Pictionary-based fMRI paradigm to study the neural correlates of spontaneous improvisation and figural creativity

Abstract

A novel game-like and creativity-conducive fMRI paradigm is developed to assess the neural correlates of spontaneous improvisation and figural creativity in healthy adults. Participants were engaged in the word-guessing game of Pictionary(TM), using an MR-safe drawing tablet and no explicit instructions to be "creative". Using the primary contrast of drawing a given word versus drawing a control word (zigzag), we observed increased engagement of cerebellum, thalamus, left parietal cortex, right superior frontal, left prefrontal and paracingulate/cingulate regions, such that activation in the cingulate and left prefrontal cortices negatively influenced task performance. Further, using parametric fMRI analysis, increasing subjective difficulty ratings for drawing the word engaged higher activations in the left pre-frontal cortices, whereas higher expert-rated creative content in the drawings was associated with increased engagement of bilateral cerebellum. Altogether, our data suggest that cerebral-cerebellar interaction underlying implicit processing of mental representations has a facilitative effect on spontaneous improvisation and figural creativity.

Full text

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SCIENTIFIC RepoRts | 5:10894 | DOI: 10.1038/srep10894
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Pictionary-based fMRI paradigm
to study the neural correlates of
spontaneous improvisation and
gural creativity
Manish Saggar1, Eve-Marie Quintin1, Eliza Kienitz1,2, Nicholas T. Bott1,2, Zhaochun Sun1,7,
Wei-Chen Hong3, Yin-hsuan Chien1,4, Ning Liu1, Robert F. Dougherty5, Adam Royalty6,
Grace Hawthorne6 & Allan L. Reiss1,8
A novel game-like and creativity-conducive fMRI paradigm is developed to assess the neural
correlates of spontaneous improvisation and gural creativity in healthy adults. Participants were
engaged in the word-guessing game of PictionaryTM, using an MR-safe drawing tablet and no explicit
instructions to be “creative”. Using the primary contrast of drawing a given word versus drawing
a control word (zigzag), we observed increased engagement of cerebellum, thalamus, left parietal
cortex, right superior frontal, left prefrontal and paracingulate/cingulate regions, such that activation
in the cingulate and left prefrontal cortices negatively inuenced task performance. Further, using
parametric fMRI analysis, increasing subjective diculty ratings for drawing the word engaged
higher activations in the left pre-frontal cortices, whereas higher expert-rated creative content in the
drawings was associated with increased engagement of bilateral cerebellum. Altogether, our data
suggest that cerebral-cerebellar interaction underlying implicit processing of mental representations
has a facilitative eect on spontaneous improvisation and gural creativity.
Creativity – the ability to create novel but appropriate outcomes, is considered as the driving force behind
all human progress. Given the wide import of creativity and its association with mental health across
the life span1,2, it is quintessential to examine the neural networks associated with creative thinking so
that novel interventions to foster creativity can be developed. Previously several neuroimaging studies
of creativity have been conducted. However, these studies have produced varied ndings3, with little
overlap4. Methodological issues might account for this variation, particularly, the inherent elusiveness of
the creativity construct itself, diversity in assessments, and the wide range of experimental procedures
currently employed5,6.
Recent neuroimaging studies have devised new avenues for exploring the neural basis of applied
creativity. For example, by comparing functional brain activation in artists with non-artists, research-
ers examined the neural correlates of enhanced artistic creativity7–9. Similarly, the neural correlates of
1Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford
University School of Medicine, 401 Quarry Road, Stanford, CA 94305. 2Pacic Graduate School of Psychology-
Stanford University Psy.D. Consortium, 1791 Arastradero Road, Palo Alto, CA 94304. 3Institute of Biomedical
Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan. 4Taipei City Hospital,
Zhong-Xing Branch, No. 145, Datong Rd, 10341, Taipei, Taiwan. 5Center for Cognitive and Neurobiological Imaging,
Stanford University, 450 Serra Mall, Building 420, Stanford, CA 94305. 6Hasso Plattner Institute of Design, Stanford
University, Building 550, 416 Escondido Mall, Stanford, CA 94305. 7Brain and Language Lab, School of English
for International Business, Guangdong University of Foreign Studies, Guangzhou, 510420.China. 8Department of
Radiology, Stanford University School of Medicine, 300 Pasteur Road, Stanford, CA 94305. Correspondence and
requests for materials should be addressed to M.S. (email: saggar@stanford.edu)
Received: 29 October 2014
Accepted: 22 April 2015
Published: 28 May 2015
OPEN

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musical improvisation have been examined to better understand the brain processes that give rise to
enhanced extemporaneity and creativity in musicians10–15. Additionally, the brain basis of specic com-
ponents of creativity, e.g., the “Aha! moment“16 and visual creativity17, have also been recently examined.
Despite this recent progress, experimental paradigms that are both conducive to creative thinking
and facilitate examination of applied creativity remain scarce. Such paradigms could play an essential
role in reducing variation in creativity neuroimaging results by minimizing confounding inuences of
cognitive processes that might not be related to creative thinking but are employed, in part, due to the
task design. For example, administering creativity assessments in a test-like setting as opposed to a
fun/game-like style can negatively inuence creativity3,18. However, most previous neuroimaging studies
of creativity have used traditional test-like assessments. Similarly, performance anxiety can negatively
impact creativity19, thereby potentially leading to methodological confounds when researchers explicitly
ask participants to be “creative”. Lastly, few neuroimaging paradigms allow participants to express their
creative potential in a direct/unrestricted manner, as opposed to pressing buttons or “thinking” creatively.
To address some of these issues, we present a novel game-like and creativity-conducive fMRI par-
adigm to assess the neural correlates of spontaneous improvisation and gural creativity. Here, par-
ticipants played the word-guessing game of PictionaryTM, using an MR-safe drawing tablet, and drew
representations of a given word in 30s with a caveat that others would later guess the word by their
drawing alone (Fig.1). e drawings were later scored for creative content and subjective ease of guessing
by two experts. us, with no explicit instructions to be “creative”, our game-like paradigm was designed
to putatively reveal the neural correlates of spontaneous improvisation and applied creativity in healthy
adults.
Results
Behavioral Results on the fMRI task. e mean rating scores for representation and creativity
(on a scale of 1 to 5), across participants, were 3.56 (SD = 0.39) and 2.69 (SD = 0.25), while the mean
self-reported diculty rating (on a scale of 1 to 3) score was 1.83 (SD = 0.25). e representation and
creativity rating scores were positively correlated (r(30) = 0.71, p < 0.001), indicating that drawings that
were good representations of the given word were also creative. No other signicant correlation was
observed between diculty ratings and representation or creativity ratings (p′s > 0.05).
Figure 1. (A) Task was setup as a block design with two conditions (word-drawing and zigzag-drawing).
(B) MR-safe tablet and pen. (C) Cartoon depicting how the participants used MR-safe tablet while lying
down in the fMRI scanner. (D) Representative drawings from the word-drawing condition, drawn while
participants were lying down in the fMRI scanner.

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SCIENTIFIC RepoRts | 5:10894 | DOI: 10.1038/srep10894
Neural correlates of spontaneous improvisation and gural creativity. We hypothesized that
by contrasting word-drawing blocks with control zigzag-drawing blocks we could reveal the neural corre-
lates of spontaneous improvisation and creativity. Using this contrast, increased activation was observed
in six dierent clusters with peak cluster activations bilaterally in the areas of paracingulate gyrus, mid-
dle frontal gyrus, superior frontal gyrus, precentral gyrus, thalamus, cerebellum, le lateralized in the
occipital cortex, superior parietal lobule, precuneus, and right lateralized in the inferior frontal gyrus
(pars triangularis). e cluster with peak activation in the paracingulate gyrus, also extended to the
regions of anterior cingulate cortex (ACC), le dorsolateral prefrontal cortex (DLPFC), and le frontal
operculum/anterior insula complex (fO-AI). Figure2 shows the activation map for the primary contrast
(in red-yellow color scale) and Table 1A provides information pertaining to the number of voxels and
cluster-corrected p-values for each cluster.
For the reverse contrast, i.e., comparing zigzag-drawing with word-drawing condition, we observed
widespread activation in the medial-prefrontal cortices, posterior cingulate/precuneus cortex, inferior
parietal lobule, lingual gyrus, temporal pole, middle/superior temporal gyrus (posterior division), infe-
rior temporal gyrus (anterior division), postcentral gyrus, paracingulate gyrus, parahippocampal gyrus
(posterior division), central/parietal opercular cortex, planum polare, heschl’s gyrus, and planum tem-
porale (Fig.2 and Table1B). is result, consistent with known activation of resting-state networks20–22,
is not surprising given the fact that, compared to word-drawing, zigzag-drawing required minimal cog-
nitive eort from the participants.
To examine how dierential activation from the primary contrast (i.e., word- versus zigzag-drawing)
is associated with fMRI task performance, we examined the relations between beta-estimates from all
the six clusters and behavioral measures of task performance. We observed a signicant negative relation
between the percentage beta values extracted from the cluster with a peak activation in the paracingulate
gyrus (with activations extending into ACC/DLPFC) and representation rating scores (r(30) = − 0.430
(95% CI: − 0.6843 to − 0.0825), p = 0.018; Fig.2B). is negative relation suggests that higher engage-
ment of the paracingulate (and ACC/DLPFC) regions could potentially lead to lower performance on
the fMRI task of word-drawing.
Parametric modulation of brain activity pattern using rating scores. For each presented word,
we obtained a self-reported subjective diculty rating score from the participants at a post-scan session.
As noted above, experts also rated each drawing on task performance (representation and creativity).
By parametrically modulating fMRI activation during the word-drawing condition with these three rat-
ing scores (in a multiple regression with the two task conditions also included), we identied brain
regions that are uniquely and increasingly recruited with corresponding increases in subjective di-
culty in word-drawing, and expert ratings of representation and creativity (Fig.3,Table 1C). Increased
recruitment of the le lateralized middle frontal gyrus, frontal pole, precentral gyrus, and DLPFC was
observed in association with increasing subjective diculty in word-drawing. Increased recruitment in
the bilateral cerebellum, lingual gyrus, brain stem, le occipital fusiform, right temporal fusiform, and
right inferior temporal gyrus was observed with increasing creativity ratings. No signicant results were
observed with increasing representation scores. In summary, the le prefrontal regions were increasingly
Figure 2. (A) Neural correlates of spontaneous improvisation and gural creativity. e red-yellow scale
depicts contrast of word-drawing versus zigzag-drawing, while the blue-green scale represents the reverse
contrast. (B) Correlations between beta-estimates from the word-drawing versus zigzag-drawing contrast
and expert representation ratings.

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SCIENTIFIC RepoRts | 5:10894 | DOI: 10.1038/srep10894
(A) Primary contrast: Word-drawing versus Zigzag-drawing
MNI Coordinates
Cluster
Index
Cluster size
(number of
voxels) P-value Z-max X Y Z Hemisphere Brain Region
6 9761 1.35E-17 6.22 − 4 14 46 Bilateral Paracingulate Gyrus
6.02 − 30 4 46 Le Middle Frontal Gyrus
5.92 − 24 6 50 Le Superior Frontal Gyrus
5.63 − 42 2 24 Le Precentral Gyrus
5.26 − 38 − 4 46 Le Precentral Gyrus
5.17 − 42 − 2 48 Le Precentral Gyrus
5 5875 2.91E-12 6.15 0 − 54 − 28 Vermis Cerebellum
6.12 − 34 − 46 − 36 Le VI Cerebellum
6.04 − 10 − 58 − 26 Cerebellum
5.88 34 − 66 − 32 R. Crus Cerebellum
5.82 0 − 58 − 32 Vermis Cerebellum
5.8 − 2 − 62 − 30 Vermis Cerebellum
4 3349 5.96E-08 5.34 − 34 − 78 22 Le Lateral Occ. Cortex
5.33 − 34 − 70 18 Le Lateral Occ. Cortex
5.17 − 28 − 48 40 Le Superior Par. Lobule
4.88 − 26 − 74 28 Le Lateral Occ. Cortex
4.85 − 8 − 68 52 Le Precuneus
4.82 − 38 − 82 14 Le Lateral Occ. Cortex
3 1175 0.0015 4.53 − 10 − 8 6 Le alamus
4.27 − 10 − 4 2 Le alamus
4.23 0 − 10 4 Le alamus
4.22 0 − 22 6 Le alamus
4.19 − 2 − 1 4 Le alamus
3.64 12 − 14 2Right alamus
2 801 0.0156 4.91 28 4 50 Right Middle Frontal Gyrus
4.09 30 − 6 44 Right Precentral Gyrus
3.95 32 − 2 58 Right Middle Frontal Gyrus
3.74 20 4 50 Right Superior Frontal Gyrus
3.24 18 12 56 Right Superior Frontal Gyrus
3.06 42 2 62 Right Middle Frontal Gyrus
1 649 0.0442 5.29 44 6 22 Right Precentral Gyrus
2.46 46 26 8 Right Inf. Frontal Gyrus
2.4 40 22 14 Right Inferior Frontal Gyrus
(B) Reverse contrast: Zigzag-drawing versus Word-drawing
2 71900 0 7.65 46 − 8 − 8 Right Central Oper. Cortex
6.59 42 10 − 22 Right Temporal Pole
6.51 − 56 − 14 2 Le Planum Temporale
6.48 4 54 − 10 Bilateral Frontomedial Cortex
6.39 38 4 − 22 Right Temporal Pole
6.37 − 56 − 18 4 Le Planum Temporale
1 744 0.0229 5.08 − 48 − 66 42 Le Lateral Occ. Cortex
4.99 − 54 − 64 30 Le Lateral Occ. Cortex
4.55 − 52 − 64 38 Le Lateral Occ. Cortex
3.16 − 42 − 64 30 Le Lateral Occ. Cortex
Continued

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recruited as subjective diculty increased, while the bilateral cerebellum and inferior temporal gyrus was
increasingly recruited in association with more highly rated creative drawings.
Discussion
We present a novel game-like and creativity-conducive fMRI paradigm to assess the neural correlates of
spontaneous improvisation and gural creativity in healthy adults. We strived to keep the environment
conducive to intuitive creative thinking by providing an MR-safe drawing tablet and no explicit instruc-
tions to be “creative” during the word-guessing game of PictionaryTM. e primary contrast of word- ver-
sus zigzag-drawing revealed increased engagement of the cerebellum, thalamus, le parietal cortex, right
superior frontal, le prefrontal and paracingulate/cingulate regions. Further, higher activation in the
cingulate and prefrontal regions was linked to lower expert representation rating scores. e parametric
fMRI approach revealed increasing subjective word-drawing diculty was associated with increasing
activation in the le pre-frontal cortices, whereas increasing expert creativity rating was associated with
higher activation of the bilateral cerebellum.
Since J. P. Guilford’s seminal lecture on the need to study creativity23, a wide range of behavioral and
neuroimaging studies have been undertaken to better understand creativity. Unfortunately, the extant
literature does not provide converging evidence in terms of specic brain regions/networks that are
associated with and/or engaged during creative thinking3,4. Apart from the methodological issues (e.g.,
varied experimental designs), such lack of convergence could also be due to several theoretical factors.
For example, it has been argued that treating creativity as a monolithic entity is one reason for such var-
iegated ndings24. Creative thinking, like any other thought process, undoubtedly requires a multitude of
explicit brain processes and networks to dene the problem/opportunity at hand, to ideate and evaluate
dierent solutions, and to prototype solutions in an iterative fashion. Further, each of these brain pro-
cesses can in turn be facilitated and adapted to a given problem/situation as a result of the implicit brain
processing (e.g., cerebellar facilitation of mental representation manipulations25). us, moving forward,
it is essential to assess creative thinking in terms of neural models of brain processes and their associated
interactions. Such neural models would also provide a framework for generating testable hypotheses and
for making valid inferences from neuroimaging studies of creativity, with the goal of moving the eld of
creativity neuroscience towards convergence.
As a starting point, we propose to adapt the neural model proposed by Ito (2008), which includes
both explicit and implicit processes potentially engaged during a problem-solving thought25. e explicit
processes include (a) the working-memory system (to retain information regarding the problem and
its constraints within a mentally “graspable” range26,27); (b) the two task-control attentional systems
(A) Primary contrast: Word-drawing versus Zigzag-drawing
MNI Coordinates
Cluster
Index
Cluster size
(number of
voxels) P-value Z-max X Y Z Hemisphere Brain Region
(C) Parametric fMRI Analysis
With subjective diculty rating
1 2152 1.08E-05 3.91 − 32 12 54 Le Middle frontal gyrus
3.88 − 32 10 58 Le Middle frontal gyrus
3.74 − 30 40 40 Le Frontal pole
3.65 − 52 22 24 Le Inferior frontal gyrus
3.56 − 32 2 34 Le Precentral gyrus
3.52 − 46 6 46 Le Middle frontal gyrus
With expert creativity rating
1 4822 5.96E-08 4.65 32 − 50 − 36 Right Cerebellum, Right VI
4.36 20 − 82 − 28 Right Cerebellum, Right Crus
4.36 − 8 − 32 − 44 —Brain Stem
3.88 6 − 36 − 44 —Brain Stem
3.85 20 − 70 − 30 Rights Cerebellum, Right Cru
3.82 − 36 − 48 − 34 le Cerebellum, Le VI
Table 1. Cluster statistics and locations for the (a-b) primary and reverse contrasts, and (c) parametric
analysis.

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(adaptive system and stable goal-directed system)28; (c) a novelty system to evaluate whether each ten-
tative solution is novel or not29; and (d) a system to store mental models and representations, on which
all other systems perform actions. e implicit processes, on the other hand, include cerebral-cerebellar
interactions to create inverse and forward models that facilitate and increase eciency of repetitive
actions on mental representations. ese implicit processes are thought to enhance the likelihood of
more creative solutions25,26. Previous theoretical papers have suggested extending Ito’s and related models
for understanding creative thinking25,26. For example, when using divergent thinking tasks to assess cre-
ative capacity, the model predicts explicit systems (especially, adaptive attentional and novelty systems)
to be highly activated as the participants are explicitly trying to generate alternative, novel and unique
solutions to an open-ended problem. Similarly, for an ‘intuitive leap’ or Aha! moment to happen, the
model predicts use of implicit processing (via inverse/forward modeling), where the leap occurs when
the solution reaches conscious awareness.
Using Ito’s model as a framework, here we did not expect signicant engagement of the novelty sys-
tem because the participants were not explicitly asked to create novel solutions. Further, as the partic-
ipants were asked to spontaneously improvise drawings for a given verb/action, we expected increased
engagement of regions implicated in implicit processing for both ecient manipulation of mental rep-
resentations and enhanced creative content in the drawings. As expected, by contrasting word-drawing
with zigzag-drawing, we did not observe dierential recruitment in the well described novelty system
(consisting of hippocampal regions and ventral tegmental area30). However, increased engagement of
the paracingulate cortex, dorsal ACC, le DLPFC, and le fO-AI complex during the word-drawing
condition as compared to zigzag-drawing was observed. Activation in the le fronto-parietal regions
suggest involvement of the central executive and “visual sketchpad” of the working memory system31,32.
Further, engagement of the DLPFC, dorsal ACC, fO-AI complex, superior parietal lobule and thalamus
suggest activation of both fronto-parietal and cingulo-opercular components of task-control attentional
systems during the word-drawing condition. e fronto-parietal component has been proposed to initi-
ate and adjust control on a trial-by-trial basis, whereas the cingulo-opercular component provides stable
‘goal-maintenance’ over the entire task33.
In a recent study, and the only other to use an MR-safe drawing tablet, Ellamil and colleagues (2012)
used a book-cover design task to examine the neural correlates of creative thinking34. e authors sep-
arately administered and compared generative and evaluative phases of designing book-covers and
observed preferential recruitment of the DLPFC, ACC, and le fO-AI regions during the evaluative as
Figure 3. Parametric modulation of fMRI activation during word-drawing condition using self-reported
diculty ratings (in red-yellow color scale) and expert creativity ratings (in blue-green color scale) . No
signicant eect was found for the expert representation ratings.

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compared to generative phase. Other neuroimaging studies, where participants were asked to gener-
ate a unique/unusual response to a given stimuli, have also suggested increased recruitment of similar
task-control attentional networks during creative thinking7,17. In our fMRI task, we did not separate
generate and evaluate phases of the task to keep the creative thought process as close to a real-world
experience as possible. However, building upon previous studies, recruitment of the task-control regions
during word-drawing suggests that even with no explicit instructions to produce novel solutions, partic-
ipants were accentuating idea evaluation more than idea generation during our task.
To choose the most unique or unusual response, idea selection and evaluation is required and is evi-
dently facilitated by task-control networks. It is, however, unclear how such preferential recruitment of
task-control networks facilitates creativity and spontaneity during an improvisation. Interestingly, during
a musical improvisation task, Limb and Braun found that enhanced creativity in expert musicians was
associated with reduced recruitment of task-control networks13. Other, more recent studies, also done in
expert musicians, show deactivations in the DLPFC during musical improvisation as a sign of reduced
monitoring and volitional control10,12. In line with their ndings, we also observed a negative relation
between the beta-estimates from the cluster encompassing the le ACC/DLPFC regions and representa-
tion ratings on our fMRI task in a non-artist population, thereby providing suggestive evidence for a
negative role of higher engagement of task-control regions during spontaneous improvisation.
e role of implicit processing, especially via cerebral-cerebellar connectivity, during creative thinking
has been previously hypothesized25,26, based on the anatomical claim that the cerebellum can facilitate
ecient manipulation of movements and mental representations alike26,35–38. Recent work by Pinho et al.
bolsters this claim, by showing increased cerebral-cerebellar functional connectivity in expert musicians
during improvisation10. In our fMRI task, participants would have required both manipulations of move-
ments as well as mental representations to successfully draw the given word. us, one would expect
the cerebellum to be progressively engaged with increasing representation as well as creativity ratings of
each word. Interestingly, we found activation in the cerebellum to uniquely and linearly increase with
increasing creativity ratings only and not with representation ratings.
e extant literature, mainly from the work in primates, points towards motor control and motor
learning as a primary role for cerebral-cerebellar interactions39–41. However, recent research compar-
ing topographical organization and origins of cerebral peduncle bers in human and macaque brains
provides support for the role of cerebral-cerebellar interactions in higher order cognitive function in
humans. For example, Ramnani et al. (2006) showed that while macaque brains had a large propor-
tion of cerebral peduncle bers originating from the cortical motor system, human brains, on the other
hand, had the largest contribution of cerebral peduncle bers arising from the prefrontal cortex42. e
prefrontal cortex has been associated with processing of more abstract information as compared to the
cortical motor system43, suggesting that the human cerebellum is involved in neural functions beyond
that associated with control of movement.
A theoretical analogue of control theory models that were used to explain the role of the cerebel-
lum in motor control in cerebellum25,39 could also be employed to hypothesize how cerebral-cerebellar
interaction might facilitate enhanced improvisation and creativity skills. To achieve speed, accuracy,
and automaticity in motor command executions, researchers have proposed that the motor commands
directed towards the movement control systems are also copied as “internal models” in the cerebel-
lum. Accordingly, these internal models serve as cerebellar representations that can simulate natural
body movements44. rough repeated and parallel simulations, the cerebellum facilitates acquisition of
advanced motor skills and eventually provides automaticity. As proposed by others25,26,39,43, this theo-
retical model of motor control and learning can be extended to higher order cognitive functioning and
thought processing. Along the same lines, we extend the putative role of the cerebellum to improvisation
and creative thinking.
During our fMRI task, participants manipulate and amalgamate existing mental representations to
express the given word in a sketch using the MR-safe tablet. We hypothesize that internal models of the
cerebellum could facilitate such manipulations of mental representations, by simulating and parallelizing
the sketching of the given word in multiple ways. Such simulations, would in turn, allow participants to
more eciently draw the target word. It remains unclear, however, how such greater eciency is trans-
lated to creativity. Future research paradigms are required to systematically dissociate the cerebellum’s
role in dierent aspects of creative thinking (e.g., elaboration, exibility, uency, originality, etc.). In sum,
our results provide preliminary evidence of cerebellar activity associated with spontaneous improvisation
and gural creativity and extend previous results to non-musicians/artists.
A potential limitation of our fMRI task arises from the possibility that zigzag-drawing might not
fully account for the amount of language processing and/or overall cognitive load that is required during
word-drawing. us, while contrasting these two conditions some activation could also be attributed to
language processing and/or higher cognitive load. However, this potential limitation would not inuence
the results from the parametric analysis of the word-drawing condition. In the future, we plan to use
control conditions that can better account for overall cognitive load (e.g., moving a pen through a maze,
without touching boundaries) and language processing associated with the word-drawing condition.
We specically developed our novel fMRI task using an ecient block design, with a total of 10
blocks per condition. However, the lack of signicant correlation between subjective diculty ratings
and expert rating scores (both on creativity and representation scales) could be partially attributed to

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the small number of blocks per condition. Lastly, due to the nature of our experimental design and the
fact that we could not record a timestamp for every stroke made by the participants, we cannot discern
the neural resources employed purely during (pre-drawing) creative thinking versus implementation of
the drawings. In the future, we can achieve such discernment by instructing participants to “imagine” or
visually construct the representation before drawing/depicting one using the MR-safe tablet.
In sum, our results indicate a putative negative role of conscious monitoring and volitional control
and a potentially positive role of implicit processing via cerebral-cerebellar interaction during spontane-
ous improvisation and gural creative thinking.
Methods
Participants and study design. irty-six healthy adults (18M, 18F) were initially enrolled in the
study. Of these, one participant was excluded due to the use of prescription antidepressants; two par-
ticipants were excluded due to excessive motion in the scanner, while three participants had incom-
plete data. us, the nal data analyses were limited to 30 adult participants (16F, Mean Age = 28.77
years (S.D. = 5.54 years) and Mean IQ = 120 (S.D. = 10.53)). Participants were included in the study if
they could undergo a magnetic resonance imagining (MRI) scan of the head and were right-handed.
Participants were excluded if they self-reported a current or past history of psychiatric or neurological
conditions that had lead them to consult a medical professional, or had metallic devices or implants in
the head or body that are contraindicated for MRI. We recruited participants by sending out yers via
emails, message boards, list-servers, and word of mouth. Participants were recruited on or around the
Stanford campus and surrounding areas. All experiments were performed in accordance with the rel-
evant guidelines and regulations of Stanford University’s Institutional Review Board (Human Subjects
Division), which approved all the experimental protocol and procedures. Written informed consent was
obtained for every participant in the study.
The fMRI Task. e word-guessing fMRI task was based on the game of PictionaryTM, 45, and was
developed using Matlab (http://www.mathworks.com) and Psychtoolbox version 3 (http://psychtoolbox.
org) soware. We used a block-design with 30 seconds block duration for each of the two conditions
(word-drawing and zigzag-drawing). In the rst condition, word-drawing, participants were asked to
draw a given word (mainly actions or verbs) to the best of their ability using the MR-safe drawing tablet,
with the caveat that others would later try to guess the word by their drawing alone. To control for the
basic motor and visuospatial aspect during the word-drawing condition, participants were also asked to
make a drawing representing the control word (“zigzag”) in the second condition. Each block was sepa-
rated by a xation period with a random duration within the range of 10–15 seconds (see Fig.1). ere
were a total of 10 blocks per condition and the total duration of the task was approximately 14.5 minutes.
In each condition, participants were shown a word on the top-le corner of the screen. Participants were
asked to fully utilize the given 30 seconds in each block and continue to add elements to the illustration
in case they wanted to nish early.
e words in the word-drawing condition were chosen from the pool of “action words” from the game
of PictionaryTM. To balance the diculty level in drawing dierent words, across participants, the chosen
words were rated by a separate set of participants (N = 10) as “Dicult”, “Medium”, and “Easy” to draw.
For example, drawing “cry” was rated as easy, while drawing “exhaust” was rated as dicult. Overall, we
chose 3 dicult, 4 medium, and 3 easy words. e order of words was randomly chosen and was kept
consistent across all participants. Additionally, participants in the fMRI study self-rated diculty level
(dicult, medium, or easy) for drawing each word during a post-scan questionnaire.
e MR-safe drawing tablet was designed and developed specically for this study using an
MR-compatible touch-sensitive surface. It uses a KEYTEC 4-wire resistive touch glass connected to a
Teensy 2.0 with custom rmware. is device connected via USB port. It streamed the absolute posi-
tion using a simple serial protocol. e tablet case was build out of clear acrylic using a laser cutter.
e rmware for this tablet is made open-source and is available at https://github.com/cni/widgets/tree/
master/touch.
Behavioral Assessments. General intelligence. e Wechsler Abbreviated Scale of Intelligence-II
(WASI-II) was used to measure general intelligence46. e WASI-II is designed to be administered indi-
vidually in approximately 30 min. e measure consists of four subtests: Vocabulary, Similarities, Block
Design, and Matrix Reasoning were used to obtain the Full Scale IQ (FSIQ). e WASI-II has a mean
standard score of 100 with a standard deviation of 15.
Task performance. Two expert raters from the Stanford Design School (authors A.R. and G.H.) were
chosen to blindly rate each drawing (from the word-drawing condition) on the scales of (a) representa-
tion and (b) creativity. Each drawing was de-identied and a web-based interface was developed for the
raters for easy access to all illustrations. e instructions for the “representation” scale were as follows:
“how easily do you think another person can guess the word represented by the drawing”. e ratings
were obtained on a ve-point scale (1-5), where 1 is “Not Representative”, 2 is “Little Representative”, 3
is “Moderately Representative”, 4 is “Representative”, and 5 is “Very Representative”.

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SCIENTIFIC RepoRts | 5:10894 | DOI: 10.1038/srep10894
e “creativity” rating of each drawing was assessed based on the three subscales of – uency, elab-
oration, and originality. ese subscales were chosen based on established standardized tests of gural
creativity47. Scores from these three subscales were averaged to get the nal score of creativity. Each
subscale was dened as follows: (a) Fluency - total number of elements in the drawing; (b) Elaboration
- imagination and exposition of detail; and (c) Originality - the statistical infrequency and unusualness/
uniqueness of the response. e rating for each subscale was also done on a ve-point scale (1-5).
Importantly, if any drawing was not clear (e.g., due to unintentional lines drawn by a multi-touch
on the tablet), the raters marked those drawings as “confusing” and were not included in the analysis.
Overall, less than 4% out of 300 drawings were excluded. e two raters were trained on a small sample
of drawings (36 drawings) and their inter-rater reliability index for all the drawings (as measured by
Intra Class Correlation Coecient (ICC)) was 0.80 for representation and 0.884 for the creativity scale.
Lastly, as mentioned before, participants also self-rated the diculty level for each drawing (as di-
cult, medium, or easy) in terms of diculty in drawing during the post-scan questionnaire.
MRI image acquisition. Participants were imaged on a 3Tesla scanner (GE MR750, Milwaukee, WI) at
the Stanford University’s Center for Cognitive and Neurobiological Imaging (CNI) using a 32-channel
radiofrequency receive head coil (Nova Medical, Inc., Wilmington, MA). e participant’s head was
stabilized by packing foam between the temples and the inner surface of the receiver coil to mini-
mize motion during the scan, and a plethysmograph was placed on a nger on the le hand to moni-
tor peripheral pulse. To restrict additional movement of hands, cushions were placed under the tablet
and under participants’ arms. A total of 435 whole-brain volumes were collected on 42 axial-oblique
slices (2.9 mm thick) prescribed parallel to the intercommissural (AC-PC) line, using a T2*-weighted
gradient echo pulse sequence sensitive to blood oxygen level-dependence (BOLD) contrast with the
following acquisition parameters: Echo Time (TE) = 30 ms, repetition time (TR) = 2000 msec, ip
angle = 77°, FOV = 23.2 cm, acquisition matrix = 80 × 80, approximate voxel size = 2.9 × 2.9 × 2.9 mm.
To reduce blurring and signal loss arising from eld in-homogeneities, an automated high-order shim-
ming method based on gradient echo acquisitions was used before acquisition of functional MRI scans.
A high-resolution T1-weighted three-dimensional BRAVO pulse sequence acquisition was acquired for
co-registration with the following parameters: Echo Time (TE) = 2.8 ms, repetition time (TR) = 7.2 ms, ip
angle = 12°, FOV = 23 cm, slice thickness = 0.9 mm, 190 slices in the sagittal plane; matrix = 256 × 256;
acquired resolution = 0.9 × 0.9 × 0.9 mm. e images were reconstructed as a 256 × 256 × 190 matrix.
fMRI Data Analysis. Functional MRI data processing was carried out using FEAT (FMRI Expert
Analysis Tool) Version 6.00, part of FSL (FMRIB’s Soware Library, www.fmrib.ox.ac.uk/fsl). e fol-
lowing pre-statisticsal processing steps were applied: motion correction using MCFLIRT48, non-brain
removal using BET49, spatial smoothing using a Gaussian kernel of FWHM 5 mm, grand-mean inten-
sity normalization of the entire 4D dataset by a single multiplicative factor, highpass temporal ltering
(Gaussian-weighted least-squares straight line tting, with sigma= 50.0s), and probabilistic independent
component analysis49,50 as implemented in MELODIC (Multivariate Exploratory Linear Decomposition
into Independent Components) Version 3.10, part of FSL. Aer preprocessing, the functional data were
registered to each individual’s high-resolution T1-weighted image, followed by registration to the MNI152
standard-space by ane linear registration using FMRIB’s Linear Image Registration Tool (FLIRT)50. For
each participant, independent components were classied as “artifact” or non-artifact using an in-house
semi-automatic artifact removal tool (SMART; similar to a tool made for the EEG data51). SMART uses
the following rules to categorize each component as an artifact: (a) when the time series of a component
is highly correlated (r > 0.4) with motion prole only and not at all with the task design; or (b) when
a component has most of its power (> 70%) in the high frequency range. Once categorized, SMART
produces an HTML based web-tool for quality check, where the operator can easily override SMART’s
automatic classication. e quality check step was incorporated to make sure that the categorization
of a component as an artifact was accomplished conservatively; e.g., if the time course of a component
showed transient correlation with the task design, the components were retained as potentially con-
taining BOLD signal. Aer this quality check, SMART uses fsl_reglt utility (supplied with FSL) to
regress out the artifactual components to recreate 4-D datasets to be used in generalized linear model
analysis. Additionally, sharp motion peaks were detected using fsl_motion_outliers script (supplied with
FSL) and were regressed out in addition to the six motion parameters (from MCFLIRT). Registration to
high-resolution structural and standard space images was carried out using FLIRT. Time-series statisti-
cal analysis was carried out using FILM with local autocorrelation correction. Group-level analysis was
carried out using FEAT (FMRI Expert Analysis Tool). Z (Gaussianised T/F) statistic images were thresh-
olded using clusters determined by Z > 2.3 and a (corrected) cluster signicance threshold of P = 0.0548,52.
Featquery tool (supplied by FSL) was used to extract percent change in parameter estimates for function-
ally dened (clusters of activations) regions of interests. MRIcron was used to visualize neuroimaging
results on structural brain images. Talaraich Client was used to search for Brodmann areas for peak clus-
ter activations. For the fMRI parametric modulation analysis, at the participant (Pi) level, we subtracted
the average value (across the 10 drawings made by participant Pi) of expert creativity ratings from each
creativity rating received by Pi. Similarly, we subtracted the average value of subjective diculty ratings
from each of the 10 subjective diculty rating provided by Pi for his/her drawings. is procedure of

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SCIENTIFIC RepoRts | 5:10894 | DOI: 10.1038/srep10894
demeaning (or normalizing) was done before running the individual level multiple regression to reduce
the eect of individual variance during the group-level analysis.
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Acknowledgements
is work was supported by a Hasso Plattner Design inking Research Program grant to A.L.R. We thank
the sta at Stanford’s Hasso Plattner Institute of Design (aka: the d.school), Center for Interdisciplinary
Brain Sciences Research, and Center for Cognitive and Neurobiological Imaging for their support.
Author Contributions
M.S. designed the task, collected and analyzed data, and wrote the manuscript. E.M.Q., E.K., N.T.B.,
Z.S., D.H., N.L., Y.H.C. and A.R. helped in study design and data collection. G.H. and A.R. blindly
rated the drawings for representation and creativity scores. R.F.D. and M.S. contributed to design and
development of MR-safe drawing tablet and R.F.D. also helped with development of neuroimaging
protocol. A.L.R. contributed to all aspects of the study, including design, interpretation of results and
writing of manuscript.
Additional Information
Competing nancial interests: e authors declare no competing nancial interests.
How to cite this article: Saggar, M. et al. Pictionary-based fMRI paradigm to study the neural
correlates of spontaneous improvisation and gural creativity. Sci. Rep. 5, 10894; doi: 10.1038/
srep10894 (2015).
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