Journal of Behavioral and Brain Science, 2011, 1, 102-110
doi:10.4236/jbbs.2011.13014 Published Online August 2011 (http://www.SciRP.org/journal/jbbs)
Gender Differences in Frontal Activation while Perceiving
Pathologically Thin Female Body Forms
Toru Uehara1, Yoko Ishige2, Masashi Suda2, Perminder Sachdev3,4
1General Health Su p port Center, Gunma University, Maebashi, Japan
2Department of Neur opsychiatry, Gunma University Graduate School of Medicine, Maebashi, Japan
3School of Psychiatry, University of New South Wales, Sydney, Australia
4Neuropsychiatric Institu te , Prince of Wales Hospital, Sydney, Australia
Received March 23, 201 1; revised May 13, 2011; accepted June 18, 2011
Abstract
Brain mechanisms underlying body image disturbances are a focus of research in the realm of eating disor-
ders, and functional imaging studies have revealed gender differences in the processing of body shape. In
this study, using 16-channel near infrared spectroscopy, we investigated frontal lobe activation in 46 healthy
university students during viewing of photographs of pathologically thin female forms, and compared gender
differences in activation, and examined the correlations between the relative changes in cerebral blood vol-
ume, eating attitudes, and perceived feelings. Participants completed the Eating Attitudes Test (EAT26) and
rated a visual analogue scale for anxiety/disgust. Significant gender differences in the pattern of activation
were noted in the prefrontal region (predominantly right side, dorsolateral to ventral), with male participants
showing greater and more widespread frontal activation. The total and subscale scores on EAT26 were sig-
nificantly correlated with the frontal activation, and perceived feelings were significantly associated with
increased prefrontal activation on the left side. Gender differences in frontal activation suggest differential
expectations between men and women of pathologically thin female body forms. The study results suggest
that anorexic psychopathology may be associated with abnormal right frontal activation while viewing thin
bodies of others.
Keywords: Body Image, Anorexia Nervosa, Prefrontal Cortex, Eating Attitudes, Near Infrared Spectroscopy
1. Introduction
The brain mechanisms underlying body image process-
ing have been explored in previous studies, using a vari-
ety of methodologies. An investigation of the neural
processing of body shape revealed activation in a distrib-
uted network, including the lateral fusiform gyrus, lateral
prefrontal cortex (PFC), and right parietal cortex [1]. The
dorsolateral PFC and the insular, inferior parietal, fusi-
form, and anterior cingulate (AC) cortical areas have been
demonstrated to respond to images of one’s own distorted
body [2]. Other studies have shown that lesions in the
dorsolateral PFC and/or parietal cortex were associated
with impaired performances on tasks requiring on-line
coding of the body posture [3]. Derogatory words con-
cerning one’s own body image have been reported to
provoke activation s in the temporomesial area, including
the amygdala, in young women [4].
In terms of clinical correlations, a recent fMRI study
[5] reported that anorexia nervosa (AN) patients demon-
strated stronger activation of the insula and lateral PFC
cortex during the satisfaction rating of thin self-images,
and the study indicated a stronger emotional involvement
while the subjects were presented with distorted images
close to their own ideal body size. Friederich et al. [6]
summarized that a neural network could be involved in
the general processing of body images (the lateral fusi-
form, parietal and dorsolateral PFC) and a related ‘emo-
tional’ network (AC, insula, amygdala) may be activ ated
when body-shape-related stimuli induce self-related or
emotional reactions. Consequently, these neural connec-
tions might play a significant role in the pathophysiology
of eating disorders, and possibly in the gender-related
difference in the prevalence of eating di sorders.
However, several complex processes could be involved
in the brain response, as seen in the detected activations,

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according to the content or focus of the stimuli. For ex-
ample, activation of the left ventrolateral and apical PFC
was found in a previous fMRI study in which line draw-
ings of underweight body shapes were used [1]; on the
other hand, exposure to distorted images of one’s own
body showed activations of the left [7] or bilateral [2]
ventrolateral PFC. On the other hand, the dorsal division
of the right AC (BA 32) has been suggested to be impor-
tant for self-referential processing of emotions [8,9]. In
anxiety responses to pictures of slim body shapes, Fried-
erich et al. [6] commented that differential activation of
the fear network, including the amygdala and the AC,
was associated with substantial body dissatisfaction.
Based on these studies, it seems reasonable to divide task
profiles into individual factors, including pure self-ref-
erence and objective thin stimulation. In response to the
respective paradigms, one could evaluate subjective and
objective feelings or reactions while presenting visual
stimuli.
We assume that body image processing can be divided
into several components according to the cognitive proc-
esses involved; first, simple self-monitoring while com-
paring with others; second objective stimulation by body
forms (e.g., emaciation or obesity); and last, disturbance
of body image perception, such as drawings of body
shapes or distorted graphics. Therefore, we conducted the
present study with the objective of selecting one compo-
nent of these processes, i.e. “how do young adults feel
about or process others’ pathological emaciation?” We
used stimuli that have been previously used successfully
(body forms of female patients with AN) [10]. This
aforementioned study indicated that processing of non
self-images by control subjects activated the inferior and
middle frontal gyri, and the superior and inferior parietal
lobules. AN patients had a similar pattern of activation
with greater activation of the medial frontal gyrus. Inter-
estingly, when the differential activation in the 2 groups
was investigated with self vs. non-self-images, control
subjects showed greater activation in the middle frontal
gyri and insula than the patients, while the patients did
not show greater activation in any region. Sachdev et al.
[10] concluded that discrepant emotional and perceptual
processing may underlie the distortion of self-images by
AN patients. It is very important to determine whether
Sachdev et al.’s study findings apply equally to men and
women from a different cultural background.
We used near infrared spectroscopy (NIRS) for this
study because of its noninvasiveness and ease of per-
formance. NIRS employs near-infrared light emitted and
detected on the skull skin [11]. It allows the monitoring
of hemodynamic changes, including both cerebral blood
volume changes and the oxygenation state, using a small
apparatus with a high time resolution of about 0.1 sec-
onds. It also allows the monitoring of changes in the
oxygenated hemoglobin concentration (o-Hb) and de-
oxygenated hemoglobin concentration (d-Hb). NIRS is
considered to be suitable for studies of higher brain func-
tions because it enables measurements in the natural set-
ting as compared with other brain imaging techniques:
for example, subjects can undergo NIRS examination in
the sitting position, with th eir eyes open, or while speak-
ing and sitting on a bed [12]. Taking advantage of these
characteristics, several NIRS studies have been con-
ducted in patients with psychiatric disorders, such as
schizophrenia [13,14], depression [15], and attention-
deficit/hyperactivity disorder [16]. These characteristics
have also enabled the investigation of subjective, delicate
experiences in healthy subjects, such as subjective
sleepiness and conversations [17,18].
2. Methods
2.1. Participants
The study participants were 46 healthy university stu-
dents (32 females), with a mean age of 20.5 years (S.D.
2.2). Two were left-handed, and 3 were visiting students
from China, the others being Japanese. Their average
body weight and height were 55 (± 7.0) kg and 162 (±
8.9) cm, respectively, and the body mass index (BMI)
ranged from 18 to 24 (mean 21.3 for all; 22.5 for male
and 20.8 for female). None of the participants had any
significant medical/psychiatric history. They were vol-
untarily recruited as subjects of this scientific study, and
were paid 1600 yen, according the official provision, for
their co-operation in the exam. All subjects gave written
informed consent prior to their participation in the study,
in conformity with the provisions of the Declaration of
Helsinki, revised in Edinburgh in 2000. We strictly pro-
tected the privacy of the subjects, carefully preserving
their anonymity. The data collection was conducted from
August to December in 2009 and from August to De-
cember in 2010.
2.2. NIRS Machine
NIRS allows calculation of the changes in the hemoglo-
bin parameters, including oxygenated hemoglobin [o-Hb]
and deoxygenated hemoglobin [d-Hb], by measuring
attenuation of the near-infrared light around the wave-
length of 800 nm. The neural activation induces regional
cerebral blood dynamics. It induces hemodynamic
changes in the brain tissue, almost identical in pattern to
spontaneous cerebral neural activity. Cortical activation
is typically detected as an [o-Hb] increase and [d-Hb]
decrease; however, the direction of changes of [d-Hb]

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can be ambiguous in the frontal lobe [19]. Mainly,
changes of [o-Hb] at a depth of 2 - 3 cm from the scalp,
that is, the surface of the cerebral cortex, are considered
to be correlated with the hemodynamic changes in PET
[20] and blood oxygenation level-dependent (BOLD)
signal changes in fMRI [21,22]. NIRS does not measure
cerebral luminescence, but measures attenuation of the
irradiated light intensity. Therefore, the combination of
optical irradiation and photon detection determines the
resolution. NIRS characteristically measures not the 1:1
combination of irradiation and detection, but the light
from one light source with 2 or more detectors arranged
geometrically in the NIRS measurement system. Thus,
information on which detector measures the signal of
which portion becomes important. There are a number of
methods for judging this channel separation. The first
method, Time Division Multiple Access (TDMA), makes
a light source turn on in turns and separates a signal on a
time-axis. The second method, Frequency Division Mul-
tiple Access (FDMA), is for modulating and irradiating 2
or more light sources with different frequencies and se-
parating a signal based on the frequency information
after detection. The third method is Code Division Mul-
tiple Access (CDMA) which uses spectrum diffusion
attenuation and is applied GPS or mobile phone tech-
nology. The NIRS machine we used, OEG-16 (Spec-
tratech, Inc, Japan), employs CDMA methodology and is,
therefore, very convenient and portable. It can provide
NIRS data under natural conditions non-invasively, and
artifacts induced by hair can be avoided because of the
adjustments which allow reading only of the front of the
head. The OEG-16 measures 16 channels on the frontal
lobe (according to Broadman’s map, it provides data
approximately on sections 10, 11, 12, 44, 45, and 46). Its
time resolution is 0.5 seconds and space resolution is 2
cm. The headset was placed according to the 10/20 sys-
tem, by which a central hole was coordinated with Fz.
From channels 1 to 8, the measurement points were
placed from the right to the central pole. From channels 9
to 16, the measurement points were placed from the ven-
tral/rostral to the left lateral (to refer the video content).
It provides relative changes of hemoglobin concentration
and values are obtained in arbitrary units (concentration
× path length).
2.3. Task
A visual paradigm was used in which 3-dimensional
(front, side, and back) photos of 2 pathologically thin
women (BMI are ranged from 15-16), used in a previous
fMRI study, were presented [10]. The women were
dressed in standardized clothing (white cropped singlet
with the abdomen showing, black bike pants and no jew-
ellery), so as to reveal the body contour. The faces of all
the images were digitally masked t o avoi d dist racti on from
the body form. The body images were placed against a
uniform grey background of medium-intensity. Digital
images were prepared using Photoshop®. To maintain
the subjects’ attention during the viewing, the subjects
were instructed to express how he/she thinks or feels
about comparing the images with him/herself. The dif-
ferent body forms were shown in 3 different profiles for
an equal duration, i.e., front, back, and side, switched
every 5 seconds. The baseline image was a uniformly
grey screen of medium intensity which was identical to
the background of the body imag es (pre-task 5 sec, inter-
task 5 sec, and post-task 5 sec).
2.4. Perceived Feeling and EAT
Perceived feeling about thinner images was rated on a
scale of 0 - 10, according to the participants’ overall im-
pression. The anxiety (how have you felt anxiety?) and
disgust (how have you felt disgust?) levels were scaled
on a visual analogue scale soon after viewing pictures
subjectively.
Eating attitudes were assessed in all of the subjects
using the Japanese version of the Eating Attitude Test
(EAT-26) [23,24] at the time of the examination. EAT-26
is a 26-item self-rated questionnaire for evaluating eating
problems. The results are presented as the total score
(range, 0-78), as well as the scores on the 3 subscales,
namely, diet (range, 0 - 39), bulimia and food preoccupa-
tion (range, 0 - 18), and oral control (range, 0 - 21),
which respectively reflect avoidance of fattening foods
and preoccupation with being thinner, thoughts about
food as well as items indicating bulimia, and self-co ntrol
in relation to eating and perceived pressure from others
to gain weight.
2.5. Data Analysis
The continuous waveforms of [Hb] changes in all of the
16 channels were acquired from each of the subjects
during the paradigm. The individually averaged [Hb]
waveforms were obtained as the average sum of 2 trials:
the baseline realignment for 5 sec before and after the
task periods, and a task segment averaging 2 sets of 15-
sec image viewing periods. Thereafter, the grand average
values of the baseline and task segments in each channel
were calculated for all of the participants’ data. W e used
only [o-Hb] values as cerebral blood volume changes
were based on a previous report [25,26]. Topography
(video content) was presented according to the time
course on the frontal portion. For this grand averaged data,
channels which carried significant activations were ana-

T. UEHARA ET AL.
105
lyzed using the t-test between the pre-task and task peri-
ods (http://www.brsystems.jp). Differences in the mean
values were tested by combined variance, as the number
of samples for the 2 periods was not equal.
Combined variance: Ue2 = {(na – 1)ua2 +
(nb – 1)u b2} / {na + nb – 2}
Variance and numbers of sample a: ua2 and na
Variance and numbers of sample b: ub2 and nb
T-value: t0 = |mXa – mXb|/root{ue2(1/na + 1/nb)}
Average of each sample: mXa, mXb
In the next step, gender differences were investigated
using a t-test for each channel during the task period by
analyzing the differences in the grand average data be-
tween males and females. In the last step, the relation-
ships of the [o-Hb] changes with the perceived anxiety/
disgust and EAT scores (total and subscales) were inves-
tigated. Channels with significantly correlated changes
(diet, bulimia and food preoccupation, and oral control
scores for EAT-26, BMI, VAS score) were analyzed by
non-parametric correlation. An imaging software was
used to analyze NIRS parameters (Data Viewer ver.1.1a,
BR Systems. Inc., Japan). The other statistical analyses
were conducted using SPSS version 17.0 (SPSS Japan,
Inc.).
3. Results
3.1. Waveform and Gender
The Figure 1 demonstrates the grand average waveforms
for the [Hb] changes for all participants according to
gender (for males on the left side). The red polygonal
line indicates the relative changes of [o-Hb], the blue
indicates those of [d-Hb], and the green indicates the
changes of the total-Hb (sum of o- and d-Hb). Overall,
fluctuating alterations were generally seen in many chan-
nels during the task. The video content depicts the to-
pography of the [o-HB] changes (for females on the right
side), and redder areas represent higher activations
(grading to yellow, green, and opposite in deeper blue).
<Video content can be viewed on http://www.youtube.
com/watch?v=Z24uVzzEZO4>
Comparisons between the baseline and task periods
are ummarized in Table 1 according to gender. The re-
spective t-value for each channel showed significant ac-
tivations (p < 0.05, DoF = 24) on chann els 1, 2 , 3, and 11
(dominantly right lateral PFC) in males, and significant
activations (p < 0.05, DoF = 24) on channel 5 and 8
(right PFC) and significant deactivations in channel 6
(right lower ventral PFC) in females. Comparisons of
activations for the task periods between the males and
females are presented in Table 2; t-values for every
Table 1. Comparisons of oxy-hemoglobin changes between
rest and task periods.
male female
rest task t rest task t
sample4 22 4 22
channel 1–0.0030.043–2.78* 0.002 0.0000.41
2 –0.006 0.021 –2.11* 0.004 –0.0041.99
3 0.0020.022–2.48* –0.001 –0.0011.01
4 0.0050.037–1.96 –0.001 0.000–1.35
5 0.0000.004–1.10 –0.001 0.003–3.52*
6 0.0030.020–1.80 –0.001 –0.004 2.30*
7 0.0000.011–1.48 –0.001 0.000–0.63
8 –0.003 0.020–1.58 –0.002 0.003–4.56*
9 –0.003 –0.0140.52 –0.001 –0.001 –0.06
10 0.0050.0040.09 0.000 –0.0021.51
11 0.0020.021–2.01 0.000 0.0000.54
12 0.0020.006–0.52 –0.001 –0.0010.07
13 0.0040.029–1.87 0.002 –0.0010.78
14 0.0090.025–1.14 0.001 0.0010.24
15 0.004–0.005 1.00 0.004 0.0011.15
16 0.008–0.027 1.64 0.009 0.0060.41
Degree of freedom = 24, *; p < 0. 0 5 , rest vs. task.
Table 2. Comparisons of activations in task periods between
male and female.
channel male female t p
1 0.045 0.000 7.47 **
2 0.022 –0.004 5.44 **
3 0.024 –0.001 9.31 **
4 0.039 0.000 6.82 **
5 0.005 0.003 1.64
6 0.022 –0.004 8.26 **
7 0.012 0.000 4.61 **
8 0.020 0.003 3.21 *
9 –0.014 –0.001 –1.66
10 0.005 –0.002 1.50
11 0.023 0.000 8.08 **
12 0.007 0.000 3.09 *
13 0.032 0.000 8.03 **
14 0.028 0.000 5.18 **
15 –0.003 0.002 –1.56
16 –0.029 0.006 –4.13 **
Degree of freedom = 24, *; p < 0.05, rest vs. t ask.

T. UEHARA ET AL.
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Figure 1. This figure demonstrates the grand average waveforms for the [Hb] changes in all of the participants according to
gender (for males on the left side). The red polygonal line indicates the relative changes of the [o-Hb], the blue polygonal line
indicates those of [d-Hb], and the green polygonal line indicates the total [Hb] (sum of o-HB and d-HB) relative changes. The
vertical axis shows the relative changes of [Hb] (mMmm), and the vertical grey lines show the mean start and end of the task
periods. The numbers on the axis of abscissas indicate the time-point of the measurements (seconds). The number besides
each waveform indicates respective channel as follows; from channels 1 to 8, the measurement points were placed from the
right to the central pole. Fr om channels 9 to 16, the measurement points were placed from the ventral/rostral to the left lat-
eral (to refer the video content).
channel and significant differences are presented. Overall,
males showed more activations than females in frontal
functioning, with especially significant differences (p <
0.01, DoF = 23.4) on channels 1 - 4, 7 - 8 (right PFC), 11,
13 - 14 (left PFC). In contrast, channel 16 (left lateral)
showed significantly greater activations in females than
in males (p < 0.01).
3.2. Correlation with EAT and Perceived
Feelings
Spearman’s correlation coefficients were calculated for
the relative changes of [o-Hb] and the scores on EAT26
or perceived feelings (rho > 0.30, *p < 0.05; rho > 0.41,
**p < 0.01). Table 3 shows the correlation coefficients
on the respective channels. The total EAT 26 scores were
correlated with activations of channels 1-4**, 6**, 7*
(right lateral to ventral PFC), 12*, and 15* (left lateral
PFC). The OC subscale scores were correlated with ac-
tivations of chann els 1*, 2**, 3-4*, 6**, 7* (righ t lateral
to medial PFC), and the diet subscale scores were corre-
lated with activations of channels 1*, 2**, 4*, and 6*
(right PFC). The BE subscale scores were correlated with
activations of channels 1-2*, 3-4**, 5*, 7* (right PFC)
and 12*, 15**, 16* (left lateral PFC). The scores for
perceived disgust were correlated with activations of the
left prefrontal channels (11**, 13**, and 14*), and those
for perceived anxiety were correlated with activations of

T. UEHARA ET AL.
107
Table 3. Correlations between perceived feelings or EAT and NIRS activations.
ch1 ch2 ch3 ch4 ch5 ch6ch7ch8ch9ch10ch11ch12 ch13 ch14 ch15ch16
r 0.08 –0.03 0.15 0.16 0.06 0.130.200.110.080.020.260.02 0.317* 0.23 0.090.04
anxiety p 0.60 0.82 0.32 0.30 0.70 0.410.190.470.620.900.080.87 0.03 0.12 0.570.77
r 0.25 0.18 0.17 0.24 0.06 0.160.230.000.150.030.403** 0.11 0.421** 0.321* –0.07 –0.10
disdust p 0.10 0.25 0.26 0.11 0.71 0.310.130.990.320.850.010.46 0.00 0.03 0.640.50
r 0.487** 0.632** 0.409** 0.422** 0.29 0.461** 0.319*0.17 0.13 0.200.280.376* 0.22 0.21 0.354*0.16
EAT total p 0.00 0.00 0.01 0.00 0.05 0.000.030.250.390.180.060.01 0.14 0.17 0.020.28
r 0.348* 0.424** 0.23 0.313* 0.10 0.297*0.110.110.070.150.230.25 0.13 0.19 0.170.03
diet p 0.02 0.00 0.13 0.04 0.51 0.050.460.490.640.320.120.10 0.38 0.20 0.260.87
r 0.324* 0.462** 0.315* 0.28 0.23 0.425** 0.295*–0.01–0.010.050.230.29 0.18 0.21 0.230.14
oral
control p 0.03 0.00 0.04 0.06 0.13 0.000.050.930.950.750.130.05 0.25 0.17 0.130.37
r 0.313* 0.342* 0.429** 0.381** 0.365* 0.28 0.367*0.29 0.16 0.17 0.140.344* 0.21 0.09 0.439**0.313*
binge
eating p 0.04 0.02 0.00 0.01 0.01 0.060.010.060.300.270.370.02 0.17 0.57 0.000.04
r 0.07 0.03 –0.19 –0.14 –0.12 –0.13–0.20–0.030.02–0.09–0.21–0.11 –0.35 –0.14 –0.367*–0.33
b
ody mass
index p 0.70 0.89 0.30 0.48 0.52 0.510.300.850.930.640.270.58 0.06 0.47 0.050.08
Spearman’s correlation coefficients, **; p < 0.05, *; p < 0.05 ch: channel.
channel 13*.
4. Discussion
The present study attempted to estimate the prefrontal
activations induced by visual stimuli consisting of the
body forms of anorexia patients, using noninvasive and
simple-to-perform functional brain imaging. Importantly,
the subjects were asked to compare the abnormally thin
forms presented with their own body image, thereby
making the presentations emotionally relevant. This vis-
ual task was associated with widespread and fluctuating
activations, but predominantly of the right PFC. This
finding was in agreement with the results of previous
studies, for instance, the fMRI study by Sachdev and col-
leagues [10] which indicated that non-self-images in-
duced activations in the medial inferior frontal gyrus
(BA46), and the fMRI study in which females without
eating disorder (ED) presented with pictures of slim
models showed activations in the right PFC and the infe-
rior frontal pole (BA9) [6].
Interestingly, obvious gender differences were ob-
served in the widespread PFC activations, and male stu-
dents showed significantly stronger activations bilater-
ally in the PFCs than females. There are a number of
possible explanations for this gender difference. If bilat-
eral activation of the PFC indicates more focused atten-
tion and active processing of the images, males seem to
have been more actively engaged with these images. On
the other hand, women were either not so engaged or in
fact actively suppressed the processing of the images
which they were visualizin g. Female participan ts showed
deactivations in the right lower ventral PFC, which may
indicated active suppression during body image process-
ing in females. In fact, the rostral PFC is involved in the
processing of conflicting and salient stimuli and top-down
inhibition of amygdala activity [27]. It might be inter-
preted as suggesting that deactivation of the right PFC
(maybe rostral) in healthy young women reflects the sen-
sitivity to negative body forms by self-comparison. An-
other possible explanation is that the body image of
women has changed toward thinness to such an extent
that they did not find the stimuli too discordant from
their own bodies, and thereby showed low interest and
activation. Men, on the other hand, still found these fe-
male forms to be pathologically thin.
The other interesting findings from the present study
were specific correlations between the PFC activations
and the scores on EAT and for perceived feelings. In
students with higher diet or oral control scores, presenta-
tion of thinner images produced activations in the right
PFC. On the other hand, students with higher food pre-
occupation scores showed bilateral activations along the
left PFC. In contrast, students with higher perceived
feelings (disgust or anxiety) when presented with thinn er
others’ images showed only left PFC activations. These
findings suggest specific links between the respective
eating patterns and the laterality of PFC activation. In

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our related study on eating disorders [28], we found
functional relationships between eating psychopathology
and brain activations and concluded that the clinical
symptoms of ED consisted of 2 components: dieting
tendency, correlated with th e righ t fronto tempor al co rtex ,
and eating behavior problems (including binge eating)
correlated with the left orbitofrontal cortex. Linkage of
left PFC activation to fear of fatness or desire for thin-
ness, and linkage of right PFC activation to bulimia
might also be a continuum in healthy subjects. The exis-
tence of relationships between body images, eating atti-
tudes, emotion and PFC activations might be speculated
to indicate specific ED-related neural mechanisms. In
addition, Friederich et al. [6] commented that individual
differences in anxiety ratings in response to presentation
of body shape models were positively associated with
activations of the fear network, including in the rostral
PFC and the inferior lateral PFC. According to that au-
thor, activation of the ventrolateral PFC (BA 47) is in-
volved in anxiety processing [29] and anger [30]. An-
other study suggests that ventrolateral PFC activation
reflects the intensity dimension of emotional perception
rather than the processing of discrete emotions [31]. The
present study also found that females showed signifi-
cantly higher activations only in the left lateral PFC
(channel 16) as compared to males, and this may be re-
lated to gender-specific emotional status and processing.
Some limitations of this study should be noted. We
used NIRS for studying brain activ ation, unlike previous
studies that have mostly used BOLD fMRI. When ap-
plying NIRS to the clinical setting, relative changes of
cerebral hemoglobin data should be cautiously inter-
preted as these are not absolute values. Also, anatomical
identification of the measurement points is one of the
major methodological challenges in multichannel NIRS;
however, a virtual registration method has been devel-
oped recently to improve placement and detection [32].
The spatial resolution in NIRS ranges from 2 to 3 cm;
therefore, it is possible to discuss functional dynamism in
the context of broader frontal connectivity. NIRS does
offer high time resolution, which can contribute to clari-
fication of the networking or circuit by allowing analyses
of the time-course and pattern of activations. NIRS, as
used in our study, is limited to the examination of the
frontal lobes and we therefore cannot comment on other
brain regions involved in these processes, which is an-
other limitation of our work. The study of body image
processing can benefit from many paradigms. Most pre-
vious studies have focused on self-images, distorted thin
images, or estimating body weights. It may be reasona-
bly expected that the extrastriate body area, which is
located in the lateral occipitotemporal cortex, must re-
spond to visual images of human bodies and body parts
[33]. In fact, one study suggested the importance of the
right parietal cortex in developing and maintaining body
representation, as well as in the pathogenesis of anorexia
[34]. This study used a frontal-sp ecific metho dology, and
should therefore be complemented with whole-brain mea-
surement. Considering the importance of body image
disturbance, especially for the mental health of women,
and the interest in the neurobiological basis of gender dif-
ferences in EDs, the clinic al impl icat ions o f these fin din g s
should be investigated. Recently, Miyake et al. [35] re-
ported significant activation of the occipitotemporal cor-
tex, right parietal cortex, and DLPFC (BA 9) in restrict-
ing AN using the other fat-image task versus the other
real-image task. The occipitotemporal and right parietal
cortices were significantly activated in the binge eat-
ing/purging type of AN (AN-BP) and control subjects,
whereas in the bulimia nervosa (BN) patients, the right
occipital (BA 18) and right parietal lobes were signifi-
cantly activated. Their series of studies used negative
word stimuli concerning body, and the left medial PFC
was activated both in patients with BN and in patients
with AN-BP [36].
The other limitations of this study should be noticed.
Female participants were over two folds than male par-
ticipants in this study; therefore, it must be careful to
generalize this finding. And people in different cultures
may have different response to pathologically thin fe-
male body forms. Actually regarding psychopathology of
eating disorders, thinner body image could be common
within various Westernized countries. But it should be
mentioned that gender difference may not be observed
for people in alternative areas.
In conclusion, gender differences in frontal activation
suggest differential processing by men and women of
pathologically thin female body forms. The study also
suggests that anorexic psychopathology may be associ-
ated with abnormal right frontal activations while view-
ing the thin body forms of others.
5. Acknowledgements
This study was supported by the Intramural Research
Grant (20-1) for Neurological and Psychiatric Disorders
of NCNP, Japan. The summary of this study have been
presented at a national meeting (33rd Japanese Society of
Biological Psychiatry in Tokyo, 2011) and will be on an
international conferen ce (17th Eating Disorders Research
Society in Edinburgh, 2011).
6. References
[1] R. Uher, T. Murphy, H. C. Friederich, T. Dalgleish, M. J.
Brammer, V. Giampietro, M. L. Phillips, C. M. Andrew,

T. UEHARA ET AL.
109
V. W. Ng, S. C. Williams, I. C. Campbell and J. Treasure,
“Functional Neuroanatomy of Body Shape Perception In
Healthy and Eating-Disordered Women,” Biological Psy-
chiatry, Vol. 58, No. 12, 2005, pp. 990-997.
doi:10.1016/j.biopsych.2005.06.001
[2] A. Wagner, M. Ruf, D. F. Braus and M. H. Schmidt,
“Neuronal Activity Changes and Body Image Distortion
in Anorexia Nervosa,” NeuroReport, Vol. 14, 2003, pp.
2193-2197. doi:10.1097/00001756-200312020-00012
[3] J. Schwoebel and H. B. Coslett, “Evidence for Multiple,
Distinct Representations of the Human Body,” Journal of
Cognitive Neuroscience, Vol. 17, No. 4, 2005, pp. 543-
553. doi:10.1162/0898929053467587
[4] N. Shirao, Y. Okamoto, T. Mantani, Y. Okamoto and S.
Yamawaki, “Gender Differences in Brain Activity Gen-
erated by Unpleasant Word Stimuli Concerning Body Im-
age: An fMRI Study,” British Journal of Psychiatry, Vol.
186, 2005, pp. 48-53. doi:10.1192/bjp.186.1.48
[5] H. M. Mohr, J. Zimmermann, C. Röder, C. Lenz, G.
Overbeck and R. Grabhorn, “Separating Two Compo-
nents of Body Image in Anorexia Nervosa Using fMRI,”
Psychological Medicine, Vol. 40, 2010, pp. 1519-1529.
doi:10.1017/S0033291709991826
[6] H. C. Friederich, R. Uher, S. Brooks, V. Giampietro, M.
Brammer, S. C. Williams, W. Herzog, J. Treasure and I.
C. Campbell, “I’m Not as Slim as That Girl: Neural Bases
of Body Shape Self-Comparison to Media Images,” Neuro-
Image, Vol. 37, No. 2, 2007, pp. 674-681.
doi:10.1016/j.neuroimage.2007.05.039
[7] M. Kurosaki, N. Shirao, H. Yamashita, Y. Okamoto and
S. Yamawaki, “Distorted Images of One’s Own Body
Activates the Prefrontal Cortex and Limbic/Paralimbic
System in Young Women: A Functional Magnetic Reso-
nance Imaging Study,” Biological Psychiatry, Vol. 59,
No. 4, 2006, pp. 380-386.
doi:10.1016/j.biopsych.2005.06.039
[8] P. Fossati, S. J. Hevenor, S. J. Graham, C. Grady, M. L.
Keightley, F. Craik and H. Mayberg, “In Search of the
Emotional Self: An FMRI Study Using Positive and Nega-
tive Emotional Words,” American Journal of Psychiatry,
Vol. 160, No. 11, 2003, pp. 1938-1945.
doi:10.1176/appi.ajp.160.11.1938
[9] K. N. Ochsner, K. Knierim, D. H. Ludlow, J. Hanelin, T.
Ramachandran, G. Glover and S. C. Mackey, “Reflecting
upon Feelings: An fMRI Study of Neural Systems Sup-
porting the Attribution of Emotion to Self and Other,”
Journal of Cognitive Neuroscience, Vol. 16, No. 10, 2004,
pp. 1746-1772. doi:10.1162/0898929042947829
[10] P. Sachdev, N. Mondraty, W. Wen and K. Guilford,
“Brains of Anorexia Nervosa Patients Process Self-Images
Differently from Non-Self-Images: An fMRI Study,” Nu-
eropsyhologia, Vol. 46, 2008, pp. 2161-2168.
[11] D. A. Boas, A. M. Dale and M. A. Franceschini, “Diffuse
Optical Imaging of Brain Activation: Approaches to Op-
timizing Image Sensitivity, Resolution, and Accuracy,”
Neuroimage, Vol. 23, Supplement 1, 2004, pp. 275-288.
doi:10.1016/j.neuroimage.2004.07.011
[12] M. Suda, M. Fukuda, T. Sato, S. Iwata, M. Song, M.
Kameyama and M. Mikuni, “Subjective Feeling of Psy-
chological Fatigue Is Related to Decreased Reactivity in
Ventrolateral Prefrontal Cortex,” Brain Research, Vol.
1252, Supplement 1, 2009, pp. 152-160.
doi:10.1016/j.brainres.2008.11.077
[13] S. Grignon, K. Forget, M. Durand and T. Huppert, “In-
creased Left Prefrontal Activation During Staring/Mutism
Episodes in a Patient with Resistant Catatonic Schizo-
phrenia: A Near Infrared Spectroscopy Study,” Cognitive
Behavior Neurology, Vol. 21, No. 1, 2008, pp. 41-45.
doi:10.1097/WNN.0b013e3181684d87
[14] R. Takizawa, K.Kasai, Y. Kawakubo, K. Marumo, S. Ka-
wasaki and H. Yamasue, “Reduced Frontopolar Activation
during Verbal Fluency Task in Schizophrenia: A Mul-
tichannel Near-Infrared Spectroscopy Study,” Schizophre-
nia Research, Vol. 99, 2008, pp. 250-262.
doi:10.1016/j.schres.2007.10.025
[15] K. Matsuo, Y. Onodera, T. Hamamoto, K. Muraki, N. Kato
and T. Kato, “Hypofrontality and Microvascular Dysregu-
lation in Remitted Late-Onset Depression Assessed by
Functional Near-Infrare d Spectroscopy,” Neuroimage, Vol.
26, No. 1, 2005, pp. 234-242.
doi:10.1016/j.neuroimage.2005.01.024
[16] A. C. Ehlis, C. G. Bähne, C. P. Jacob, M. J. Herrmann and
A. J. Fallgatter, “Reduced Lateral Prefrontal Activation in
Adult Patients with Attention-Deficit/Hyperactivity Dis-
order (ADHD) during a Working Memory Task: A Func-
tional Near-Infrared Spectroscopy (fNIRS) Study,” Jour-
nal of Psychiatric Research, Vol. 42, No. 3, 2008, pp.
1060-1067. doi:10.1016/j.jpsychires.2007.11.011
[17] M. Suda, Y. Takei, Y. Aoyama, K. Narita, T. Sato, M.
Fukuda and M. Mikuni, “Frontopolar Activation During
Face-to-Face Conversation: An in Situ Study Using
Near-Infrared Spectroscopy,” Neuropsychologia, Vol. 48,
No. 2, 2010a, pp. 441-447.
doi:10.1016/j.neuropsychologia.2009.09.036
[18] M. Suda, T. Sato, M. Kameyama, M. Ito, T. Suto, Y.
Yamagishi, T. Uehara, M. Fukuda and M. Mikuni, “De-
creased Cortical Reactivity Underlies Subjective Daytime
Light Sleepiness in Healthy Subjects: A Multichannel
Near-Infrared Spectroscopy Study,” Neuroscience Re-
search, Vol. 60, No. 3, 2008, pp. 319-326.
doi:10.1016/j.neures.2007.12.001
[19] T. Sato, M. Ito, T. Suto, M. Kameyama, M. Suda, Y.
Yamagishi, A. Ohshima, T. Uehara, M. Fukuda and M.
Mikuni, “Time Courses of Brain Activation and Their Im-
plications for Functio n: A M ultichannel Near-Infrared Spec-
troscopy Study during Finger Tapping,” Neuroscience Re-
search, Vol. 58, No. 3, 2007, pp. 297-304.
doi:10.1016/j.neures.2007.03.014
[20] E. Ohmae, Y. Ouchi, M. Oda, T. Suzuki, S. Nobesawa, T.
Kanno, E. Yos hikawa, M. F utatsubashi, Y. Ueda, H. Okada
and Y. Yamashit a, “Cerebral Hemody namics Evaluati on by
Near-Infrared Time-Resolved Spectroscopy: Correlation
with Simultaneous Positron Emission Tomography Mea-
surements,” Neuroimage, Vol. 29, No. 3, 2006, pp. 697-
705.
[21] V. Toronov, A. Webb, J. H. Choi, M. Wolf, A. Michalos,
E. Gratton and D. Hueber, “Investigation of Human Brain

T. UEHARA ET AL.
110
Hemodynamics by Simultaneous Near-Infrared Spec-
troscopy and Functional Magnetic Resonance Imaging,”
Medical Physiology, Vol. 28, No. 4, 2001, pp. 521-527.
doi:10.1118/1.1354627
[22] D. J. Mehagnoul-Schipper, B. F. van der Kallen, W. N.
Colier, M. C. van der Sluijs, L. J. van Erning, H. O. Thi-
jssen, B. Oeseburg, W. H. Hoefnagels and R. W. Jansen,
“Simultaneous Measurements of Cerebral Oxygenation
Changes During Brain Activation by Near-Infrared Spec-
troscopy and Functional Magnetic Resonance Imaging in
Healthy Young and Elderly Subjects,” Human Brain
Mapping, Vol. 16, No. 1, 2002, pp. 14-23.
doi:10.1002/hbm.10026
[23] D. M. Garner, M. P. Olmsted, Y. Bohr and P. E.
Garfinkel, “The Eating Attitudes Test: Psychometric Fea-
tures and Clinical Correlates,” Psychological Medicine,
Vol. 12, No. 4, 1982, pp. 871-878.
doi:10.1017/S0033291700049163
[24] K. Nakamura, Y. Hoshino, A. Watanabe, K. Honda, S.
Niwa, K. Tominaga, S. Shimai and M. Yamamoto, “Eat-
ing Problems in Female Japanese High School Students: A
Prevalence Study,” International Journal of Eating Dis-
orders, Vol. 26, No. 1, 1999, pp. 91-95.
doi:10.1002/(SICI)1098-108X(199907)26:1<91::AID-EA
T12>3.0.CO;2-M
[25] M. Kameyama, M. Fukuda, Y. Yamagishi, T. Sato, T.
Uehara, M. Ito and M. Mikuni, “Frontal Lobe Function in
Bipolar Disorder: A Multichannel Near-Infrared Spec-
troscopy Study,” Neuroimage, Vol. 29, No. 1, 2006, pp.
172-184. doi:10.1016/j.neuroimage.2005.07.025
[26] T. Suto, M. Fukuda, M. Ito, T. Uehara and M. Mikuni,
“Multichannel Near-Infrared Spectroscopy in Depression
and Schizophrenia: Cognitive Brain Activation Study,”
Biological Psychiatry, Vol. 55, No. 5, 2005, pp. 501-511.
doi:10.1016/j.biopsych.2003.09.008
[27] A. Etkin, T. Egner, D. M. Peraza, E. R. Kandel and J.
Hirsch, “Resolving Emotional Conflict: A Role for the
Rostral Anterior Cingulate Cortex in Modulating Activity
in the Amygdale,” Neuron, Vol. 51, No. 6, 2006, pp. 871-
882. doi:10.1016/j.neuron.2006.07.029
[28] M. Suda, T. Uehara, M. Fukuda, T. Sato, M. Kameyama,
and M. Mikuni, “Dieting Tendency and Eating Behavior
Problems in Eating Disorder Correlate with Right Fron-
totemporal and Left Orbitofrontal Cortex: A Near-Infra-
red Spectroscopy Study,” Journal of Psychiatry Research,
Vol. 44, No. 8, 2010b, pp. 547-555.
doi:10.1016/j.jpsychires.2009.11.005
[29] T. A. Kimbrell, M. S. George, P. I. Parekh, T. A. Ketter,
D. M. Podell, A. L. Danielson, J. D. Repella, B. E. Ben-
son, M. W. Willis, P. Herscovitch and R. M. Post, “Re-
gional Brain Activity During Transient Selfinduced An-
xiety and Anger in Healthy Adults,” Biological Psychia-
try, Vol. 46, No. 4, 1999, pp. 454-465.
doi:10.1016/S0006-3223(99)00103-1
[30] D. D. Dougherty, L. M. Shin, N. M. Alpert, R. K. Pitman,
S. P. Orr, M. Lasko, M. L. Ma cklin, A. J. Fischman, S. L.
Rauch, “Anger in Healthy Men: A Pet Study Using
Script-Driven Imagery,” Biological Psychiatry, Vol. 46,
No. 4, 1999, pp. 466-472.
doi:10.1016/S0006-3223(99)00063-3
[31] S. Grimm, C. F. Schmidt, F. Bermpohl, A. Heinzel, Y.
Dahlem, M. Wyss, D. Hell, P. Boesiger, H. Boeker and G.
Northoff, “Segregated Neural Representation of Distinct
Emotion Dimensions in the Prefrontal Cortex—An fMRI
Study,” Neuroimage, Vol. 30, No. 1, 2006, pp. 325-340.
doi:10.1016/j.neuroimage.2005.09.006
[32] D. Tsuzuki, V. Jurcak, A. K. Singh, M. Okamoto, E. Wa-
tanabe and I. Dan, “Virtual Spatial Registration of Stand-
Alone fNIRS Data to MNI Space,” Neuroimage, Vol. 34,
No. 4, 2007, pp. 1506-1518.
doi:10.1016/j.neuroimage.2006.10.043
[33] P. E. Downing, Y. Jiang, M. Shuman and N. Kanwisher,
“A Cortical Area Selective for Visual Processing of the
Human Body,” Science, Vol. 293, No. 5539, 2001, pp.
2470-2473. doi:10.1126/science.1063414
[34] D. Nico, E. Daprati, N. Nighoghossian, E. Carrier, J. R.
Duhamel and A. Sirigu, “The Role of the Right Parietal
Lobe in Anorexia Nervosa,” Psychological Medicine, Vol.
40, No. 9, 2010, pp. 1531-1539.
doi:10.1017/S0033291709991851
[35] Y. Miyake, Y. Okamoto, K. Onoda, M. Kurosaki, N.
Shirao, Y. Okamoto and S. Yamawaki, “Brain Activation
during the Perception of Distorted Body Images in Eating
Disorders,” Psychiatry Research, Vol. 181, No. 3, 2010a,
pp. 183-192. doi:10.1016/j.pscychresns.2009.09.001
[36] Y. Miyake, Y. Okamoto, K. Onoda, N. Shirao, Y. Oka-
moto, Y. Otagaki and S. Yamawaki, “Neural Processing
of Negative Word Stimuli Concerning Body Image in Pa-
tients with Eating Disorders: An fMRI Study,” Neuroi-
mage, Vol. 50, No. 3, 2010b, pp. 1333-1339.
doi:10.1016/j.neuroimage.2009.12.095