Regional Gray Matter Density Associated with Cognitive Reflectivity–Impulsivity: Evidence from Voxel-Based Morphometry
Regional Gray Matter Density Associated with Cognitive Reflectivity-Impulsivity: Evidence from Voxel-Based Morphometry
Ryoichi Yokoyama 0 1
Takayuki Nozawa 0 1
Hikaru Takeuchi 0 1
Yasuyuki Taki 0 1
Atsushi Sekiguchi 0 1
Rui Nouchi 0 1
Yuka Kotozaki 0 1
Seishu Nakagawa 0 1
Carlos Makoto Miyauchi 0 1
Kunio Iizuka 0 1
Takamitsu Shinada 0 1
Yuki Yamamoto 0 1
Sugiko Hanawa 0 1
Tsuyoshi Araki 0 1
Hiroshi Hashizume 0 1
Keiko Kunitoki 0 1
Mayu Hanihara 0 1
Yuko Sassa 0 1
Ryuta Kawashima 0 1
0 1 Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University , Sendai , Japan , 2 Japan Society for the Promotion of Science , Tokyo , Japan , 3 Smart Ageing International Research Center, Institute of Development, Aging and Cancer, Tohoku University , Sendai , Japan , 4 Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University , Sendai , Japan , 5 Division of Medical Neuroimaging Analysis, Department of Community Medical Supports, Tohoku Medical Megabank Organization, Tohoku University , Sendai , Japan , 6 Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University , Sendai , Japan , 7 Human and Social Response Research Division, International Research Institute of Disaster Science, Tohoku University , Sendai, Japan, 8 Graduate Schools for Law and Politics , The University of Tokyo , Bunkyo, Tokyo , Japan , 9 Faculty of Medicine, Tohoku University , Sendai , Japan
1 Academic Editor: Tom Denson, The University of New South Wales , AUSTRALIA
When faced with a problem or choice, humans can use two different strategies: cognitive reflectivity, which involves slow responses and fewer mistakes, or cognitive impulsivity, which comprises of quick responses and more mistakes. Different individuals use these two strategies differently. To our knowledge, no study has directly investigated the brain regions involved in reflectivity-impulsivity; therefore, this study focused on associations between these cognitive strategies and the gray matter structure of several brain regions. In order to accomplish this, we enrolled 776 healthy, right-handed individuals (432 men and 344 women; 20.7 1.8 years) and used voxel-based morphometry with administration of a cognitive reflectivity-impulsivity questionnaire. We found that high cognitive reflectivity was associated with greater regional gray matter density in the ventral medial prefrontal cortex. Our finding suggests that this area plays an important role in defining an individual's trait associated with reflectivity and impulsivity.
Funding: This study was supported by JST/RISTEX,
JST/CREST, a Grant-in-Aid for Young Scientists (B)
(KAKENHI 23700306), a Grant-in-Aid for Young
Scientists (A) (KAKENHI 25700012) from the Ministry
of Education, Culture, Sports, Science, and
Technology, the Japan Society for the Promotion of
Science, and Tohoku University international
Advanced Research and Education Organization.
The funders had no role in study design, data
collection and analysis, decision to publish, or
preparation of the manuscript.
Human problem-solving canonically requires the adoption of one of two cognitive strategies.
Throughout the literature, these strategies [1, 2] have been widely categorized as reflective and
intuitive , explicit and implicit , controlled and automatic , or system 1 and system
Competing Interests: The authors have declared
that no competing interests exist.
2 . Furthermore, it has been suggested that the utilization of these strategies varies among
In psychology, the two problem-solving approaches have been identified as types of
cognitive style and are classically referred to as reflectivity and impulsivity . The cognitive
reflectivity strategy is seen in individuals who are slow responders and commit fewer mistakes,
whereas cognitive impulsivity is observed in individuals who respond quickly, committing
more mistakes . Importantly, impulsivity as a reference to cognitive style in psychology
should not be compared to impulsivity as it is used in psychiatric studies such as addiction
research . This is because impulsivity has a negative connotation in the field of psychiatry
since it has been defined as a trait related to poor notion, premature execution, undue risk, or
inappropriate actions that often result in undesirable consequences . In contrast,
impulsivity as a concept in psychology does not have a negative connotation [12, 13]; rather, it is
considered necessary to maintain a balance between the rapidness and accuracy of an action. This
view is supported by several findings indicating that psychiatric and psychological
measurements involve different aspects of impulsivity .
Recently, magnetic resonance imaging (MRI) has been used as a tool to investigate how
white and grey matter (GM) structure can predict individual differences in a variety of human
cognitive functions . For example, previous psychiatric studies have been able to identify a
relationship between the orbitofrontal cortex (OFC) volume and impulsivity [18, 19]. However,
to our knowledge, no study has directly investigated the brain structures involved in cognitive
reflectivityimpulsivity. As previously mentioned, impulsivity in psychiatry is a different
concept than impulsivity in psychology; therefore, we assume that their neural basis will also
Previous neuroimaging studies have identified several regions of the brain responsible for
reflectivity and impulsivity. For example, the reflective system, which includes the dorsolateral
prefrontal cortex (DLPFC), anterior cingulate, insula cortex, and hippocampus, is thought to
play a central role in reflectivity , while the impulsive system, which includes the striatum
and amygdala, is associated with impulsive behavior . The neural system that integrates
information from both the reflective and impulsive systems has been identified [21, 22]; we
termed it the integration system and have used this term hereafter. For example, the
ventromedial prefrontal cortex (vmPFC) is thought to be part of the integration system because it is
associated with aspects of both reflectivity and impulsivity; some studies have categorized the
vmPFC as a reflective system , while others have categorized it as an impulsive system .
However, the brain structure responsible for representing individual differences in cognitive
reflectivityimpulsivity is still unknown. For the neural basis of cognitive reflectivityimpulsivity,
we focus on two primary, non-mutually-exclusive possibilities: (1) individual differences in
cognitive reflectivityimpulsivity could be mediated by brain regions involved in reflective and/or
impulsive processing or (2) individual differences in cognitive reflectivityimpulsivity can be
represented in the integration system. On the basis of previous studies, if the first possibility is
true, then the reflective system and/or the impulsive system may be responsible for the individual
differences in cognitive reflectivityimpulsivity. On the other hand, if the second possibility is
true, then the integration system may be responsible for the individual differences in cognitive
reflectivityimpulsivity. Thus, our first hypothesis is that the reflective system and/or the
impulsive system is responsible for individual differences in cognitive reflectivityimpulsivity, while
our second hypothesis is that the integration system mediates these differences.
To test our hypotheses, we investigated the association between individual differences in
cognitive reflectivityimpulsivity and regional GM density (rGMD) by using voxel-based
morphometry (VBM) . For assessing cognitive reflectivityimpulsivity, we used a cognitive
reflectivityimpulsivity questionnaire . Further, in order to adjust for the effects of
intelligence on brain structure, the Ravens Advanced Progressive Matrix (RAPM) test 
was conducted and used for an analysis.
In accordance with the Declaration of Helsinki (1991), written informed consent was obtained
from the participants prior to their participation in the present study. The Tohoku University
School of Medicine Ethics Committee approved the study protocol.
Seven hundred and seventy-six healthy, right-handed individuals (432 men and 344 women;
20.7 1.8 years) participated in this study as part of an ongoing project investigating
associations among brain region, cognitive function, age, genetics, and daily habits . Data
generated from the subjects in this study will likely be used in other studies unrelated to the theme
of the current investigation, and some of the subjects who participated in this study became
subjects of intervention studies (only psychological and imaging data recorded before the
intervention was used in this study). All subjects were university, college, or postgraduate students
or subjects who had graduated one year before the study onset. All participants had normal
vision and no history of neurological or psychiatric illness. Handedness was evaluated for all
participants using the Edinburgh Handedness Inventory .
The cognitive reflectivityimpulsiveness questionnaire
The cognitive reflectivityimpulsiveness questionnaire [24, 36] was used to assess individual
differences in reflectivity and impulsivity. This self-reported questionnaire contains 10 items
and employs a four-point Likert scale with responses ranging from I dont agree at all to I
agree very much . The questionnaire was developed as a substitute for the matching
familiar figures (MFF) test (illustration test), which has been used to measure cognitive reflectivity
and impulsivity in children . The one-factor structure of the scale for this questionnaire has
been supported by factor analyses . Answers to all questions were compiled into a single
score (with the score totaling 40, and responses from reverse items were reverted by 5x
before the summation). A high score indicated higher cognitive reflectivity, whereas a low score
indicated higher cognitive impulsivity. A previous validation study using adult subjects showed
that the MMF test and the cognitive reflectivityimpulsiveness questionnaire show significant
correlation (r = -0.314, p < 0.01) .To test the validity of the cognitive
reflectivityimpulsiveness questionnaire, we examined the correlation of the cognitive reflectivityimpulsiveness
questionnaire scores with the impulsiveness scores of novelty-seeking from the Temperament
and Character Inventory [38, 39]. The Temperament and Character Inventory scores were
acquired from our sample at the same time as the cognitive reflectivityimpulsiveness
questionnaire scores. We observed a significant correlation between the two parameters (r = -0.64,
p < 0.01), supporting the validity of the questionnaire in parallel with the previous validation
studies described above. Moreover, the internal consistency (measured using Cronbachs
coefficient ) and test-retest reliability of this questionnaire were estimated to be 0.842 and 0.827,
respectively . These values indicate the high reliability of the questionnaire, supporting the
criterion-related validation of reflectivity and impulsivity .
Assessment of psychometric measures of general intelligence
Ravens Advanced Progressive Matrix (RAPM), one of the purest psychometric measures of
general intelligence , was used to assess intelligence in our study in order to adjust for the
well-known effect of individual psychometric measures of intelligence on brain structures [29,
40, 41]. RAPM  contains 36 nonverbal items requiring fluid reasoning ability. Each item
consists of a 3 3 matrix with a missing piece to be completed by selecting the best of eight
alternatives. How subjects scored on this test (number of correct answers in 30 min) was used as
an index of individual psychometric measure of intelligence.
Image acquisition and analysis
All MRI data acquisition was performed using a 3-T Philips Achieva scanner. High-resolution
T1-weighted structural images (T1WIs: 240 240 matrix, TR = 6.5 ms, TE = 3 ms, FOV = 24
cm, slices = 162, slice thickness = 1.0 mm) were collected using a magnetization-prepared
rapid gradient echo sequence.
Preprocessing of T1-weighted structural data
Preprocessing of the structural data was performed using the Statistical Parametric Mapping
software (SPM8; Wellcome Department of Cognitive Neurology, London, UK) implemented in
Matlab (Mathworks Inc., Natick, MA, USA). The procedure conducted in our previous study
was used ; using the new segmentation algorithm implemented in SPM8, T1-weighted
structural images of each individual were segmented into six tissue sections. In this process, the
gray matter tissue probability map (TPM) was manipulated from the original SPM8 gray matter
TPM in such a way that the signal intensities of voxels (gray matter tissue probability of the
default tissue gray matter TPM + white matter tissue probability of the default TPM) with
intensity more than 0.25 became 0. When this manipulated gray matter TPM was used, the dura
matter was less likely to be classified as gray matter (compared with when the default gray
matter TPM was used), without other substantial segmentation problems. Default parameters were
used in this new segmentation process with the exception that affine regularization was
performed with the International Consortium for Brain Mapping template for East Asian brains.
We then progressed to the diffeomorphic anatomical registration through exponentiated lie
algebra (DARTEL) process implemented in SPM8. In this process, we used DARTEL-imported
images of the five TPMs (extracranial space was not used because it is not consistent across
subjects) from the abovementioned new segmentation method. First, we prepared a template
which we had created and used in our previous studies (see  and , respectively). Using
this template, we then performed DARTEL (using default parameters) for all of the subjects.
The resulting images were spatially normalized to the Montreal Neurological Institute (MNI)
space to yield images with 1.5 1.5 1.5 mm3 voxels. Subsequently, all images were smoothed
by convolving them with an isotropic Gaussian kernel of 12 mm full width at half maximum
(FWHM) for the reasons described below.
We investigated rGMDs associated with individual differences in cognitive reflectivity
impulsivity. Statistical analyses of morphological data were then performed using VBM5
software (http://dbm.neuro.uni-jena.de/vbm/), an extension of SPM5 .
In the analyses, we included only voxels that showed rGMD values more than 0.05 in all
subjects. The primary purpose for using GM thresholds was to cut the periphery of the GM
areas so that the areas for analysis were effectively limited.
Note: RAPM = Ravens Advanced Progressive Matrices
A whole-brain approach was used in this study. In the whole-brain multiple regression
analysis, we tested for a relationship between cognitive reflectivityimpulsiveness (as assessed by
the cognitive reflectivityimpulsiveness questionnaire) and rGMD. The age, sex, and total
intracranial volume (TIV; total GM volume + total WM volume + total CSF volume) were used
as additional covariates for the analysis. Furthermore, analyses were performed both with and
without the RAPM score as an additional covariate in addition to the covariates used above to
assess the effect of general intelligence. Of note, when total brain volume was included as a
covariate in the density measures analysis, the results of the analysis showed tissue densities that
could not be explained by total brain volume.
The statistical significance level in this study was set at P < 0.05, and corrected at the
nonisotropic adjusted cluster level  with an underlying voxel level of P < 0.0025 [47, 48]. We
used VBM5/SPM5 for statistical analyses (please see  for our rationale behind selecting the
settings for the current study). The previously mentioned validation study using VBM5 
showed that in this non-isotropic cluster-size test of random field theory, a relatively higher
cluster-determining threshold combined with high smoothing values of more than six voxels
leads to appropriate conservativeness in real data. With high smoothing values, an uncorrected
threshold of P < 0.01 seems to lead to anti-conservativeness, whereas that of P < 0.001 seems
to lead to slight conservativeness . However, there are substantial differences in the way
SPM8 and SPM5 estimate the actual FWHM in the areas analyzed, and this directly affects the
cluster test threshold [47, 48]. Therefore, regardless of which version is more appropriate, we
believe that the conditions for this non-isotropic adjusted cluster size test shown by the
previous study  are no longer guaranteed in SPM8. Thus, we used the VBM5/SPM5 version for
statistical analyses performed in this study as in our previous studies [47, 48].
Additional analysis of the gender effect
As described in the results section, the behavioral analysis indicated an effect of gender on
cognitive reflectivityimpulsivity (Table 1). Thus, sex likely plays a role in individual differences
in reflectivity, impulsivity, and brain structure. Therefore, an additional analysis of this gender
effect was conducted. We investigated whether the relationship between rGMDs and the
cognitive reflectivityimpulsivity scores differed between sexes (whether the interaction between
sex and the cognitive reflectivityimpulsivity score affected rGMD). In the whole brain
analysis, we used a voxel wise analysis of covariance (ANCOVA) in which sex difference was a
group factor (using the full factorial option of SPM5). In this analysis, age, RAPM score, and
total brain volume were covariates. All of these covariates, except total brain volume, were
modeled so that each covariate's unique relationship with rGMD could be seen in each sex
(using the interactions option in SPM8), which would allow the interaction effects of sex and
the covariates to be investigated. The total brain volume was modeled so that this covariate
Note: RAPM = Ravens Advanced Progressive Matrices
had a common relationship with rGMD across sexes. The interaction effect between sex and
the self-handicapping scale score on rGMD was assessed using t-contrasts.
Table 2 shows the average and standard deviation (SD) of age, RAPM scores, and cognitive
reflectivityimpulsiveness among subjects. A distribution of the cognitive reflectivityimpulsiveness
score is indicated in Fig. 1 (right side of the figure).
Fig 1. Anatomical correlates of cognitive reflectivityimpulsiveness. (a) The region of correlation is overlaid on a sagittal section (top left), a coronal
section (top right), and an axial section (bottom left) of the skull stripped image of the averaged normalized T1-weighted structural images of a portion of the
subjects that participated in this study. The redyellow color scale indicates the T score of the positive correlation between rGMD and the cognitive
reflectivityimpulsiveness score. rGMD was positively correlated with individual cognitive reflectivityimpulsiveness in a cluster in the medial part of the
ventral prefrontal cortex (vmPFC). Results are shown with P < 0.05, corrected for multiple comparisons at the non-isotropic adjusted cluster-level with an
underlying voxel-level of P < 0.0025, uncorrected. (b) A scatterplot between the cognitive reflectivityimpulsiveness score and the mean rGMD value in the
significant cluster in (a) is shown for visualization purposes only. The X-axis indicates the mean rGMD value, and the Y-axis indicates the cognitive
reflectivityimpulsiveness score. The upper histogram indicates the distribution of the mean rGMD value, and the right histogram indicates the distribution of
the cognitive reflectivityimpulsiveness score. The distribution of these two parameters shows a significant positive correlation.
A multiple regression analysis with cognitive reflectivityimpulsiveness score as the
dependent variable and age, sex, and RAPM score as independent variables revealed that females
showed significantly lower cognitive reflectivityimpulsiveness scores (Table 1).
Correlation between rGMD and cognitive reflectivityimpulsiveness
We investigated the association between rGMD and individual differences in cognitive
reflectivityimpulsiveness. A multiple regression analysis including age, sex, RAPM score, and
TIV revealed that the cognitive reflectivityimpulsiveness score was significantly and positively
correlated with the rGMD in the vmPFC (peak MNI coordinates x, y, z = 15, 47, -29; peak
t value = 3.70; cluster size = 1851; P < 0.001, corrected for multiple comparisons at the
nonisotropic [non-stationary] adjusted cluster level with a cluster-determining uncorrected
threshold of P < 0.0025; Fig. 1).
Effects of the RAPM on the VBM results
In order to confirm the effects of the RAPM on VBM results, we conducted a VBM analysis
without RAPM in the model. Positive correlations were still observed between rGMD and
vmPFC with the use of the same statistical threshold as that described above (peak MNI
coordinates x, y, z = 15, 47, -29; peak t value = 3.72; cluster size = 1962). In addition, we assessed
brain regions, which correlated with RAPM, by focusing on the RAPM regressor. However, no
brain regions were identified using the same statistical threshold as that described above. At
the behavioral level, intelligence did not affect cognitive reflectivity-impulsivity (see the basic
data section). In addition, intelligence did not affect the VBM analysis results. Thus, we
concluded that general intelligence did not significantly impact the neural basis of cognitive
reflectivity-impulsivity. This result is not consistent with those of previous studies, which
identified the relationship between general intelligence and brain structure . This discrepancy
may be because of the sample used in this study; the participants were all high-achieving
university students; thus, the general intelligence may not vary significantly thereby resulting in
weak statistical results.
Effects of the gender
The ANCOVA using data from both sexes revealed that there were no interaction effects
between the score on the cognitive reflectivityimpulsivity questionnaire and gender on rGMD.
In this study, we demonstrated that a higher cognitive reflectivityimpulsiveness score was
associated with more rGMD in the vmPFC.
Structural differences in the vmPFC can determine how information from the impulsive and
reflective systems is utilized. Previous studies on the somatic marker hypothesis suggest that
the vmPFC plays an important role in switching between impulsive and reflective strategies
[21, 22]. In addition, the vmPFC has strong relationships with both the DLPFC, which is
categorized as a reflective system, and the limbic system, which is categorized as an impulsive
system. Specifically, the vmPFC interacts with either the DLPFC  or the limbic system 
depending on the requirement for reflective or impulsive thoughts, respectively. Therefore, the
vmPFC could act as a mediator between reflective and impulsive systems. This interpretation
aids in understanding the discrepancy of the vmPFC being categorized as a reflective system in
some studies but as an impulsive system in others [5, 20]. The vmPFC may be neither
impulsive nor reflective; rather, it might serve as an integration area for information received from
both the reflective and impulsive systems. Thus, our second hypothesisthe vmPFC forms the
neural basis of cognitive reflectivity-impulsivitywas supported.
As an alternative interpretation of our results, an individuals preference for reflective thoughts
could result in structural differences in the vmPFC. The vmPFC is involved in complicated
information processing, such as self-control and situational comprehension , and in
highlevel processing of information related to social interaction [52, 53]. It is possible that
information processing becomes more complicated with increasing reflectivity, and that this would
require more activation of the vmPFC. Finally, this increase in activity might also result in
structural changes in the vmPFC.
Divergence between previous results and our current findings
Lastly, there is some divergence between the results reported the literature and our current
findings. Specifically, previous studies revealed a significant negative correlation between
impulsivity and the lateral OFC [18, 19]. This is in contrast to findings that revealed a significant
positive correlation between cognitive reflectivity and the vmPFC. This discrepancy might
reflect the difference between the psychiatric concept of impulsivity and the psychological
concept of cognitive reflectivityimpulsivity. The previous studies used an impulsiveness scale (the
BIS-11 questionnaire) that was based on psychiatric measures ; unlike the concept of
cognitive impulsiveness in psychology [12, 13], impulsiveness in psychiatry indicates
inappropriate behaviors and does not consider risk . Therefore, the BIS-11 questionnaire used in
previous studies [54, 55] might be risk insensitive. Further, it has been suggested that the
medial OFC (vmPFC) and lateral OFC have different functions . In particular, the lateral OFC
responds to risk . Thus, the relationship that was found in the previous study between a
smaller lateral OFC and impulsivity  is likely to be related to risk insensitivities that result
in inappropriate behaviors.
At the behavioral level, we observed a gender effect in that the average cognitive reflectivity
impulsivity score was higher for females compared to males. However, we did not observe a
gender effect at the brain structure level. Thus, the neural basis of cognitive reflectivity
impulsivity may be common between genders.
In this study, we analyzed data from 776 participants; such a large sample enabled detection of
even small associations between brain structure and cognitive reflectivity-impulsivity.
Associations between brain structure and personality traits have reported weak but significant
relationships . However, to find a more extensive relationship between brain function and
cognitive reflectivity-impulsivity, investigating only brain structure would not be sufficient.
Thus, more research using different approaches, such as a multivariate study or a resting
connectivity study, would be beneficial.
One possible direction that can be pursued in future studies is the relationships among individual
differences in reflectivity, impulsivity, and brain structure. This possibility focuses on other
impulsiveness measurements based on the fact that other impulsiveness questionnaires exhibit
different relationships with brain structures . If this is the case, then our understanding of what
a questionnaire actually measures might be clarified by its relationship with specific brain
structures. In addition, one might think that cognitive reflectivityimpulsivity is not on a common
axis. Thus, modeling cognitive reflectivityimpulsivity separately and determining its correlation
in a brain structure will be a promising way to better understand cognitive reflectivity
To the best of our knowledge, this is the first study investigating associations between brain
structure and cognitive reflectivityimpulsivity, and our results provided direct neurobiological
identification of the brain structures that were associated with cognitive reflectivityimpulsivity.
Specifically, we demonstrated a significant positive correlation between rGMD in the vmPFC
and the cognitive reflectivityimpulsiveness scores. This finding suggests that the vmPFC may
bridge the impulsive and reflective systems in the brain.
We thank Yuki Yamada for operating the MRI scanner, Haruka Nouchi for conducting the
psychological tests, all other assistants for helping with the experiments and the study, the
study participants, and all of our other colleagues at IDAC, Tohoku University for
Conceived and designed the experiments: RY TN HT RK. Performed the experiments: RY TN
HT YT AS RN YK SN CMM KI TS YY SH TA HH KK MH YS. Analyzed the data: RY TN HT.
Contributed reagents/materials/analysis tools: RK. Wrote the paper: RY TN HT.
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