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\begin{document}

\title{Anxiety symptoms without depression are associated with cognitive
control network (CNN) dysfunction: an fNIRS study}



\author[1]{Qinqin Zhao}%
\author[2]{Zheng Wang}%
\author[3]{Caihong Yang}%
\author[4]{Han Chen}%
\author[2]{yan zhang}%
\author[2]{Irum Zeb}%
\author[5]{Pu Wang}%
\author[2]{Huifen Wu}%
\author[2]{Qiang Xiao}%
\author[2]{Fang  Xu}%
\author[2]{Yueran Bian}%
\author[2]{Nian Xiang}%
\author[2]{Min Qiu}%
\affil[1]{Dean’s Office, MianYang Teachers’ College, Mianyang, China}%
\affil[2]{Huazhong University of Science and Technology}%
\affil[3]{Central China Normal University}%
\affil[4]{President Office, MianYang Teachers’ College, Mianyang, China}%
\affil[5]{The Seventh Affiliated Hospital Sun Yat-sen University}%


\vspace{-1em}



  
  \date{June 16, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Anxiety is a common psychological disorder associated with affective
disorders and associated with other mental disorders, with depression
being the most common comorbidity. Few studies have examined the neural
mechanisms underlying anxiety after controlling for depressive symptoms.
This study aimed to explore whether there are differences in cortical
activation in anxiety patients with different severity whose depressive
symptoms is normal. In current study, depression levels were normal for
366 subjects---139 healthy subjects, 117 with mild anxiety, and 110 with
major anxiety. The Hospital Anxiety and Depression Scale (HADS) and a
verbal fluency task (VFT) tested subjects' anxiety and depression and
cognitive function, respectively. A 53-channel guided near-infrared
spectroscopic imaging technology (fNIRS) detected the concentration of
oxyhemoglobin (oxy-Hb). Correlation analysis between anxiety severity
and oxy-Hb concentration in the brain cortex was performed, as well as
ANOVA analysis of oxy-Hb concentration among the three anxiety severity
groups. Results showed that anxiety severity was significantly and
negatively correlated with oxy-Hb concentration in the left frontal eye
field (lFEF) and in the right dorsolateral prefrontal area (rDLPFC). The
oxy-Hb concentration in the lFEF and the rDLPFC were significantly lower
in the major anxiety disorder group than that in the control group. This
suggests that decreased cortical activity of lFEF and rDLPFC may be
neural markers of anxiety symptoms after control depressive symptoms.
Anxiety symptoms without depressive symptoms may be result from the
dysfunction of cognitive control network (CCN) which includes lFEF and
rDLPFC.%
\end{abstract}%



\sloppy


\textbf{KEYWORDS}

Anxiety, depression, fNIRS, cognitive control network

\textbf{1 \textbar{} INTRODUCTION}

Anxiety is an irrational state of fear of helplessness and insecurity, a
state of fear of danger in which the danger is unrealistic. Moderate
anxiety can remind people to pay attention to things around them.
However, when the fear state becomes irrational and burdens the
individual, general anxiety and even panic ensue. Pathological anxiety,
also known as anxiety disorder, is a common psychological disorder
associated with affective disorders, which is beyond the control of
individuals and interfere with their normal functioning (Carey, et al.,
2021). Therefore, this study mainly aims to explore whether there are
functional abnormalities in the cognitive control network of the brain
in anxiety disorders.

The key aspect of cognitive decline in anxiety is the internal sense of
loss of control or impaired attention to inhibiting emotional responses
to unrealistic danger or unwanted thoughts. This sense of loss of
control in anxiety disorders may be due to dysfunction of the top-down
control. Some studies have reported that anxiety is associated with
decreased neural activity in the frontal cortex (Basten, Stelzel, \&
Fiebach, 2012; Bishop, Duncan, Brett, \& Lawrence, 2004; Bishop, 2009).
Bishop found that in threat-related attention tasks, higher trait
anxiety is associated with decreased neural activity in the prefrontal
(PFC) (Bishop, Duncan, Brett, \& Lawrence, 2004). They then allowed
participants to perform conflict tasks, and also found that high trait
anxiety is associated with decreased neural activity in PFC (Bishop,
2009). This suggests that trait anxiety is associated with broader
attention control disorders in the PFC, even without threat-related
stimuli. Basten and Comte found that during the working memory task,
high trait anxiety levels were related to stronger deactivation of the
region related to the brain default pattern network; namely, the
rostral-ventral anterior cingulate cortex (ACC) (Basten, Stelzel, \&
Fiebach, 2012; Comte, et al., 2015). Similarly, Ansari and Derakshan
(2011) used ERP research to find that in response-inhibition tasks,
high-anxious individuals have a longer latency in response to the
inhibited target (Ansari \& Derakshan, 2011). Moreover, in a period of
time before the inhibited target appears, the ERP activity in the
frontal lobe center and the central recording site is lower for these
individuals than those of the low-anxiety individuals. This shows that
anxiety interferes with top-down inhibitory processing.

We still do not fully understand the underlying neural mechanism of
anxiety disorder even though there are some studies on social anxiety
disorder (SAD) (Holas, Krejtz, Cypryanska, \& Nezlek, 2014; Kawashima,
et al., 2016; Yokoyama, et al., 2015) , trait anxiety (Basten, Stelzel,
\& Fiebach, 2012; Bishop, 2009), and anxiety accompanied by depression
(Delaparte, et al., 2017; Kessler, et al., 2015; Wu, et al., 2022).
Whether anxiety causes activation or deactivation of the brain during
cognitive processes is still a controversial question (Kawashima, et
al., 2016; Yokoyama, et al., 2015). Yokoyama et al., using the fNIRS
technique, found that, compared with the healthy control group, the
concentration of oxygenated hemoglobin (oxy-Hb) in the ventrolateral
prefrontal cortex (VLPFC) of patients with anxiety changed less during a
verbal fluency task (VFT) (Yokoyama, et al., 2015). This suggests that
the decreased VLPFC activity among the task-related patients may be a
neural marker for the SAD patients. However, Kawashima et al. (2016)
also used the fNIRS technique to find that the left frontal cortex is
overactivated during VFT among anxiety disorders (Kawashima, et al.,
2016). The results of these studies are inconsistent regarding changes
in neural function in anxiety disorders. Therefore, it is necessary to
further explore the changes in its neural function, which will help to
further intervene and treat anxiety disorders.

More importantly, it is worth noting that most anxiety disorders are
associated with other mental disorders, with depression being the most
common comorbidity. According to the World Health Organization's World
Mental Health Survey, the comorbidity rate of anxiety and depression was
45.7\% (Kessler, et al., 2015). Therefore, in most existing studies,
anxiety and depression are often studied together, and anxiety disorders
are often regarded as accompanying symptoms of depression (Delaparte, et
al., 2017; Kessler, et al., 2015; Wu, et al., 2022). However, functional
impairment is more severe in patients with combined anxiety and
depressive disorders (Crane, et al., 2016), and their brain functions
may be different from those of patients who suffer from anxiety alone.
Few studies have focused on the symptoms of anxiety after controlling
for depression to study the underlying neural mechanisms of its
pathogenesis. Therefore, in this study, we controll for depressive
symptoms and the concerned about whether the severity of anxiety
symptoms affects brain cortical activation.

Multi-channel fNIRS is an optical neuroimaging technology that has been
applied to the research of several neuropsychiatric disorders (Rahman,
Siddik, Ghosh, Khanam, \& Ahmad, 2020), such as anxiety disorders
(Ishikawa, et al., 2014). The fNIRS technology uses a NIR light source
and detector placed on the scalp to measure the change in reflected NIR
light intensity, which is mainly due to the difference in the absorbance
of oxyhemoglobin (oxy-Hb) and deoxyhemoglobin (deoxy-Hb) in the
subcortical blood vessels. Therefore, fNIRS can quantify concentration
changes in oxy-Hb and deoxy-Hb. Previous studies have shown that the
cerebral cortex hemoglobin signal recorded by fNIRS is similar to that
recorded by functional MRI, and this hemodynamic signal is considered a
representation of brain activity (Duan, Zhang, \& Zhu, 2012; Pinti, et
al., 2018; Sato, et al., 2013). The prefrontal cortex is closely
associated with the primary formation of fear memories, thus offering
new possibilities for studying the neurobiology of the underlying
abnormal fear in anxiety disorders (Dejean, et al., 2015). Therefore, in
this study, the fNIRS system channel covers the frontotemporal region.

Impaired verbal fluency is common in patients with mental disorders;
therefore, the verbal fluency task (VFT) is usually used to diagnose
them (Raucher-Ch\selectlanguage{ngerman}éné, Achim, Kaladjian, \& Besche-Richard, 2017; Yeung \&
Lin, 2021). The VFT is a commonly used neuropsychological tool that can
measure executive function (Whiteside, et al., 2016) and is often used
to assess cognitive changes and impairment (McDonnell, et al., 2020).
Neuroimaging studies have found that the VFT depends on frontotemporal
lobe function, especially on the dominant side (Henry \& Crawford,
2004). Therefore, in this study, we investigated whether changes in
frontal activation during the VFT correlate with anxiety disorder
severity. To exclude the effect of depression, we selected only
individuals with different levels of anxiety whose depression was at
normal levels. We allowed the participants to perform the VFT while
recording the hemodynamic response of the fNIRS system.

Based on the evidence of an association between anxiety disorders and
frontal dysfunction found in previous studies (Basten, Stelzel, \&
Fiebach, 2011; Basten, Stelzel, \& Fiebach, 2012; Bishop, Duncan, Brett,
\& Lawrence, 2004; Bishop, 2009; Comte, et al., 2015), we predicted that
generalized anxiety severity during the VFT would be associated with the
degree of frontal function activation, as well as significantly lower
frontal function activity in patients with severe anxiety disorders.

\textbf{2 \textbar{} MATERIALS AND METHODS}

\textbf{2.1 \textbar{} Participants}

Participants were recruited from the psychiatry department of the
hospital of the Huazhong University of Science and Technology in Wuhan,
China from September 2020 to May 2023. All subjects were assessed with
the Mini International Neuropsychiatric Interview (MINI-Chinese version)
and the Hospital Anxiety and Depression Scale (HADS).

According to the Hospital Anxiety and Depression Scale (HADS), there
were 366 subjects whose depression levels were normal (HAD, D \selectlanguage{english}[?] 7). The
139 healthy subjects (HAD, A \selectlanguage{english}[?] 7) comprised 75 males and 64 females
(mean age +- SD: 20.34 +- 2.55). The 117 mild anxiety subjects (HAD, 0 \selectlanguage{english}[?] A
\textless{} 11) comprised 57 males and 60 females (mean age +- SD: 21.59
+- 2.25). The 110 major anxiety subjects (HAD, A \selectlanguage{english}[?] 11) comprised 55 males
and 55 females (mean age +- SD: 21.02 +- 2.17).

\textbf{2.2 \textbar{} Measures}

In the area of anxiety, there are several outcome tools to measure the
overall level of anxiety, such as the Beck Anxiety Inventory (BAI)
(Beck, Epstein, Brown, \& Steer, 1988), the Hamilton Rating Scale for
Anxiety (HAMA) (Hamilton, 1959), the State-Trait Anxiety Inventory
(STAI-S tate and STAI-Trait) (Spielberger, 1970) and Hospital Anxiety
and Depression Scale (HADS) (Snaith, 1992). This study used the HADS to
test depression and anxiety. HADS is a scale designed as a short
self-reported measure depression and anxiety. It performs well in
assessing the symptoms of anxiety disorder and depression in physical,
psychiatric and the general population (Bjelland, Dahl, Haug, \&
Neckelmann, 2002; White, Leach, Sims, Atkinson, \& Cottrell, 1999). The
reliability and validity of the scale were verified in different
languages (including Chinese) (Herrero, et al., 2003; Whelan-Goodinson,
Ponsford, \& Schonberger, 2009). HADS is a 14 item self-reported scale
including 7 items measure depression and 7 items measure anxiety
(Snaith, 2003). Each item is scored on a four-point scale, ranging from
0 to 3. 3 indicates a high frequency of symptoms. The total score of
each subscale ranges from 0 to 21 points: normal (0 to 7 points),
suspicious (8 to 10 points) and diagnostic (11 to 21 points).

\textbf{2.3 \textbar{} Activation task}

We selected VFT task to distinguish the cognitive executive function of
anxious depression and non-anxious depression. VFT requires subjects to
perform word formation task according to the given Chinese characters
when they hear specific Chinese characters such as ``\begin{CJK}{UTF8}{gbsn}上,\end{CJK}\selectlanguage{english}'' ``\begin{CJK}{UTF8}{gbsn}时,\end{CJK}\selectlanguage{english}''
``\begin{CJK}{UTF8}{gbsn}说,\end{CJK}\selectlanguage{english}'' and ``\begin{CJK}{UTF8}{gbsn}家\end{CJK}\selectlanguage{english}''). This block contains a 30 second pre task rest, a 60
second VFT task, and a 60 second post task rest (Figure 1).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

FIGURE 1 The verbal fluency task protocol.

\textbf{2.4 \textbar{} fNIRS measurement}

A 53-channel fNIRS device (BS-7000, Wuhan Znion Technology Co., Ltd.,
China) was used to measure the three types of relative concentration of
oxy-Hb, deoxy-Hb, and total-Hb by near-infrared light of a specific
wavelength. The instrument has 16 pairs of emission and detector probes
(Fig. 2). The wavelengths of light emitted are 760 nm and 850 nm, and
the frequency is 100 Hz. The distance between each emitter and detector
is 2.9--3.1 cm. The area between the emitter and detector probes
consists of a channel. Each probe was positioned on the scalp of the
forehead. The optode arrangement is shown in Figure 2 and is based on
the 10/20 System of Electrode Placement method, which is also commonly
used in EEGs (Okamoto, et al., 2004).

\textbf{2.5 \textbar{} Regions of interest (ROIs)}

ROIs were set at the following regions: Left Broca's area (detected by
the 2\textsuperscript{nd}, 3\textsuperscript{rd},
5\textsuperscript{th},7\textsuperscript{th}, 8\textsuperscript{th} and
13\textsuperscript{th} channels), Right Broca's area (detected by the
44\textsuperscript{th}, 49\textsuperscript{th}, 50\textsuperscript{th},
46\textsuperscript{th} ,51\textsuperscript{st} and
53\textsuperscript{rd} channels), Left Dorsolateral prefrontal cortex
(detected by the 6\textsuperscript{th}, 11\textsuperscript{st},
14\textsuperscript{th}, 17\textsuperscript{th}
,18\textsuperscript{th}and 20\textsuperscript{th} channels), Right
Dorsolateral prefrontal cortex (detected by the 31\textsuperscript{st},
32\textsuperscript{nd}, 34\textsuperscript{th}, 39\textsuperscript{th}
,42\textsuperscript{nd}and 45\textsuperscript{th} channels), Left
Frontal eye fields (detected by the 12\textsuperscript{nd} and
24\textsuperscript{th} channels), Right Frontal eye fields (detected by
the 26\textsuperscript{th} and 38\textsuperscript{th} channels), Left
Frontopolar area (detected by the 9\textsuperscript{st},
15\textsuperscript{th}, 16\textsuperscript{th}, 19\textsuperscript{th},
21\textsuperscript{rt}, 22\textsuperscript{nd}, 23\textsuperscript{th}
and 27\textsuperscript{th} channels), Right Frontopolar area (detected
by the 30\textsuperscript{th}, 33\textsuperscript{th},
35\textsuperscript{th}, 36\textsuperscript{th}, 37\textsuperscript{th},
41\textsuperscript{rt}, 43\textsuperscript{th}and 48\textsuperscript{th}
channels), Left Motor and Supplementary Motor Cortex (detected by the
1\textsuperscript{rt}, 4\textsuperscript{th} and 10\textsuperscript{th}
channels) and Right Motor and Supplementary Motor Cortex (detected by
the 40\textsuperscript{th}, 47\textsuperscript{th}and
52\textsuperscript{nd} channels) (Figure 2).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

FIGURE 2 Locations of the 53-channels for the functional near-infrared
spectroscopy (fNIRS). Estimated cortical areas corresponding to each
channel using the virtual registration method in (A) the right parietal
and temporal areas, (B) the frontal area, and (C) the left parietal and
the temporal areas.

\textbf{2.6 \textbar{} Statistical analysis}

In the fNIRS data preprocessing section. The fNIRS data were analyzed
using Homer2 package based on Matlab (M., et al., 2016). Firstly, the
raw light intensity file has been converted to homer2 file format (.
nirs). Then, the raw fNIRS data were first converted to optical density
(function: \emph{hmrIntensity2OD} ), and using the manufacturer's
recommendations, channels with a variation coefficient greater than
7.5\% are considered bad channels and deleted from the analysis. A
wavelet transform was used to correct for motion artifact
(function:\emph{hmrMotionCorrectWavelet} ) using the default
interquartile range (0.1), as this is optimal for motion correction. Any
remaining motion artifact was then removed through the motion artifact
detection tool (function: \emph{hmrMotionArtifact} , tMotion = 0.5,
tMask = 1.0, STDEVthresh = 15, AMPthresh = 3.0). The signal was then
bandpass filtered (function: \emph{hmrBandpassFilt} , hpf = 0.00, lpf =
0.10) to remove baseline drift and physiological noise. Finally, the
concentration changes of oxy-Hb and the deoxy-Hb were then computed
according to the Modified Beer-Lambert Law. The oxy-Hb and the deoxy-Hb
values were then saved as text files for each subject. Finally, the
oxy-Hb and the deoxy-Hb time series for each subject was z-scored by
channel.

\textbf{3 \textbar{} RESULTS}

\textbf{3.1 \textbar{} Correlation between anxiety severity and cortical
activity}

According to previous studies, we predicted that the severity of anxiety
was negatively correlated with the activation of the prefrontal cortex
during the VFT. Pearson correlation results showed that there was a
negative trend between anxiety severity and the mean oxy-Hb
concentration during VFT in the right dorsolateral prefrontal area
(rDLPFC) (\emph{r} = -0.226,p = 0.066) (Figure 3AB) and in the left FEF
during VFT (\emph{r} = -0.258,p = 0.035) (Figure 3 CD). This shows that
the anxiety may result from the decrease of cortical activity in the
prefrontal lobe (including right DLPFC and left FEF).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

FIGURE 3 The correlation between oxy-Hb concentration and generalized
anxiety. (A) the right dorsolateral prefrontal cortex. (B) The
correlation between oxy-Hb concentration of right dorsolateral
prefrontal cortex and generalized anxiety. (C) the left frontal eye
fields. (D) The correlation between oxy-Hb concentration of left frontal
eye fields and generalized anxiety.

\textbf{3.2 \textbar{} Comparison of cortical activity of oxy-Hb among
study groups}

According to previous studies and the Pearson correlation results in
this study, we predicted that the activation in the right DLPFC and the
left FEF during the VFT in major anxiety disorder is decreased compared
with healthy individuals. Therefore, first, we took the mean oxy-Hb
concentration in the right DLPFC during VFT as the dependent variable,
did ANOVA analysis between the three anxiety severity groups, and found
that the main effect of groups was also
significant,\emph{R\textsuperscript{2}} \textless{} 0.001,
F\emph{\textsubscript{(df=2)}} = 3.587, \emph{p} = 0.033. The post
analysis results showed that the mean oxy-Hb concentration in the right
DLPFC during VFT in the major anxiety group (\emph{m} = 0.047,
\emph{SE}= 0.606) was significantly lower than that in the control group
(\emph{m} = 0.896, \emph{SE} = 0.942, \emph{p} = 0.009) and was lower
than that in the mild anxiety group (\emph{m} = 0.730, \emph{SE} =
1.019), and the statistical results reached the marginal significant
level \emph{p} = 0.059 (Figure 4A). This suggests that the decreased
cortical activity in right DLPFC in patients with major anxiety may be a
neural marker of their anxiety symptoms.

Second, we took the mean oxy-Hb concentration in the left FEF during VFT
as the dependent variable and did ANOVA analysis between the three
anxiety severity groups. The results show that the main effect of groups
is significant, \emph{R\textsuperscript{2}} = 0.000,
F\emph{\textsubscript{(df=2)}} = 4.935, \emph{p} = 0.010. Post analysis
showed that the mean oxy-Hb concentration in the left FEF of the major
anxiety group (\emph{m} = -1.255, \emph{SE} = 2.215) was significantly
lower than that in the control group (\emph{m} = 0.601, \emph{SE} =
1.646) (\emph{p} = 0.003) and was lower than that in the mild anxiety
group (\emph{m} = 0.050, \emph{SE} = 1.608), and the statistical results
reached the marginal significant level \emph{p} = 0.057 (Figure 4B).
This suggests that the decreased cortical activity in left FEF in
patients with major anxiety may be a neural marker of their anxiety
symptoms.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

FIGURE 4 (A) The oxy-Hb concentration in the rDLPFC during VFT between
three anxiety severity groups; (B) The oxy-Hb concentration in the lFEF
during VFT between three anxiety severity groups.

\textbf{4 \textbar{} DISCUSSION}

Anxiety is an irrational state of fear of helplessness and insecurity,
or a state of fear of unrealistic danger that deeply impacts our
psychological functioning (Nechita, Nechita, \& Motorga, 2018), leading
to problems such as the decreasing ability to read text (Calvo \&
Carreiras, 1993), feeling a lack of success or failure (Yajima \& Arai,
1996), making decisions with low risk but low return (Raghunathan \&
Pham, 1999), and having lower levels of learning aptitudes (Cassady,
2004). The global incidence rate of anxiety disorder within one year is
as high as 13\% (Steel, et al., 2014), and the lifetime prevalence has
reached 5.7\% (Kessler, et al., 2005). Although anxiety disorders deeply
impact psychological functioning and their prevalence is high, the
underlying neural mechanism is still largely unclear. Although previous
studies have examined the neural mechanisms of social anxiety, trait
anxiety, and anxious depression, few have examined the neural mechanisms
of anxiety after controlling for depressive symptoms. Therefore, this
study aimed to explore whether there are differences in brain cortical
activity based on severities of anxiety disorders among patients with
normal depressive symptoms.

We selected people aged 18--24 years as the subjects because, among
people of all ages, the proportion of anxiety disorders among this age
group is the highest (Alonso, Angermeyer, Bernert, Bruffaerts, \&
Vollebergh, 2004). The Hospital Anxiety and Depression Scale (HADS) was
used to test anxiety and depression. fNIRS was used to detect oxy-Hb
concentration during the VFT. Anxiety severity was significantly and
negatively correlated with oxy-Hb concentration in the lFEF. The oxy-Hb
concentration in the lFEF of patients with major anxiety disorder was
significantly lower than that of healthy controls. In addition, there
was a negative correlation between anxiety severity and oxy-Hb
concentrations in the rDLPFC. The oxy-Hb concentration in the rDLPFC
among patients with major anxiety disorders was significantly lower than
that in the control group. These results indicate that decreased
cortical activity in the rDLPFC and lFEF in major anxiety disorder
patients may be a neural marker of anxiety symptoms.

\textbf{4.1 \textbar{} Anxiety is closely related to dysfunctional
cortical activity in the cognitive control network (CCN)}

Previous studies have found that the severity of GAD is related to the
uncontrollability of unrealistic worries (Hallion \& Ruscio, 2013;
Hallion, Tolin, Assaf, Goethe, \& Diefenbach, 2017), and cognitive
control can inhibit such unrealistic worries. Therefore, cognitive
control plays an important role in anxiety disorders. The core network
of cognitive control is the CCN, comprising the dorsolateral prefrontal
cortex (DLPFC), anterior cingulate cortex (ACC), and posterior parietal
cortex (Niendam, et al., 2012). Previous fNIRS-related studies of
individuals with high social anxiety found that in state rumination, the
activation of the brain region of the CCN was reduced, which resulted in
reduced cognitive and attention control in individuals with higher
social anxiety (Laicher, et al., 2022). Our results showed a negative
correlation between anxiety severity and cortical activation of the
rDLPFC, a part of the CCN. Our results also found that anxiety severity
was significantly and negatively correlated with cortical activation of
the lFEF, a part of the frontoparietal cortex. The frontoparietal region
is the brain region predominantly involved in cognitive control
(Niendam, et al., 2012). These results indicate that anxiety is closely
related to cognitive dysfunction during the VFT.

\textbf{4.2 \textbar{} Decreased cortical activity of the CCN in
patients with major anxiety disorder may be a neural marker for anxiety
symptoms}

The oxy-Hb concentrations in both the lFEF and rDLPFC in the major
anxiety disorder group were significantly lower than that in the control
group. Both the rDLPFC and lFEF are part of the brain region
predominantly involved in cognitive control. Consistent with our
results, some fNIRS studies found that cognitive control is impaired in
anxiety-related disorders. For example, Ohta et al. used fNIRS to find
that in the non-emotional VFT experiment, panic disorder showed
hypofrontality activation (Ohta, et al., 2008), indicating that
cognitive control impairment does not seem to be related to emotional
processes. Nishimura et al., using fNIRS, found that the cortical
activity in the left inferior frontal cortex in patients with panic
disorder during the VFT was significantly less than that among the
healthy control group (Nishimura, et al., 2007); thus, they believed
that the left frontal lobe, which is critical for active attention
control, may be damaged in patients with panic disorder. Bishop also
found that trait anxiety is related to impaired prefrontal control of
attention (Bishop, 2009). In addition, some ERP-related studies have
reported that cognitive control is impaired (Righi et al., 2009;
Cavanagh et al., 2017. Righi et al. (2009), using ERP, found that in a
continuous attention task, N2 amplitude increased among participants
with high trait and state anxiety, and so they believed that the
attention function of anxiety disorder is impaired (Righi, Mecacci, \&
Viggiano, 2009). Cavanagh et al. (2017), using ERP, found that in the
executive control task, ERR, or error related to \selectlanguage{greek}θ \selectlanguage{english}power, and the
correlation between reaction time and \selectlanguage{greek}θ \selectlanguage{english}power, could predict GAD status
(Cavanagh, Meyer, \& Hajcak, 2017). Further, they believed that the
executive control function is impaired with GAD. Thus, decreased
cortical activity of the CCN in major anxiety disorders may be a neural
marker for anxiety symptoms.

However, inconsistent with our results, some studies have reported
anxiety is associated with increased neural activity (Basten, Stelzel,
\& Fiebach, 2011; Basten, Stelzel, \& Fiebach, 2012; Comte, et al.,
2015) and decreased functional connectivity (Basten, Stelzel, \&
Fiebach, 2011; Comte, et al., 2015). Anxiety tendency is closely related
to changes in emotional function, showing that, in selective attention,
anxious individuals are highly sensitive to threat-related and vague
emotional stimuli. Some researchers have found that, in
affective-related tasks, the DLPFC (Basten, Stelzel, \& Fiebach, 2011)
and ACC (Comte, et al., 2015) of individuals with high trait anxiety
showed stronger task-related activation; however, the connectivity
between the PFC and ACC was reduced (Basten, Stelzel, \& Fiebach, 2011;
Comte, et al., 2015). This shows that, when faced with incompatible
emotional information, the neural processing efficiency of attention
control in trait anxiety disorders is inefficient. The overactivated
DLPFC attempts to compensate for inefficient connectivity. Basten et al.
(2012) found that in working memory tasks, trait anxiety is related to
stronger functional coupling between the right DLPFC and ventrolateral
prefrontal cortex, and higher levels of trait anxiety are related to
stronger activation of the rDLPFC and left inferior frontal sulcus
(Basten, Stelzel, \& Fiebach, 2012). This shows that anxiety can affect
cognitive processing even in the absence of threat-related stimuli. The
overactivation of the rDLPFC and left inferior frontal sulcus is a
compensatory effect. These findings, which are inconsistent with our
results, may reflect the different task types used. In conclusion,
patients with anxiety disorders showed low efficiency in terms of
cortical functional activation and cognitive processing efficiency.

\textbf{4.3 \textbar{} Limitations}

However, this study also has some limitations. First, Hospital Anxiety
and Depression Scale (HADS) was used to evaluate the symptoms of anxiety
and depression among all groups. However, the mainstream psychiatric
diagnostic scale Hamilton Depression Scale (HAM)was not used (Ho, et
al., 2020). Different measurements may lead to different research
results. Therefore, HAM, Montgomery Asperger Depression Scale or Beck
Depression Scale should be used for further verification. Second, in
this study, the 53 channels of fNIRS only covered the frontal and
temporal cortex, but not the posterior and deeper brain regions, such as
amygdala.

\textbf{5 \textbar{} CONCLUSION}

The anxiety is closely related to the dysfunction of cortical activity
of the cognitive control network (CCN). The decreased cortical activity
of CCN in major anxiety disorders may be a neural marker of their
anxiety symptoms.

\textbf{AUTHOR CONTRIBUTIONS Yan Zhang:} Supervision; conceptualization;
review and editing.\textbf{Caihong Yang:} Conceptualization; data
collection; data analysis; methodology; visualization; writing.
\textbf{Pu Wang:}supervision; conceptualization; review and editing.
\textbf{Huifen Wu:}Methodology; review. Yueran Bian: Data collection.
\textbf{Fang Xu:}Data collection. \textbf{Qiang Xiao:} Data collection.
\textbf{Nian Xiang:} Data collection. \textbf{Min Qiu:} Data collection.

\textbf{ACKNOWLEDGMENTS}

This research was supported by Philosophy and Social Science Research in
the Universities of Hubei Province. Project number: 21Q247. We
gratefully acknowledge the staff of the Department of Psychiatry,
Hospital of Huazhong University of Science and Technology in China. We
would like to appreciate all the participants in this study.

\textbf{CONFLICT OF INTEREST STATEMENT}

We have no known conflict of interest to disclose.

\textbf{DATA AVAILABILITY STATEMENT}

Raw data within this article will be made available upon request. If you
would like to access the raw data and analysis, please email Caihong
Yang at the following e-mail address: ychpsychology@163.com.

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