Psychological resilience mediates the association of the middle frontal gyrus functional connectivity with sleep quality
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Psychological resilience mediates the association
of the middle frontal gyrus functional connectivity
with sleep quality
Yan Shi
Hunan Normal University
Youling Bai
Hunan Normal University
Li Zhang
Hunan Normal University
Yang Chen
Hunan Normal University
Xiaoyi Liu
Hunan Normal University
Yunpeng Liu
Hunan Normal University
Huazhan Yin ( yhz1979@sina.com )
Hunan Normal University https://orcid.org/0000-0001-6722-7507
Research Article
Keywords: psychological resilience, sleep quality, MFG, resting-state functional connectivity
Posted Date: July 22nd, 2022
DOI: https://doi.org/10.21203/rs.3.rs-1841535/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Read Full License
Page 1/18
Abstract
The ability to recover from stress, which is essential for sleep quality, is characterized as psychological
resilience. Meanwhile, poor sleep quality is commonly related to low psychological resilience. However,
the neurological underpinnings of psychological resilience, as well as the neural substrates of the links
between psychological resilience and sleep quality in healthy brains, remain not well understood. Based
on the recent studies that the middle frontal gyrus (MFG) acted a important role in psychological
resilience. The current study sought to determine the association between functional connectivity of the
MFG and psychological resilience in 144 young college students using resting-state functional
connectivity (rs-FC) analysis. Then, we examined how psychological resilience-related functional
connectivity was linked to sleep quality. Results indicated that the MFG functional connectivity was
observed to associate with psychological resilience. These connectivities involved the right middle
cingulum gyrus (rMCG), the right precentral gyrus (rPreCG), the left postcentral gyrus (lPoCG), and the left
thalamus. In addition, mediation analysis suggested that psychological resilience played a mediating role
in the relationship among the rMFG-rMCG, the rMFG-rPreCG, the rMFG-lPoCG, and the lMFG-left thalamus
and sleep quality. Overall, the current study offers new evidence for the neurological underpinnings of
psychological resilience and brings to light an underlying mechanism that the relationship between MFG
connectivity and sleep quality is mediated through psychological resilience.
1. Introduction
The capacity to deal effectively with stress and actively with difficulties in life is called psychological
resilience (Smith et al., 2010), which is also regarded as a stable trait (Daniels et al., 2011; Jacelon, 1997;
Waugh et al., 2011). An abundance of studies discovered the positively predictive role of psychological
resilience in sleep quality (Arora et al., 2022; Edwards et al., 2017; Lenzo et al., 2022; Segovia et al., 2013).
However, the neurological underpinnings of psychological resilience, as well as the neural substrates of
the links between psychological resilience and sleep quality in healthy brains, remain not well understood.
As a result, the current study tried to uncover the neural substrates of psychological resilience and its
mediation role in the link between psychological resilience-related functional connectivity and sleep
quality with rs-FC.
1.1 The brain neural mechanism of psychological resilience
There are numerous variables that affect psychological resilience, but processing on an emotional and
cognitive level is a major one (Curtis & Cicchetti, 2003; Philippe et al., 2009). For instance, Shi et al. (2019)
discovered that positive and negative impacts were directly linked to psychological resilience.
Furthermore, strong cognitive control or attention control may operate as resilience-enhancing
characteristics (Benight & Cieslak, 2011). During top-down attentional control, resilient participants
showed higher frontal area activity (Blair et al., 2013). On the other hand, the cognitive appraisal of
resilience (CAR) model also indicated that cognitive control and emotional regulation were involved in
resilience, allowing people to shift their attention away from the suffering experienced as a result of
Page 2/18perceived unpleasant occurrences (Yao & Hsieh., 2019). In summary, both the capacity to regulate
emotions to preserve emotional stability and cognitive control played significant roles in an individual’s
resilience level.
Meanwhile, neuroimaging research has examined the neurobiological correlates of psychological
resilience, and it has been shown that psychological resilience is linked to the structure of the middle
frontal gyrus (MFG), which is important for both cognitive control and emotional regulation (Anderson et
al., 2004; Blair et al., 2007; Hedden & Gabrieli, 2010; Kohn et al., 2014; Levens et al., 2011; Lévesque et al.,
2003; Ohira et al., 2006; Yamasaki et al., 2002; Yang et al., 2016). For instance, Daniels et al. (2011)
discovered that resilience was associated with increased activity in the MFG, which has previously been
implicated in emotion regulation. In addition, Burt et al. (2016) indicated that adolescents with high
resilience levels had significantly greater gray matter volume in right MFG and right superior frontal
regions compared with adolescents with low resilience levels. Hsieh et al. (2021) also found that the MFG
was related to individual resilience strength by magnetic resonance imaging (MRI). Additionally, higher
resilient participants showed weaker covariation in the MFG and middle temporal gyrus (Park et al.,
2022). Given the role of the MFG in psychological resilience as well as the process of emotional
regulation and cognitive control, we speculate that the MFG may be the underlying neural marker in
psychological resilience.
1.2 Psychological resilience played a mediator between MFG connectivity and sleep quality
Low psychological resilience is one of the reasons for poor sleep quality (Arora et al., 2022).
Psychological resilience is able to mitigate the negative impact of stress on sleep, whereas higher levels
of resilience can protect individuals from sleep disturbances (Liu et al., 2016). Increasing behavioral
evidence revealed that psychological resilience was enabled to predict sleep quality (Arora et al., 2022; Du
et al., 2022; Liu et al., 2016). For example, Arora et al. (2022) demonstrated an apparent positive
association between sleep quality and psychological resilience, which suggested that greater sleep
quality was linked to higher levels of psychological resilience. Additionally, Du et al. (2022) indicated that
enhanced psychological resilience attenuated the impact of perceived stress on sleep quality in 2254
students. What’s more, according to the hyperarousal theory of insomnia, insomnia can disrupt sleep
processes that are necessary for emotional processing and lead to an increased risk of heightened
emotional reactivity during the day (Perlis et al., 2001). While psychological resilience can forecast
negative emotional responses to stressful life events and facilitate individual’s sleep quality (Liu et al.,
2016).
Importantly, the MFG was also found to be implicated in sleep quality. For example, reduced functional
connectivity between the right precuneus and right MFG was brought on by sleep deprivation (Li et al.,
2020). Furthermore, insomnia patients had a significantly lower gray matter concentration in the left MFG
and postcentral gyrus, compared to normal controls (Dai et al., 2014). Insomnia patients also showed a
lower fractional amplitude of low-frequency fluctuations (ALFF) values in the inferior frontal gyrus and
right MFG (Li et al., 2015). Given the importance of psychological resilience in predicting sleep quality
Page 3/18and the relevance of MFG in both psychological resilience and sleep quality, we hypothesized that
psychological resilience may mediate the link between MFG connectivity and sleep quality.
In summary, previous behavioral studies have discovered the predictive role of psychological resilience in
sleep quality (Edwards et al., 2017; Lenzo et al., 2022; Segovia et al., 2013) and neuroimaging research
has investigated the role of MFG in psychological resilience based on the brain structural research.
However, the MFG connectivity in psychological resilience based on functional research and how the
neurobiological link between psychological resilience and sleep quality is not well known. The rs-FC
method is helpful for assessing the temporal correlation in low-frequency blood oxygen level dependent
(BOLD) signal fluctuations between various brain areas (Friston et al., 1993). Therefore, we used rs-FC to
identify whether the MFG functional connectivity is related to psychological resilience and further to
probe how the links between psychological resilience-related functional connectivity and sleep quality. We
proposed two hypotheses: (a) the MFG connectivity might be correlated to psychological resilience, and
(b) The association between MFG connectivity and sleep quality might be mediated through
psychological resilience.
2. Materials And Methods
2.1 Participants
144 college students (female = 77, age range is from 20 to 25, Mage = 22.04, SD = 1.01) participated in the
current study. Students were recruited via an advertisement on the university website or bulletin boards.
We excluded left-handed subjects, and all remaining subjects had no previous or concurrent psychiatric
disorders. The Institutional Review Board of Southwestern University's Brain Imaging Center gave its
approval for this study to be carried out. The study was supported by the Ethics Committee of Southwest
University, Chongqing, China. We did not start the experiment until subjects signed the written informed
consent and provided them with a small gift for their participation.
2.2 Measures
Psychological resilience scale
Psychological resilience is measured using the Resilience Scale, which consists of 25 items on a one-
factor structure (e.g., “I can seek out solutions to problems get out of them”). Subjects were asked to rate
each item, with ratings range from 1 (totally disagree) to 7 (totally agree) for each item on the scale. The
individual's level of psychological resilience is reflected in the scale, with higher total scores indicating
better levels of psychological resilience. The scale had been widely used in previous studies and had
great reliability and validity (Cronbach’s α = 0.93) (Shi et al., 2019).
Pittsburgh Sleep Quality Index
Page 4/18The Pittsburgh Sleep Quality Index (PSQI) was utilized to evaluate sleep quality. It consisted of 19 self-
assessment questions and 5 other-assessment questions (Cronbach’s α = 0.83) (Buysse et al., 1989). The
5 other-assessment items were typically utilized for clinical diagnosis and were therefore excluded from
the total score. The 19 self-assessment items were included in seven components, including subjective
sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping
medication, and daytime dysfunction. Each component score can range from 0 to 3. Total scores of PSQI
were composed of these seven component scores, with a minimum score of 0 and a maximum score of
21. Poor sleep quality is indicated if an individual has a total PSQI score of more than five. The PSQI
scale demonstrates excellent sensitivity (89.6%) and specificity (86.5%) for evaluating sleep quality
(Buysse et al., 1989).
2.3 MRI Data Acquisition
The equipment used to acquire resting-state fMRI data is a Siemens 3T Trio scanner at the brain imaging
center of Southwest University. During the instrument scans, participants were instructed to relax and
close their eyes, but were unable to doze off until the operation was over. The gradient echoplanar
imaging (EPI) sequence was used to obtain 242 conterminous whole-brain resting-state functional
images using the following settings: repetition time = 2000 ms; slices =32; echo time = 30 ms; thickness =
3 mm; resolution matrix = 64 ×64; flip angle = 90◦; field of view = 220 × 220 mm2; slice gap =1 mm; and
voxel size = 3.4 ×3.4 ×4 mm3
2.4 Imaging pre-processing
We used MATLAB 2014a (Math Works, Natick, MA) software for image pre-processing. All resting-state
image data were analyzed using SPM8 (http://www.fil.ion.ucl.ac. uk /spm) and the Data Processing
Assistant for resting-state software (DPARSF V3.2) (Zhang et al., 2020). The first 10 EPI scans, which are
thought to show how people adjust to their surroundings and prevent the equilibration effect, were
removed. The leftover scans were then re-sampled to 3 ×3 ×3 mm3 and precompiled via slice timing,
realignment, and normalization to an MNI (Montreal Neurological Institute) template. After that, in order
to remove the possible influence of physiological artifacts, the cerebrospinal fuid signal, white matter
signal, global signal, and the Friston 24-parameter model (Friston et al., 1996) were all regressed. A 8
mm full width at half maximum Gaussian kernel was then used to smooth the data. Finnally, to minimize
the impact of low-frequency fluctuation and high-frequency noise, the smoothed images were filtered with
band pass (0.01-0.1HZ). According to earlier research (Zhang et al., 2020; He et al., 2020), the pre-
processing of resting-state images was similar to that method.
2.5 Functional connectivity construction
The functional connectivity was built using Power's 264 functional node templates. Previous studies
demonstrated that emotional processing and cognitive processing significantly predicted psychological
resilience (Genet & Siemer 2011; Gloria et al., 2013; Mestre et al., 2017; Shi et al., 2019). Emotional
processing and cognitive process are involved in MFG (Beauregard et al., 2001; Kropf et al., 2018;
Page 5/18Lévesque et al., 2003; Ohira et al., 2006; Porro et al., 1996; Quidé et al., 2021). Neuroimaging studies have
examined the neurobiological correlates of psychological resilience and shown that psychological
resilience was related to the structure of MFG. Thus, we selected MFG as ROI to build functional
connectivity.
2.6 Statistical analysis
The software of SPSS 24.0 was used for the statistical analysis of the relationship between functional
connectivity in MFG and behavior. Specifically, the relationships among psychological resilience, sleep
quality, and functional connectivity were calculated by Pearson’s correlation analysis.
Then, we utilized the PROCESS macro in SPSS 24.0 to conduct a mediation analysis to look into the
mediating role of psychological resilience between MFG functional connectivity and sleep quality. In the
mediation model, psychological resilience played the role of mediating variable (MV), the independent
variable (IV) was brain functional connectivity, and sleep quality was regarded as the dependent variable
(DV). According to previous studies, the indirect effect was equal to the multiplication of path a and path
b. Path a represented the effect of IV on MV. Path b represented the effect of the was the relation of MV
on DV. Path c represented the direct effect of IV on DV. Path c’ represented the effect of IV on DV after
controlling MV. To establish the statistical significance of the mediation effect, the bootstrapping
approach was performed. We used 5000 samples to generate 95% confidence intervals (CI). The
mediation effect is significant if the 95% confidence interval fails to contain zero.
3. Results
3.1 Behavioral results
Behavioral results were listed in Table 1. Including the mean, standard deviation, score range, and the
association between psychological resilience and sleep quality. Results revealed a significant negative
correlation between psychological resilience and PSQI (r = – 0.206, p < 0.05), which meant that
psychological resilience was positively related to sleep quality. Additionally, there was no significant
gender differences in psychological resilience scores (t = 0.046, p > 0.05).
Insert Table 1
3.2 Functional connectivity in psychological resilience and sleep quality
To investigate the relationship between psychological resilience and functional connectivity, we
accomplished a Pearson’s correlation analysis. We selected the MFG as ROI to build functional
connectivity. The results showed that the MFG connectivity can predict psychological
resilience. Specifically, we found four functional connectivity, which were the rMFG-rMCG, the rMFG-
rPreCG, the rMFG- lProCG, and the lMFG-left thalamus (See Table 2 and Fig. 1). No other significant
association was acquired in our results.
Page 6/18Insert Table 2
Insert Fig 1.
We discovered that psychological resilience significantly predicted sleep quality in our sample (β = -
0.206, t = - 2.513, p < 0.05). In order to proving the hypothesis that psychological resilience mediates the
link between functional connectivity and sleep quality. We further examined the association between
functional connectivity and sleep quality. Results showed that four FC significantly predicted sleep
quality, including the rMFG–rMCG (β = 0.183, t = 2.217, p < 0.05), the rMFG– rPreCG (β = 0.171, t = 2.063,
p < 0.05), the rMFG–lPoCG (β = 0.178, t = 2.160, p < 0.05), and the lMFG–left thalamus (β = - 0.166, t = -
2.011, p < 0.05) (See Table 2).
3.3 Mediation analysis
To explore the role of psychological resilience in MFG functional connectivity and sleep quality, we carried
out four mediation analyses to test whether psychological resilience can explain the linkage between
MFG functional connectivity and sleep quality. Results revealed that psychological resilience played a
mediating role in the relations among the rMFG-rMCG (path a = – 14.841, p = 0.024; path b = – 0.025, p =
0.033; path ab = 0.372, bootstrapped 95% CI = 0.018, 1.156 ), the rMFG-rPrCG (path a = – 13.215, p =
0.026; path b = – 0.025, p = 0.031; path ab = 0.337, bootstrapped 95% CI = 0.017, 1.040 ), the rMFG-lPoCG
(path a = – 13.034, p = 0.025; path b = – 0.025, p = 0 .032; path ab = 0.328, bootstrapped 95% CI = 0.009,
1.024 ), and the lMFG-left thalamus (path a = 11.748, p = 0.047; path b = - 0.026, p = 0.028; path ab =
0.303, bootstrapped 95% CI = -0.901, -0.025 ) and sleep quality (Fig 2.).
Insert Fig 2.
4. Discussion
In the current study, we used rs-FC to probe the neurological underpinnings of psychological resilience as
well as the neural substrates of the links between psychological resilience and sleep quality in healthy
brains. Two major results were found. Firstly, psychological resilience was positively related to the lMFG-
left thalamus and negatively related to the rMFG-rMCG, the rMFG-rPreCG, and rMFG-lPoCG. Second,
mediation analysis revealed that rMFG-rMCG, rMFG-rPreCG, rMFG-lPoCG, and lMFG-left thalamus were
related to psychological resilience and offered a potential mechanism by which psychological resilience
mediates the association between functional connectivity in MFG and sleep quality. Overall, our findings
extended the earlier research by exposing the neurological underpinnings of psychological resilience and
bringing to light an underlying mechanism that the relationship between MFG connectivity and sleep
quality is mediated through psychological resilience.
4.1 The brain neural mechanism of psychological resilience
Results showed a negative relationship between psychological resilience and MFG connectivity, which
was mainly involving rMCG, rPreCG, lPoCG, and left thalamus. This was similar to prior research in which
Page 7/18psychological resilience was linked to structural changes or functional connectivity of these brain regions
(Burt et al., 2016; Filippi et al., 2021; Keith et al., 2016; Kennis et al., 2015; Kong et al., 2015). First, the
MFG, rMCG, rPreCG, lPoCG, and left thalamus were implicaed in the process of emotional processing
(Beauregard et al., 2001; Corbetta et al., 2002; Porro et al., 1996; Quidé et al., 2021; Torta et al., 2011).
Therefore, the activity of these brain regions was likely to associate with psychological resilience through
emotion regulation. For example, Yamasaki et al. (2002) conclude that the MFG may be a neural
substrate for attention–emotion interactions, and damaging this neural pathway will increase negative
emotional interference (Levens et a., 2011). Increased activity in lPoCG was observed during emotion
recognition of both positive and negative emotions (Adolphs et al., 2000; Hooker et al., 2012). Similarly,
emotion processing has been associated with psychological resilience (Curtis & Cicchetti, 2003; Genet &
Siemer, 2011; Gloria et al., 2013). Thus, emotion processing was an important mechanism for the
association between the brain regions of the MFG, rPreCG, lPoCG, rMCG, and left thalamus and
psychological resilience. Besides, this might be indicated that the FC of rMFG- rPreCG, rMFG - lPoCG,
rMFG - rMCG, and lMFG - left thalamus were also associated with emotion processing.
An alternative explanation may be based on the role of the MFG, rPreCG, lPoCG, rMCG, and left thalamus
in cognitive processing (Corbetta et al., 2002; Fox et al., 2006; Saalmann & Kastner, 2015). For instance,
previous studies found that the functions of inhibition and attention control were localized to the MFG
(Corbetta et al., 2002; Fox et al., 2006; Japee et al., 2015; Kincade et al., 2005; Munakata et al., 2011).
Besides, the interactions between the thalamus and cortical areas are integral to behavioral flexibility and
cognition in general (Saalmann & Kastner, 2015). Meanwhile, psychological resilience has been found to
associate with cognitive processing (Burns, et al., 2011; Liu, et al., 2012; Shi, et al., 2019). Thus, cognitive
processing was an important mechanism equally for the association between the brain regions of the
MFG, rPreCG, lPoCG, rMCG, and left thalamus and psychological resilience. Based on the role of emotion
processing and cognitive processing in psychological resilience and brain regions, our results that
psychological resilience was negatively associated with the rMFG-rPreCG, rMFG-lPoCG, and rMFG-rMCG
and positively associated with the lMFG-left thalamus might suggest that emotion processing and
cognitive processing were the underlying neural mechanism linking psychological resilience and FC.
Besides, our results showed that MFG connectivity might be the potential neuroimaging biomarker of
psychological resilience.
More interestingly, brain regions (i.e., rPreCG, lPoCG, rMFG, and rMCG) involved in our functional
connectivity results have been identified as being parts of the SSN and FPN (Londei et al., 2010).
Previous studies showed that both the SSN and the FPN were considered important networks crucial for
regulation and healthy brain functioning (Kropf et al., 2018; Zanto & Gazzaley., 2013). The FPN is
important for top-down cognitive processes (Zanto & Gazzaley., 2013) and the SSN is crucial for
processing sensory information and regulation of emotion (Kropf et al., 2018). The CAR model also
suggested that cognitive control and emotional regulation can be seen as key factors for an individual’s
resilience to conquer adverse experiences (Yao & Hsieh., 2019). In a word, our study extends previous
findings on the neural mechanisms of psychological resilience and suggests that SSN and FPN may be
Page 8/18potential neural biomarkers of psychological resilience, which needs to be further explored in future
studies.
4.2 Psychological resilience played a mediator between MFG connectivity and sleep quality
Importantly, our findings reveals that psychological resilience mediates the relations between MFG
connectivity and sleep quality. First, behavioral findings discovered that psychological resilience was
associated with sleep quality. This result was in line with previous studies in which psychological
resilience was linked to sleep quality (Arora et al., 2022; Du et al., 2022). Based on the hyperarousal theory
of insomnia, insomnia might disturb sleep processes required for emotional processing, increasing the
risk of heightened emotional reactivity throughout the day. Psychological resilience can predict negative
emotional reactions to stressful life situations, whereas higher levels of resilience can protect an
individual from sleep disruptions (Liu et al., 2016). One explanation might be that people with high levels
of resilience don't constantly consider all the possible consequences of the stresses in daily life, which
could lead to better sleep patterns and higher-quality sleep than people with lower levels of resilience.
The relationships between MFG connectivity and sleep quality were further discovered to be mediated by
psychological resilience. Interestingly, we discovered four pairs of FC associated with sleep quality, which
were the rMFG-rPreCG, the rMFG- lPoCG, the rMFG-rMCG, and the lMFG- left thalamus. These results were
similar to earlier research (Bai et al., 2022; Li et al, 2018; Li et al, 2020; Killgore et al, 2013; Perlis et al.,
2001; Wang et al., 2016). For example, an increased FC between the primary visual cortex and PreCG was
discovered, and this increased FC was related to the trouble of falling asleep ( Killgore et al., 2013). One
reason offered was that the increased rPreCG activity could be linked to enhanced sensitivity during the
period just before falling asleep (Bai et al., 2022). Besides, Bai et al. (2022) reported that sleep quality
was negatively related to the increased functional connectivity in bilateral PreCG and bilateral PoCG. The
PoCG is the primary receptive area for external stimuli and might cause excessive hyperarousal, leading
to increased processing of sensory information (Perlis et al., 2001; Sung et al., 2020). These findings were
interpreted in the hyperarousal theory of insomnia, which suggested that increased cognitive arousal and
sensory processing were responsible for difficulty falling or staying asleep. In addition, Li et al. (2020)
found sleep deprivation induced decreased functional connectivity between the right precuneus and the
rMFG. The rMFG was related to emotion regulation and sustained attentional impairment after sleep
deprivation (Cai et al., 2021). What’s more, the rMCG is also implicated in emotion and related to sleep
quality. For instance, Li et al. (2018) showed that rMCG had increased functional connectivity strength in
the right hippocampus in chronic insomnia disorder. The MCG is a vital area in the limbic system that is
associated with emotion processing (Wang et al., 2016). The disruption of emotion regulation in chronic
insomnia disorder might be further demonstrated that the rMCG involved in sleep quality. Meanwhile, the
thalamic abnormality is related to sleep dysfunction (Lunsford-Avery et al., 2013). Similar neuroimaging
research has also discovered that the rs-FC between cerebellum posterior lobe and thalamus in normal
sleep is distinct from sleep deprivation (Liu et al. 2015; Dai et al. 2015). Given the role of the MFG
connectivity in psychological resilience was crucial for sleep quality, this may be indicated that the rMFG-
the rPreCG, the rMFG-the lPoCG, the rMFG-the rMCG, and the lMFG-left thalamus were also associated
Page 9/18with sleep quality. At the same time, the rMFG-rPreCG, the rMFG-lPoCG, the rMFG-rMCG, and the lMFG-left
thalamus might be the neuromarkers linking psychological resilience to sleep quality.
What’s more, based on the hyperarousal hypothesis of insomnia, the reason of being difficulty falling or
staying asleep was increased cognitive arousal and emotional dysregulation (Perlis et al., 2001). While
psychological resilience is associated with emotion regulation and enables buffering of the negative
impact of stress on sleep (Liu et al., 2016). The higher levels of resilience can protect an individual from
sleep disturbances (Burns, et al., 2011; Curtis & Cicchetti, 2003). Similarly, lower ability of resilience
resulted in increased pre-sleep cognitive hyperarousal, which leads to poor sleep quality (Palagini et
al.,2018). Therefore, based on the role of the MFG functional connectivity in psychological resilience and
sleep quality, the mediation findings showed that psychological resilience facilitated sleep quality by
inhibiting unwanted thoughts and taking more effective emotion regulation strategies.
There are limitations to this study. Firstly, the subjects in our study are all young college students, so it is
not known whether our findings can be replicated in other populations (e.g., older adults). Second, since
this is cross-sectional research, causal inferences cannot be made. Therefore, the next investigations
should adopt longitudinal designs to reveal the causal relationships between brain function and
behavioral data.
5. Conclusion
In conclusion, this study adopted the resting-state functional connectivity to investigate the neural
substrates of psychological resilience and how psychological resilience-related functional connectivity
was linked to sleep quality in young college students. According to the findings of our results, the
psychological resilience level of college students may rely on the rsFC of MFG, which primarily involves
the rMCG, the rPreCG, the lPoCG, and the left thalamus, thus revealing a potential functional basis of
psychological resilience. Furthermore, by showing that psychological resilience mediates the relationship
between functional connectivity in MFG and sleep quality, our findings offer preliminary support for a
similar functional basis linking psychological resilience to sleep quality.
Declarations
Author contribution Yan Shi and Youling Bai conducted the studies, Yan Shi, Youling Bai, Li Zhang and,
YangChen collected and analyzed the data. Yan Shi, Youling Bai, Li Zhang, YangChen, Xiaoyi Liu,
Yunpeng Liu, and HuanzhanYin prepared and revised the manuscript.
Funding sources This work was supported by the Hunan social science achievement evaluation
committee, china (Grant No.XSP22YBC047).
Data Availability The datasets generated during and/or analyzed during the current study are available
from the corresponding author on reasonable request.
Page 10/18Compliance with Ethical Standards All procedures followed were in accordance with the Helsinki
Declaration of 1975, and the applicable revisions at the time of the investigation. Informed consent was
obtained from all participants included in the study.
Consent to participate Informed consent was obtained from all participants included in the study.
Consent for publication Not applicable
Conflict of Interest On behalf of all authors, the corresponding author states that there is no confict of
interest.
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Tables
Table 1. Participant demographics (N = 144; males = 67, females = 77).
Mean SD Range Psychological resilience PSQI
Age 22.04 1.01 22–25
Psychological resilience 102.24 14.96 71–141 – – 0.206*
PSQI 4.51 2.10 1–12 – –
N: number, SD: standard deviation, PSQI: Pittsburgh Sleep Quality Index.
* p < 0.05.
Table 2. Functional connectivity of nodes links correlated with the Pittsburgh sleep quality index score
and psychological resilience.
Node 1 Node 2 psychological resilience PSQI
r p r p
(corrected) (corrected)
rMFG rMCC –0.188 0.024 0.183 0.028
rMFG rPrCG –0.185 0.026 0.171 0.041
rMFG LPoCG –0.019 0.025 0.178 0.032
lMFG left thalamus 0.166 0.047 –0.166 0.046
Figures
Page 16/18Figure 1
The functional connectivity of MFG and its correlation with the psychological resilience score. (a)
Correlation between psychological resilience scores and the strength of rsFC between the rMFG (right
middle frontal gyrus) and rMCG (right middle cingulum gyrus). (b) Correlation between psychological
resilience scores and the strength of rsFC between the rMFG (right middle frontal gyrus) and rPreCG (right
precentral gyrus). (c) Correlation between psychological resilience scores and the strength of rsFC
between the rMFG (right middle frontal gyrus) and lPoCG (left postcentral gyrus). (d) Correlation between
psychological resilience scores and the strength of rsFC between the lMFG (left middle frontal gyrus) and
lPoCG (left postcentral gyrus).
Page 17/18Figure 2
Psychological resilience mediates the relationship among the rMFG-rMCG, rMFG-rPreCG, rMFG-lPoCG,
and lMFG-left thalamus and sleep quality. *P < 0.05. Note: rMFG: right middle frontal gyrus, rMCG: right
middle cingulum gyrus, rPreCG: right precentral gyrus, lPoCG: left postcentral gyrus, lMFG: left middle
frontal gyrus.
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