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Altered Resting-State Functional Connectivity of Multiple Networks and Disrupted Correlation With Executive Function in Major Depressive Disorder

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Frontiers in Neurology
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10.3389/fneur.2020.00272
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April, 2020
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ORIGINAL RESEARCH
published: 28 April 2020
doi: 10.3389/fneur.2020.00272

Altered Resting-State Functional
Connectivity of Multiple Networks
and Disrupted Correlation With
Executive Function in Major
Depressive Disorder
Yujie Liu 1,2 , Yaoping Chen 1 , Xinyu Liang 1 , Danian Li 3 , Yanting Zheng 1,2 , Hanyue Zhang 1 ,
Ying Cui 4 , Jingxian Chen 5 , Jiarui Liu 6 and Shijun Qiu 2*
1

Edited by:
Boldizsar Czeh,
University of Pécs, Hungary
Reviewed by:
Feng Liu,
Tianjin Medical University General
Hospital, China
Long Jiang Zhang,
Medical School of Nanjing Univeristy,
Nanjing, China
*Correspondence:
Shijun Qiu
qiu-sj@163.com
Specialty section:
This article was submitted to
Applied Neuroimaging,
a section of the journal
Frontiers in Neurology
Received: 14 January 2020
Accepted: 24 March 2020
Published: 28 April 2020
Citation:
Liu Y, Chen Y, Liang X, Li D, Zheng Y,
Zhang H, Cui Y, Chen J, Liu J and
Qiu S (2020) Altered Resting-State
Functional Connectivity of Multiple
Networks and Disrupted Correlation
With Executive Function in Major
Depressive Disorder.
Front. Neurol. 11:272.
doi: 10.3389/fneur.2020.00272

Frontiers in Neurology | www.frontiersin.org

First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China, 2 Department of Radiology,
The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China, 3 Cerebropathy Center, The
First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China, 4 Cerebropathy Center, The Third
Affiliated Hospital of Guangzhou Medical University, Guangzhou, China, 5 Department of Radiology, Shunde Hospital of
Southern Medical University, Shunde, China, 6 Department of Radiology, Zhuhai Hospital of Southern Medical University,
Zhuhai, China

Background: Major depressive disorder (MDD) is one of the most common and costly
psychiatric disorders. In addition to significant changes in mood, MDD patients face an
increased risk of developing cognitive dysfunction. It is important to gain an improved
understa; nding of cognitive impairments and find a biomarker for cognitive impairment
diagnosis in MDD.
Methods: One hundred MDD patients and 100 normal controls (NCs) completed
resting-state fMRI (rs-fMRI) scan, in which 34 MDD patients and 34 NCs had scores
in multiple cognitive domains (executive function, verbal fluency, and processing speed).
Twenty-seven regions of interest from the default mode network (DMN), central executive
network (CEN), salience network (SN), and limbic system (LS) were selected as seeds
for functional connectivity (FC) analyses with the voxels in the whole brain. Finally,
partial correlations were conducted for cognitive domain scores and FCs with significant
differences between the MDD and NC groups.
Results: Significant FC differences between groups were identified among the
seeds and clusters in the DMN, CEN, LS, visual network, somatomotor network,
ventral attention network, and dorsal attention network. In the MDD patients, the
magnitude of the Stroop interference effect was positively correlated with the illness
duration, and the illness duration was negatively correlated with the FC between
the right ventral hippocampal gyrus and the left inferior frontal gyrus. However,
the correlation between the Stroop interference effect and the FC of the right
anterior prefrontal cortex with the left cerebellum_4_5 was disrupted in these patients.

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Conclusions: The MDD patients have altered FCs among multiple brain networks and
a disrupted correlation between the FC of prefrontal cortex and executive function. The
disrupted correlation could present before the symptoms develop and may be the core
process in the development of executive function impairment.
Keywords: major depressive disorder, functional connectivity, resting state, fMRI, neuropsychological test,
executive function

INTRODUCTION

voxel-wise FC alteration in MDD only chose seeds from a single
network, but seeds from multiple cognition-related networks
would be more helpful in detecting the cognitive impairments.
Second, the sample size in the previous studies is relatively
small, and the majority of the patients in the study have a
treatment history. In this scenario, the medication effect cannot
be distinguished from those associated with the disease itself.
Therefore, a larger sample composed purely of first-episode and
drug-naïve MDD patients may eliminate possible confounding
factors such as different episodes and medication use and achieve
a more reliable result. In our previous study (26), we applied
spectral dynamic causal modeling to estimate the effective
connectivity of a large-scale network consisting of 27 ROIs
(from the DMN, CEN, SN, and LS) in the 100 MDD patients
and 100 NCs, and found that reduced excitatory and increased
inhibitory connections coexisted within the DMN, underlying
disrupted self-recognition and emotional control in MDD. We
also proposed a new dynamic FC-based metric, high-order FC
(27), to measure the temporal synchronization of long-range FC
dynamics; we found that high-order FC significantly improved
MDD diagnostic accuracy compared to conventional FC (28).
Since the above studies did not analyze cognitive performance in
MDD patients, we will do so in this paper.
In this study, we hypothesized that the FC alterations of four
likely involved resting-state networks (DMN, CEN, SN, and LS)
were associated with the cognitive impairments in first-episode
and drug-naïve MDD patients. We applied a seed-based method
to examine the whole-brain voxel-wise FC of 27 predefined seeds
from DMN, CEN, SN, and LS based on rs-fMRI data collected
from a relatively large sample size, including 100 first-episode
and drug-naïve MDD and 100 normal controls (NCs). We also
correlated the significantly altered FC with scores on a battery
of neuropsychological tests that covered cognitive domains
including executive function, verbal fluency, and processing
speed in 34 MDD patients and 34 NCs. The results showed that
MDD patients possessed altered FCs in multiple brain networks
and a disrupted correlation between the FC of prefrontal cortex
and executive function. The disrupted correlation could present
before the symptoms develop and may be the core process in the
development of executive function impairment.

Major depressive disorder (MDD) is one of the most common
and costly psychiatric disorders (1). This condition has been
ranked as one of the top 10 leading causes of disability
among 191 countries (2) and is the second leading cause of
disability worldwide, affecting 4.7% of the global population (3).
MDD patients suffer significant changes in mood, characterized
by sadness along with various other symptoms, such as
fatigue, altered appetite, and/or sleep (4). Moreover, cognitive
impairments are commonly detected in MDD patients (5–7).
In a recent study, cognitive impairments related to MDD were
grouped into four cognitive domains: (i) verbal learning and
memory, (ii) visuospatial learning and memory, (iii) executive
function (EF)/attention, and (iv) psychomotor speed; 8.9–37.5%
of MDD patients were impaired in two or more cognitive
domains (8). High rates of persisting cognitive impairments were
also found in MDD patients that nearly 60% of MDD patients
remained cognitively impaired at 6-month follow-up (9). There
are data suggesting that proper medical treatment may lead to
improved cognitive functioning (10–12), while worse treatment
outcomes and higher rates of recurrence are associated with
poorer cognition (13). In light of these findings, it is important
to gain an improved understanding of cognitive impairments in
MDD and find a potential biomarker for cognitive impairment
diagnosis in MDD, which could be of great clinical importance
in terms of allowing early, accurate diagnosis and reducing the
chances of chronic relapse and recurrence (5).
Resting-state functional MRI (rs-fMRI) has been widely used
to investigate the neural mechanisms of brain dysfunctions
(14) and to explore potential imaging biomarkers in various
diseases (e.g., MDD, social anxiety disorder, and Alzheimer’s
disease) (15–17). By measuring fluctuations in blood-oxygenlevel-dependent (BOLD) signals, rs-fMRI can be used to assess
brain functional connectivity (FC). Researchers have indicated
that cognitive impairments in MDD are related to significant
FC changes within and between several brain networks, such
as the default mode network (DMN), central executive network
(CEN), salience network (SN), and limbic system (LS) (18–
23). For example, the fronto-limbic system, a key network
for emotional regulation and memory, is potentially linked to
cognitive impairment in unmedicated MDD patients (24). The
alterations in the large-scale brain FC network in MDD may
demonstrate the depressive biases toward internal thoughts at the
cost of engaging with the external world, resulting in lapses in
cognitive problems (15, 25).
However, limitations exist in the previous studies. First, most
studies that used seed-based methods to examine the whole-brain

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METHODS AND MATERIALS
Participants
A total of 119 first-episode, treatment-naïve MDD patients and
109 NCs from two datasets were used in this study. MDD patients
were recruited from the psychological counseling outpatient

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clinic of the First Affiliated Hospital of Guangzhou University of
Chinese Medicine from August 2015 to June 2018. The diagnosis
of treatment-naïve, first-episode depression was made by two
attending psychiatrists, each of whom had more than 10 years of
experience with the Diagnostic and Statistical Manual of Mental
Disorders (DSM)-5 (29); the Structured Clinical Interview for
the DSM (SCID) was used to assess whether the diagnostic
criteria were met (30). The 17-item Hamilton Depression Rating
Scale (HDRS-17) (31) was also used to evaluate the severity of
depression (32). Each patient self-reported a rough estimate of
illness duration. The other inclusion criteria for MDD patients
were as follows: (1) aged between 18 and 55 years old, (2) HDRS17 score > 17, (3) right-handed native Chinese speaker, and (4)
free of any history of neurological illness or any other psychiatric
disorder according to the DSM-5. Exclusion criteria included (1)
a history of any significant illness, (2) alcohol abuse [a total score
≥ 8 on the Alcohol Use Disorders Identification Test (33)], and
(3) contraindications to MRI scans. The NCs were all volunteers
who were physically healthy based on their self-reported medical
history and mentally healthy according to the Mini-International
Neuropsychiatric Interview (MINI) (34) as applied by two
psychologists. Besides, the HDRS-17 score of NCs was <7. This
study was conducted in accordance with the Declaration of
Helsinki. All participants provided written informed consent,
and the study was approved by the Ethics Committee of the
First Affiliated Hospital of Guangzhou University of Chinese
Medicine, Guangzhou, China.

certain letters (as in the phonemic VFT) (39). In this study, we
gave the participants 1 min to name as many words from the
category of “animals” or items starting with a certain Chinese
word (Fa) as possible. The dependent measure reported was the
number of words generated.
Participants were also assessed by the most reported
processing speed task, the Symbol Digit Modalities Test (SDMT).
The dependent measure was the number of items correctly
completed within 90 s.
Statistical analyses were performed using IBM SPSS Statistics
version 23.0 (Chicago, IL, USA). Age and education level were
compared using two-sample t-tests, gender was compared using
a chi-squared test, and SCWT (SIE_time and SIE_accuracy), VFT
(semantic VFT and phonemic VFT), and SDMT scores between
MDD patients and NCs were compared by using linear regression
analyses (age, gender, and education level as covariates).

Image Acquisition
All MRI data were acquired using a 3.0-T GE Signa HDxt
scanner with an 8-channel head-coil within 3 days of diagnosis.
The participants were instructed to close their eyes and refrain
from thinking anything in particular. Two radiologists made
consensus decisions that all participants were free of visible brain
abnormalities or any form of lesions based on thick-slice axial T1and T2-weighted images as well as T2-weighted fluid-attenuated
inversion recovery (T2-FLAIR) images. In order to increase
sample size, this study included two image datasets, which were
acquired during two different periods but had largely the same
parameters, including TR/TE = 2000/30 ms, flip angle = 90◦ ,
matrix size = 64 × 64, and slice spacing = 1.0 mm for rs-fMRI
and slice thickness = 1 mm, no slice gap, matrix size = 256 ×
256, field of view (FOV) = 256 × 256 mm2 for three-dimensional
T1-weighted images (3D-T1WI). The different parameters are as
follows. For the first dataset [82 MDD patients and 72 NCs, also
used in (28)], the parameters included FOV = 240 × 240 mm2 ,
slice thickness = 4 mm, slice number = 33, and scanning time
= 8′ 20′′ (250 volumes) for rs-fMRI and TR/TE = 10.4/4.3 ms,
FA = 15◦ , and 156 slices for 3D-T1WI. For the second dataset
(37 MDD patients and 37 NCs), the parameters included FOV
= 220 × 220 mm2 , slice thickness = 3 mm, slice number = 36,
scanning time = 6′ 10′′ (185 volumes) for rs-fMRI and TR/TE =
6.9/1.5 ms, FA = 12◦ , and 188 slices for 3D-T1WI. Compared to
the first dataset, the second dataset has a slightly increased spatial
resolution for rs-fMRI (we used the same number of volumes
for each of the two datasets). The effect of different datasets was
removed in the statistical analysis later.

Clinical Assessment and
Neuropsychological Testing
Participants in the second dataset (37 MDD patients and
37 NCs) were assessed by a battery of neuropsychological
tests that covered cognitive domains including executive
function, verbal fluency, and processing speed. The tests were
administered by a trained psychometric technician supervised by
a clinical neuropsychologist.
First, participants were subjected to a Stroop Color-Word
Test (SCWT) (35), as research has consistently found inhibitory
control impaired in MDD (36), and we hypothesized that the
FC changes were associated with this cognitive impairment.
The SCWT included three parts. Two of them represent the
“congruous condition” in which participants were required to
read out the name of a color written in black (W) and name
different color patches (C). Conversely, in the third part, named
color-word (CW) condition or incongruent condition, colorwords were printed in an inconsistent color ink (i.e., the word
“blue” printed in yellow) and participants were required to name
the color of the ink instead of reading the word itself. The
difficulty in inhibiting reading the word was called the Stroop
interference effect (SIE) (35). Speed and accuracy scores were
recorded for calculation of the SIE (SIE_time and SIE_accuracy)
to evaluate inhibitory control ability (37). The calculation method
has been detailed in (38).
Verbal fluency test (VFT) is used to measure the ability to
generate words in a limited period of time, either from given
semantic categories (as in the semantic VFT) or starting with

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Image Preprocessing
Image preprocessing was performed using SPM12 (www.fil.
ion.ucl.ac.uk/spm) and DPARSF version 2.3 (http://rfmri.org/
DPARSF). For each rs-fMRI scan, 180 volumes remained for
further analyses. The remaining images were corrected for
acquisition time intervals between slices and head motion
between volumes. Data from 19 MDD patients and 10 NC
were discarded because their maximum cumulative head motion
exceeded 2 mm in translation or 2◦ in rotation along any
direction, or the mean framewise displacement (FD) exceeded

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education, and center as covariates) to determine areas with
significantly different FCs to the ROIs between MDD patients
and NCs. We used P < 0.001 for the cluster-forming threshold
and implemented a familywise error (FWE) correction approach
at the cluster level. All results survived whole-brain cluster
correction (PFWE < 0.05).

0.2 mm (40). Next, 3D-T1WI data were coregistered to the
rs-fMRI data of the same subject and further segmented
using unified segment (http://www.fil.ion.ucl.ac.uk/spm) and
registered to the standard Montreal Neurological Institutes
(MNI) space using diffeomorphic anatomical registration
through exponentiated Lie algebra (DARTEL). The rs-fMRI data
were then warped to MNI space according to the generated
deformation field and smoothed with a Gaussian kernel of 6 mm
full width at half maximum (FWHM). Several nuisance signals,
including the Friston-24 head motion parameters and mean
signals from cerebrospinal fluid and white matter, were regressed
out from the rs-fMRI data. Then, linear detrending and bandpass
filtering (0.01–0.08 Hz) were performed to reduce low-frequency
drift and high-frequency noise.

Correlation Between FC and Clinical
Scores
The correlations between significantly different FCs and
clinical scores (illness duration, HDRS-17 scores, SIE_time,
SIE_accuracy, semantic VFT, phonemic VFT, and SDMT scores)
were calculated using partial correlation analysis. P < 0.05 after
Bonferroni correction was considered significant. Age, gender,
and education were included as covariates in the correlation
analyses of cognitive scores.

FC Analysis
We specified 27 predefined ROIs (see the detailed ROI definition
in Table 1) from DMN, CEN, SN, and LS based on their vital
role in MDD neuropathology (22, 23, 41). The coordinates of the
ROIs from the DMN, CEN, and SN were adopted from Raichle
(42), and those from the LS were taken from Drysdale et al. (41).
Using DPARSF version 2.3 (http://rfmri.org/DPARSF), we
computed Pearson correlation coefficients between the mean
time series of each ROI (each ROI was a sphere centering at
the above coordinates with a radius of 5 mm) and that of each
voxel of the whole brain. Then, a Fisher r-to-z transformation
was used to convert the correlation coefficient to z values to
improve normality. Finally, we obtained z-FC maps of each
individual for further analysis. Next, we used SPM 12 (www.fil.
ion.ucl.ac.uk/spm) to perform two-sample t-tests (gender, age,

RESULTS
Demographic and Clinical Characteristics
A total of 100 MDD patients (66 females, 34 males; mean age:
29.46 years) and 100 NCs (59 females, 41 males; mean age: 29.59
years) entered the following analysis, where 34 MDD patients (25
females, 9 males; mean age: 29.41 years) and 34 NCs (24 females,
10 males; mean age: 30.09 years) with neuropsychological test
scores were included in the correlation analysis. No significant
differences in age, gender, and education were found between
the 100 MDD patients and the 100 NCs. The demographic and
clinical data of 100 MDD patients and 100 NCs are summarized
in Table 2.

TABLE 1 | Names and MNI coordinates of 27 ROIs from four networks.
Seed

MNI coordinates (mm)

Seed

Default mode network
1

Posterior cingulate
cortex/precuneus

2
3

MNI coordinates (mm)

Salience network
0 −52 7

15

Dorsal anterior cingulate
cortex

Medial prefrontal cortex

−1 54 27

16

L-anterior prefrontal cortex

−35 45 30

L-lateral parietal cortex

−46 −66 30

17

R-anterior prefrontal cortex

32 45 30

4

R-lateral parietal cortex

49 −63 33

18

L-insula

−41 3 6

5

L-inferior temporal gyrus

−61 −24 −9

19

R-insula

6

R-inferior temporal gyrus

58 −24 −9

20

L-lateral parietal cortex

−62 −45 30

21

R-lateral parietal cortex

62 −45 30

7

Medial dorsal thalamus

0 −12 9

8

L-posterior cerebellum

−25 −81 −33

9

R-posterior cerebellum

25 −81 −33

0 21 36

41 3 6

Limbic system

Central executive network

22

L-subgenual anterior
cingulate cortex

−4 15 −11

23

R-subgenual anterior
cingulate cortex

4 15 −11

10

Dorsal medial prefrontal cortex

0 24 46

24

L-amygdala

11

L-anterior prefrontal cortex

−44 45 0

25

R-amygdala

12

R-anterior prefrontal cortex

44 45 0

26

L-ventral hippocampus

−27 −15 −18

27

R-ventral hippocampus

27 −15 −18

13

L-superior parietal lobule

−50 −51 45

14

R-superior parietal lobule

50 −51 45

−19 −2 −21
19 −2 −21

R, Right; L, left.

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hippocampus and the left inferior frontal gyrus (opercular part),
(I) the left ventral hippocampus and the left inferior frontal
gyrus (orbital part), (J) the right ventral hippocampus and the
right inferior frontal gyrus (opercular part), and (K) the right
ventral hippocampus and the left inferior frontal gyrus (opercular
part). Additionally, lower FC was also observed between the left
posterior cerebellum and the left postcentral gyrus (L).

TABLE 2 | Demographic and clinical characteristics of participants.
MDD (N = 100)

NC (N = 100)

t/χ2

P-value

29.46 ± 9.34§

29.59 ± 10.33

−0.09

0.93†

66/34

59/41

1.05

0.31‡

12.46 ± 3.22§

12.88 ± 2.77

−0.09

0.32

Illness duration (months)

8.64 ± 10.86§

NA

NA

NA

HDRS-17

22.15 ± 3.18§

NA

NA

NA

Characteristics
Age (years)
Gender (F/M)
Education (years)

†

Correlations Between Altered FC and
Clinical Scores

MDD, major depressive disorder; NC, normal control; HDRS-17, 17-item hamilton
depression rating scale.
§ Mean ± standard deviation.
†
The P-values were obtained through a two-sample t-test.
‡ The P-value was obtained through a chi-squared test.

For all 100 MDD patients, FC between the right ventral
hippocampus in the LS and the left inferior frontal gyrus in
the CEN was negatively correlated with illness duration (r =
−0.25, Pcorrected = 0.01). In the 34 MDD patients and 34 NCs
with neuropsychological test scores, the SIE_accuracy score was
correlated with illness duration in the MDD group (r = 0.44,
Pcorrected = 0.03). Although there was no difference in SIE_time
or SIE_accuracy scores between the two groups, we still found
that the SIE_accuracy score was positively correlated with the
FC between the right anterior prefrontal cortex in the CEN and
the left cerebellum_4_5 in the visual network (r = 0.43, Pcorrected
= 0.03) in the NCs but not the MDD patients (Figure 2). In
addition, there were significant differences in semantic VFT and
SCWT scores between the two groups. However, no correlation
was found between FCs and HRDS-17, semantic VFT, phonemic
VFT, or SDMT scores.

TABLE 3 | Demographic and clinical characteristics of the participants with
neuropsychological tests.
Characteristics

MDD (n = 34)

NC (n = 34)

t/χ2

P-value

Age (years)

29.41 ± 8.27§

30.09 ± 10.88§

−0.29

0.77†

25/9

24/10

0.07

0.787‡

−0.86

0.395†
NA

Gender (F/M)
Education (years)

13.00 ± 3.44

§

§

13.68 ± 3.07

§

Illness duration (months)

7.81 ± 8.46

NA

NA

HDRS-17

21.85 ± 2.25§

NA

NA

NA

SIE_time

1.17 ± 0.37§

1.10 ± 0.32§

−0.95

0.35¶

SIE_accuracy

−0.05 ± 0.06§

−0.03 ± 0.04§

1.57

0.12¶

Semantic VFT

18.15 ± 5.77§

21.47 ± 4.82§

2.44

0.02¶

Phonemic VFT

8.15 ± 4.34§

9.91 ± 3.98§

1.86

0.07¶

53.21 ± 12.28§

62.03 ± 14.12§

3.40

0.00¶

SDMT

DISCUSSION
In this study, we analyzed the FC differences of 27 seeds from
the DMN, CEN, SN, and LS with the voxels of the whole
brain between 100 first-episode, drug-naïve MDD patients and
100 NCs. We also correlated the significantly altered FC with
scores on a battery of neuropsychological tests that covered
cognitive domains including executive function, verbal fluency,
and processing speed in 34 MDD patients and 34 NCs. The
result showed that significant FC differences between groups
were identified among the seeds and clusters in the DMN, CEN,
LS, visual network, somatomotor network, ventral attention
network, and dorsal attention network. In the MDD patients,
the magnitude of the Stroop interference effect was positively
correlated with the illness duration, and the illness duration
was negatively correlated with the FC between the right ventral
hippocampal gyrus and the left inferior frontal gyrus. However,
the correlation between the Stroop interference effect and
the FC of the right anterior prefrontal cortex with the left
cerebellum_4_5 was disrupted in the MDD patients. Our findings
offer a novel insight into the pathophysiological mechanisms of
executive function in MDD.

MDD, major depressive disorder; NC, normal control; HDRS-17, 17-item hamilton
depression rating scale; CTQ, childhood trauma questionnaire; SIE_time, interference
effect of time during the Stroop test; SIE_accuracy, interference effect of accuracy during
the Stroop test.
§ Mean ± standard deviation (SD).
† The P-values were obtained by two-sample t-tests.
‡ The P-value was obtained by a chi-squared test.
¶ The P-values were obtained by linear regression analyses. Age, gender, and education
level were included as covariates.

No significant difference was found between the 34 MDD
patients and the 34 NCs in terms of age, gender, education,
SIE_time, SIE_accuracy or phonemic VFT scores, but the MDD
patients had significantly lower semantic VFT and SDMT scores
than the NCs (P < 0.05). See details in Table 3.

MDD-Related FC Alterations
Significant differences were found in the FC of seven ROIs
between MDD and NCs. As shown in Table 4 and Figure 1,
MDD patients had higher FC than NCs between the following
ROI and clusters: (A) the posterior cingulate cortex/precuneus
and the right paracentral gyrus, (B) the left inferior temporal
gyrus and the right cuneus, (C) the right anterior prefrontal
cortex and the left cerebellum_4_5 (part extend to right
cerebellum_4_5), (D) the right anterior PFC and the right middle
frontal gyrus, (E) the right amygdala and the left inferior frontal
gyrus (triangular part), (F) the right amygdala and the left
rolandic operculum, (G) the left ventral hippocampus and the
right inferior frontal gyrus (opercular part), (H) the left ventral

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MDD-Related FC Alterations
The DMN, CEN, SN, and LS support emotion regulation and
higher cognitive functions in MDD (43). In this study, we
observed several discriminative brain regions contributing to
MDD-related FC alterations, including the posterior cingulate
cortex, left inferior temporal gyrus and left posterior cerebellum
in the DMN, the right anterior prefrontal cortex in the CEN,

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TABLE 4 | MDD-related FC alterations.
FC number

Seed

Brain region

MNI coordinates (mm)

Cluster size

Peak T

MDD > NC

Default mode network

A

Posterior cingulate cortex/precuneus

R-paracentral gyrus

12 −24 69

73

4.94

B

L-inferior temporal gyrus

R-cuneus

21 −69 24

72

4.06

L-cerebellum_4_5

−3 −45 0

118

4.72

R middle frontal gyrus

27 45 33

50

4.35

L-inferior frontal gyrus, triangular part

−33 30 18

90

4.80

L-rolandic operculum

−45 −3 21

97

4.45

R-inferior frontal gyrus, opercular part

54 9 21

47

4.45

H

L-inferior frontal gyrus, opercular part

−48 6 24

60

4.31

I

L-inferior frontal gyrus, orbital part

−36 24 −3

44

4.08

R-inferior frontal gyrus, opercular part

51 6 21

47

4.24

L-inferior frontal gyrus, opercular part

−54 9 27

59

3.80

−33 −24 66

54

−4.35

Central executive network
C

R-anterior prefrontal gyrus

D
Limbic system
E

R-amygdala

F
G

J

L-ventral hippocampus

R-ventral hippocampus

K
NC > MDD

Default mode network

L

L-posterior cerebellum

L-precentral gyrus

R, right; L, left.

were contributed by recurrent MDD patients, not by first-episode
and drug-naïve patients, which need to be confirmed by more
future studies.
Multiple MDD studies have focused on other typically
impaired brain networks such as CEN and LS because of
their roles in emotion processing, executive functioning and
antidepressant action (21). Our results also indicated that the
fronto-limbic system has altered in the first-episode and drugnaïve MDD patients. We found increased FC of the right
amygdala with the left inferior frontal gyrus and the left rolandic
operculum, and increased FC of bilateral ventral hippocampus
with the bilateral inferior frontal gyrus. The amygdala and
hippocampus are the core regions in the LS and have widespread
connections to diverse cortical areas, such as the frontal cortex,
which is the region known to constitute the neuroanatomical
network of cognitive function (54). In addition, increased FC
of CEN with ventral attention network and visual network, and
increased FC of LS with dorsal attention network may reflect
altered or biased salience monitoring.

and the right amygdala and bilateral ventral hippocampus in
the LS. The DMN provides the neural substrate for depressive
rumination and is the network that receives the most attention
in clinical MDD imaging research (44, 45). In our study, the
results showed that MDD patients had altered FC between the
DMN and the somatomotor network—we reported the novel
finding of increased FC between the posterior cingulate cortex
and the right paracentral gyrus, while decreased FC between
the left posterior cerebellum and left precentral gyrus in MDD
patients. The posterior cingulate cortex plays a pivotal role
in the DMN (46), and from the current body of research,
it has been demonstrated to have increased engagement in
MDD and predicts disease severity (47, 48). The left posterior
cerebellum (crus II) is believed to be coupled with the DMN,
showing a possible role in memory and planning processing
(49). Also, according to our previous study, crus II may be a
promising biomarker for MDD diagnosis (28). The paracentral
gyrus and precentral gyrus belong to the somatomotor network.
They are not simply motor structures but also involved in
more “cognitive” processes, including response inhibition, action
sequencing,working memory, speech and language processing
(50–52). Previous studies have reported the altered FC between
the DMN and somatomotor network. Bessette et al. (53) found
that the remitted MDD patients had weaker connectivity between
the DMN seed (right hippocampus) and the SMN seed (right
paracentral lobule). These altered FCs between the DMN and
SMN may reflect ongoing rumination and underlie deficits in
cognitive control. Besides, in MDD patients, altered FC was also
found between the DMN and the visual network. Our result
showed an increased FC between the left inferior temporal gyrus
and the right cuneus, suggesting abnormal processing in the
DMN and visual network in MDD. This was contradicting to the
results of other studies; they found reduced FC between these two
networks in MDD patients (45). However, most of these results

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Relationship Between Altered FC and
Clinical Scores
SCWT is a famous test for measuring the executive function,
especially inhibition (55). In the SCWT, the difficulty in
inhibiting reading the word in the incongruent condition was
called the SIE (35). Traditionally, MDD patients had higher
SIE score than the NCs, indicating poor executive function
in MDD (36). An event-related fMRI study concluded that
higher SIE was correlated with reduced activation in the dorsal
anterior cingulate cortex and the left dorsolateral prefrontal
cortex (56). In our study, no difference of SIE was found
between the MDD patients and the NCs, which is in accordance
with the result of Wagner et al. (57), but the correlation
between the SIE and the FC of the right anterior prefrontal

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Executive Function Impairment in MDD

FIGURE 1 | Clusters of between-group differences of FC with age, gender, education level, and center adjusted (P < 0.05, FWE corrected). Compared to the NCs,
significantly increased FCs in MDD patients were found between (A) the posterior cingulate cortex/precuneus and the right paracentral gyrus; (B) the left inferior
temporal gyrus and the right cuneus; (C) the right anterior prefrontal cortex and the left cerebellum_4_5 (part extend to right cerebellum_4_5); (D) the right anterior
PFC and the right middle frontal gyrus; (E) the right amygdala and the left inferior frontal gyrus (triangular part); (F) the right amygdala and the left rolandic operculum;
(G) the left ventral hippocampus and the right inferior frontal gyrus (opercular part); (H) the left ventral hippocampus and the left inferior frontal gyrus (opercular part); (I)
(Continued)

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Executive Function Impairment in MDD

FIGURE 1 | the left ventral hippocampus and the left inferior frontal gyrus (orbital part); (J) the right ventral hippocampus and the right inferior frontal gyrus (opercular
part); and (K) the right ventral hippocampus and the left inferior frontal gyrus (opercular part). Decreased FC in MDD patients was found between the left posterior
cerebellum and the left postcentral gyrus (L). Color scale denotes the t values; x, y, z, Montreal Neurological Institutes coordinates; L, left; R, right. The bar graph
shows the z value of the above FCs (means and SD; * indicates P < 0.05, FWE corrected).

FIGURE 2 | Correlations between altered FC and clinical scores. (A) The z score of the FC (zFC) between the right ventral hippocampus (R-vHPC) and the left inferior
frontal gyrus (L-IFG) was negatively correlated with illness duration (r = −0.25, Pcorrected < 0.01) in 100 MDD patients. (B) The zFC between the right anterior
prefrontal cortex (R-aPFC) and the left cerebellum_4_5 (L-cerebellum_4_5) was positively correlated with the SIE_accuracy score (r = 0.43, Pcorrected < 0.05) in the 34
NCs. (C) The SIE_accuracy score was correlated with illness duration in the 34 MDD patients (r = 0.44, Pcorrected < 0.05).

Limitations

cortex with the left cerebellum_4_5 (part extend to the right
cerebellum_4_5) was disrupted in the patients. The disrupted
correlation indicates that the altered FC may present before
symptoms develop, suggesting that it is the core process in
the development of executive function impairment rather than
being produced by the symptoms. In addition, we found that
in the MDD patients, the SIE accuracy was related to the
illness duration. However, the inconsistency of the above studies
highlighted the importance of replicating the results of previous
studies (58). Besides, we found that the longer illness duration
in MDD was correlated with decreased FC between the right
ventral hippocampus and the left inferior frontal gyrus in the
MDD patients, supporting the notion that the fronto-limbic
system is the key network in MDD (21). Collectively, our
observation may indicate that altered FC of seeds in the CEN
and LS could be associated with illness duration and executive
function impairment via wide-ranging connections to cortical
and subcortical brain regions.
In our study, MDD patients produced significantly fewer
words than NCs in the semantic VFT but not in the phonemic
VFT. Some previous studies indicated that MDD patients
had worse performance than NCs in both the semantic and
phonemic VFT, but with a significantly larger effect size for
semantic fluency (59–61). Our study also showed that the
SDMT score was significantly lower in MDD patients than in
NCs, indicating a slower processing speed of MDD patients
(62–64). We may need other metrics from fMRI to study the
above cognitive impairments since no correlation was found
between the altered FCs and VFT or SDMT scores in the
present study.

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Our study had some limitations. First, we did not compute the
sample size formula before the experiment. Although the sample
size of MDD patients in our study was larger than those of
most MDD studies, it is still insufficient, especially the number
of MDD patients with available cognitive domain scores. The
very small sample size will reduce the statistical power and
the reproducibility of a study (65). We will do sample size
calculation and continue to recruit more MDD patients with
cognitive scores in further work. Second, we did not divide
the MDD patients into mild, moderate, and severe depression
subgroups. As we know, more subgroups of MDD subjects
based on the disease severity could lead to more meaningful
findings, as the depression severity may also be correlated
to a different degree of cognitive impairments. However, in
our study, only moderate and severe subgroups could be
identified from the patient group. Besides, creating different
subgroups will cause small sample size in each subgroup,
which could negatively affect the result of our study. We will
recruit mild depression patients in further work and separately
investigate different subgroups. Besides, we only recruited
only first-episode, drug-naïve MDD patients. Selecting this
group of MDD patients eliminates possible confounding factors
such as illness duration and medication use (66). However,
different MDD subtypes could have different neurobiological
mechanisms and should be investigated separately in the future
(67). Third, we used only one imaging modality, but other
modalities also provide valuable diagnostic information and
could be used jointly with our protocol in order to improve
diagnosis. In addition, conventional FC is commonly used in

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Executive Function Impairment in MDD

fMRI studies, but if we wish to further our understanding
of its biological meaning, other advanced methods, such as
dynamic FC and high-order FC, should be applied to our future
MDD study.

provided their written informed consent to participate in
this study.

CONCLUSIONS

YL, YCh, XL, and SQ contributed to conception and design of
the study. DL, YZ, HZ, YCu, JC, and JL organized the data.
YL performed the data analysis and drafted the manuscript.
All authors revised the manuscript, and read and approved the
submitted version.

AUTHOR CONTRIBUTIONS

The MDD patients have altered FCs among multiple brain
networks and a disrupted correlation between the FC of
prefrontal cortex and executive function. The disrupted
correlation could present before the symptoms develop and
may be the core process in the development of executive
function impairment. This study offers a novel insight into
the pathophysiological mechanisms of executive function
impairment in MDD.

ACKNOWLEDGMENTS
YL, YCh, XL, YZ, HZ, and SQ were supported by the National
Natural Science Foundation of China—Major International
(Regional) Joint Research Project (81920108019), Major
Project (91649117), and General Project (81771344, and
81471251), Innovation and Strong School Project of Education
Department of Guangdong Province (2014GKXM034), and
Science and Technology Plan Project of Guangzhou (20181002-SF-0442). YL was also supported by China Scholarship
Council (201708440259) and Excellent Doctoral and PhD
Thesis Research Papers Project of Guangzhou University of
Chinese Medicine (A1-AFD018181A55). DL was supported
by Tranditional Chineses Medicine Bureau of Guangdong
Province (20202059).

DATA AVAILABILITY STATEMENT
The datasets generated for this study are available on request to
the corresponding author.

ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by The First Affiliated Hospital of Guangzhou
University of Chinese Medicine. The patients/participants

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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2020 Liu, Chen, Liang, Li, Zheng, Zhang, Cui, Chen, Liu and Qiu.
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April 2020 | Volume 11 | Article 272