Failing to deactivate: The association between brain activity during a working memory task and creativity

NeuroImage 55 (2011) 681–687

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Failing to deactivate: The association between brain activity during a working memory task and creativity Hikaru Takeuchi a,⁎, Yasuyuki Taki a, Hiroshi Hashizume a, Yuko Sassa a, Tomomi Nagase b, Rui Nouchi c, Ryuta Kawashima a,c a b c

Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan Faculty of Medicine, Tohoku University, Sendai, Japan Smart Ageing International Research Center, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan

a r t i c l e

i n f o

Article history: Received 21 June 2010 Revised 23 September 2010 Accepted 15 November 2010 Available online 25 November 2010

a b s t r a c t Working memory (WM) is an essential component for human higher order cognitive activities. Creativity has been essential to the development of human civilization. Previous studies from different fields have suggested creativity and capacity of WM have opposing characteristics possibly in terms of diffuse attention. However, despite a number of functional imaging studies on creativity, how creativity relates to brain activity during WM has never been investigated. In this functional magnetic resonance imaging (fMRI) study, we investigated this issue using an n-back WM paradigm and a psychometric measure of creativity (a divergent thinking test). A multiple regression analysis revealed that individual creativity was significantly and positively correlated with brain activity in the precuneus during the 2-back task (WM task), but not during the non-WM 0-back task. As the precuneus shows deactivation during cognitive tasks, our findings show that reduced task induced deactivation (TID) in the precuneus is associated with higher creativity measured by divergent thinking. The precuneus is included in the default mode network, which is deactivated during cognitive tasks. The magnitude of TID in the default mode network is considered to reflect the reallocation of cognitive resources from networks irrelevant to the performance of the task. Thus, our findings may indicate that individual creativity, as measured by the divergent thinking test, is related to the inefficient reallocation of attention, congruent with the idea that diffuse attention is associated with individual creativity. © 2010 Elsevier Inc. All rights reserved.

Introduction Working memory (WM) is an essential component for human higher order cognitive activities (Baddeley, 1986; Just and Carpenter, 1992; Osaka and Nishizaki, 2000). Creativity has been essential to the development of human civilization. Creativity is commonly agreed to be the ability to produce work that is both novel and appropriate (Sternberg, 2005). Creativity has been often measured by divergent thinking tests psychometrically. Divergent thinking pertains primarily to information retrieval and the call for a number of varied responses to a certain item (1967). Divergent thinking has been proposed to be a key aspect of creativity (Guilford, 1967). Thus, although there are many aspects of creativity and many methods for measuring creativity (such as divergent thinking, remote association of ideas, insight, improvisation, creative achievement), we focused on creativity measured by divergent thinking in this study and refer to it as creativity if not otherwise specified. WM is the limited capacity ⁎ Corresponding author. Division of Developmental Cognitive Neuroscience, IDAC, Tohoku University, 4-1, Seiryo-cho, Aoba-ku, Sendai 980-8575, Japan. Fax: +81 22 717 7988. E-mail address: [email protected] (H. Takeuchi). 1053-8119/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2010.11.052

storage system involved in the maintenance and manipulation of information over short periods of time (Baddeley, 2003). It is a functionally important system for the facilitation of a wide range of cognitive activities such as reasoning, learning and comprehension (Baddeley, 2003). Previous studies from different fields have suggested creativity and WMC have opposing characteristics possibly in terms of diffuse attention. Creativity and WMC both positively correlate with psychometric intelligence (creativity is correlated with psychometric intelligence among subjects with normal and inferior intelligence) (Baddeley, 2003; Sternberg, 2005) and to our knowledge, no studies have shown that WMC and creativity negatively correlate. However, psychological studies have shown that while creative people are inefficient at dealing with a selective attention task (Necka, 1999), and subjects with higher creativity, as expressed by the ability to write poems (Kasof, 1997), are characterized by the inability to ignore irrelevant external stimuli, subjects with lower WMC have difficulty in blocking out, or inhibiting, distracting information (Conway et al., 2001) and are more easily distracted in selective attention tasks (Engle et al., 1999). This subject has also been studied well with a dichotic listening paradigm in which subjects have to pay attention to information received in one ear, but not in the other (e.g. Rawlings,

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1985). In this task, subjects with higher creativity, and normal subjects with lower WMC showed more information intrusion errors coming from the unattended ear, indicating that they could not filter nor suppress unattended information processing (Rawlings, 1985; Dykes and McGhie, 1976; Moray, 1959). The association among divergent attention, working memory and creativity are further supported by a study that found hyperactive children, who are characterized by a decreased ability to focus their attention easily and working memory impairments, are usually more creative than their peers with no attention disorder (Shaw, 1992; Kuntsi et al., 2001). On the other hand, Ritalin (methylphenidate) administration significantly decreased symptoms of attention deficit hyperactive disorder and creativity (Swartwood et al., 2003) while improving WMC (Mehta et al., 2004). Finally, our recent study showed that training on WM using mental calculation improves WMC while reducing creativity measured by divergent thinking in normal young adults (Takeuchi et al., unpublished). These findings may indicate that creativity and WMC have opposing characteristics in certain aspects (possibly diffuse attention). The association between creativity and WMC is at least partly mediated by genetics. The prevalent genotype of the neuregulin 1 gene that increases the risk of psychosis (Hall et al., 2006; Kéri et al., 2009) is associated with lower WMC (Stefanis et al., 2007) and increased creativity (Kéri, 2009). On the other hand, previous neuroimaging studies revealed the neural activities of WM and how these activities alter in subjects with lower WMC. Regions in the lateral prefrontal and parietal cortices are activated during WM tasks, while regions in the medial prefrontal cortices (mPFC) and the posterior cingulate cortices are deactivated (Owen et al., 2005; Sambataro et al., 2010). The deactivated regions are referred to as the default-mode network (DMN), which is active at rest and usually suspended during externally directed, attentiondemanding tasks (Raichle et al., 2001). It is known that the reduced magnitude of task induced deactivation (TID) in the DMN is a characteristic of subjects with lower WMC (Whitfield-Gabrieli et al., 2009; Sambataro et al., 2010). It has been argued that the magnitude of TID in the DMN during cognitive tasks reflects attentional reallocation from the task irrelevant cognitive processes to the cognitive processes involved in performing the task (McKiernan et al., 2003). As for other neuroimaging studies of creativity, besides the ones described above, previous studies have investigated functional activity during creative tasks and the structural characteristics of creative subjects (for review see Arden et al., 2010). Yet the functional characteristics of creative subjects during WM have not been investigated. It is important to disentangle the association between WM, an essential component for human higher order cognitive activities, and creativity, which has been essential to the development of human civilization. Using functional magnetic resonance imaging (fMRI), we investigated the functional characteristics of creative subjects during WM. We hypothesized that activity in the DMN is altered in creative subjects. The reason for focusing on the DMN is that TID in the DMN is thought to reflect attentional reallocation, which is difficult for creative subjects (Dykes and McGhie, 1976). Methods Subjects Sixty-three healthy, right-handed individuals (32 men and 31 women) participated in this study as part of our ongoing project to investigate the association between brain imaging, cognitive function, and aging. All subjects who took part in this study also became subjects of our intervention studies. Psychological tests and MRI scannings not described in this study were performed together with those described in this study (psychological data and imaging data recorded before the intervention were used in this study). The mean

age of subjects was 21.6 years (standard deviation [SD], 1.68). All subjects were university students or postgraduate students. All subjects had normal vision and none had a history of neurological or psychiatric illness. Having a history of psychiatric illnesses was assessed based on a routine questionnaire in our laboratory in which each subject answered questions about whether they had or have any of a list of illnesses. Handedness was evaluated using the Edinburgh Handedness Inventory (Oldfield, 1971). Written informed consent was obtained from each subject for the projects in which they participated. The procedures for all studies were approved by the Ethics Committee of Tohoku University. Creativity assessment The S-A creativity test (Society for Creative Mind, 1969), which is a divergent thinking test, was used to assess creativity. A detailed discussion of the psychometric properties of this instrument and how it was developed is found in the test's technical manual (Society for Creative Mind, 1969). The test was used to evaluate creativity through divergent thinking (Society for Creative Mind, 1969) and it involved three types of tasks. The first task required subjects to generate unique ways of using typical objects. The second task required subjects to imagine desirable functions in ordinary objects. The third task required subjects to imagine the consequences of ‘unimaginable things’ happening. The S-A creativity test provided a total creativity score, which was used in this study. It also scored the four dimensions of the creative process (fluency, originality, elaboration, and flexibility). For more details, including the psychometric properties of this test, sample answers to the questionnaire, the manner in which they were scored, and why total creativity score was used in the data analysis, see our previous works (Takeuchi et al., 2010a,b). The split half reliability estimate used for testing the internal consistency of the total scores of the S-A creativity test was 0.80 according to the manual of this test (Society for the Creative Mind, 1969). The total score of the S-A creativity test has been shown to be highly correlated with various other external measures of creativity as described below. For example, the total score of the S-A creativity test has been shown to be positively correlated with (a) grades in each basic academic subject {language (Japanese), arithmetic, science, society} in elementary school children, (b) an academic aptitude test (an achievement test) in junior high school children, and (c) white-collar worker job achievement in a company (Society for Creative Mind, 1969). Furthermore, the scores of all four dimensions of the S-A creativity test score have been shown to be significantly and positively correlated with (a) the openness of NEO-FFI (Costa and McCrae, 1992) and (b) the score on a test for practical problem solving abilities in daily life (Denney et al., 1982; Shimonaka and Nakazato, 2007). Results of these studies suggest the external validity of this test and the test's ability to predict creative performance in everyday situations (Society for Creative Mind, 1969; Shimonaka and Nakazato, 2007). Furthermore, the scores of the S-A creativity test, have been shown to be significantly correlated with one's frequency of visual hypnagogic experiences, which in turn correlate with the vividness of mental imagery and neuroticism (Watanabe, 1998). Nevertheless, the association between creative achievement and the scores of this test have not been demonstrated in peer reviewed journals, as have the scores of the Torrance Tests of Creative Thinking (TTCT) (for the associations between creative achievement and the TTCT, see Kim, 2008; Plucker, 1999). However, the nature of the S-A creativity test is similar to that of the TTCT in that it consists of three questions which are similar to the three questions in the TTCT (Torrance, 1966). In these questions, subjects are asked to (a) improve a product (list ways to change a certain product so that it will have more desirable characteristics), (b) find interesting and unusual uses for an object and (c) list all the consequences should an improbable situation occur (Torrance, 1966).

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Assessment of psychometric measures of general intelligence Raven's Advanced Progressive Matrix (RAPM; Raven, 1998), which is often shown to be the most correlated with general intelligence and thus the best measure of general intelligence (RAPM; Raven, 1998), was used to assess intelligence and adjust for the effect of general intelligence on brain activities (e.g., Gray et al., 2003). For more details of how RAPM was performed, see our previous work (Takeuchi et al., 2010a,b). Assessment of verbal WMC Computerized forward and backward digit span tests were used to assess verbal WMC. These tests were used in our study to adjust for the effect of individual psychometric measures of verbal WMC on brain activities during verbal WM processes. Subjects were asked to view a progressively increasing number of random digits visually presented one-digit per second on a computer screen. They were then asked to repeat the sequence by pressing numbered buttons on the screen in the presented order (digit-span forward) or in the reverse order (digit-span backward), starting from two digits. Three sequences were given at each level, until the participants responded incorrectly to all three sequences, at which point the task was ended. The score of each test is equal to the sum of the number of digits correctly repeated in the digit span forward and digit span backward tasks. fMRI task The n-back task was performed during the fMRI scanning session. Participants received instructions and practiced the tasks before entering the scanner. During scanning, they viewed stimuli on a screen via a mirror mounted on a head coil. Visual stimuli were presented using Presentation Software (Neurobehavioral Systems, Inc., Albany, CA, USA). A fiber-optic, light-sensitive key press interfaced with a button box was used to record participants' behavior. We used a simple block design and the n-back WM task (Callicott et al., 1999) to tap brain activities during the WM task. There were two conditions (0-, and 2-back). Each condition had six blocks and all the n-back tasks were performed in one session. Subjects were instructed to recall stimuli {visually presented Japanese letters (there were four types of letters)} seen ‘n’ previously (e.g. two letters previous for the 2-back and the currently presented letter for the 0back). Using two buttons during the 0-back task, subjects were asked to push the first button when the target stimuli were presented and to push the second button when the other stimuli were presented. During the 2-back task, subjects were asked to push the first button when the currently presented stimuli and the stimuli presented two letters previously were the same, and to push the second button when the currently presented stimuli and the stimuli presented two letters previously were different. Our version of the n-back task was designed to require individuals to push buttons continuously during the task period. The task level of the memory load was shown above the stimuli for 2 s before the task started, and remained visible during the task period. Each letter was presented for 0.5 s and a fixation cross was presented for 1.5 s between each item. Each block consisted of 10 stimuli. Thus, each block lasted for 20 s. A baseline fixation cross was presented for 13 s between the task and the presentation of the next condition's task level (2 s) (Thus the rest period lasted for 15 s). There were six runs for each 2-back and 0-back condition. As there are various strategies for performing n-back tasks, the subjects were requested to follow a specific, indicated strategy. In the 2-back task, participants were told to update the items in their memory two at a time, not one at a time. To clarify, in the 2-back tasks, they first had to remember two numbers presented and then, while they were pushing

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either of the two buttons based on the rule, they were asked to remember the two incoming numbers. Image acquisition All MRI data acquisition was conducted with a 3-T Philips Intera Achieva scanner. Forty-two transaxial gradient-echo images (echo time = 30 ms, flip angle = 90°, slice thickness = 3 mm, FOV = 192 mm, matrix = 64 × 64) covering the entire brain were acquired at a repetition time of 2.5 s, using an echo planar sequence. For the n-back session, 174 functional volumes were obtained. Furthermore, the diffusion-weighted data and a data set with no diffusion weighting (b value = 0 s/mm2) (b = 0 image) were acquired using a spin-echo EPI sequence (TR = 10293 ms, TE = 55 ms, FOV = 22.4 cm, 2 × 2 × 2 mm3 voxels, 60 slices). The total scan time was 7 min 17 s. The b = 0 image in the diffusion weighted data was used for spatial normalization of the EPI data in this study. EPI geometric distortion was not corrected for in this b = 0 image. Pre-processing and individual level functional imaging data analysis Pre-processing and data analysis were performed using statistical Parametric Mapping software (SPM5; Wellcome Department of Cognitive Neurology, London, UK) and implemented in Matlab (Mathworks Inc., Natick, MA, USA). Prior to the analysis, the BOLD images were re-aligned and re-sliced to fit the mean image of the series and they were corrected for slice timing. The skull and skin parts of all the BOLD images of each subject were then stripped by masking the raw BOLD images of each subject with the threshold of a given signal intensity in the spatially smoothed (8 mm FWHM) mean image of all the BOLD images of each subject. By masking the raw BOLD images by intensity thresholding of the spatially smoothed (using 8 mm FWHM) mean image of all the BOLD images of each participant, we were able to delete the skin and skull parts of the images, but not the brain parenchyma parts. This is because, the skin and skull parts of the BOLD images have little signal just over or just under themselves. Thus, by spatial smoothing, the signal intensity of the skin parts decreases compared with the brain parenchyma parts (signals of small portions of voxels that have an unusually high signal compared with the voxels around themselves also decrease). The threshold to perform the skull stripping was the same for all subjects and it was determined by visual inspection that the skulls of the subjects were stripped but the brain parenchyma of the subjects were not deleted. The first smoothing for skull stripping was performed to make mask images for skull stripping the unsmoothed BOLD images. The processed BOLD images that went on to the next processing step were unsmoothed images. The masked BOLD images were coregistered to the skull stripped b = 0 image (made by a similar method to the one used for making skull stripped BOLD images) and they were spatially normalized on to a skull-stripped b = 0-image-template which was made from data obtained in our scanner (see Takeuchi et al., 2010a for details on the creation of this template). We did not use T1-weighted structural images for the process of coregistering because the process of coregistering the BOLD images taken in our studies to the T1-weighted structural images used in our laboratory often fails according to visual inspection. This failure might possibly be caused by differences in the two images due to distortion of the BOLD images. We used b = 0 images for the normalization procedures for the following reasons. First, the process of coregistering the BOLD images taken in our studies to the b = 0 images goes well according to the visual inspection of all images. This might be because both the images are taken using an EPI sequence and have similar characteristics including distortion of the images (Reber et al., 1998). Second, b = 0 images have more clear anatomical characteristics which allow precise normalization procedures. Third, the structure of the orbitofrontal cortex (OFC) was

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typically lost in the BOLD images taken in the 3-T scanner (Stenger, 2006). In a few cases, the direct normalization of the BOLD images to SPM5's EPI template distorted the structures around the OFC to compensate for the loss of the OFC in the BOLD images taken in our study. On the other hand, b = 0 images have apparently relatively more of the structure around the OFC according to visual inspection, that prevents this kind of distortion and allows for better normalization procedures. However, since b = 0 images and BOLD images in the same subjects have similar characteristic, b = 0 images and BOLD images probably also match well in the coregistration process, despite the difference in the structures around the OFC. The normalization step gave images of the BOLD images with 3 × 3 × 3 mm voxels. We removed low frequency fluctuations with a high-pass filter using a cut-off value of 128 s. Individual-level statistical analyses were performed using a general linear model (GLM). A design matrix was fitted to each participant with one regressor in each task condition (0- or 2- back in the n-back task) using a standard hemodynamic response function (HRF). The cue phases of the n-back task were modeled in the same way, but not analyzed further. The design matrix weighted each raw image according to its overall variability to reduce the impact of movement artifacts (Diedrichsen and Shadmehr, 2005). The design matrix was fit to the data of each participant. After estimation, the beta images were smoothed (8 mm FWHM) and used for a secondlevel random-effects analysis. For the fMRI analysis, contrasting images of the 0-back task compared with the resting period, and the 2-back task compared with the resting period, were estimated for each subject after preprocessing. Data were subject to a random-effects analysis which allowed inferences derived from this subject sample to be generalized to the population. To study the effect of individual differences in creativity on brain activation, we entered the first-level contrast images into a second-level regression analysis. Group level functional imaging data analysis At the group level analysis, we tested for a relationship between individual creativity and brain activities during the 0-back task and the 2-back task. In the whole brain analysis, we used a multiple linear regression analysis to look for areas where the beta-estimates of the contrast were significantly related to individual creativity measured by the divergent thinking test (total creativity score in the S-A creativity test). The effect of sex, age, accuracy and reaction time during the n-back task (2-back performance in the case of analyzing 2-back brain activity, 0-back performance in the case of analyzing 0back brain activity), the score of RAPM, and the score of the digit span were corrected by entering these parameters into the model of the multiple regression analyses as covariates. Regions of significance were inferred using cluster-level statistics (Friston et al., 1996). Only clusters with a P b 0.05, after correcting for multiple comparisons (controlling for family wise error) at the cluster size, with a voxel-level cluster-determining threshold of P b 0.005 uncorrected, were considered statistically significant in this analysis.

Table 1 Average and SD of scores from the S-A creativity test, RAPM score, computerized digit span score, age, and performance of the n-back task in our sample.

S-A creativity test score RAPM score Computerized digit span score Age 0-back, RT (ms) 0-back, accuracy (%) 2-back, RT (ms) 2-back, accuracy (%)

Average

SD

38.6 27.3 35.9 21.6 4568 99.0 7061 98.4

11.1 3.88 6.65 1.68 765 1.67 1601 2.58

Correlation between brain activity during the 0-back task and creativity After controlling for sex, age, accuracy and reaction time on the 0back task, the score of RAPM and the score of the digit span, a multiple regression analysis revealed no significant correlations between creativity and brain activities during the 0-back task. Correlation between brain activity during the 2-back task and creativity After controlling for sex, age, accuracy and reaction time on the 2-back task, the score of RAPM and the score of the digit span, a multiple regression analysis revealed that creativity scores were significantly and positively correlated with brain activity during the 2back task in the precuneus (x, y, x = 9, −57, 27, t = 3.88, p = 0.030 corrected for multiple comparisons at the cluster size with an underlying voxel level of P b 0.005; Figs. 2a and b). This region belongs to a set of regions that were deactivated during the task (Fig. 2c), suggesting that the correlation seen in the precuneus is a negative correlation between the magnitude of deactivation during the task and the creativity scores (in other words, the higher the creativity scores, the less the deactivation during the task in this region, see the scatter plot in Fig. 2b). There were no significant negative correlations between brain activity during the 2-back task and the creativity scores. Discussion To our knowledge, this is the first study to investigate the association between individual creativity and functional activity during WM. Creativity and WM performances did not correlate in this study of young healthy and cognitively intact subjects. However, congruent with our hypothesis, our findings showed that reduced TID during the WM task in the precuneus (one of the key nodes of the DMN) is associated with creativity measured by the divergent thinking test. In this study, among the regions that belong to the DMN, only the activity (deactivation) of the precuneus shows significant correlation with creativity. The precuneus and surrounding posteromedial areas are amongst the brain structures displaying the highest resting metabolic rates (Cavanna and Trimble, 2006). Thus, the maximum magnitude of the deactivation in this region during

Results Behavioral data Table 1 shows the average and the SD of scores from the S-A creativity test, Raven's Advanced Progressive Matrices (RAPM) score, computerized digit span score, age, reaction times and accuracies of the 0- and 2-back tasks in our sample. Neither the RAPM score, computerized digit span score, age, nor performance on n-back tasks correlated significantly with the total S-A creativity test score (P N 0.05). For complete data showing the distribution of the total S-A creativity test score, see Fig. 1.

Fig. 1. Distribution of the scores from the S-A creativity test in our sample.

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Fig. 2. (a) Regions of correlation between brain activity during the 2-back task and creativity test scores (P b 0.05, uncorrected for visualization purposes). Regions of correlation are overlaid on a single subject T1 image of SPM5. Creativity scores were significantly and positively correlated with brain activity during the 2-back task in the precuneus. (b) A scatter plot of the relationship between the creativity scores and brain activity during the 2-back task in the precuneus (9, − 57, 27). (c) Regions that are deactivated specifically in response to WM demand (2-back condition) are displayed at a height threshold of 0.05, uncorrected for visualization purposes.

externally directed attention demanding tasks is supposed to be bigger than that of other regions in the DMN. This might lead to variations in the magnitude of deactivation among subjects and lead

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to a higher sensitivity for correlations between the deactivation of this region and the external factor (creativity). This kind of correlation in the precuneus was not observed in the 0-back task (with a threshold of P b 0.005) which might be attributable to the lack of variability in the deactivations of this task, since the magnitude of TID in the DMN increased with the task difficulty (McKiernan et al., 2003). These results cannot be explained by either a difference in performance during the fMRI task or by the cognitive load during the fMRI task because adjustments for the effects of performance during the fMRI task, general intelligence, and WMC have been performed. Thus, our results revealed one aspect of the association between WM, an essential component for human higher order cognitive activities, and creativity, which has been essential to the development of human civilization. The pattern of activity during WM in the precuneus (the key node of the DMN) among creative subjects may reflect their impaired ability to suppress irrelevant cognitive activity during a cognitive task. The precuneus is one of the regions that is deactivated during cognitive tasks (Sambataro et al., 2010 and see also Fig. 2c). The precuneus is an important node in the DMN where subsystems of the DMN are thought to converge (Buckner et al., 2008). The precuneus is supposed to be engaged in cognitive activities such as self-related mental representations and episodic memory retrieval during rest (Cavanna and Trimble, 2006). It has been argued that the magnitude of TID in the DMN during cognitive tasks reflects the reallocation of attention from the semantic processes that occur during rest to the cognitive processes involved in performing the task (McKiernan et al., 2003). This idea is consistent with the reported positive correlation between task performance and the magnitude of TID in the DMN (Sambataro et al., 2010). Thus, observed reduced TID in the precuneus in creative subjects may indicate that they are not able to inhibit or suppress cognitive activity irrelevant to the task performance (task unrelated thought; Mckiernan et al., 2006) during a complex task such as WM. We are also unable to rule out the possibility that reduced TID in the DMN may reflect reduced brain activity in the DMN during rest (Kennedy et al., 2006). Nevertheless, creative subjects are characterized by cognitive ‘disinhibition’ (the inability to inhibit or suppress certain cognitions) such as the inability to habituate to sensations (low latent inhibition; Carson et al., 2003) and reduced negative priming (Stavridou and Furnham, 1996). Perhaps, the disinhibitions observed in creative subjects have common biological backgrounds. One speculation about the implication that creative subjects are unable to suppress DMN activity is that such an inability to suppress seemingly unnecessary cognitive activity may actually help creative subjects in associating two ideas represented in different networks. This idea of creativity has been long stated. For example, James (1890) and Spearman (1931) suggested that creativity generally requires a combination of elements that have little association and are isolated. Furthermore, it was suggested that greater creativity might be achieved using brain networks in which knowledge in one domain helps organize a quite different domain that might, nevertheless, share some attributes (Heilman et al., 2003). The network consisting of the lateral frontal and parietal cortices, which is activated during WM performance, is commonly recruited by externally focused attention-demanding tasks (Fox et al., 2005), while the DMN is involved in internally focused tasks including autobiographical memory retrieval, envisioning the future, and taking the perspectives of others (Buckner et al., 2008). These two networks usually ‘anticorrelate’; in other words, when one network is activated, the other is deactivated (Fox et al., 2005). The inability to suppress an irrelevant network when one network is recruited may lead to the intrusion of thoughts from the irrelevant network and may allow two isolated ideas to combine. The present imaging results might be due to differences in diffuse attention in creative subjects, and similar results may be obtained from other externally directed attention demanding tasks. Attention

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and WM share neural substrates and they are cognitively deeply associated (Awh and Jonides, 2001). The lateral prefrontal and parietal regions, which play a key role in WM (Klingberg, 2006), are engaged in a wide range of externally directed attention demanding tasks (Buckner et al., 2008). The medial prefrontal regions and the posterior cingulate regions, which are deactivated during WM tasks (Sambataro et al., 2010), and the deactivation of which is also associated with WM performance (Sambataro et al., 2010), are also deactivated during a wide range of externally directed attention demanding tasks (Raichle et al., 2001). Thus, WM might be just a representative of externally directed (selective) attention demanding cognitive tasks and our results may be observed during other externally directed selective attention demanding cognitive tasks. Present findings are comparable to the previous study that showed reduced TID during a WM task in the DMN in relatives with schizophrenia (Whitfield-Gabrieli et al., 2009) and further support the notion that creativity and schizotypy are associated and both of them are associated with diffuse attention. One of the most robust findings about the association between creativity and capacity of working memory (WMC) is that schizotypal personality disorders, or schizotypy, are characterized by facilitated creativity and impaired WMC (Horan et al., 2008; Matheson and Langdon, 2008; Fisher et al., 2004). Schizotypy refers to the personality trait of experiencing ‘psychotic’ symptoms (Claridge, 1997) which are observed in normal populations (Johns and van Os, 2001), while the continuity of psychotic symptoms with normal experience, has been pointed out (Johns and van Os, 2001). Furthermore, schizotypy may be conceptualized as a predisposition to schizophrenia at the level of the organization of the personality among not only clinical populations but also normal populations (Meehl, 1989; Vollema and van den Bosch, 1995; van't Wout et al., 2004). Numerous studies have shown that both facilitated creativity and impaired WMC are associated with schizotypy and seen in the relatives of people with schizophrenia (Horan et al., 2008; Matheson and Langdon, 2008, also for a review of the association between creativity and schizotypy, see Fisher et al., 2004. For the inconsistency of the latter association, see Miller and Tal, 2007). Furthermore, subjects with schizotypy (Braunstein-Bercovitz, 2000; Rawlings, 1985; Dykes and McGhie, 1976) are characterized by the inability to ignore irrelevant external stimuli like creative subjects and subjects with lower WMC as described in the Introduction. The association between creativity and schizotypy is also supported by functional and structural neuroimaging studies. Functional imaging studies have shown that the creative thinking process is associated with increased activity of the lateral prefrontal cortex (PFC) (Folley and Park, 2005; Gibson et al., 2009; Chávez-Eakle et al., 2007, see also Arden et al., 2010) and schizotypes increasingly recruit the right lateral PFC during a divergent thinking test (Folley and Park, 2005). On the other hand, structural studies showed that creativity is associated with (1) reduced white matter structural integrity in the left inferior frontal white matter (Jung et al., 2010a), (2) reduced neurometabolites in the anterior gray matter region (Jung et al., 2009), (3) reduced cortical thickness in orbitofrontal regions and other regions (Jung et al., 2010b) and (4) increased regional gray matter volume of the caudate (Takeuchi et al., 2010b). These structural characteristics or overlapped characteristics, are also observed in patients with schizophrenia or subjects with schizotypal personality disorders (although numbers of differences exist between the structural characteristic of patients with schizophrenia and those of creative subjects) (Ende et al., 2000; Goldman et al., 2009; Sussmann et al., 2009; McIntosh et al., 2008; Staal et al., 2000; Nakamura et al., 2005). Finally, it is known that the reduced magnitude of task induced deactivation (TID) in the DMN is a shared characteristic of subjects with schizophrenia, their relatives, and subjects with lower WMC (Whitfield-Gabrieli et al., 2009; Sambataro et al., 2010). Thus, present results are comparable to (a) the association between creativity and schizotypy including that among

normal adults and (b) the association between reduced TID in the DMN and schizotypy. There is at least one limitation in this study. We used young healthy subjects with a high-level of education. Limited sampling of the full range of intellectual abilities is a common hazard when sampling from college cohorts. However, given the correlation between intelligence and creativity among subjects with normal and inferior intelligence (but not subjects with higher intelligence, Sternberg, 2005), focusing on highly intelligent subjects (or subjects with higher education) was certainly warranted for the purpose of this study. On the other hand, not only does a nonlinear relationship between intelligence and creativity exist (Sternberg, 2005), but also some studies reported that the relationship between creativity and neurometabolites among subjects with lower IQ and that among subjects with higher IQ, are different (Jung et al., 2009). We did not design the study to focus on these kinds of complex relationships by recruiting subjects with a wide range of cognitive functions. This is because neuroimaging studies have usually focused on the linear relationship between imaging measures and creativity (Jung et al., 2010a,b; Takeuchi et al., 2010ab), and the nonlinear relationship between creativity and psychometric intelligence is well known but controversial and an elusive concept (e.g., Preckel et al., 2006). It might be dependent upon the type of creativity measure. However, by recruiting the subjects of a full range of population samples, we may be able to fully investigate the possible nonlinear relationship between creativity and brain characteristics. In summary, the present results showed an association between brain activity during the WM task in the precuneus, the key node of the DMN, and creativity. Failure of the DMN to deactivate during cognitive tasks is observed in numbers of psychiatric or neurological disorders (for a review, see Buckner et al., 2008). Thus, the fact that creative subjects show reduced TID in the DMN may provide a basis for new insights into creativity and its association with pathologies (Nettle, 2008).

Acknowledgments We thank Y. Yamada for operating the MRI scanner, the participants, the testers for the psychological tests, and all our other colleagues in IDAC, Tohoku University for their support. This study was supported by JST/RISTEX, JST/CREST.

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