A Study of the Mechanisms by Which Mood and Sleep Disorders Interact
Conditions: Sleep Disorder Insomnia Chronic
Sponsor: Qilu Hospital of Shandong University

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Research on the Mechanisms of the
Interaction between Emotions and Sleep
Disorders
Project leader:
Jiang Wenjing
Research
Organization:
Qilu
Hospital,
Shandong University

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Summary of research program
Project Title
Research on the mechanism of the interaction between
mood and sleep disorders
Purpose of Research
Primary purpose: To explore the mechanisms by which
mood and sleep disorders interact.
Exploratory Aims: 1. Discover the correlation between
ECG/cardio-magnetic
and
EEG/Encephalomagnetic
features and mood and sleep disorders;
2. correlation of mind-brain monitoring to
explore mood and sleep disorders interactions.
Research hypotheses
1.
Intrinsic mapping mechanism between mood and
sleep disorders.
2.
Possible reasons for the interaction between mood
conduction and sleep disorders.
Study design
□Case-control study
□Cohort study
□√
Cross-sectional study
Subjects
Criteria
(1) Normal healthy group
Inclusion Criteria:
Physical,
mental,
psychological,
and
social
relationships in a completely good state.
Exclusion criteria:
①Neurological and psychiatric diseases;
②History of sleep disorders;
③history of other chronic or systemic diseases.
(2) Mood disorders with insomnia
Inclusion criteria:
2 ( a) Meet the above diagnostic criteria for
insomnia.
②Age 18～80 years old.

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Date: 2024-10-11
③
PSQI ＞
5, ISI 7 ＞
, HAMA 7 ＞
and/or
HAMD≥7; and
④No communication barriers; and
⑤Signed informed consent.
Exclusion Criteria
(i) Secondary insomnia caused by other somatic
diseases; (ii) Other types of sleep disorders and mood
disorders.
Other types of sleep disorders and mood disorders.
③Environmental or other human factors disturbing
sleep for a long time.
(3) Mood disorder without insomnia group
Inclusion Criteria
(1) Do not meet the above diagnostic criteria for
insomnia ;)
②Age is 18～80 years old.
③PSQI≤5, ISI≤7, HAMA＞7 and/or HAMD
≥7; and
④No communication barriers; and
⑤Signed informed consent.
Exclusion criteria.
(i) Presence of insomnia and other sleep disorders
(4) Primary insomnia
Inclusion Criteria
①meets the above diagnostic criteria for insomnia;
②
does not meet the diagnostic criteria for mood
disorders
(ii) Do not meet the above diagnostic criteria for
mood disorders.
③Age 18～80 years old; ④PSQI 5 years old.
④PSQI＞5, ISI＞7, HAMA≤7 and HAMD＜

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7; and
⑤No communication barriers;
⑥Signed informed consent.
Exclusion criteria:
①
secondary insomnia caused by other somatic
diseases; ②other types of sleep disorders were excluded.
②Excluding other types of sleep disorders.
③Environmental or other human factors interfere
with sleep for a long time.
1.3 Experimental grouping: the study is divided into
four groups, namely: normal health group; mood disorder
with insomnia group; mood disorder without insomnia
group; primary insomnia without mood disorder group.
Diagnostic criteria
The diagnostic criteria of insomnia were met:
Patients were screened by two neurologists with -15
years of experience using a semi-standardized psychiatric
and sleep related interview according to the Diagnostic
and Statistical Manual of Mental Disorders, 5th edition
(DSM- V ) criteria . The selected subjects met the
following criteria:
①Self-reported difficulty falling asleep, difficulty
maintaining sleep, or early awakening;
②symptoms at least three times per week and for 3
months;
③
Concomitant with at least one associated
impairment in daytime functioning (e.g., fatigue, mood
disturbance, or cognitive impairment);
④No other sleep disorder (e.g., obstructive sleep
apnea
or
sleep-related
movement
disorders),
serious
organic disease, or psychiatric illness, such as depression

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or generalized anxiety;
⑤
All patients selected were not taking any
psychoactive drugs for at least 2 weeks before and during
the study period to eliminate drug effects;
Sample
size
and
calculation basis
PASS software was applied to determine sleep
improvement based on PSG sleep data, PSQI scores and
ISI scores, and 50 cases of mood disorders with insomnia
group, 50 cases of mood disorders without insomnia
group, 50 cases of primary insomnia group, and 50 cases
of normal healthy group were included according to the
literature reports
Main exposure factors
Includes general clinical information, scale assessments,
ambulatory
electrocardiograms,
and
electrocardiographic/magnetic
and
electroencephalographic/magnetic
monitoring
data.
(100KB/copy)
Endpoint Variables
Primary endpoint: there is an intrinsic mapping and
interaction between mood and sleep disorders.
Secondary endpoints:
1.
to
reveal
the
manifestation
of
mind-brain
interactions in emotions and their changes in sleep
disordered states using simultaneous measurements of
heart-brain electro/magnetic signals. Provide information
about
the
spatiotemporal
properties
of
mind-brain
interactions, synchronization, and responses to external
stimuli and internal regulation.
2. It is expected to obtain a complex mapping
relationship between mood-mind-brain-sleep quality, to
discover the mechanism by which mood and sleep
disorders influence each other, and to provide new
perspectives for understanding the interactions between

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mood disorders and sleep disorders.
Follow-up plan
(To be completed for
cohort study)
None
Sample collection
□NoYes
Brief description of type of organization, volume
Include general clinical information, scale assessment,
blood pressure, ambulatory ECG, EEG, basal metabolic
rate. ECG/MAG & EEG/MAG monitoring: sympathetic
nervous
system
activity.
(Full
30
minutes)
(100KB/sample)
Screening
for
Non-Medical Purposes
No □Yes
e.g. non-essential CT, imaging, laboratory tests, etc.
Statistical methods
For
continuous
variables,
statistical
descriptions were made using mean ± standard
deviation,
and
for
categorical
variables,
frequency
and
rate
(%).
For
comparison
of
parameters between two groups, t-test or rank-sum
test
was
used
for
continuous
variables
and
chi-square test for categorical variables. For
one-way correlation analysis, Pearson or Spearman
correlation analysis was used. For multifactor
analysis, generalized linear model or logistic
regression
model
was
used.
All
of
the
above
parameters
were
considered
statistically
significant at p<0.05
Study
risk
self-assessment
□High risk studies
□Medium risk studies
Low risk
studies
Brief description:
This study is a low-risk study, the research process,
fully protect the patient's right to privacy and informed
consent, the data obtained from the study can be published
for academic needs, but the patient's privacy (such as

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name, medical record number, etc.) will not be published
to absolute confidentiality, the patient before participating
in the experiment are signed informed consent, and have
the right to terminate participation in the study at any time.
Confidentiality Statement:
The information contained in this study protocol will be made available for review only by the
investigator(s) of this project, the Clinical Research Management Committee, and the
appropriate agencies. It is strictly forbidden to communicate any information to third parties
not related to this study without the approval of the principal investigator.

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1. Background of the study (state the basis and significance of the study in the
light of the current state of internal and external research)
1. Rationale for the establishment of the project
(I) Significance of the study
Sleep is vital to human beings, and sleep can promote brain development , help
consolidate memory, enhance human immunity and so on. About one-third of life is
spent in sleep. In recent years, the incidence of sleep disorders, especially insomnia,
has been on the rise. The 2024 White Paper on Sleep Health of Chinese Residents
shows that 59% of people in China suffer from insomnia. Insomnia will directly lead
to the patient's daytime function damage, such as daytime drowsiness, inattention,
etc.[1] , affecting people's normal work life, and even for the traffic and other safety
hazards. And insomnia is related to the dysfunction of cardiovascular and
cerebrovascular system, endocrine system and digestive and respiratory system, etc.,
and plays a vital role in the development of chronic diseases, so insomnia has become
an increasingly serious medical and social problems.
Meanwhile, with the development of society, the incidence of mental diseases is
gradually rising. China's mental health survey shows that the prevalence of anxiety
disorders in China is 4.98%, and the lifetime prevalence of depression is 6.9%[2] ,
Mood disorders can greatly affect the quality of life, and even seriously jeopardize
people's physical health and life safety. Insomnia and mood disorders have a close
relationship. Insomnia not only increases the risk of depression[3] , but also raises the
risk of suicide[4] among young patients. In addition, mood disorders are also closely
related to insomnia, anxiety and depression and other mood disorders will also
increase the incidence of insomnia[5] , and some studies have shown that the quality
of life of patients with major depression is related to insomnia[6] .
The effects of emotional states on sleep are multidimensional.Meneo et al.
summarized multiple mechanisms by which emotions affect
sleep, such as
dysfunctions in the prefrontal cortex and amygdala, among others, as well as
cognitive-behavioral
factors.
Among
these
mechanisms,
dysfunction
of
the
Autonomic Nervous System (ANS) is considered to be a central link[7] . The
Autonomic Nervous System (ANS) is mainly composed of two major branches, the

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sympathetic and parasympathetic. On the one hand, under emotional stress or anxiety,
the activity of the Sympathetic Nervous System (SNS) is enhanced, leading to
physiological responses such as increased heart rate and blood pressure, which
increases alertness and reduces the tendency to sleep[8] . On the other hand, during
sleep, the activity of the Parasympathetic Nervous System (PNS) is enhanced,
particularly through an increase in the high-frequency component of Heart Rate
Variability (HRV), which contributes to the depth and quality of sleep[9] .
Although the association between mood and sleep disorders is widely
recognized, research on the underlying mechanisms is still limited and it is difficult to
develop effective intervention strategies accordingly. As an emerging biomonitoring
method that can simultaneously monitor and analyze the activities of the brain and
heart and interpret the connection of the heart-brain axis from a new perspective,
mind-brain homoeostasis can help to elucidate the related physiological activities,
provide evidence for determining the mechanism of the influence of emotions on
sleep disorders, and improve the accuracy of diagnosis and the evaluation of the
effectiveness of treatments. The heart-brain synesthesia method plays a crucial role in
revealing the complex connection between mood and sleep disorders, and is expected
to improve our research on the mechanism of mood and sleep disorders and find new
ways of intervention, thus enhancing the public's physical and mental health.
(II) Current status of domestic and international research
The effects of emotional states on sleep are mediated by a series of complex
physiological and psychological processes. Studies have shown that mood disorders
significantly affect autonomic function. In severely depressed patients and patients
with concomitant anxiety, HRV is reduced, suggesting impairment of autonomic
function in patients with mood disorders[8] , while the parasympathetic nervous
system, through its neural activity, influences cardiac function and participates in the
regulation of anxiety-related responses[10] . In addition, patients with various types of
sleep disorders also experience autonomic dysfunction. For example, patients with
chronic insomnia show enhanced sympathetic activity during sleep, especially during
the rapid eye movement stage of sleep, while it is less pronounced during slow wave
sleep[11] . Obstructive sleep apnea is associated with dysregulation of the

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sympathetic
nervous
system
and
sympathetic
excitation[12]
.
However,
the
mechanisms involved remain unclear.
There are several ways to measure autonomic activity in mood disorders or
sleep disorders. For example, classical HRV reflects the function and interconnection
of sympathetic and parasympathetic nerves[13] . In addition, other measures such as
plasma norepinephrine and cortisol levels, muscle sympathetic activity, changes in
blood pressure, body temperature and basal metabolic rate, and sympathetic skin
responses can also reflect autonomic function[12] . Although these methods of
assessing sympathetic nervous system function provide different perspectives on
sympathetic activity, they are unable to accurately differentiate between systemic and
localized differences in sympathetic activity, and they are subject to the interference
of a variety of physiological and environmental factors, which in turn affects the
stability and accuracy of the assessment.
With the rapid development of quantum information technology, atomic
magnetometer technology based on the SERF (Spin-Exchange Relaxation-Free) effect
has made a breakthrough in the field of sensing biological weak electrophysiological
signals[14] . Magnetic cardiography (MCG) technology utilizes highly sensitive
multi-channel atomic magnetometer arrays to non-destructively and non-invasively
record the weak magnetic field signals generated by myocardial electrical activity
during the cardiac cycle, providing a new way to assist in the diagnosis of
cardiovascular diseases, especially ischemic heart disease[15] . Compared with
electrocardiography (ECG), magnetoencephalography (MEG) is free of interference
from human tissues and can capture the weak magnetic field signals generated by the
electrical activity of the heart more acutely, providing richer information[16] .
Magnetoencephalography (MEG) is a technique that can measure neural activity in
the brain with high temporal and spatial resolution by detecting the biomagnetic field
formed by the weak currents generated by the excitatory postsynaptic potentials of
neurons[17] . MEG technology has been used in many clinical studies for brain
disorders such as Parkinson's disease, Alzheimer's disease, and autism to search for
brain magnetic biomarkers[18-20] . These heart-brain magnetic imaging techniques
based on ultra-high sensitivity and very weak magnetic field sensing provide new

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non-invasive and high-resolution means for the diagnosis and research of heart-brain
diseases, and have important clinical application value and research potential.
Based on the development of heart and brain measurement technology,
heart-brain synesthesia has shown its unique advantages in the field of mood and
sleep. The University of Konstanz has revealed the role of heart-brain signal
measurements in understanding emotional responses by analyzing the effects of
emotional images on magnetoencephalography and heart rate[21] , and Japanese
scholars' research on the moderating effect of fasting on physiological-psychological
responses to emotional pictures[22] . For sleep research, Beijing Institute of
Technology proposed a sleep quality assessment method based on the combination of
EEG and ECG multimodal features[23] in 2023 , which not only improves the
accuracy of sleep disorder diagnosis, but also provides strong support for clinical
diagnosis and treatment. Combining EEG and ECG, the study also demonstrated that
the sympathetic-parasympathetic activity of the heart plays an initiating role in the
process of emotional arousal, and that there is a causal relationship between this
activity and brain dynamics[24] . Heart-brain co-testing can synchronize the recording
of cardiac dynamics and the activation of brain structures, providing the underlying
mechanisms related to the activity of the heart-brain axis and, accordingly, providing
effective interventions and efficacy assessment methods. The development of
cardio-cerebral homoeostasis provides new perspectives and methods for the study of
physical and mental health, and opens up a new path for the diagnosis and treatment
of sleep disorders.
2. Research Problems, Research Objectives (Elaborate the scientific problems
and research objectives of the study)
2.1 Research question:
Intrinsic mapping mechanism between heart-brain electrical/magnetic features
and other features and emotions and sleep disorders: to reveal the manifestation of
heart-brain interactions in different emotions and the changes in sleep disordered
states.
To
study
the
intrinsic
mapping
mechanism
between
mind-brain
electrical/magnetic features and mood and sleep disorders, to extract the mind-brain
electrical/magnetic features associated with sleep disorders, and to study the possible

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reasons for the interaction between mood and sleep disorders.
2.2 Primary research objective: Primary objective: to explore the mechanism of
the interaction between mood and sleep disorders.
2.3 Secondary and exploratory research aims:
Exploratory Aims: 1. to discover the relationship between ECG/cardio-magnetic and
EEG/Encephalomagnetic characteristics and mood and sleep disorders;
2. correlation of mind-brain monitoring to explore mood and sleep disorders
interacting with each other.
3. research methodology (this section is the focus of the presentation)
3.1 Research Design
3.1.1 Research methodology through cross-sectional studies
3.1.2 General information collection: detailed baseline data were collected from
all participants, including basic demographic characteristics such as age, gender,
education level, health status, sleep and lifestyle.
3.1.3 Prior to the start of the experiment, the Pittsburgh Sleep Quality Index
(PSQI), Insomnia Severity Index (ISI), Montreal Cognitive Assessment Scale
(MoCA), Brief Intelligence Mental State Examination (MMSE), Hamilton Anxiety
Scale (HAMA), Hamilton Depression Scale (HAMD), Epworth Sleepiness Scale
(ESS ), SF-36 Quality of Life Scale (QOLS) and other relevant scale information and
dynamic electrocardiogram.
3.1.4 Measurement of POMS scale and blood pressure and basal metabolic rate,
EEG 0 minutes before the start of the experiment.
3.1.5 ECG/magnetic and EEG/magnetic monitoring: sympathetic nervous
system activity. (Full 30 minutes)
3.2 Study site and study population (source and inclusion exclusion criteria)
3.2.1 Data collection: all data in this study were obtained from outpatients and
inpatients/healthy volunteers of Qilu Hospital of Shandong University.
3.2.2 Diagnostic and Inclusion and Exclusion Criteria of Study Subjects:
Insomnia diagnostic criteria:
Patients were screened according to Diagnostic and Statistical Manual of Mental
Disorders, 5th edition (DSM-V) criteria by two neurologists with 15 years of
experience using a semi-standardized psychiatric and sleep-related interview. The

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selected subjects met the following criteria:
①Self-reported difficulty falling asleep, difficulty maintaining sleep, or early
awakening;
(ii) Symptoms were present at least three times per week and had persisted for 3
months;
(iii) Concomitant with at least one associated impairment in daytime
functioning (e.g., fatigue, mood disorders, or cognitive impairment);
④
No other sleep disorders (e.g., obstructive sleep apnea or sleep-related
movement disorders), serious organic diseases, or psychiatric disorders, such as
depression, generalized anxiety;
⑤All patients selected were not taking any psychoactive drugs for at least 2
weeks before and during the study to eliminate drug effects;
NAC criteria
(1) Normal healthy group
Inclusion criteria:
Physical, mental, psychological, and social relationships in a completely good
state.
Exclusion criteria:
①Neurological and psychiatric diseases;
②History of sleep disorders;
③history of other chronic or systemic diseases.
(2) Mood disorders with insomnia
Inclusion criteria:
①Meet the above diagnostic criteria for insomnia.
②Age 18～80 years old; ③PSQI 5.
③PSQI＞5, ISI＞7, HAMA＞7 and/or HAMD≥7.
④No communication barriers; and
⑤Signed informed consent.
Exclusion Criteria
①Secondary insomnia caused by other somatic diseases; ②Other types of
sleep disorders and mood disorders.

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Other types of sleep disorders and mood disorders.
③Environmental or other human factors disturbing sleep for a long time.
(3) Mood disorder without insomnia group
Inclusion Criteria
(1) Do not meet the above diagnostic criteria of insomnia; (2) Aged 18
80
years old.
②Age is 18～80 years old.
③PSQI≤5, ISI≤7, HAMA＞7 and/or HAMD≥7.
④No communication barriers; and
⑤Signed informed consent.
Exclusion criteria.
(i) Presence of insomnia and other sleep disorders
(4) Primary insomnia
Inclusion Criteria
①Meet the above diagnostic criteria for insomnia.
①Does not meet the above diagnostic criteria for mood disorders.
③Age 18～80 years old.
④PSQI＞5, ISI＞7, HAMA≤7 and HAMD＜7; and
⑤No communication barriers;
⑥Signed informed consent.
Exclusion criteria:
①secondary insomnia caused by other somatic diseases; ②other types of
sleep disorders were excluded.
②Excluding other types of sleep disorders.
③environmental or other human factors disturbing sleep for a long time.
3.3 Study Variables (Factors) and Measurement
(1) Measurement of variables before the start of the experiment
Including general clinical information, scale assessment, and ambulatory
electrocardiogram
(10KB/copy).
Scale
assessment
included:
measurement
of
Pittsburgh Sleep Quality Index (PSQI) (description: 0-5 very good, 6-10 okay, 11-15
fair, 16-21 very poor), Insomnia Severity Index (ISI) (description: total score range:

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0-28,0-7 no clinically significant insomnia, 8-14 subthreshold insomnia, 15-21
clinically insomnia moderate-severe, 22- 28 clinically significant insomnia), Montreal
Cognitive Assessment Scale (MoCA) (description ≥26 normal), Brief Mental State
Examination (MMSE) (description: ≥27 normal, 21-26 mild, 12-20 moderate, <12
severe), Hamilton Anxiety Scale (HAMA) (description: ≥probably severe anxiety,
≥21 definitely significant anxiety, and ≥14 definitely anxious, >7 probably anxious,
≤7 normal), Hamilton Depression Scale (HAMD) (description: <7 no depression,
7-12 mild depression, 18-24 moderate depression, >24 severe depression), Epworth
Sleepiness Scale (ESS ), SF-36 Quality of Life Scale, and other related scale
information. Simplified formulas were used to estimate basal metabolic rate, such as
the Gale formula: basal metabolic rate % = (pulse rate +
pulse pressure)-111.
(2) Measurement of variables while the experiment was in progress:
0 minutes to 30 minutes after the lights were turned off Measurements The
following data (100 KB/copy)
The POMS scale of state of mind and blood pressure and basal metabolic rate
were measured 0 minutes before the start of the experiment, and EEG.
ECG/magnetic and EEG/magnetic monitoring: sympathetic nervous system
activity. (full 30 minutes)
3.4 Study Outcomes
3.4.1 Primary outcome variable: there is an intrinsic mapping and interaction
between mood and sleep disorders.
3.4.2 Secondary outcome variables:
1. Using simultaneous measurements of mind-brain electrical/magnetic signals,
reveal how mind-brain interactions manifest themselves in emotions and how they
change in sleep-disordered states. Provide information about the spatiotemporal
properties of mind-brain interactions, synchronization, and response to external
stimuli and internal regulation.
2.
It
is
expected
to
obtain
a
complex
mapping
relationship
between
mood-mind-brain-sleep quality, to discover the mechanism by which mood and
sleep disorders influence each other, and to provide new perspectives for
understanding the interactions between mood disorders and sleep disorders.

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3.5 Follow-up (to be filled in by cohort study)
3.6 Sample size
Applying PASS software, sleep improvement was determined based on PSG
sleep data, PSQI score and ISI score, and 50 cases of mood disorders with insomnia
group, 50 cases of mood disorders without insomnia group, 50 cases of primary
insomnia group, and 50 cases of normal healthy group were included according to the
literature reports
3.7 Data collection and management
Data collection was firstly collected by applying the paper version of the case
report form, and the subjects' information (including general clinical data, scale
assessment, blood pressure, dynamic ECG, EEG, basal metabolic rate) was then
entered into the computer by specialized personnel. ECG/ECG and EEG/MEG data
were measured by specialized personnel. Statistical analysis was performed by a
clinical statistician.
3.8 Statistical Analysis Methods
For continuous variables, statistical descriptions were performed using mean
±
standard deviation, and for categorical variables, frequency and rate (%). For
comparison of parameters between two groups, t-test or rank sum test was used for
continuous variables and chi-square test was used for categorical variables. For
one-way correlation analysis, Pearson or Spearman correlation analysis was used. For
multifactor analysis, generalized linear model or logistic regression model was used.
All of the above parameters were considered statistically significant at p<0.05
Quality control
1) Clinical data disease diagnosis: determined by two chief physicians of sleep
disorder group;
2) Scale assessment: determination by two scale-trained neurologists;
(3) Sleep apnea monitoring: determination by two specialized PSG charting
physicians;
(4) Ambulatory electrocardiogram (ECG): performed by two specialized physicians
specializing in the interpretation of ambulatory electrocardiograms;
(5)
Electroencephalogram
(EEG):
determination
by
two
specialized
EEG
interpreting doctors;
6) ECG/magnetic and EEG/magnetic monitoring: determination by two specialized

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professionals;
7) Statistics: performed by clinical statisticians;
4. Research feasibility analysis
4.1 Theoretical feasibility
This topic is well-founded, reasonably designed and operable: the group has referred
to a large amount of literature, the topic is reasonably designed, the technical route is
correct, and the source of the required samples is sufficient; the group has been
engaged in the research related to the pathogenesis and treatment of sleep disorders
and mood disorders for a long time, and it has a good foundation for the work.
4.2 Feasibility of methodology and personnel
The research group has a reasonable composition of personnel with rich research
experience, a reasonable mix and a clear division of labor, which can ensure the
smooth completion of all steps in this project. The clinical scales and related indexes
required for this study have been widely used in medical research, and the technology
is mature and reliable; the number of patients with mood disorders and sleep disorders
in Qilu Hospital of Shandong University is large, and there is a base of sample size.
The R&D team has many years of R&D experience in ultra-high sensitivity weak
magnetic field measurement, and has developed two generations of ultra-high
sensitivity magnetic field and inertia measurement experimental devices since 2013
with the support of the National Natural Fund Committee of China. Based on the
above technology foundation, the team has independently innovated and developed a
weak cardiac magnetic imaging device and a weak brain magnetic measurement
device, which are close to the best international level, and offer transformative new
technologies for cardiac disease research, neuroscience, and brain science. The team
has provided transformative new technologies for heart disease research, neuroscience
and brain science. The project leader has been deeply engaged in psychosomatic and
sleep disorders for a long time. He is the young vice-chairman of the Chinese Medical
Association's Psychosomatic Medicine Branch and the vice-chairman of the
Shandong Provincial Medical Association's Geriatrics Society, and he is in charge of
the Shandong Provincial Key Research and Development Program "Caring for You
and Me, Caring for Others", the National Mental Health Project, and he has carried
out the sleep clinic and polysomnography. As the main researcher, he has undertaken
a number of clinical trials on psychosomatic and sleep disorders, and has conducted a

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number of clinical and scientific studies on psychosomatic disorders and sleep
disorders.
5. Time plan
Annual arrangement of the research program and expected research results
2024.10-2026.02
Screening
of
enrolled
subjects,
completion
of
baseline
assessment, measurement of cardiac/magnetic and cerebral/electromagnetic and
related auxiliary examination indexes;
2026.03-2027.02
Collect data and group samples for the period;
2027.03-2027.10
Data analysis and writing of the article.
6. References
[1]
PAVLOVA M K, LATREILLE V. Sleep Disorders [J]. American Journal of
Medicine, 2019, 132(3): 292-9.
[2]
HUANG Y, WANG Y, WANG H, et al. Prevalence of mental disorders in
China: a cross-sectional epidemiological study [J]. Lancet Psychiatry, 2019,
6(3): 211-24.
[3]
BYRNE E M, TIMMERMAN A, WRAY N R, et al. Sleep Disorders and Risk
of Incident Depression: a Population Case-Control Study [J]. Twin Research
and Human Genetics, 2019, 22(3): 140-6.
[4]
TSENG W-C, LIANG Y-C, SU M-H, et al. Sleep apnea may be associated
with suicidal ideation in adolescents [J]. European Child & Adolescent
Psychiatry, 2019, 28(5): 635-43.
[5]
MERRILL R M. Mental Health Conditions According to Stress and Sleep
Disorders [J]. International Journal of Environmental Research and Public
Health, 2022, 19(13).
[6]
JERMANN F, PERROUD N, FAVRE S, et al. Quality of life and subjective
sleep-related measures in bipolar disorder and major depressive disorder [J].
Quality of Life Research, 2022, 31(1): 117-24.
[7]
MENEO D, SAMEA F, TAHMASIAN M, et al. The emotional component of
insomnia disorder: a focus on emotion regulation and affect dynamics in
relation to sleep quality and insomnia [J]. J Sleep Res, 2023, 32(6): e13983.
[8]
KEMP A H, QUINTANA D S, FELMINGHAM K L, et al. Depression,
Comorbid Anxiety Disorders, and Heart Rate Variability in Physically Healthy,
Unmedicated Patients: Implications for Cardiovascular Risk [J]. Plos One,
2012, 7(2).
[9]
JURYSTA F, LANQUART J P, SPUTAELS V, et al. The impact of chronic
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