Motomura et al.
Journal of Physiological Anthropology(2023) 42:18
https://doi.org/10.1186/s40101-023-00336-1

ORIGINAL ARTICLE

Journal of
Physiological Anthropology

Open Access

Alexithymia characteristics are associatedwith salience network activity in healthy
participants: an arterial spin labeling study
Yuki Motomura1*, Ayaka Fukuzaki2, Sanami Eto3, Naoki Hirabayashi3, Motoharu Gondo4, Satoshi Izuno3,5,
Osamu Togao6, Koji Yamashita6, Kazufumi Kikuchi6, Nobuyuki Sudo3 and Kazufumi Yoshihara3

Abstract
Background Alexithymia, a personality trait characterized by difficulties in identifying and expressing their emotions
despite having a range of emotional experiences, can impact individuals’ stress coping mechanisms. While manystudies have investigated brain functions associated with specific tasks in relation to emotion processing, research
focusing on resting-state brain functions has been limited. Thus, the aim of this study was to investigate the relationship between alexithymia and brain function by analyzing arterial spin labeling (ASL) data obtained during the restingstate.
Methods A brain structural and functional imaging study was conducted on 42 healthy adult men and womenusing ASL and the 20-item Toronto Alexithymia Scale (TAS-20) questionnaire survey. Cerebral blood flow and functional connectivity values were calculated for regions of interest in the default mode network, saliency network,and central executive network from the ASL data. Correlation analysis was performed with TAS20 scores, and partial
correlation analysis was conducted to control for anxiety and depression.
Results The functional connectivity analysis revealed a negative correlation between the functional connectivityof the right insular cortex and left anterior cingulate cortex and the total score of TAS, as well as difficulty identifying
feelings and difficulty describing feeling subscores, indicating that the higher the scores, the weaker the functional
connectivity between these regions (T = -3.830, p = 0.0013, R = -0.5180). This correlation remained significant evenafter controlling for anxiety and depression using partial correlation analysis.
Conclusion The present study revealed differences in the activity of the Saliency Network at rest as measured
by ASL, which were independent of anxiety and depression, and varied depending on the severity of alexithymia. Thisfunctional change may underlie the neural basis of decreased emotional processing observed in alexithymia. Thesefindings may contribute to the elucidation of the neural mechanisms of alexithymia, which can lead to social impairments, and suggest the usefulness of ASL measurement as a biomarker of alexithymia.
Keywords Alexithymia, Arterial spin labeling, Anterior cingulate cortex, Insular cortex, Salience network, fMRI

*Correspondence:
Yuki Motomura
motomura@design.kyushu-u.ac.jp
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Motomura et al. Journal of Physiological Anthropology(2023) 42:18

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Background
Alexithymia is a personality trait characterized by individuals having emotions such as joy, anger, and sadness,but having difficulty recognizing and expressing these
emotions. It is considered as one of the personality
traits related to emotional processing that has individual differences and is present in the general population[1]. Using the cutoff value of the Toronto Alexithymia
Scale, a questionnaire for alexithymia, it has been foundthat individuals with a high tendency for alexithymia exist to some extent in the general population, at
approximately 10.3% [2]. Recent studies have reported
on the relationship between problems in interpersonal
relationships, empathy deficiency, and healthy individuals. For example, people with a high tendency foralexithymia have weak affection and connection to others, tend to take the initiative in relationships with others, and have difficulty dealing with social problems [3].
It has also been reported that people with a high tendency for alexithymia evaluate pain significantly lower
in response to tasks that present others’ pain images
and have low scores on scales evaluating “Seeing fromanother person’s perspective” and “empathic concern”
in questionnaires measuring capacity for empathy [4].
Such obstacles related to interpersonal relationships
and empathy are thought to have negative effects on
daily life.
Conversely, alexithymia is deeply related to mental disorders, and previous studies have shown that approximately 45% of people with major depressive disorder(MDD) has alexithymia [5], suggesting that impaired
emotional processing is also related to the onset of physical and mental symptoms [6]. It is known that patientswith mental and physical disorders accompanied by alexithymia are resistant to psychotherapy [7].
In recent years, brain function studies based on cognitive neuroscience have been attracting attention forelucidating the neural mechanisms of alexithymia [8,9]. Studies using mainly functional magnetic resonance imaging (fMRI) have been conducted. In a study
by Berthoz et al., a decrease in activity was observed in
the left anterior cingulate gyrus and prefrontal cortex
in response to negative emotional pictures presented
using the International Affective Picture System in thealexithymia group [10]. In a study that presented facial
expressions of sadness unconsciously, individuals withhigh alexithymia tendencies showed decreased activity inseveral brain regions, including the left amygdala, insula,and superior temporal gyrus [11]. Additionally, differences in the activity of the anterior cingulate cortex wereobserved in response to different types of visual emotional stimuli and emotional valence between individuals
with and without alexithymia [12].

This study focuses on the activity of the resting state
network (RSN) in individuals with alexithymia. RSNs
have been shown to exist in various functions, and each
RSN autonomously processes information even during rest and in the unconscious state. Due to the easeof measurement, investigating RSNs as a biomarker
has been conducted in diseases such as MDD [13] andneurotic overeating [14]. In a previous study, the functional connectivity between 21 emotion-related brain
regions was evaluated as regions of interest (ROI) in
resting-state fMRI studies of alexithymia. It was foundthat alexithymia patients had weak functional connectivity in the left amygdala basal nucleus and the left
superior temporal gyrus, as well as in the functional
connectivity between the left amygdala basal nucleus
and the supplementary motor area (SMA) compared
to non-alexithymic patients [15]. Furthermore, a study
that examined the functional and structural connectivity between brain regions, with intergroup differences in the amplitude of low-frequency fluctuation (anindex that evaluates the amplitude of low-frequency
oscillations thought to reflect neural activity in the
blood-oxygen-level-dependent [BOLD] signal), founddifferences in functional connectivity in regions such as
the right inferior temporal gyrus to the middle occipital gyrus, left inferior temporal gyrus to the insula, andboth anterior cingulate cortices to the frontal pole in
the alexithymia group [16].
Thus, given the correlation between alexithymia
and mental illness, as well as the lack of empathy, it
is considered useful to investigate resting-state brain
function for understanding the neural mechanisms
underlying this disorder. However, there have beenfew studies on this topic. Therefore, this study utilized arterial spin labeling (ASL), an imaging technique that uses magnetic resonance imaging (MRI) toobtain perfusion images, to investigate resting-state
brain activity. ASL can non-invasively quantify cerebral blood flow (CBF), which is believed to reflect
local neural activity and is related to local oxygen consumption and glucose metabolism. ASL is less susceptible to low-frequency noise than functional MRI and
can quantitatively measure resting-state brain activity.
CBF is a typical parameter obtained from perfusion
images, representing blood flow in tissue. There havebeen few studies using ASL to investigate the RSN in
alexithymia. Therefore, this study aims to examine therelationship between alexithymia and brain function
by analyzing ASL data obtained during resting-statescans. Specifically, this study investigated the relationship between the alexithymia questionnaire score andthe mean CBF value, as well as the functional connectivity between each RSN.

Motomura et al. Journal of Physiological Anthropology(2023) 42:18

Methods
Analysis data

The data analyzed in this study were obtained froman experiment conducted to investigate the integrated
effects of nutritional status on the mind and body ofpatients with eating disorders. Only the data from participants measured as healthy controls for the eating
disorder patient group were used in the analysis, whiledata from the actual patient group were excluded.
Participants

The participants in the ASL experiment were
49 healthy men and women aged 20 to 56 years(31.75 ± 10.17 years) (16 men and 33 women). Participants were selected for the study based on the following criteria: 1. They were healthy right-handed men
and women aged 15 to 69 years; 2. their body massindex (body mass index = weight [kg]/height [­m]2) was
between 18.5 and 25; 3. they had no diagnosis or suspicion of mental or physical illness; 4. they did not haveany metal implants such as pacemakers; 5. they did
not have any eye diseases including color vision abnormalities; 6. they had no symptoms of claustrophobia; 7.
they were not pregnant or had no possibility of being
pregnant; 8. the study doctor deemed the participant
suitable for the study. Participants who satisfied these
conditions participated in this experiment.
Experimental procedures

The study was conducted from April 2013 to February
2019 at the Department of Psychosomatic Medicine,
Kyushu University Hospital, and the Department of
Psychosomatic Medicine, Kyushu University Graduate
School of Medical Sciences. The research was approvedby the research ethics committee at Kyushu University.
The participants underwent various tests within 7 days,
which included blood biochemistry tests (fasting blood
sampling), brain structural imaging tests (MRI, Diffusion
Tensor Imaging), brain functional imaging tests (fMRI,
Magnetoencephalogram, and ASL), electroencephalography, autonomic nervous system function tests, bodycomposition tests, gastrointestinal transit tests, basal
metabolism, and psychological tests. Participants were
instructed to keep their head still, not to fall asleep, tokeep their eyes closed except when instructed, and tofollow other similar instructions during the imaging
tests, which took approximately 30 min each. Several
questionnaires, such as the 20-item Toronto Alexithymia
Scale (TAS-20) and the State-Trait Anxiety Inventory(STAI), were administered for the psychological tests.

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MRI measurement environment

The MRI scans were conducted using a 3.0 T scanner(PHILIPS Achieva, the Netherlands) and the settings
for the acquisition of functional (ASL) and structuralbrain images were as follows:
ASL image acquisition
The whole-brain perfusion image was acquired
using pseudo-continuous arterial spin labeling. The
imaging parameters for the pseudo-continuous arterial spin labeling perfusion imaging were as follows:
2D single-shot gradient-echo echo planar imaging(EPI) in combination with parallel imaging (sensitivity encoding factor 2.0); TR/TE, 4200/8.56 ms;
matrix, 64 × 64; FOV, 240 × 240 mm; in-plane resolution, 3.75 × 3.75 mm; 20 slices acquired in ascendingorder; slice thickness, 6 mm; slice gap, 1 mm; labeling duration, 1650 ms; post-labeling delay (PLD),
2000 ms. Thirty-two pairs of control/label images
were acquired and then averaged. The total scan duration for both control and label imaging was 4.72 min.
Structural image acquisition
To obtain spatial reference images for analysis, T1-weighted MRI was performed using a
T1-weighted magnetization-prepared rapid gradientecho sequence. The imaging parameters were as follows: TR = 7.0 s, TE = 3.2 ms, slice thickness = 1 mm,
flip angle = 9°, voxel size = 1 mm × 1 mm × 1 mm, andtotal scan time = 6.52 min.
Questionnaires

Below are the details on the questionnaires used in thisstudy.
TAS‑20

TAS-20 is a self-administered questionnaire consistingof 20 items used to evaluate alexithymia. It was originallycreated by Taylor et al. [17] and is considered to be oneof the useful self-report assessment scales worldwide [18].
TAS-20 consists of three subscales: difficulty identifying feelings (DIF), difficulty describing feelings (DDF),
and externally oriented thinking (EOT). In this study,
we used the Japanese version of TAS-20 [18] to conduct
the investigation.
Center for Epidemiologic Studies Depression Scale (CES‑D)

CES-D is a self-administered questionnaire consisting of 20 items used to evaluate depressive states. It
was developed by the US National Institute of Mental

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Health to investigate the distribution and related factors of depressive symptoms in the general population.
In this study, the Japanese version of CES-D [19] wasused for the survey.

each participant. The correlation analysis was performed
for each voxel, which is the smallest unit of a 3D brain
map, and regions of the brain that showed a significant
correlation between alexithymia and CBF were estimatedbased on the results. The average CBF map was calculated using the absolute value of CBF, as we believed thatfactors such as head size may affect the analysis results.
Therefore, in this study, we selected the analysis of covariance option in the global normalization module and performed the analysis by converting the signal of CBF to theratio of the whole brain signal. The significance level was
set at p < 0.05 (family-wise error, peak level correction).

STAI

STAI is a questionnaire consisting of two subscales,
STAI-State and STAI-Trait, each with 20 items for a
total of 40 items, used to evaluate the degree of anxiety. STAI-State measures state anxiety, which refers totransient anxiety that is experienced in a specific situation and changes rapidly, while STAI-Trait measures
trait anxiety, which measures how much anxiety the
respondent generally feels and is not affected by situational factors. In this study, the Japanese version of
the STAI (Nakazato et al., Published by Sankyobo) wasused for the survey.
ASL analysis

The data of three participants who showed abnormalities
in the obtained average CBF map and four participants
who did not provide data on TAS-20 were excluded, andthe data of 42 participants (age, male-to-female ratio)
were used for analysis.
For ASL data analysis, the ASLtbx tool (https://​www.​cfn.​upenn.​edu/​zewang/​ASLtbx.​php), SPM12 7771 and its
plugin CONN, and Microsoft Excel (for Office 365) wereused. Additional analyses were conducted using the statistical software R version 4.1.3 and Microsoft Excel (forOffice 365).
Preprocessing of ASL data and creation of CBF maps

Realignment correction was applied to compensate forhead motion during the measurement, and the coregister process was used to match the structural image ofthe participant with the average image obtained by therealignment correction. After performing the smoothing process with a 6-mm full width at half maximum
Gaussian kernel to reduce noise generated by the entire
process, the CBF map was created by measuring thedifference between the control and label images. The
32 obtained CBF maps were then averaged (hereafterreferred to as the mean CBF map) and normalized to thestandard brain of MNI templates. As noise was observedin the obtained mean CBF map, the smoothing process
was repeated. The mean CBF map was used to investigate the relationship between alexithymia and CBF, andthe CBF map was used for ASL functional connectivity
analysis, which will be described later.
The correlation between alexithymia and CBF

Correlation analysis was performed between the average
CBF values and TAS-20 using the average CBF map of

Functional connectivity analysis using ASLThis study focuses on three RSNs based on the “triple
network theory” proposed by Menon: the default mode
network (DMN), the saliency network (SN), and the central executive network (CEN). Menon has proposed thatthe relationship between these three core networks plays
an important role in many neurological disorders (e.g.,chronic pain and depression) [20]. Using these regions
as ROIs, functional connectivity analysis was conductedusing CBF maps.
The method for calculating functional connectivity
involves computing the correlation coefficient between
ASL signals from anatomically distant brain regions, andwhen a significant correlation is observed, functional
connectivity between those regions is recognized. Functional connectivity has two types based on the directionof the connection. Positive functional connectivity represents a state where ASL signals from two distant brain
regions show positive correlation, indicating a relationship where both regions are simultaneously active when
one region is active. Conversely, negative functional connectivity represents a state where ASL signals from two
distant brain regions show negative correlation, indicating a relationship where the activity of one region
decreases when the other region is active.
The CBF maps were preprocessed and created during the data preprocessing and CBF map creation (1.6.2)
through the realignment, coregister, and segmentation
processes. Therefore, in this study, only the normalization and smoothing processes were performed for preprocessing. Subsequently, the denoising process was
conducted using Conn. In this study, ROI-to-ROI analysis was performed by determining the ROIs in advanceand dividing the brain region into arbitrary regions foranalysis.
The ROI-to-ROI analysis is one of the fundamental methods in functional connectivity analysis. It is ahypothesis-driven analysis that evaluates the correlationof brain activities by selecting seed regions, and one of
its advantages is that it provides a clear meaning when

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quantifying functional connectivity. However, as thenumber of ROIs increases, multiple comparison correction is required for all combinations, making it difficult tofind significant results, and caution is needed in selecting
ROIs. In this study, the brain regions that constitute each
RSN (DMN, SN, and CEN) were selected as ROIs, but
to address the issue of multiple comparison correction,
the ROIs were limited to the core regions of each RSN(DMN, SN, and CEN). The selected regions are shown in
Table 1.
The ROIs were created with WFU pick atlas tool
(https://​www.​nitrc.​org/​proje​cts/​wfu_​picka​tlas/) in SPM,
following the automated anatomical labeling (AAL) template (https://​www.​gin.​cnrs.​fr/​en/​tools/​aal/). In cases
where regions (Medial Prefrontal Cortex, Dorsal lateral
Prefrontal Cortex, and Posterior Parietal Cortex) could
not be created using the AAL template, the corresponding areas were selected from the “network” ROI provided
by default in CONN and used in the analysis.
This analysis was conducted in two levels. First, individual-level analysis (1st level analysis) was performed,
where ROI-to-ROI analysis was conducted within
individuals to calculate functional connectivity values
between all selected ROIs. For each functional connectivity value, functional connectivity between each region
within DMN was defined as functional connectivity
within DMN. Similarly, functional connectivity within
SN and CEN was also defined, and these were defined
as functional connectivity within the network. Additionally, the combination of each region in DMN (7) and SN(4) was defined as functional connectivity between DMN
and SN (a total of 28). Functional connectivity between
DMN and CEN and between SN and CEN were also

defined in the same manner, and these were defined asfunctional connectivity between networks. After the individual-level analysis, in the group-level analysis (2nd level
analysis), a test was conducted to determine whether thefunctional connectivity values between the target ROIs
were significantly greater than “0” as a group.

Table 1 ROIs used for functional connectivity analysis
Network

Brain region name

AbbrevDMNMedial Prefrontal Cortex

MPFC

Right Posterior Cingulate Cortex

R.PCC

Left Posterior Cingulate Cortex

L.PCC

Right Precuneus

R.PREC

Left Precuneus

L.PREC

Right Inferior Parietal Lobule

R.IPL

Left Inferior Parietal Lobule

L.IPL

Right Insula

R.INS

Left Insula

L.INS

Right Anterior Cingulate Cortex

R.ACC​Left Anterior Cingulate Cortex

L.ACC​

Right Lateral Prefrontal Cortex

R.LPFC

Left Lateral Prefrontal Cortex

L.LPFC

Right Posterior Parietal Cortex

R.PPC

Left Posterior Parietal Cortex

L.PPC

SN

CEN

Correlation between alexithymia and functional
connectivity

A regression analysis was performed using the total
scores and subscale scores (DIF, DDF, and EOT) of TAS20 for each participant. The regression analysis was
conducted for functional connectivity of the significant
positive or negative functional connections identified
in the ASL functional connectivity analysis at the grouplevel (2nd level analysis) among all ROIs. The significancelevel was set to 5%, and analysis-level false discovery rate(FDR) correction was used for multiple comparisons.
Additional analysis
Investigation of subscales of TAS‑20

To investigate which component of alexithymia isstrongly associated with the areas of functional connectivity with alexithymia that were found to be significant,we conducted a regression analysis using the scores ofeach subscale of TAS-20 (DIF, DDF, and EOT) for eachparticipant, in addition to the analysis of the total TAS
score. We set the significance level at 5% and used analysis-level FDR correction for multiple comparisons.
Partial correlation analysis

There have been reports of a relationship between alexithymia and anxiety or depression. For instance, Lee et al.
[21] revealed a positive correlation between the total
score of TAS-20, DIF, and STAI-S (state anxiety). In a
study conducted with Japanese participants, Kojima et al.
[22] reported a positive correlation between TAS-20 anda depression index. Additionally, studies investigating
functional connectivity at rest in MDD, anxiety disorder, and anxiety traits have reported abnormal functional
connectivity in the DMN in both depression and anxiety[23, 24]. Therefore, even if there is a relationship between
alexithymia and functional connectivity, the relationshipmay be a spurious correlation due to confounding factors such as depression or anxiety. In addition, age andgender have been reported to affect TAS-20 scores [25].
Accordingly, this study conducted additional investigations that considered age, dummy variable of gender,
anxiety (STAI) and depression (CES-D) indices for thesignificant relationship between alexithymia and functional connectivity.
For the analysis method, partial correlation coefficientswere calculated between the scores of TAS-20 (total

Motomura et al. Journal of Physiological Anthropology(2023) 42:18

Page 6 of 13score and DIF score) and the values of functional connectivity, while controlling for the scores of age, gender,
STAI-state, STAI-trait, and CES-D as covariates. The
pcor function in R version 4.1.3 was used to conduct thepartial correlation analysis. The values of functional connectivity were extracted from the individual analysis (1stlevel analysis) of ASL functional connectivity analysis
and used for the analysis. The significance level was set
at 5%. Because the STAI, CES-D data for one participant
was lost due to a technical error, this analysis was performed on 41 persons.

between the right insula and the left anterior cingulate
cortex.

Results
Demographic data

Demographic data for the 42 subjects included in theanalysis are shown below (mean ± SD, 28 female): age,
31.19 ± 9.46; TAS20, 31.19 ± 9.46; TAS20-DIF, 31.19 ± 9.46;
TAS20-DDF, 31.19 ± 9.46; TAS20-EOT, 31.19 ± 9.46;
CES-D, 31.19 ± 9.46; STAI-state, 31.19 ± 9.46; STAI-trait,
31.19 ± 9.46. Four participants scored above the TAS-20
cut-off point (61), with a maximum score of 63. No participant scored above 70, confirming that alexithymia tendencies were generally within the healthy range in the current
group of participants.
Correlation between alexithymia and CBF

The analysis of the correlation between alexithymia and
CBF did not reveal any significant voxel with a significant
relationship.
ASL functional connectivity analysis

Figure 1 shows the results of functional connectivity
analysis in ASL data. ROI-to-ROI analysis was conductedusing core regions that comprise the DMN, SN, and CEN
as ROIs. Positive functional connectivity was observed inmany regions within networks, and negative functional
connectivity was observed between the DMN and SN insome cases.
Correlation between alexithymia and functional
connectivity

The results of the regression analysis concerning the correlation between alexithymia and functional connectivity
are shown in Fig. 2.
Based on the results in Fig. 2, a scatter plot was createdto show the significant functional connections between
brain regions identified by functional connectivity analysis and the total score of TAS-20 with a significant positive or negative correlation (Fig. 3).
The scatter plot revealed a significant negative correlation (T = -3.830, p-FDR = 0.0013, r = -0.5180) between
the total score of TAS-20 and the functional connectivity

TAS‑20 sub‑scale analysis

The DIF and DDF scores of TAS-20 were negatively correlated with the functional connectivity between the rightinsula and the left anterior cingulate cortex (T = -4.004,
p-FDR = 0.0008, r = -0.5349; T = -3.142, p-FDR = 0.0094,
r = -0.4450). Furthermore, the DDF score showed a negative correlation with the functional connectivity between
the right insula and the right anterior cingulate cortex(T = -2.885, p-FDR = 0.0094, r = -0.4150). No significant
correlation was found between EOT score and functional
connectivity.
Scatter plots were created for the results that showed asignificant negative correlation (Figs. 4 and 5).
Partial correlation analysis

Table 2 shows the correlation coefficients before controlling for age, gender, anxiety and depression, while Table 3
shows the partial correlation coefficients when age, gender, STAI-state, STAI-trait, and CES-D scores were used
as control variables in the partial correlation analysis.
After controlling for age, gender, anxiety and depression,a significant negative correlation was found between
the total score of TAS-20 and the functional connectivity between the right insula and left anterior cingulate
cortex (T = -2.2930, p = 0.02858, r = -0.3660). Similarly, asignificant negative correlation was found between the
DIF score and the functional connectivity between theright insula and left anterior cingulate cortex (T = -2.624,
p = 0.01293, r = -0.4103). A significant negative correlation was also found between the DDF score and thefunctional connectivity between the right insula andright anterior cingulate cortex (T = -2.2412, p = 0.03165,
r = -0.3588), and a marginally significant correlation was
found between the DDF score and the functional connectivity between the right insula and left anterior cingulate
cortex (T = -1.7812, p = 0.08380, r = -0.2922).

Discussion
This study investigated the association between alexithymia and brain function by analyzing ASL data obtained
during resting state. Although no significant correlation
was found between CBF measured by ASL and alexithymia, a negative correlation was found between the total
score and scores on the DIF subscale (difficulty identifying feelings) of the TAS-20 and the functional connectivity between the right insula and left anterior cingulate
cortex. Additionally, a negative correlation was foundbetween the DDF subscale score (difficulty describing feelings to others) of the TAS-20 and the functional

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Fig. 1 Results of ASL functional connectivity analysis (group-level analysis). The results of the group analysis (2nd level analysis) are shownin the table, where the presence or absence of functional connectivity between regions is indicated by color (p < 0.05 analysis-level FDR correction).
The numbers in the table represent t-values. The red colors indicate significant positive functional connectivity, the blue colors indicate significant
negative functional connectivity, and the color intensity reflects the strength of the functional connectivity. The uncolored areas indicate
no significant functional connectivity. See Table 1 for abbreviations of ROIs. TAS-20 = The 20-item Toronto Alexithymia Scale; DMN = default mode
network; SN = salience network; CEN = central executive network; FDR = false discovery rate; ROI = region of interest

connectivity between the right insula and right anterior
cingulate cortex.
The insula and anterior cingulate cortex are regionsthat constitute the SN, and it has been found that they
are often co-activated in various situations, includinginteroception, self-recognition, and emotion recognition [26]. Terasawa et al. [27] examined brain activity
during the evaluation of sentences related to emotional
states and bodily conditions (e.g., “I am happy,” “Mypulse is fast”) and found that the right anterior insulaand bilateral anterior cingulate cortex were activated in
the evaluation of the current emotional state. Furthermore, SN is now recognized as part of the emotional network [28]. Previous fMRI studies using emotional stimuli
have reported that the activity of the anterior cingulate
cortex and insula is related to alexithymia [10, 11, 29].
Therefore, based on the fact that the functional connectivity between the insula and anterior cingulate cortex
was weakened as the scores of DIF (difficulty identifying feelings) and DDF (difficulty describing feelings) inMotomura et al. Journal of Physiological Anthropology(2023) 42:18

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Fig. 2 Results of the regression analysis on the relationship between alexithymia and functional connectivity. The table shows the resultsof the regression analysis between the TAS-20 total score and functional connectivity. The numbers in the table represent t-values. The color
indicates the nature of the correlation, with red indicating a significant positive correlation, blue indicating a significant negative correlation,and the intensity of the color representing the strength of the correlation. Blank cells indicate areas where no significant results were foundin the analysis (p < 0.05 analysis-level FDR correction). See Table 1 for abbreviations of ROIs. TAS-20 = The 20-item Toronto Alexithymia Scale;
DMN = default mode network; SN = salience network; CEN = central executive network; FDR = false discovery rate; ROI = region of interest

the present study increased, it can be considered that therelationship between alexithymia and functional connectivity observed in this study is closely related to emotion
processing.
The negative correlation between functional connectivity in the insula-anterior cingulate cortex and alexithymia
was observed, and the insula functions in change perception in internal bodily states and promoting awareness[30]. In contrast, the anterior cingulate cortex has beenreported to be involved in the evaluation, regulation, andcognitive control of emotions [31, 32]. Based on thesefindings, it can be suggested that individuals with a hightendency towards alexithymia may have problems withthe coordination between the insula, which is involvedin the perception of emotions, and the anterior cingulate
cortex, which is involved in emotional control.
Based on the results of this study, the anterior cingulate
cortex was associated with alexithymia on both the left
and right sides, while the insula was only associated withalexithymia on the right side. Several previous studies

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Fig. 3 Correlation between alexithymia and functional connectivity between the right insula and the left anterior cingulate cortex. The left sideshows the location of the ROIs on a 3D brain map, while the right side shows the scatter plot. The total score of TAS-20 exhibited a significant
negative correlation with the functional connectivity between the right insula and the left anterior cingulate cortex.(r[40] = 0.52, p = 0.0013, FDR
corrected) FDR = false discovery rate; ROI = region of interest; R.INS = right Insula; L.ACC = left anterior cingulate cortex

Fig. 4 Correlation between subscales of alexithymia and functional connectivity between the right insular cortex and left anterior cingulate
cortex. On the left, a scatter plot shows the correlation between TAS-20 DIF score and the functional connectivity between the right insula
and the left anterior cingulate cortex. On the right, a scatter plot shows the correlation between TAS-20 DDF score and the functional connectivity
between the same regions. Both DIF and DDF scores showed a significant negative correlation with functional connectivity. (DIF: r[40] = 0.53,
p = 0.0008, FDR corrected; DDF: r[40] = 0.45, p = 0.0094, FDR corrected) TAS-20 = The 20-item Toronto Alexithymia Scale; DIF = Difficulty Identifying
Feeling subscale; DDF = Difficulty Describing Feelings subscale; FDR = false discovery rate; ROI = region of interest; R.INS = right insula; L.ACC = left
anterior cingulate cortex

Motomura et al. Journal of Physiological Anthropology(2023) 42:18

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Fig. 5 Correlation between subscales of alexithymia and functional connectivity between the right insular cortex and right anterior cingulate
cortex. The left shows the location of the region of interest (ROI) on a 3D brain map, and the right panel shows a scatter plot. A significant negative
correlation was observed between the DDF score of TAS-20 and the functional connectivity between the right insula and the right anterior
cingulate cortex. (r[40] = 0.42, p = 0.0094, FDR corrected). TAS-20 = The 20-item Toronto Alexithymia Scale; DDF = Difficulty Describing Feelings
subscale; FDR = false discovery rate; ROI = region of interest; R.INS = right insula; R.ACC = right anterior cingulate cortex

Table 2 Relationship between alexithymia and functional connectivity
before controlling for age, gender, anxiety and depression
Functional Connectivity

TAS-20 score
Total Score DIF

Right Insula-left anterior cingulate
cortex-0.5180**

DDF-0.5349*** -0.4450**-0.4150**

Right insula-right anterior cingulate
cortex1)n = 422)*p < 0.05, **p < 0.01, ***p < 0.001

Table 3 Relationship between alexithymia and functional connectivity
after controlling for age, gender, anxiety and depression
Functional Connectivity

TAS-20 score
Total score DIF

Right Insula-left anterior cingulate
cortexRight insula-right anterior cingulate
cortex1)n = 412)† p < 0.1, * p < 0.05

-0.3660*

DDF*-0.4103-0.2922†-0.3588*

have reported that the right insula in particular is involvedin the recognition of emotional states [26, 33, 34]. If theobserved association between alexithymia and functional
connectivity is closely related to emotions, it is possiblethat the right insula plays an important role in emotion
recognition even during a resting-state brain activity.
Prior studies using fMRI data have not demonstrated a
relationship between alexithymia and SN; however, a comparison of functional connectivity between resting-state
fMRI and ASL data has revealed some differences in thesame resting-state brain function data. Specifically, fMRI
showed strong functional connectivity in the posterior
regions of the default mode network (precuneus and bilateral angular gyrus), whereas ASL showed strong functional
connectivity in the medial prefrontal cortex [35], suggesting that different regions are sensitive to each method.
This difference in functional connectivity between fMRI
and ASL has also been confirmed in studies using different analysis methods, such as seed-to-voxel analysis, and
it is believed to be related to differences in MRI imaging
protocols [36]. In this study, ASL showed a relationshipbetween alexithymia and the frontal brain regions, such as
the anterior cingulate cortex and insula. Furthermore, ASL
has lower signal-to-noise ratio compared to fMRI [37],and ASL has less data compared to fMRI, which may haveMotomura et al. Journal of Physiological Anthropology(2023) 42:18

Page 11 of 13

contributed to the differences in the results. The difference
in data may be due to the fact that ASL needs to acquire
two types of images, control and label images, to calculate
the CBF map. Therefore, the differences in the characteristics of the measurement methods between fMRI and ASL
may have influenced the results of this study.
There are several limitations to this study. The first limitation concerns the selection of ROIs for functional connectivity analysis. When ROIs are subdivided into smaller
regions, the results may differ. In this study, ROIs wereselected based on the AAL atlas, without considering thesubregions of each area. For example, the anterior cingulate
cortex can be subdivided into multiple regions that belong
to different networks [38]. Specifically, the ventral anterior cingulate cortex is part of the default mode network(DMN) [39], while the dorsal anterior cingulate cortex is
identified as part of the SN [40]. The posterior cingulate
cortex can also be subdivided into several regions, withthe ventral subregion showing strong functional connectivity with the rest of the DMN, and the dorsal subregion
showing high functional connectivity with the prefrontal
network involved in cognitive control [41]. Therefore, asfunctional roles may differ within a single area, future studies examining functional connectivity within and between
networks should also consider the subregions of each area.
The second limitation of this study pertains to the needfor caution when measuring alexithymia. In this study,
the degree of alexithymia was assessed using the TAS-20
questionnaire. While the TAS-20 is a highly useful evaluation scale worldwide [18], studies have emphasized thelimitations of measuring alexithymia through a questionnaire survey. Specifically, a study that measured alexithymia using both structured interviews and questionnaire
surveys found that although the total scores were correlated, there were discrepancies in the results between
the clinical assessment of emotional recognition and the
DIF score (difficulty in identifying feelings) of the TAS20 [42]. As both the fMRI and ASL analyses in this study
showed a correlation between the DIF score and functional connectivity, the accuracy of evaluating the degreeof difficulty in identifying emotions may have influenced
the analysis results. Thus, in the future, more careful consideration is necessary when measuring alexithymia.
The third limitation is that no assessment of sleepiness
was performed. Since the main aim of this entire project was to compare patients with eating disorders, theevaluation of sleepiness was not included in the protocol. Sleepiness has been shown to affect functional connectivity [43]. Although this study is a comparison in theabsence of sleep-related interventions and therefore is
not expected to have the same impact as previous studies, it is possible that sleepiness at the time of imaging
may have had a small impact.

One of the indicators of brain activity used in thisstudy, functional connectivity, is estimated based on thecorrelation between parameters of neural activity such
as ASL signals, and it is difficult to determine whether
the functional connectivity between two regions is adirect or causative relationship [44]. In the future, investigating other approaches, such as effective connectivity,
which can estimate direct causality between functionallyconnected regions, will be useful for elucidating the neural mechanisms of alexithymia. Furthermore, given theneed for careful consideration in determining whether
changes in functional connectivity are disease-specificor dependent on the state of arousal [45], it is important
to conduct further research in this field to provide morerobust evidence.

Conclusions
We have demonstrated that there are differences in theactivity of the SN during rest, as measured by ASL, thatare independent of anxiety and depression and are basedon the degree of alexithymia. This functional change
may underlie the neural basis of reduced emotion processing observed in alexithymia. These results have thepotential to contribute to the elucidation of the neuralmechanisms of alexithymia, which can lead to social
impairment, and demonstrate the usefulness of ASL
measurements as a biomarker for alexithymia; in particular, based on previous studies showing that impaired
emotional processing is associated with the developmentof psychosomatic and mental disorders [6], and thatpatients with psychosomatic and mental disorders withapraxia are resistant to psychotherapy [7], ASL imaging
as a biomarker may contribute to treatment selection
and differentiation of high-risk groups.
Abbreviations
AAL	Automated anatomical labeling
ASL	Arterial spin labeling
BOLD	Blood-oxygen-level-dependent
CBF	Cerebral blood flow
CEN	Central executive network
CES-D	Center for Epidemiologic Studies Depression Scale
DDF	Difficulty describing feelings
DIF	Difficulty identifying feelings
DMN	Default mode network
EOT	Externally oriented thinking
ET	Echo time
FDR	False discovery rate
fMRI	Functional magnetic resonance imaging
MDD	Major depressive disorder
MRI	Magnetic resonance imaging
ROI	Regions of interest
RSN	Resting state network
SMA	Supplementary motor area
SN	Saliency network
STAI	State-Trait Anxiety InventoryTAS-20	20-Item Toronto Alexithymia Scale
TR	Repetition time

Motomura et al. Journal of Physiological Anthropology(2023) 42:18

Acknowledgements
We appreciate the time and effort spent by our participants. We would like tothank Editage (www.​edita​ge.​com) for the English language editing.
Authors’ contributions
SE, NH, MG, SI and KYo designed the experiment. SE, NH, MG, SI, OT, KK, Kya
and KYo performed the experiment. AF and YM analyzed the data with advice
from KYo. AF and YM wrote the manuscript with useful discussion from all
authors. NS supervised the conduct of this study. All authors have read andapproved the final manuscript.
Funding
This research was supported by the operating expenses for Kyushu University
departments, JSPS Grant-in-Aid for Scientific Research (19K16244), and AMED
under grant number JP19dm0307104.
Availability of data and materials
The datasets generated during and/or analyzed during the current study areavailable from the corresponding author on reasonable request.

Declarations
Ethics approval and consent to participate
This study was approved by the Research Ethics Committee of Kyushu University (Fukuoka, Japan, Approval Number:26–107). Written informed consent
was obtained from all participants.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Human Life Design, Faculty of Design, Kyushu University,
4‑9‑1 Shiobaru, Minamiku, Fukuoka 815‑8540, Japan. 2 Department of Kansei
Science, Graduate School of Integrated Frontier Science, Kyushu University,
4‑9‑1 Shiobaru, Minamiku, Fukuoka 815‑8540, Japan. 3 Department of Psychosomatic Medicine, Graduate School of Medical Sciences, Kyushu University,
Fukuoka, Japan. 4 Department of Psychosomatic Medicine, Kyushu University
Hospital, Fukuoka, Japan. 5 Division of Cerebral Integration, Department
of System Neuroscience, National Institute for Physiological Sciences, Okazaki,
Japan. 6 Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Received: 29 March 2023 Accepted: 3 August 2023

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