the CAP rate, which in young people reflects primarily the A1 subtype, also correlated with originality (R = .765; p = .038). Conclusions: NREM sleep is associated with low levels of cortical arousal, and low cortical arousal may enhance the ability of people to access to the remote associations that are critical for creative innovations. In addition, A1 CAP subtypes reflect frontal activity, and the frontal lobes are important for divergent thinking, also a critical aspect of creativity. Ó 2011 Elsevier B.V. All rights reserved.
1. Introduction Fredrich August von Kekule, a famous German chemist, attempted to determine the shape of the benzene molecule, which was known to have six carbon atoms. In 1865, reflecting upon his discovery of the hexagonal-ring-like structure, he asserted that the solution came to him in a dream. ‘‘I turned my chair to the fire and dozed. Again the atoms were gamboling before my eyes... My mental eyes... could not distinguish larger structures, of manifold conformation; long rows, sometimes more closely fitted together; all twining and twisting in snakelike motion. But look! What was ⇑ Corresponding author at: IRCCS San Giovanni Di Dio Fatebenefratelli, Laboratorio LENITEM, Via Pilastroni, 4, 25125 Brescia, Italy. Tel.: +39 030 3501313; fax: +39 030 3501592. E-mail address: [email protected] (V. Drago). 1389-9457/$ - see front matter Ó 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.sleep.2010.11.009
that? One of the snakes had seized hold of its own tail, and the form whirled mockingly before my eyes. As if by a flash of lighting I awoke...’’ [1]. Although many people claimed that Kekule fabricated this story, there is no strong evidence to support these doubters and there remains a good possibility that falling asleep did indeed help him solve this problem. Whereas sleep enhanced his creativity, what remains unknown is if he was in rapid eye movement (REM) sleep, dreaming or if he was in Non-REM (NREM) sleep, using imagery. Sleep is primarily subdivided into REM and NREM sleep. During REM sleep, as the name implies, there are rapid eye movements, decreased muscle tone and a typical electroencephalogram (EEG) pattern characterized by high frequency waves with low voltage. Within NREM sleep there are four different stages primarily defined by the frequency of EEG wave activity. Slow-wave sleep corresponds to the ‘‘old’’ stages three and four [2] or to the ‘‘new’’
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stage N3 [3], and is characterized by a prominent presence of slowwave EEG activity (SWA) in the theta and delta range (theta: 4–7 Hz; delta: 0.5–4 Hz). Cyclic Alternating Pattern (CAP) occurs during NREM [4] and is characterized by periodic transient events (phase A of CAP) arising from background activity (phase B). These amplitude changes in the EEG during NREM sleep can reoccur with intervals as long as two minutes. CAP pattern sequences are defined as three or more A phases separated from each other by no more than 60 s. The percentage of NREM sleep occupied by CAP sequences defines the CAP rate. The remaining NREM sleep that is devoid of CAP sequences is called NCAP. This CAP–NCAP dichotomy has been defined as an expression of arousal instability/stability [5]. The A phase of CAP can be subdivided into A1, A2 and A3 subtypes, based on the relative proportions of SWA and faster EEG rhythms. In particular, the A1 subtype is characterized by a prevalence of high-voltage slow waves (EEG synchrony) while the A3 subtype has a preponderance of fast lower-amplitude rhythms (EEG desynchrony); the A2 subtype is a mixture of slow and fast EEG rhythms. A1 is also the most common subtype of CAP, normally accounting for the majority of all CAP A phases during normal sleep, and occurs approximately 200–400 times per night [6,7]. The A1 subtype of CAP is recorded primarily from the leads that are over the frontal and prefrontal regions of the scalp [8]. This distribution of CAP slow waves suggests that they might have a role in sleep-related cognitive processing, and support for this postulate has already been reported [9–11]. It has also been shown that CAP slow components are modified by a learning task during the day preceding sleep [12]. In previous research, CAP activity, particularly A1, has been linked to cognitive activities primarily performed by frontal lobe networks [13]. One of the first steps in the creative process is divergent thinking, and this aspect of creativity also appears to be mediated by frontal lobe networks [14,15]. Therefore, the specific aims of this study were (1) to test the hypothesis that CAP rate during the night is related to creativity during the following day, (2) to test the hypothesis that CAP A1 is positively correlated with measures of creativity, and (3) to test the hypothesis that CAP A2 and A3 are negatively correlated with creativity.
Table 1 Demographic and clinical features of the participants included in this study. Mean (SD) Age, years Education, years Weight, kg Height, cm Body mass index Epworth sleepiness scale Handedness Presence of snoring Cigarette smoking a
27.8 (4.31) 16.9 (2.20) 61.8 (8.73) 168.1 (0.63) 21.7 (1.72) 3.4 (1.30) Right = 8, left = 0 No = 7, yes = 1 (mild) No = 5, yes = 3a
One subject <10/day; two participants <5/day.
Troina (Italy). The first night was used as an adaptation night (data not used for this study). Polysomnographic recordings from the subsequent two nights (Night 1 and Night 2) provided the physiologic data for this study. The participants took a creativity test either on the morning after Night 1 (Morning 1) or on the morning following Night 2 (Morning 2), together with other neuropsychological tests which have been reported elsewhere [13]. 2.2.2. Polysomnographic recordings All participants were asked to abstain from caffeinated beverages for the duration of the study. Polysomnographic recordings were carried out in a sleep laboratory with controlled sound (noise level to a maximum of 30 dB). Lights-out time was based on the individual habitual bed time and ranged between 09:30 and 11:30 P.M. Participants were allowed to sleep until they spontaneously awoke in the morning. Polysomnographic recordings included an electrooculogram (EOG, electrodes placed 1 cm above the right outer cantus and 1 cm below the left outer cantus), an electroencephalogram (EEG, 19 channels, electrodes placed according to the 10–20 International System referred to linked earlobes), an electromyogram (EMG) of the submentalis muscle and an electrocardiogram (ECG). Recordings were carried out using a Brain Quick Micromed System 98 recording machine. Signals were sampled at 256 Hz, 12bit A/D precision and stored on hard disk for further analysis. EEG signals, in particular, were digitally band-pass filtered at 0.1– 50 Hz.
2. Methods 2.1. Participants Eight self reported right-handed healthy volunteers (four women and four men) with a mean age of 27.8 (SD = 4.31), 16.9 (SD = 2.20) years of education, and no history of neurologic or psychiatric illness served as participants. These participants reported no sleep problems. At the time of this study none of the participants were taking any form of medication. Five out of eight were not cigarette smokers, and the other three participants consumed less than 10 cigarettes/day (two of them less than 5/day). None of our participants were alcohol abusers. See Table 1 for a summary of their demographic information. The subjects’ consent was obtained according to the Declaration of Helsinki (BMJ 1991; 302: 1194) and the study was approved by the institution’s ethical committee. 2.2. Apparatus and procedures 2.2.1. Overview The participants underwent a series of three consecutive night polysomnographic recordings at the Sleep Research Centre of the Oasi Institute for Research on Mental Retardation and Brain Aging,
2.2.3. Sleep scoring Sleep stages were scored following standard criteria [3] with 30-s epochs. Subsequently, based on the absence of artifacts, each CAP phase A was detected using the criteria by Terzano et al. [4], from the C3 or C4 electrode. The side of the EEG recording should not influence the detection of CAP, because CAP components have been shown to be symmetric [16]. As mentioned above, CAP is a periodic EEG activity during NREM sleep which is characterized by repeated spontaneous sequences of transient events (phase A), recurring at intervals up to 2 min long. The return to background activity identifies the interval that separates the repetitive elements (phase B). In addition, phase-A subtypes are scored within a CAP sequence only if within 60 s they are followed by another phase A. This is because the CAP procedure is based on the succession of complete CAP cycles (phase A + phase B). Fig. 1 shows a polysomnographic recording with an example of a typical CAP sequence in one of our participants. CAP A phases have been subdivided into a 3-stage hierarchy of arousal strength: the A1 subtype is an A phase with synchronized EEG patterns (intermittent alpha rhythm in Stage 1; sequences of K-complexes or delta bursts in the other NREM stages), associated with mild or trivial polygraphic variations; the A2 subtype is an A
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Fig. 1. Example of CAP during sleep stage 2 of one participant; three A1 subtypes are shown, along with one A3 subtype. Also the scalp topographic mapping of the EEG slowwave component of one A1 subtype is shown, together with the scalp topographic mapping of the EEG high-frequency component of the A3 subtype, obtained as already reported by Ferri et al. [8].
phase with desynchronized EEG patterns preceded by or mixed with slow high-voltage waves (K-complexes with alpha and beta activities, k-alpha, arousals with slow wave synchronization), linked with a moderate increase of muscle tone and/or cardiorespiratory rate; the A3 subtype is an A phase with desynchronized EEG patterns alone, (transient activation phases or arousals) or exceeding 2/3 of the phase A length, coupled with an enhancement of muscle tone and/or cardiorespiratory rate. CAP was detected by the sleep analysis software Hypnolab 1.2 (SWS Soft, Italy) which allows computer-assisted detection of CAP A phase subtypes. With this software, detection is performed by means of a human-supervised automated approach. The performance of this system has been evaluated and validated [16], but for this study the scorer also visually edited the automated detections before the computation of the various CAP parameters that were used for statistical analysis. 2.2.4. Torrance test of creativity Each participant underwent the Abbreviated Torrance Test for Adults (ATTA) which assesses creative thinking [17]. The ATTA consists of three activities, one verbal and two figural. During the verbal activity (Task 1) the subject is asked the following question: ‘‘Just suppose you could walk on air or fly without being in an airplane or similar vehicle. What problems might this create? List as many as you can.’’ In the other version the subject is asked: ‘‘Just suppose a great fog were to fall over the earth and all we could see of people would be their feet. What would happen? How would this change life on earth? List as many ideas as you can.’’ The participants were then given three minutes to write down all the ideas they could create. The second activity (Task 2) in the ATTA consisted of giving the participant a paper on which there are two incomplete drawings. The participants were then asked to create meaningful drawings
that incorporate these incomplete figures and to title their drawings. The participants were given three minutes to complete this task. Task 3 consists of giving the participants a sheet of paper that contains either nine isosceles triangles or nine pairs of straight lines. The participants were asked to make as many pictures as possible using these triangles or lines. The participants were told that every picture should have a meaning and a title. Again, the participants were given three minutes to complete this activity. The participants’ performances on these ATTA tests were scored by a researcher, who was different from the examiner who administered the test and was blinded to the participants’ physiologic data. Our dependent measures included the creative ability scores (fluency, originality, elaboration, and flexibility) from the three tasks of the ATTA. For Task 1, fluency was defined as the total number of different consequences or possibilities produced, and originality was defined primarily by the rarity-novelty of the response. For Tasks 2 and 3, fluency was defined as the number of objects or pictures made from the incomplete figure (Task 2) and triangles (Task 3). Originality was defined as the ability to produce uncommon or novel-original responses. Flexibility was defined as the ability to process information or objects in different ways given the same stimulus and involved switching from one conceptual field to another. Elaboration was the ability to embellish ideas with details. Additional dependent measures consisted of the total raw score from Task 1 (fluency and originality), Task 2 (fluency, originality, and elaboration), and Task 3 (fluency, originality, elaboration, and flexibility). In addition to measuring these aspects of the tasks, the ATTA also provides a series of 15 creativity indicators that need to be evaluated in order to properly score this test. The creativity indicators for the verbal responses (Task 1) are richness and colorfulness of imagery, emotions/feelings, future
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orientation, humor, conceptual incongruity, and ‘‘provocative questions’’ (a question that is defined as one that makes a person think of an object or situation from a different point of view). The respondent is asked to project himself/herself into a different world, a new world or a new perspective. The indicators for the figural responses (Tasks 2 and 3) are openness, resistance to premature closure, unusual visualization, different perspective, movement and/or sound, richness and/or colorfulness of imagery, abstractness of titles, context, environment for objects, articulateness in telling a story, combination/synthesis of two or more figures, internal visual perspective, expression of feeling and emotions, and fantasy. After scoring the creative ability (fluency, originality, elaboration and flexibility) each raw score is converted to a normalized scaled score, which allows assessment of the four measures on a comparable scoring scale. Each creative indicator is scored on a three point scale of 0, 1, or 2. The sum of these indicators added to the scaled scores form a composite measure which is defined as the Creativity Index (CI). This CI was the primary dependent variable used in our analysis.
3. Data reduction For the analysis of the sleep polysomnography recordings, we averaged the targeted sleep parameters, including the rates of CAP, A1, A2, and A3 as well as the total sleep time in minutes for NREM Stages 1 through 4 (S1, S2, S3, S4) for Night 1 and Night 2 and total time in REM sleep.
4. Data analyses In order to test the hypothesis that CAP rates are associated with creativity we implemented the following analysis span. To examine the relationship between CAP and creativity we conducted a series of partial correlations between these selected CAP parameters (mentioned above) and the indices of creativity. Partial correlations were conducted controlling for the effects of both age and education of the participants. Additionally, correlations between the different sleep variables were performed in order to learn if there were relationships between these variables (e.g., whether A1 rate was significantly related to S4) as well as performing correlations between the different creativity variables to understand the degree of dependence between these measures.
5. Results To examine the hypotheses that CAP rate would be related to creativity we conducted a series of correlations between CAP rate, CAP A1, CAP A2, and CAP A3 and indices of creativity derived from the ATTA, including the overall Creativity Index, the Fluency, Originality, Elaboration, and Flexibility Standard Scores (SS), as well as the total raw scores from each of the three tasks. We also examined the relationship between creativity and other sleep parameters, including total time in Stage 1, Stage 2, Stage 3, Stage 4, and REM sleep. The results indicated positive correlations between Stage 1 of NREM sleep and some measures of creativity such as the Fluency SS (r = .797; p = .029) and the Flexibility SS (r = .43; p = .002), between Stage 4 of NREM sleep and the Originality SS (r = .779; p = .034) and a global measure of figural creativity, the total raw score from Task 2 (r = .758; p = .040) as well as a negative correlation between REM sleep and the Originality SS (r = .827, p = .042). During NREM sleep the CAP rate, which in young people reflects primarily A1, also correlated with the Originality SS (r = .765; p = .038). See Table 2 for a correlation matrix.
6. Discussion The primary finding of this study indicates that CAP rate is associated with creativity. Specifically, there is a positive relationship between CAP A1 and creativity. These results are also consistent with other studies which suggest the importance of slow-wave sleep on other forms of cognitive functions, such as learning and processing speed [12,13,18,19]. Our data also indicate a correlation between Stage 1 sleep and two aspects of creativity, fluency and flexibility. But we are not certain why Stage 1 sleep would be positively correlated with these aspects of creativity. Several scientists, however, have reported that they were able to solve a difficult scientific problem during sleep or when they were falling asleep or awakening from sleep, as well as being in a relaxed state. For example, in 1897, Ramon and Cajal [20] in their book Advice for a Young Investigator wrote ‘‘If a solution fails to appear after all of this, and yet we feel success is just around the corner, try resting for a while.’’ There are other factors that can influence sleep architecture. Stress and high emotional states may also influence creativity and sleep architecture. Easterbrook [21], as well as Eysenck [22], suggested that stress causes high cortical arousal and this high arousal might suppress the emergence of remote associations. With reduced stress and a decrease in cortical arousal, unusual or remote associations are more likely to manifest. Further, it has been demonstrated that stress, which increases norepinephrine, results in focused attention on a limited number of external stimuli rather than internal representations (e.g., memories and knowledge). Support for the negative influence of stress on creativity comes from studies which have revealed that during times of anxiety people have a reduction of creativity [23]. In addition, patients with a generalized anxiety disorder also have reduced creativity [24]. During NREM sleep there is a reduction of norepinephrine [25] and perhaps it is the reduction of norepinephrine that might be a factor in the enhancement of certain elements of creativity. To understand the role of norepinephrine in creativity, Beversdorf et al. [26] assessed the influence of norepinephrine on cognitive flexibility by testing normal participants’ ability to solve anagrams when treated with placebo, ephedrine and propanolol. Ephedrine increases the level of norepinephrine, whereas propanolol (a beta noradrenergic blocker) interferes with norepinephrine influence on the brain. Beversdorf et al. [26] found that people solve anagrams better after administration of propanolol than after administration of ephedrine. In addition, Ghacibeh et al. [27] performed a creativity study of patients who had vagus nerve stimulation for medically intractable partial epilepsy. Their hypothesis was that since vagus nerve stimulation may activate the neurons in the locus coeruleus (LC), potentially increasing the release of norepinephrine, this stimulation may result in a reduction in creativity and cognitive flexibility. Their findings were consistent with this hypothesis. Physiologic support for the postulate that the level of arousal might determine the size of neural networks comes from research by Contreras and Llinas [28]. Using high speed optical imaging, they electrically stimulated subcortical white matter in slices of a brain from a guinea pig to record the portion of the neocortex activated by subcortical stimulation. They found that with low frequency stimulation, cortical activation is at first somewhat limited, but then after a few milliseconds this activation spreads to nearby areas. After high frequency stimulation, however, the cortical excitation remained fixed to a small column of neurons that were directly above the stimulating electrode. Intracellular recording from the neurons around this excited column during the rapid stimulation revealed increased inhibitory synaptic activity that probably inhibited the spread of activation to other areas.
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V. Drago et al. / Sleep Medicine 12 (2011) 361–366 Table 2 Bivariate correlations between the creativity and sleep structure or CAP variables. Each cell of this table contains correlation coefficient and (p value). CI CAP A1 rate A2 rate A3 rate S1 S2 S3 S4 REM
.542 .632 .617 .379 .457 .238 .418 .689 .289
FLSS (.133) (.089) (.096) (.229) (.181) (.325) (.205) (.065) (.318)
.040 (.470) .323 (.266) .179 (.367) .404 (.213) .797 (.029) .069 (.448) .595 (.106) .008 (.494) .118 (.425)
ORSS .765 .195 .058 .501 .148 .128 .263 .779 .827
ESS (.038) (.356) (.456) (.156) (.390) (.405) (.307) (.034) (.042)
.476 .567 .753 .031 .353 .318 .105 .632 .111
FXSS (.170) (.121) (.042) (.477) (.247) (.270) (.421) (.089) (.429)
.219 .472 .546 .152 .943 .262 .349 .221 .369
ACT1 (.338) (.172) (.131) (.387) (.002) (.308) (.249) (.337) (.270)
.409 .013 .260 .429 .039 .391 .589 .439 .647
ACT2 (.210) (.490) (.309) (.198) (.471) (.221) (.109) (.192) (.119)
.515 .809 .825 .432 .121 .696 .102 .758 .324
ACT3 (.148) (.026) (.022) (.196) (.409) (.062) (.424) (.040) (.297)
.231 (.330) .540 (.134) .731 (.049) .010 (.493) .618 (.096) .276 (.298) .207 (.347) .340 (.255) .316 (.302)
Note: Correlations printed in bold typeface are statistically significant (p < .05). Probabilities are provided in parentheses.
Additional behavioral support for the postulate that catecholamine-mediated arousal modulates the size of neuronal networks comes from the priming study of Kischka and coworkers [29]. These investigators used a lexical priming task in which either real words or pseudo words were flashed on a screen. Participants were asked to press a computer key as rapidly as possible if they saw a real word, and they were asked not to press the key if they saw a pseudo word. Sometimes these real words and pseudo words were preceded by another word called ‘‘a prime.’’ The preceding word is called a prime because if it is related to the target word, it will help the participants recognize the target word (lion and tiger, as an example) and thus reduce the response time. The greater the association between the primes and the target words (direct priming), the more rapid the recognition of the target word. In contrast, the less strongly the two words (e.g., stripes and lion) are related (indirect priming), the less the influence of the prime on word recognition and response time. When Kischka et al. [29] administered levodopa to normal participants, the indirect priming effect decreased. Although Kischka et al. suggested that dopamine reduces the spread of semantic activation, levodopa is a precursor of both dopamine and norepinephrine, and the administration of levodopa to these individuals may have also increased the level of norepinephrine. In addition to accessing remote associations, originality is dependent upon disengagement and divergent thinking. According to William James [30] divergent thinking is the ability to take a different direction from the current and past modes of thought or expression. Zangwell [14] and Milner [15], suggested and provided evidence that frontal lobe dysfunction disrupts divergent thinking. For example, the Wisconsin Card Sorting Task assesses disengagement and divergent thinking. Milner [15] demonstrated that patients with removal of portions of the frontal lobes do poorly on this test because they cannot disengage and use divergent thinking. Thus, these patients ‘‘get stuck in set.’’ Several studies have investigated the potential role of the frontal lobes in seeking behavior and originality. Comparing participants with high versus low creativity has revealed that the highly creative participants have a higher baseline frontal lobe activity and appear to use their frontal lobes while performing creative tasks [30]. In addition, Chàvez-Eakle et al. [31] correlated cerebral blood flow (CBF) with creativity, assessing dimensions such as fluency, originality and flexibility, and comparing participants with high and low creativity. Their results indicated that participants with better creative performance showed greater CBF activity in the frontal lobes. Whereas CAP A1 activity is positively correlated with performances on Task 2 of the ATTA, CAP A2 is negatively correlated with performances on the same test (as well as on Task 3). This dichotomy might also be explained by the frontal mediated disengage-avoid and temporo-parietal engage-approach behavioral dichotomy. CAP A1 activity is thought to be generated primarily
by the frontal lobes, and CAP A2 and A3 activity is thought to be generated by the posterior brain regions. The results of this study also indicate that REM sleep is associated with a decrease in creativity as measured in this study by the ATTA. The reason we found that originality is negatively correlated with REM sleep is not entirely known. During REM sleep there is physiologic evidence of high cortical activation. During this stage, however, the locus coeruleus is even more quiescent than during NREM sleep. Thus, changes of norepinephrine cannot account for the decreased originality associated with REM sleep. In contrast to the results of this study, a recent report indicated a positive role of REM sleep on creativity. Cai et al. [32] examined the role of REM sleep on creative problem solving using the Remote Associates Test. In this test the subjects are provided with a series of three words (e.g., falling, actor, dust) and asked to find a word that is associated with all three of these words (e.g., star). The Remote Association Test assesses a form of convergent or associative reasoning; this form of reasoning is also important for creative problem solving. Cai et al.’s results suggest that compared to quiet rest and NREM sleep, REM activity enhances the integration of associated information. The reason why this convergent integration occurs during REM sleep is not known; however, this integration may occur because during REM sleep there is an increase of cholinergic hemispheric activity [33] which is even greater during REM sleep than during waking states [34]. There are some studies that indicate a greater posterior than frontal activation during REM sleep [35]. Compared with waking, REM sleep activation is greater in the limbic lobe and in certain cortical association areas, but the dorsolateral prefrontal cortex remains conspicuously deactivated [36]. Frontal deactivation has also been described in the first functional magnetic resonance imaging (fMRI) study of REM sleep [37]. In this study we primarily assessed disengagement and divergent reasoning which has been demonstrated to be mediated by the frontal lobes; A1 CAP activity appears to be associated with an enhancement of this process. The creative process, however, has several major steps. In addition to the ability to disengage and use divergent thinking, in many creative tasks it is also important to perform associative and convergent reasoning. In Cai et al.’s [32] study these investigators primarily assessed convergent thinking and found that REM sleep appeared to enhance this process. Based on this current study and Cai et al’s study it would appear that the full creative process may benefit from both these stages of sleep. But future studies will have to further test this hypothesis. Whereas the mechanism accounting for the relationship between CAP rate and creativity is unclear, as mentioned, it may be driven in large part by the A1 subtype. All of our participants were young, and it is known that CAP in young people is mainly represented by the A1 subtype [38]. Several studies have suggested that creativity reduces with aging and future studies may want to investigate the relationships between creativity, CAP and aging.
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While to our knowledge, this is the first study specifically designed to examine relationships between CAP and creativity, and this study revealed several apparently strong and important relationships, this study also has some limitations. Due to sample size and a large number of important variables, correlations are the best statistical option for analyzing these data. This approach, however, does not allow for statistical control of other variables (e.g., demographics) or for elaboration of relationships (e.g., is there a relationship between ratios of CAP rate and NREM sleep to components of creativity and what are the moderators/mediators of the relationships?). Thus, the results of this study should be replicated and expanded with a larger group of participants and better statistical controls. Future research to further explore the relationships between sleep and creativity is certainly warranted. Conflict of Interest The ICMJE Uniform Disclosure Form for Potential Conflicts of Interest associated with this article can be viewed by clicking on the following link: doi:10.1016/j.sleep.2010.11.009.
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[13] Aricò D, Drago V, Foster PS, Heilman KM, Williamson J, Ferri R. Effects of NREM sleep instability on cognitive processing. Sleep Med 2010;11:791–8. [14] Zangwell OL. (1966) Psychological deficits associated with frontal lobe lesions. Int J Neurol 1966;5:395–402. [15] Milner B. Behavioural effects of frontal-lobe lesions in man. Trends Neurosci 1984;7:403–7. [16] Ferri R, Bruni O, Miano S, Smerieri A, Spruyt K, Terzano MG. Inter-rater reliability of sleep cyclic alternating pattern (CAP) scoring and validation of a new computer-assisted CAP scoring method. Clin Neurophysiol 2005;116:696–707. [17] Goff K, Torrance EP. In: Abbreviated torrance test for adults manual. Bensenville, IL: Scholastic Testing Service; 2002. [18] Ferri R, Drago V, Aricò D, et al. The effects of experimental sleep fragmentation on cognitive processing. Sleep Med 2010;11:378–85. [19] Huber R, Ghilardi MF, Massimini M, Tononi G. Local sleep and learning. Nature 2004;430:78–81. [20] Ramon Y, Cajal S. In: Advice for a young investigator (Translated by N Swanson and LW Swanson). Cambridge, MA: MIT Press; 1999. [21] Easterbrook JA. The effect of emotion on cue utilization and the organization of behavior. Psychol Rev 1959;66:183–201. [22] Eysenck HL. In: Genius. New York and Cambridge: Cambridge University Press; 1995. [23] Baas M, De Dreu CK, Nijstad BA. A meta-analysis of 25 years of mood-creativity research: hedonic tone, activation, or regulatory focus? Psychol Bull 2008;134:779–806. [24] Henning ER, Turk CL, Mennin DS, Fresco DM, Heimberg RG. Impairment and quality of life in individuals with generalized anxiety disorder. Depress Anxiety 2007;24:342–9. [25] Aston-Jones G, Bloom FE. Activity of norepinephrine-containing locus coeruleus neurons in behaving rats anticipates fluctuations in the sleep– waking cycle. Int J Neurosci 1981;1:876–86. [26] Beversdorf DQ, Hughes JD, Steinberg BA, Lewis LD, Heilman KM. Noradrenergic modulation of cognitive flexibility in problem solving. Neuroreport 1999;10:2763–7. [27] Ghacibeh GA, Shenker JI, Shenal B, Uthman BM, Heilman KM. Effect of vagus nerve stimulation on creativity and cognitive flexibility. Epilepsy Behav 2006;8:720–5. [28] Contreras D, Llinas R. Voltage-sensitive dye imaging of neocortical spatiotemporal dynamics to afferent activation frequency. J Neurosci 2001;21:9403–13. [29] Kischka U, Kammer T, Maier S, Weisbrod M, Thimm M, Spitzer M. Dopaminergic modulation of semantic network activation. Neuropsychologia 1996;34:1107–13. [30] Carlsson I, Wendt PE, Risberg J. On the neurobiology of creativity. Differences in frontal activity between high and low creative participants. Neuropsychologia 2000;38:873–85. [31] Chàvez-Eakle RA, Graff-Guerrero A, Garcìa-Reyna JC, Vaugier V, Cruz-Fuentes C. Cerebral blood flow associated with creative performance. A comparative study. NeuroImage 2007;38:519–28. [32] Cai DJ, Mednick SA, Harrison EM, Kanady JC, Mednick SC. REM, not incubation, improves creativity by priming associative networks. Proc Natl Acad Sci 2009;106:10130–4. [33] Lee MG, Hassani OK, Alonso A, Jones BE. Cholinergic basal forebrain neurons burst with theta during waking and paradoxical sleep. J Neurosci 2005;25:4365–9. [34] Vazquez J, Baghdoyan HA. Basal forebrain acetylcholine release during REM sleep is significantly greater than during waking. Am J Physiol Regul Integr Comp Physiol 2001;280:R598–601. [35] Miyauchi S, Misaki M, Kan S, Fukunaga T, Koike T. Human brain activity timelocked to rapid eye movements during REM sleep. Exp Brain Res 2009;192:657–67. [36] Hobson JA, Pace-Schott EF. The cognitive neuroscience of sleep: neuronal systems, consciousness and learning. Nat Rev Neurosci 2002;3:679–93. [37] Lovblad KO, Thomas R, Jakob PM, et al. Silent functional magnetic resonance imaging demonstrates focal activation in rapid eye movement sleep. Neurology 1999;53:2193–5. [38] Parrino L, Boselli M, Spaggiari MC, Smerieri A, Terzano MG. Cyclic alternating pattern (CAP) in normal sleep: polysomnographic parameters in different age groups. Electroen Clin Neuro 1998;107:439–50.
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Sleep Medicine journal homepage: www.elsevier.com/locate/sleep
Original Article
Cyclic alternating pattern in sleep and its relationship to creativity Valeria Drago a,e,⇑, Paul S. Foster c,d, Kenneth M. Heilman c, Debora Aricò b, John Williamson c, Pasquale Montagna e, Raffaele Ferri b a
IRCCS San Giovanni Di Dio Fatebenefratelli, Via Pilastroni, 4, 25125 Brescia, Italy Department of Neurology, Oasi Institute for Research on Mental Retardation and Brain Aging (IRCCS), Via Conte Ruggero, 73, 94018 Troina (EN), Italy c Department of Neurology, Center for Neuropsychological Studies, University of Florida College of Medicine, 100 S. Newell Drive, 32610-0015 Gainesville, FL, USA d Department of Psychology, Middle Tennessee State University, 1301 East Main Street, 37132-0001 Murfreesboro, TN, USA e Neurology Service, Department of Neurology, University of Bologna, Ugo Foscolo, 7, 40123 Bologna, Italy b
a r t i c l e
i n f o
Article history: Received 26 July 2010 Received in revised form 27 October 2010 Accepted 5 November 2010
Keywords: Sleep Creativity Cyclic alternating pattern Torrance Frontal lobe functions Arousal
a b s t r a c t Background/objectives: Sleep has been shown to enhance creativity, but the reason for this enhancement is not entirely known. There are several different physiologic states associated with sleep. In addition to rapid (REM) and non-rapid eye movement (NREM) sleep, NREM sleep can be broken down into Stages (1– 4) that are characterized by the degree of EEG slow-wave activity. In addition, during NREM sleep the cyclic alternating pattern (CAPs) of EEG activity has been described which can also be divided into three subtypes (A1–A3) according to the frequency of the EEG waves. Differences in CAP subtype ratios have been previously linked to cognitive performances. The purpose of this study was to asses the relationship between CAP activity during sleep and creativity. Methods: The participants were eight healthy young adults (four women) who underwent three consecutive nights of polysomnographic recording and took the Abbreviated Torrance Test for Adults (ATTA) on the second and third mornings after the recordings. Results: There were positive correlations between Stage 1 of NREM sleep and some measures of creativity such as fluency (R = .797; p = .029) and flexibility (R = .43; p = .002), between Stage 4 of NREM sleep and originality (R = .779; p = .034) and a global measure of figural creativity (R = .758; p = .040). There was also a negative correlation between REM sleep and originality (R = .827; p = .042). During NREM sleep the CAP rate, which in young people reflects primarily the A1 subtype, also correlated with originality (R = .765; p = .038). Conclusions: NREM sleep is associated with low levels of cortical arousal, and low cortical arousal may enhance the ability of people to access to the remote associations that are critical for creative innovations. In addition, A1 CAP subtypes reflect frontal activity, and the frontal lobes are important for divergent thinking, also a critical aspect of creativity. Ó 2011 Elsevier B.V. All rights reserved.
1. Introduction Fredrich August von Kekule, a famous German chemist, attempted to determine the shape of the benzene molecule, which was known to have six carbon atoms. In 1865, reflecting upon his discovery of the hexagonal-ring-like structure, he asserted that the solution came to him in a dream. ‘‘I turned my chair to the fire and dozed. Again the atoms were gamboling before my eyes... My mental eyes... could not distinguish larger structures, of manifold conformation; long rows, sometimes more closely fitted together; all twining and twisting in snakelike motion. But look! What was ⇑ Corresponding author at: IRCCS San Giovanni Di Dio Fatebenefratelli, Laboratorio LENITEM, Via Pilastroni, 4, 25125 Brescia, Italy. Tel.: +39 030 3501313; fax: +39 030 3501592. E-mail address: [email protected] (V. Drago). 1389-9457/$ - see front matter Ó 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.sleep.2010.11.009
that? One of the snakes had seized hold of its own tail, and the form whirled mockingly before my eyes. As if by a flash of lighting I awoke...’’ [1]. Although many people claimed that Kekule fabricated this story, there is no strong evidence to support these doubters and there remains a good possibility that falling asleep did indeed help him solve this problem. Whereas sleep enhanced his creativity, what remains unknown is if he was in rapid eye movement (REM) sleep, dreaming or if he was in Non-REM (NREM) sleep, using imagery. Sleep is primarily subdivided into REM and NREM sleep. During REM sleep, as the name implies, there are rapid eye movements, decreased muscle tone and a typical electroencephalogram (EEG) pattern characterized by high frequency waves with low voltage. Within NREM sleep there are four different stages primarily defined by the frequency of EEG wave activity. Slow-wave sleep corresponds to the ‘‘old’’ stages three and four [2] or to the ‘‘new’’
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stage N3 [3], and is characterized by a prominent presence of slowwave EEG activity (SWA) in the theta and delta range (theta: 4–7 Hz; delta: 0.5–4 Hz). Cyclic Alternating Pattern (CAP) occurs during NREM [4] and is characterized by periodic transient events (phase A of CAP) arising from background activity (phase B). These amplitude changes in the EEG during NREM sleep can reoccur with intervals as long as two minutes. CAP pattern sequences are defined as three or more A phases separated from each other by no more than 60 s. The percentage of NREM sleep occupied by CAP sequences defines the CAP rate. The remaining NREM sleep that is devoid of CAP sequences is called NCAP. This CAP–NCAP dichotomy has been defined as an expression of arousal instability/stability [5]. The A phase of CAP can be subdivided into A1, A2 and A3 subtypes, based on the relative proportions of SWA and faster EEG rhythms. In particular, the A1 subtype is characterized by a prevalence of high-voltage slow waves (EEG synchrony) while the A3 subtype has a preponderance of fast lower-amplitude rhythms (EEG desynchrony); the A2 subtype is a mixture of slow and fast EEG rhythms. A1 is also the most common subtype of CAP, normally accounting for the majority of all CAP A phases during normal sleep, and occurs approximately 200–400 times per night [6,7]. The A1 subtype of CAP is recorded primarily from the leads that are over the frontal and prefrontal regions of the scalp [8]. This distribution of CAP slow waves suggests that they might have a role in sleep-related cognitive processing, and support for this postulate has already been reported [9–11]. It has also been shown that CAP slow components are modified by a learning task during the day preceding sleep [12]. In previous research, CAP activity, particularly A1, has been linked to cognitive activities primarily performed by frontal lobe networks [13]. One of the first steps in the creative process is divergent thinking, and this aspect of creativity also appears to be mediated by frontal lobe networks [14,15]. Therefore, the specific aims of this study were (1) to test the hypothesis that CAP rate during the night is related to creativity during the following day, (2) to test the hypothesis that CAP A1 is positively correlated with measures of creativity, and (3) to test the hypothesis that CAP A2 and A3 are negatively correlated with creativity.
Table 1 Demographic and clinical features of the participants included in this study. Mean (SD) Age, years Education, years Weight, kg Height, cm Body mass index Epworth sleepiness scale Handedness Presence of snoring Cigarette smoking a
27.8 (4.31) 16.9 (2.20) 61.8 (8.73) 168.1 (0.63) 21.7 (1.72) 3.4 (1.30) Right = 8, left = 0 No = 7, yes = 1 (mild) No = 5, yes = 3a
One subject <10/day; two participants <5/day.
Troina (Italy). The first night was used as an adaptation night (data not used for this study). Polysomnographic recordings from the subsequent two nights (Night 1 and Night 2) provided the physiologic data for this study. The participants took a creativity test either on the morning after Night 1 (Morning 1) or on the morning following Night 2 (Morning 2), together with other neuropsychological tests which have been reported elsewhere [13]. 2.2.2. Polysomnographic recordings All participants were asked to abstain from caffeinated beverages for the duration of the study. Polysomnographic recordings were carried out in a sleep laboratory with controlled sound (noise level to a maximum of 30 dB). Lights-out time was based on the individual habitual bed time and ranged between 09:30 and 11:30 P.M. Participants were allowed to sleep until they spontaneously awoke in the morning. Polysomnographic recordings included an electrooculogram (EOG, electrodes placed 1 cm above the right outer cantus and 1 cm below the left outer cantus), an electroencephalogram (EEG, 19 channels, electrodes placed according to the 10–20 International System referred to linked earlobes), an electromyogram (EMG) of the submentalis muscle and an electrocardiogram (ECG). Recordings were carried out using a Brain Quick Micromed System 98 recording machine. Signals were sampled at 256 Hz, 12bit A/D precision and stored on hard disk for further analysis. EEG signals, in particular, were digitally band-pass filtered at 0.1– 50 Hz.
2. Methods 2.1. Participants Eight self reported right-handed healthy volunteers (four women and four men) with a mean age of 27.8 (SD = 4.31), 16.9 (SD = 2.20) years of education, and no history of neurologic or psychiatric illness served as participants. These participants reported no sleep problems. At the time of this study none of the participants were taking any form of medication. Five out of eight were not cigarette smokers, and the other three participants consumed less than 10 cigarettes/day (two of them less than 5/day). None of our participants were alcohol abusers. See Table 1 for a summary of their demographic information. The subjects’ consent was obtained according to the Declaration of Helsinki (BMJ 1991; 302: 1194) and the study was approved by the institution’s ethical committee. 2.2. Apparatus and procedures 2.2.1. Overview The participants underwent a series of three consecutive night polysomnographic recordings at the Sleep Research Centre of the Oasi Institute for Research on Mental Retardation and Brain Aging,
2.2.3. Sleep scoring Sleep stages were scored following standard criteria [3] with 30-s epochs. Subsequently, based on the absence of artifacts, each CAP phase A was detected using the criteria by Terzano et al. [4], from the C3 or C4 electrode. The side of the EEG recording should not influence the detection of CAP, because CAP components have been shown to be symmetric [16]. As mentioned above, CAP is a periodic EEG activity during NREM sleep which is characterized by repeated spontaneous sequences of transient events (phase A), recurring at intervals up to 2 min long. The return to background activity identifies the interval that separates the repetitive elements (phase B). In addition, phase-A subtypes are scored within a CAP sequence only if within 60 s they are followed by another phase A. This is because the CAP procedure is based on the succession of complete CAP cycles (phase A + phase B). Fig. 1 shows a polysomnographic recording with an example of a typical CAP sequence in one of our participants. CAP A phases have been subdivided into a 3-stage hierarchy of arousal strength: the A1 subtype is an A phase with synchronized EEG patterns (intermittent alpha rhythm in Stage 1; sequences of K-complexes or delta bursts in the other NREM stages), associated with mild or trivial polygraphic variations; the A2 subtype is an A
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Fig. 1. Example of CAP during sleep stage 2 of one participant; three A1 subtypes are shown, along with one A3 subtype. Also the scalp topographic mapping of the EEG slowwave component of one A1 subtype is shown, together with the scalp topographic mapping of the EEG high-frequency component of the A3 subtype, obtained as already reported by Ferri et al. [8].
phase with desynchronized EEG patterns preceded by or mixed with slow high-voltage waves (K-complexes with alpha and beta activities, k-alpha, arousals with slow wave synchronization), linked with a moderate increase of muscle tone and/or cardiorespiratory rate; the A3 subtype is an A phase with desynchronized EEG patterns alone, (transient activation phases or arousals) or exceeding 2/3 of the phase A length, coupled with an enhancement of muscle tone and/or cardiorespiratory rate. CAP was detected by the sleep analysis software Hypnolab 1.2 (SWS Soft, Italy) which allows computer-assisted detection of CAP A phase subtypes. With this software, detection is performed by means of a human-supervised automated approach. The performance of this system has been evaluated and validated [16], but for this study the scorer also visually edited the automated detections before the computation of the various CAP parameters that were used for statistical analysis. 2.2.4. Torrance test of creativity Each participant underwent the Abbreviated Torrance Test for Adults (ATTA) which assesses creative thinking [17]. The ATTA consists of three activities, one verbal and two figural. During the verbal activity (Task 1) the subject is asked the following question: ‘‘Just suppose you could walk on air or fly without being in an airplane or similar vehicle. What problems might this create? List as many as you can.’’ In the other version the subject is asked: ‘‘Just suppose a great fog were to fall over the earth and all we could see of people would be their feet. What would happen? How would this change life on earth? List as many ideas as you can.’’ The participants were then given three minutes to write down all the ideas they could create. The second activity (Task 2) in the ATTA consisted of giving the participant a paper on which there are two incomplete drawings. The participants were then asked to create meaningful drawings
that incorporate these incomplete figures and to title their drawings. The participants were given three minutes to complete this task. Task 3 consists of giving the participants a sheet of paper that contains either nine isosceles triangles or nine pairs of straight lines. The participants were asked to make as many pictures as possible using these triangles or lines. The participants were told that every picture should have a meaning and a title. Again, the participants were given three minutes to complete this activity. The participants’ performances on these ATTA tests were scored by a researcher, who was different from the examiner who administered the test and was blinded to the participants’ physiologic data. Our dependent measures included the creative ability scores (fluency, originality, elaboration, and flexibility) from the three tasks of the ATTA. For Task 1, fluency was defined as the total number of different consequences or possibilities produced, and originality was defined primarily by the rarity-novelty of the response. For Tasks 2 and 3, fluency was defined as the number of objects or pictures made from the incomplete figure (Task 2) and triangles (Task 3). Originality was defined as the ability to produce uncommon or novel-original responses. Flexibility was defined as the ability to process information or objects in different ways given the same stimulus and involved switching from one conceptual field to another. Elaboration was the ability to embellish ideas with details. Additional dependent measures consisted of the total raw score from Task 1 (fluency and originality), Task 2 (fluency, originality, and elaboration), and Task 3 (fluency, originality, elaboration, and flexibility). In addition to measuring these aspects of the tasks, the ATTA also provides a series of 15 creativity indicators that need to be evaluated in order to properly score this test. The creativity indicators for the verbal responses (Task 1) are richness and colorfulness of imagery, emotions/feelings, future
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orientation, humor, conceptual incongruity, and ‘‘provocative questions’’ (a question that is defined as one that makes a person think of an object or situation from a different point of view). The respondent is asked to project himself/herself into a different world, a new world or a new perspective. The indicators for the figural responses (Tasks 2 and 3) are openness, resistance to premature closure, unusual visualization, different perspective, movement and/or sound, richness and/or colorfulness of imagery, abstractness of titles, context, environment for objects, articulateness in telling a story, combination/synthesis of two or more figures, internal visual perspective, expression of feeling and emotions, and fantasy. After scoring the creative ability (fluency, originality, elaboration and flexibility) each raw score is converted to a normalized scaled score, which allows assessment of the four measures on a comparable scoring scale. Each creative indicator is scored on a three point scale of 0, 1, or 2. The sum of these indicators added to the scaled scores form a composite measure which is defined as the Creativity Index (CI). This CI was the primary dependent variable used in our analysis.
3. Data reduction For the analysis of the sleep polysomnography recordings, we averaged the targeted sleep parameters, including the rates of CAP, A1, A2, and A3 as well as the total sleep time in minutes for NREM Stages 1 through 4 (S1, S2, S3, S4) for Night 1 and Night 2 and total time in REM sleep.
4. Data analyses In order to test the hypothesis that CAP rates are associated with creativity we implemented the following analysis span. To examine the relationship between CAP and creativity we conducted a series of partial correlations between these selected CAP parameters (mentioned above) and the indices of creativity. Partial correlations were conducted controlling for the effects of both age and education of the participants. Additionally, correlations between the different sleep variables were performed in order to learn if there were relationships between these variables (e.g., whether A1 rate was significantly related to S4) as well as performing correlations between the different creativity variables to understand the degree of dependence between these measures.
5. Results To examine the hypotheses that CAP rate would be related to creativity we conducted a series of correlations between CAP rate, CAP A1, CAP A2, and CAP A3 and indices of creativity derived from the ATTA, including the overall Creativity Index, the Fluency, Originality, Elaboration, and Flexibility Standard Scores (SS), as well as the total raw scores from each of the three tasks. We also examined the relationship between creativity and other sleep parameters, including total time in Stage 1, Stage 2, Stage 3, Stage 4, and REM sleep. The results indicated positive correlations between Stage 1 of NREM sleep and some measures of creativity such as the Fluency SS (r = .797; p = .029) and the Flexibility SS (r = .43; p = .002), between Stage 4 of NREM sleep and the Originality SS (r = .779; p = .034) and a global measure of figural creativity, the total raw score from Task 2 (r = .758; p = .040) as well as a negative correlation between REM sleep and the Originality SS (r = .827, p = .042). During NREM sleep the CAP rate, which in young people reflects primarily A1, also correlated with the Originality SS (r = .765; p = .038). See Table 2 for a correlation matrix.
6. Discussion The primary finding of this study indicates that CAP rate is associated with creativity. Specifically, there is a positive relationship between CAP A1 and creativity. These results are also consistent with other studies which suggest the importance of slow-wave sleep on other forms of cognitive functions, such as learning and processing speed [12,13,18,19]. Our data also indicate a correlation between Stage 1 sleep and two aspects of creativity, fluency and flexibility. But we are not certain why Stage 1 sleep would be positively correlated with these aspects of creativity. Several scientists, however, have reported that they were able to solve a difficult scientific problem during sleep or when they were falling asleep or awakening from sleep, as well as being in a relaxed state. For example, in 1897, Ramon and Cajal [20] in their book Advice for a Young Investigator wrote ‘‘If a solution fails to appear after all of this, and yet we feel success is just around the corner, try resting for a while.’’ There are other factors that can influence sleep architecture. Stress and high emotional states may also influence creativity and sleep architecture. Easterbrook [21], as well as Eysenck [22], suggested that stress causes high cortical arousal and this high arousal might suppress the emergence of remote associations. With reduced stress and a decrease in cortical arousal, unusual or remote associations are more likely to manifest. Further, it has been demonstrated that stress, which increases norepinephrine, results in focused attention on a limited number of external stimuli rather than internal representations (e.g., memories and knowledge). Support for the negative influence of stress on creativity comes from studies which have revealed that during times of anxiety people have a reduction of creativity [23]. In addition, patients with a generalized anxiety disorder also have reduced creativity [24]. During NREM sleep there is a reduction of norepinephrine [25] and perhaps it is the reduction of norepinephrine that might be a factor in the enhancement of certain elements of creativity. To understand the role of norepinephrine in creativity, Beversdorf et al. [26] assessed the influence of norepinephrine on cognitive flexibility by testing normal participants’ ability to solve anagrams when treated with placebo, ephedrine and propanolol. Ephedrine increases the level of norepinephrine, whereas propanolol (a beta noradrenergic blocker) interferes with norepinephrine influence on the brain. Beversdorf et al. [26] found that people solve anagrams better after administration of propanolol than after administration of ephedrine. In addition, Ghacibeh et al. [27] performed a creativity study of patients who had vagus nerve stimulation for medically intractable partial epilepsy. Their hypothesis was that since vagus nerve stimulation may activate the neurons in the locus coeruleus (LC), potentially increasing the release of norepinephrine, this stimulation may result in a reduction in creativity and cognitive flexibility. Their findings were consistent with this hypothesis. Physiologic support for the postulate that the level of arousal might determine the size of neural networks comes from research by Contreras and Llinas [28]. Using high speed optical imaging, they electrically stimulated subcortical white matter in slices of a brain from a guinea pig to record the portion of the neocortex activated by subcortical stimulation. They found that with low frequency stimulation, cortical activation is at first somewhat limited, but then after a few milliseconds this activation spreads to nearby areas. After high frequency stimulation, however, the cortical excitation remained fixed to a small column of neurons that were directly above the stimulating electrode. Intracellular recording from the neurons around this excited column during the rapid stimulation revealed increased inhibitory synaptic activity that probably inhibited the spread of activation to other areas.
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V. Drago et al. / Sleep Medicine 12 (2011) 361–366 Table 2 Bivariate correlations between the creativity and sleep structure or CAP variables. Each cell of this table contains correlation coefficient and (p value). CI CAP A1 rate A2 rate A3 rate S1 S2 S3 S4 REM
.542 .632 .617 .379 .457 .238 .418 .689 .289
FLSS (.133) (.089) (.096) (.229) (.181) (.325) (.205) (.065) (.318)
.040 (.470) .323 (.266) .179 (.367) .404 (.213) .797 (.029) .069 (.448) .595 (.106) .008 (.494) .118 (.425)
ORSS .765 .195 .058 .501 .148 .128 .263 .779 .827
ESS (.038) (.356) (.456) (.156) (.390) (.405) (.307) (.034) (.042)
.476 .567 .753 .031 .353 .318 .105 .632 .111
FXSS (.170) (.121) (.042) (.477) (.247) (.270) (.421) (.089) (.429)
.219 .472 .546 .152 .943 .262 .349 .221 .369
ACT1 (.338) (.172) (.131) (.387) (.002) (.308) (.249) (.337) (.270)
.409 .013 .260 .429 .039 .391 .589 .439 .647
ACT2 (.210) (.490) (.309) (.198) (.471) (.221) (.109) (.192) (.119)
.515 .809 .825 .432 .121 .696 .102 .758 .324
ACT3 (.148) (.026) (.022) (.196) (.409) (.062) (.424) (.040) (.297)
.231 (.330) .540 (.134) .731 (.049) .010 (.493) .618 (.096) .276 (.298) .207 (.347) .340 (.255) .316 (.302)
Note: Correlations printed in bold typeface are statistically significant (p < .05). Probabilities are provided in parentheses.
Additional behavioral support for the postulate that catecholamine-mediated arousal modulates the size of neuronal networks comes from the priming study of Kischka and coworkers [29]. These investigators used a lexical priming task in which either real words or pseudo words were flashed on a screen. Participants were asked to press a computer key as rapidly as possible if they saw a real word, and they were asked not to press the key if they saw a pseudo word. Sometimes these real words and pseudo words were preceded by another word called ‘‘a prime.’’ The preceding word is called a prime because if it is related to the target word, it will help the participants recognize the target word (lion and tiger, as an example) and thus reduce the response time. The greater the association between the primes and the target words (direct priming), the more rapid the recognition of the target word. In contrast, the less strongly the two words (e.g., stripes and lion) are related (indirect priming), the less the influence of the prime on word recognition and response time. When Kischka et al. [29] administered levodopa to normal participants, the indirect priming effect decreased. Although Kischka et al. suggested that dopamine reduces the spread of semantic activation, levodopa is a precursor of both dopamine and norepinephrine, and the administration of levodopa to these individuals may have also increased the level of norepinephrine. In addition to accessing remote associations, originality is dependent upon disengagement and divergent thinking. According to William James [30] divergent thinking is the ability to take a different direction from the current and past modes of thought or expression. Zangwell [14] and Milner [15], suggested and provided evidence that frontal lobe dysfunction disrupts divergent thinking. For example, the Wisconsin Card Sorting Task assesses disengagement and divergent thinking. Milner [15] demonstrated that patients with removal of portions of the frontal lobes do poorly on this test because they cannot disengage and use divergent thinking. Thus, these patients ‘‘get stuck in set.’’ Several studies have investigated the potential role of the frontal lobes in seeking behavior and originality. Comparing participants with high versus low creativity has revealed that the highly creative participants have a higher baseline frontal lobe activity and appear to use their frontal lobes while performing creative tasks [30]. In addition, Chàvez-Eakle et al. [31] correlated cerebral blood flow (CBF) with creativity, assessing dimensions such as fluency, originality and flexibility, and comparing participants with high and low creativity. Their results indicated that participants with better creative performance showed greater CBF activity in the frontal lobes. Whereas CAP A1 activity is positively correlated with performances on Task 2 of the ATTA, CAP A2 is negatively correlated with performances on the same test (as well as on Task 3). This dichotomy might also be explained by the frontal mediated disengage-avoid and temporo-parietal engage-approach behavioral dichotomy. CAP A1 activity is thought to be generated primarily
by the frontal lobes, and CAP A2 and A3 activity is thought to be generated by the posterior brain regions. The results of this study also indicate that REM sleep is associated with a decrease in creativity as measured in this study by the ATTA. The reason we found that originality is negatively correlated with REM sleep is not entirely known. During REM sleep there is physiologic evidence of high cortical activation. During this stage, however, the locus coeruleus is even more quiescent than during NREM sleep. Thus, changes of norepinephrine cannot account for the decreased originality associated with REM sleep. In contrast to the results of this study, a recent report indicated a positive role of REM sleep on creativity. Cai et al. [32] examined the role of REM sleep on creative problem solving using the Remote Associates Test. In this test the subjects are provided with a series of three words (e.g., falling, actor, dust) and asked to find a word that is associated with all three of these words (e.g., star). The Remote Association Test assesses a form of convergent or associative reasoning; this form of reasoning is also important for creative problem solving. Cai et al.’s results suggest that compared to quiet rest and NREM sleep, REM activity enhances the integration of associated information. The reason why this convergent integration occurs during REM sleep is not known; however, this integration may occur because during REM sleep there is an increase of cholinergic hemispheric activity [33] which is even greater during REM sleep than during waking states [34]. There are some studies that indicate a greater posterior than frontal activation during REM sleep [35]. Compared with waking, REM sleep activation is greater in the limbic lobe and in certain cortical association areas, but the dorsolateral prefrontal cortex remains conspicuously deactivated [36]. Frontal deactivation has also been described in the first functional magnetic resonance imaging (fMRI) study of REM sleep [37]. In this study we primarily assessed disengagement and divergent reasoning which has been demonstrated to be mediated by the frontal lobes; A1 CAP activity appears to be associated with an enhancement of this process. The creative process, however, has several major steps. In addition to the ability to disengage and use divergent thinking, in many creative tasks it is also important to perform associative and convergent reasoning. In Cai et al.’s [32] study these investigators primarily assessed convergent thinking and found that REM sleep appeared to enhance this process. Based on this current study and Cai et al’s study it would appear that the full creative process may benefit from both these stages of sleep. But future studies will have to further test this hypothesis. Whereas the mechanism accounting for the relationship between CAP rate and creativity is unclear, as mentioned, it may be driven in large part by the A1 subtype. All of our participants were young, and it is known that CAP in young people is mainly represented by the A1 subtype [38]. Several studies have suggested that creativity reduces with aging and future studies may want to investigate the relationships between creativity, CAP and aging.
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While to our knowledge, this is the first study specifically designed to examine relationships between CAP and creativity, and this study revealed several apparently strong and important relationships, this study also has some limitations. Due to sample size and a large number of important variables, correlations are the best statistical option for analyzing these data. This approach, however, does not allow for statistical control of other variables (e.g., demographics) or for elaboration of relationships (e.g., is there a relationship between ratios of CAP rate and NREM sleep to components of creativity and what are the moderators/mediators of the relationships?). Thus, the results of this study should be replicated and expanded with a larger group of participants and better statistical controls. Future research to further explore the relationships between sleep and creativity is certainly warranted. Conflict of Interest The ICMJE Uniform Disclosure Form for Potential Conflicts of Interest associated with this article can be viewed by clicking on the following link: doi:10.1016/j.sleep.2010.11.009.
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