Near-infrared brain functional characteristics of mild cognitive impairment with sleep disorders

Abstract

BACKGROUND

Mild cognitive impairment (MCI) has a high risk of progression to Alzheimer’s disease. The disease is often accompanied by sleep disorders, and whether sleep disorders have an effect on brain function in patients with MCI is unclear.

AIM

To explore the near-infrared brain function characteristics of MCI with sleep disorders.

METHODS

A total of 120 patients with MCI (MCI group) and 50 healthy subjects (control group) were selected. All subjects underwent the functional near-infrared spectroscopy test. Collect baseline data, Mini-Mental State Examination, Montreal Cognitive Assessment scale, fatigue severity scale (FSS) score, sleep parameter, and oxyhemoglobin (Oxy-Hb) concentration and peak time of functional near-infrared spectroscopy test during the task period. The relationship between Oxy-Hb concentration and related indexes was analyzed by Pearson or Spearmen correlation.

RESULTS

Compared with the control group, the FSS score of the MCI group was higher (t = 11.310), and the scores of Pittsburgh sleep quality index, sleep time, sleep efficiency, nocturnal sleep disturbance, and daytime dysfunction were higher (Z = -10.518, -10.368, -9.035, -10.661, -10.088). Subjective sleep quality and total sleep time scores were lower (Z = -11.592, -9.924). The sleep efficiency of the MCI group was lower, and the awakening frequency, rem sleep latency period, total sleep time, and oxygen desaturation index were higher (t = 5.969, 5.829, 2.887, 3.003, 5.937). The Oxy-Hb concentration at T0, T1, and T2 in the MCI group was lower (t = 14.940, 11.280, 5.721), and the peak time was higher (t = 18.800, 13.350, 9.827). In MCI patients, the concentration of Oxy-Hb during T0 was negatively correlated with the scores of Pittsburgh sleep quality index, sleep time, total sleep time, and sleep efficiency (r = -0.611, -0.388, -0.563, -0.356). It was positively correlated with sleep efficiency and total sleep time (r = 0.754, 0.650), and negatively correlated with oxygen desaturation index (r = -0.561) and FSS score (r = -0.526). All comparisons were P < 0.05.

CONCLUSION

Patients with MCI and sleep disorders have lower near-infrared brain function than normal people, which is related to sleep quality. Clinically, a comprehensive assessment of the near-infrared brain function of patients should be carried out to guide targeted treatment and improve curative effect.

Key Words: Mild; Cognitive impairment; Sleep disorders; Near-infrared; Brain functional; Characteristics

Core Tip: Mild cognitive impairment (MCI) is a transitional state from normal cognitive development to dementia. Here, we analyzed 120 patients with MCI accompanied by sleep disorders and 50 healthy subjects and mainly observed the near-infrared brain function characteristics of the two groups and their relationship with the sleep status of patients with MCI. Through statistical analysis, we put forward the theory that the near-infrared brain function of MCI patients with sleep disorders has poor performance and is closely related to sleep quality and fatigue, which has a certain breakthrough.

INTRODUCTION

Mild cognitive impairment (MCI) is a common cognitive impairment with clinical manifestations between normal and dementia. It is a transitional state from normal cognitive development to dementia[1]. According to the survey, the incidence of MCI in the elderly is about 18.8%, increasing with the intensification of population ageing[2].

MCI, the main manifestation of memory loss, has not significantly affected the basic daily life and social activities of patients. However, MCI has a high risk of progression to Alzheimer’s disease (AD). Studies have shown that the incidence of AD in MCI patients is 10 times higher than that in healthy elderly people[3]. According to statistics, 10%-15% of MCI patients over 65 years old in China get worse and progress to AD every year[4]. In addition, MCI is often accompanied by a variety of sleep-wake disorders, including insomnia, sleep-related breathing disorders, rapid eye movement sleep behaviour disorders, circadian dysrhythmic sleep-wake disorders, excessive daytime sleepiness, restless leg syndrome, etc., which are mainly manifested by abnormal sleep structures such as reduced total sleep time, reduced sleep efficiency, and increased wake time and frequency. Studies have shown that the incidence of sleep disorders in patients with MCI is as high as 63%[5]. A large cohort study indicates that over 60% of MCI patients report sleep disorders, such as difficulty falling asleep, frequent awakenings during the night, and early awakenings. These sleep disorders not only severely impact the patients’ quality of life but may also accelerate further cognitive decline[6]. MCI patients exhibit significant changes in their sleep structure, including reduced deep sleep (slow-wave sleep) and increased light sleep and rapid eye movement sleep, which are closely linked to the extent of cognitive impairment[7]. Sleep disorders may affect neurotransmitter regulation, such as disrupting the balance of the cholinergic system; interfere with memory consolidation and hinder the brain’s storage of new information; cause neuroinflammatory responses and damage neurons[8]. These factors work together to lead to a more significant decline in cognitive ability. MCI accompanied by cognitive decline increases the risk of developing AD. It further aggravates the impairment of memory and cognitive function, the severity of neuropsychiatric symptoms, and the mortality rate of AD patients[9].

MCI is the early stage of the onset of AD and the optimal time window for treatment. At present, it is not clear whether sleep disorders have an impact on brain function in patients with MCI. Therefore, timely and comprehensive assessment of MCI with sleep disorders is of great significance for clinical intervention decision-making. Based on multiple indexes of deoxyhemoglobin and oxyhemoglobin (Oxy-Hb), functional near-infrared spectroscopy (fNIRS) can effectively detect brain functions related to neuronal activity, cellular energy metabolism, and hemodynamics[10]. However, there are few reports on the application of fNIRS in the assessment of MCI with sleep disorders. Based on this, this study intends to investigate the near-infrared brain functional characteristics of MCI with sleep disorders through fNIRS, to provide references for clinical research.

MATERIALS AND METHODS

Subject source

One hundred and twenty patients with MCI who were outpatients and inpatients of internal medicine in our hospital from August 2021 to July 2023 were selected and classified into the MCI group according to the designed inclusion and exclusion criteria. Inclusion conditions: (1) The patient complained of memory impairment and was diagnosed with MCI; (2) Accompanied by sleep disorders; (3) Memory impairment inconsistent with age and education level; (4) Overall cognitive function is normal and the ability to live daily was normal; (5) The treatment compliance was good; and (6) The diagnostic criteria for dementia have not been met. Exclusion criteria: (1) History of cerebrovascular disease; (2) Serious heart and lung, liver, and kidney diseases; (3) Suffering from diseases causing cognitive dysfunction such as AD, Parkinson’s disease, pulmonary cardiopathy, cardiac insufficiency, viral or autoimmune encephalitis, brain tumors, etc.; (4) Taking sleeping pills, excessive alcohol consumption; and (5) Depression was assessed according to the Hamilton Depression Scale[11].

In addition, 50 healthy persons matching the age, gender, and education level of the MCI group were selected and classified into the control group. MCI has no history of neurological disease, brain trauma, drug dependence, long-term alcohol consumption, or mental illness. This study protocol was reviewed and approved by the Medical Ethics Committee of our hospital. All subjects underwent fNIRS testing and had complete test records. We have obtained the consent of all subjects before accessing and accessing clinical data.

Diagnostic criteria for MCI and sleep disorders

Refer to the MCI-related diagnostic criteria developed by the working group of the National Institute on Aging and the Alzheimer’s Association in 2011[12]: (1) The patient’s subjective feelings and (or) the informed person confirmed that there was a decline in cognitive function (mostly memory), and the course of the disease was more than 3 months; (2) Objective examination of cognitive decline. Mini-Mental State Examination (MMSE)[13] scores ≤ 26 points, Montreal Cognitive Assessment (MoCA) scale[14] scores ≤ 24 points (years of education ≤ 12 years plus 1 point); cognitive test scores are 1-1.5 standard deviations below the norm for age and literacy; (3) The diagnostic criteria for dementia have not been met. The rating of the global deterioration scale[15] is 2-3. Clinical dementia rating[16] = 0.5 points; MMSE scale score: Primary school level > 20 points, middle school level (including technical secondary school) > 22 points, university level (including junior college) > 23 points; and (4) The basic ability of daily living remains normal, and the ability of complex instrumental activity of daily living[17] may be slightly impaired.

Clinical data and indicators were collected: (1) Baseline data: Sex, age, body mass index, years of schooling, duration of MCI disease, systolic blood pressure, diastolic blood pressure; (2) MMSE, MoCA, fatigue severity scale (FSS), Pittsburgh sleep quality index (PSQI) scores; (3) Sleep parameters: Sleep efficiency, awakening frequency, rem sleep latency period, total sleep time, oxygen desaturation index; and (4) Oxy-Hb concentration and peak time of fNIRS test during the task period.

Assessment scale: (1) The MMSE scale assessed the cognitive function status of the subjects, with a total score of 30 points, covering the test items such as orientation, calculation, language function memory, etc. The lower the score, the more serious the cognitive function was impaired, and those with a score of 26 or more were considered to have normal cognitive function; (2) The MoCA scale includes 7 cognitive domains, including directed visual space, executive function, naming, memory, attention, language, abstract generalization, and various aspects of cognitive function such as orientation, executive ability, and memory. The highest total score is 30 points, and ≤ 26 points are cognitive dysfunction. If the subject’s years of schooling were less than 12 years, the positive education bias was measured by adding 1 point. The lower the score, the more serious the degree of cognitive function impairment, 19 to 25 is classified as mild cognitive function impairment; (3) The FSS[18] was used to evaluate the degree of fatigue. The scale consists of 9 rating items, each divided into 7 points, with 1 being “strongly disagree” and 7 being “strongly agree”. A higher total score indicates more severe fatigue; and (4) Sleep quality in the last 1 month was assessed by the PSQI[19]. The questionnaire consisted of 19 items with 6 components (subjective sleep quality, sleep time, total sleep time, sleep efficiency, nocturnal sleep, disturbance, and daytime dysfunction). Each sub-item is scored on a 0-3 scale, and the total score of each sub-item is PSQI score, the range is 0-21 points, and ≥ 7 points is rated as sleep disorder.

MCI treatment

Conventional Western medicine treatment: Donepezil hydrochloride tablets (Shaanxi Fangzhou Pharmaceutical Co., LTD., Sinopath H20030583, 5 mg) taken orally before bed, 5 mg/time, once/day, for 8 weeks. For those with mild sleep disorders, relaxation training, sleep restriction, and stimulation control are used. For those with mild and severe sleep disorders, drugs such as sleeping pills and hypnotics are used.

fNIRS operations

The principle behind measuring frontal lobe activity with fNIRS technology is primarily based on the tightly coupled relationship between neuronal activation and vascular response. fNIRS selects the frontal lobe as the measurement site due to several key reasons and principles: (1) The frontal lobe is crucial for cognitive functions, with the prefrontal cortex playing a pivotal role in advanced cognitive processes such as problem-solving, memory, judgment, and impulse control. These functions are essential for understanding human behavior and cognitive mechanisms. Additionally, the prefrontal cortex is implicated in emotional processing and decision-making, and is a primary affected area in numerous neurological and psychiatric disorders, including depression, anxiety, and schizophrenia; (2) Amenable to optical measurement. Located relatively shallow beneath the scalp, the prefrontal cortex is more accessible for optical measurement compared to other deep brain structures. This allows fNIRS technology to non-invasively monitor changes in frontal lobe activity; and (3) In clinical research, variations in prefrontal cortex activity frequently serve as critical indicators for assessing neurological disease symptoms, treatment effectiveness, and cognitive function recovery. fNIRS technology offers a convenient and noninvasive monitoring approach for these studies.

fNIRS tests were performed in the MCI group before treatment (T0) and at 4 weeks (T1) and 8 weeks (T2) after treatment, while fNIRS tests were performed in the control group during physical examination. The equipment is produced by Wuhan, China Zilian Hongkang, model BS-3000. The device consists of 16 light sources and 16 detectors, forming a total of 53 channels, arranged in a rectangle of 4 rows by 9 columns, the channel spacing is 3 cm, and the sampling frequency is 20 Hz. Localization was performed with reference to the international 10-20 system, covering bilateral frontal pole, bilateral frontal eye area, bilateral Broca area, and bilateral dorsolateral prefrontal lobe. Task method: The verbal fluency task (VFT) involves sitting in a chair with arms in a quiet environment, with eyes open and body relaxed. Patients were grouped with words of “day”, “cloud”, “learning”, and “Shang” respectively, 15 seconds for each group, 60 seconds in total, and 30 seconds for rest.

Statistical analysis

SPSS 23.0 statistical software package was used for statistical analysis. Measurement data conforming to normal distribution were represented by mean ± SD, an independent sample t-test was performed, and Pearson correlation analysis was used for correlation. The measurement data that did not conform to the normal distribution were described by P50 (P25, P75) and subjected to a non-parametric test (Z), and the correlation was analyzed by Spearmen correlation. Count data use case description, row χ² test. Interpretation of Pearson or Spearmen correlation analysis results: |r| > 0.8, indicating a high correlation among the variables. 0.5 < |r|≤ 0.8 indicates a moderate correlation between the variables. 0.3 < |r|≤ 0.5 indicates a low correlation between the variables. P < 0.05 was considered to be statistically significant.

RESULTS

Baseline data

Compared with the control group, systolic and diastolic blood pressure were higher in the MCI group (P > 0.05) (Table 1).

Table 1 Baseline data.

Group	MCI group (n = 120)	Control group (n = 50)	t	P value
Sex (male/female)	67/53	30/20	0.250	0.617
Age (years)	65.39 ± 7.16	64.87 ± 8.03	0.416	0.678
Body mass index (kg/m2)	22.66 ± 2.18	22.74 ± 2.34	0.213	0.831
Years of schooling (years)	6.51 ± 2.02	6.39 ± 1.68	0.370	0.712
Duration of MCI (months)	4.38 ± 1.02	4.21 ± 1.13	0.959	0.339
Systolic pressure (mmHg)	138.21 ± 23.68	124.32 ± 10.15	3.992	< 0.001
Diastolic blood pressure (mmHg)	86.20 ± 9.23	79.22 ± 6.69	4.840	< 0.001

MCI: Mild cognitive impairment.

MMSE, MoCA, and FSS score

The MMSE scores at T0, T1, and T2 in the MCI group were 24.32 ± 1.41, 25.73 ± 1.08, and 27.38 ± 0.95, respectively, while those in the control group were 29.12 ± 1.68. MoCA scores at T0, T1, and T2 in the MCI group were 23.37 ± 1.40, 24.43 ± 1.00, and 26.42 ± 1.04, respectively, while those in the control group were 28.56 ± 1.58. In the MCI group, MMSE scores at T0, T1 and T2 were increased successively (t: T0 vs T1 = 8.696, T0 vs T2 = 19.720, T1 vs T2 = 12.570), and MoCA scores were increased successively (t: T0 vs T1 = 6.749, T0 vs T2 = 19.160, T1 vs T2 = 15.110); compared with the control group, MMSE scores at T0, T1, and T2 were all lower (t = 19.090, 15.680, 8.548) (Figure 1A), and MoCA scores were all lower (t = 26.930, 24.200, 12.390) (P < 0.001 for all) (Figure 1B). The FSS score in the MCI group was 31.25 ± 9.68 and that in the control group was 15.39 ± 3.25, and the FSS score in the MCI group was higher (t = 11.310, P < 0.001) (Figure 1C).

Figure 1 Comparison of Mini-Mental State Examination, Montreal Cognitive Assessment, and fatigue severity scale scores among different groups. A: Mini-Mental State Examination score; B: Montreal Cognitive Assessment score; C: Fatigue severity scale score. ^aP < 0.05, represents the comparison of data between the two groups. MCI-T0, MCI-T1, and MCI-T2 represent the indicator levels of the mild cognitive impairment group at T0, T1, and T2, respectively. MCI: Mild cognitive impairment; MMSE: Mini-Mental State Examination; MoCA: Montreal Cognitive Assessment; FSS: Fatigue severity scale.

PSQI and its scores in each dimension

Compared with the control group, the scores of PSQI, sleeping time, sleep efficiency, night sleep disorder, daytime dysfunction, and other dimensions were higher in the MCI group, and the scores of subjective sleep quality and total sleep time were lower (P < 0.05) (Table 2 and Figure 2).

Figure 2 Comparison of Pittsburgh sleep quality index and its various dimensions scores between mild cognitive impairment group and control group. ^aP < 0.05, represents the comparison of data between the two groups. MCI: Mild cognitive impairment; PSQI: Pittsburgh sleep quality index.

Table 2 Pittsburgh sleep quality index and its scores in each dimension.

Group	PSQI	Subjective sleep quality	Sleep time	Total sleep time	Sleep efficiency	Nocturnal sleep disturbance	Daytime dysfunction
MCI group (n = 120)	6 (6, 6)	2 (2, 2)	0 (0, 1)	2 (2, 3)	0 (0, 1)	0 (0, 1)	0 (0, 1)
Control group (n = 50)	9 (8, 10)	1 (1, 1)	2 (2, 2)	1 (1, 1)	2 (1, 2)	2 (1, 2)	2 (1.75, 2)
Z	-10.518	-11.592	-10.368	-9.924	-9.035	-10.661	-10.088
P value	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001

PSQI: Pittsburgh sleep quality index.

Sleep parameter

Sleep efficiency, awakening frequency, rem sleep latency period, total sleep time, oxygen desaturation index in the MCI group were 84.36% ± 8.52%, 3.26 ± 0.53 times, 87.35 ± 8.37 minutes, 386.58 ± 40.39 minutes, 3.52 ± 0.61 time/hour, respectively. The control group was 93.15% ± 9.28%, 2.74 ± 0.53 times, 83.22 ± 8.80 minutes, 407.26 ± 42.17 minutes, 2.95 ± 0.46 time/hour, respectively. The sleep efficiency of the MCI group was lower (t = 5.969, P < 0.001), and the awakening frequency (t = 5.829, P < 0.001), rem sleep latency period (t = 2.887, P = 0.004), total sleep time (t = 3.003, P = 0.003), and the oxygen desaturation index was higher (t = 5.937, P < 0.001) (Figure 3).

Figure 3 Comparison of sleep parameter between mild cognitive impairment group and control group. A: Sleep efficiency; B: Awakening frequency; C: Rem sleep latency period; D: Total sleep time; E: Oxygen desaturation index. ^aP < 0.05, represents the comparison of data between the two groups. MCI: Mild cognitive impairment.

Oxy-Hb concentration and peak time during the task period

The functional Oxy-Hb concentrations of T0, T1, and T2 in MCI group were 0.30 ± 0.08 mmol/L/mm, 0.35 ± 0.11 mmol/L/mm, and 0.46 ± 0.15 mmol/L/mm, respectively, while those in control group were 0.62 ± 0.20 mmol/L/mm. The peak time at T0, T1, and T2 in the MCI group was 92.39 ± 12.23 seconds, 80.52 ± 11.47 seconds, and 72.58 ± 10.29 seconds, respectively, while that in the control group was 55.82 ± 9.74 seconds.

In the MCI group, Oxy-Hb concentration in T0, T1, and T2 during the task period increased successively (t: T0 vs T1 = 4.027, T0 vs T2 = 10.310, T1 vs T2 = 6.478), and the peak time decreased successively (t: T0 vs T1 = 7.755, T0 vs T2 = 13.580, T1 vs T2 = 5.645); compared with the control group, the Oxy-Hb concentration at T0, T1, and T2 was lower (t = 14.940, 11.280, 5.721), and the peak time was higher (t = 18.800, 13.350, 9.827) (all P < 0.001) (Figure 4).

Figure 4 Comparison of oxyhemoglobin concentration and peak time during the task period among different groups. A: Oxyhemoglobin concentration; B: Peak time. ^aP < 0.05, represents the comparison of data between the two groups. MCI-T0, MCI-T1, and MCI-T2 represent the indicator levels of the mild cognitive impairment group at T0, T1, and T2, respectively. MCI: Mild cognitive impairment; Oxy-Hb: Oxyhemoglobin.

Correlation analysis of Oxy-Hb concentration in T0 phase

Oxy-Hb concentration during T0 was negatively correlated with the PSQI score and its dimensions such as sleep time, total sleep time, and sleep efficiency scores (r = -0.611, -0.388, -0.563, -0.356; all P < 0.001) (Figure 5). The functional Oxy-Hb concentration at T0 was positively correlated with sleep efficiency and total sleep time (r = 0.754, 0.650, both P < 0.001), and negatively correlated with oxygen depletion index (r = -0.561, P < 0.001) and FSS score (r = -0.526, P = 0.039) (Figure 6).

Figure 5 Scatter plot of the correlation between oxyhemoglobin concentration during the task period and Pittsburgh sleep quality index and its dimension scores. A: Pittsburgh sleep quality index; B: Subjective sleep quality; C: Sleep time; D: Total sleep time; E: Sleep efficiency; F: Nocturnal sleep disturbance; G: Daytime dysfunction. PSQI: Pittsburgh sleep quality index; Oxy-Hb: Oxyhemoglobin.

Figure 6 Scatterplot of the correlation between oxyhemoglobin concentration during the task period and sleep parameters and fatigue severity scale scores. A: Sleep efficiency; B: Awakening frequency; C: Rem sleep latency period; D: Total sleep time; E: Oxygen desaturation index; F: Fatigue severity scale score. FSS: Fatigue severity scale; Oxy-Hb: Oxyhemoglobin.

DISCUSSION

In the context of the accelerating ageing trend of the population, the number of MCI cases is increasing year by year, which has a serious impact on the quality of life of patients and their families. At present, how to accurately, scientifically and effectively evaluate MCI remains a challenge. Studies have found that factors such as high blood pressure, sleep and fatigue are risk factors for MCI[20]. MCI is commonly associated with sleep disorders, and the brain function status of these patients is unknown. fNIRS technology can detect the haemoglobin content in blood flow to evaluate the activity and characteristics of brain function[21]. Therefore, it is necessary to explore the near-infrared brain function and its relationship with sleep quality in patients with MCI accompanied by sleep disorders.

Sleep disorders can exacerbate MCI or cause more significant cognitive decline via various mechanisms, including neurotransmitter imbalances, activation of inflammatory responses, and abnormalities in brain energy metabolism[22]. These mechanisms, when combined, can impair the normal function and synaptic plasticity of brain neurons, thereby accelerating cognitive decline. Our study centers on the assessment of brain function, with the goal of using functional neuroimaging (fNIRS) to monitor and quantify these changes in real time, thereby providing a scientific basis for understanding the complex relationship between sleep and cognitive disorders. Through statistical analysis, we found that the MMSE and MoCA scores of MCI patients with sleep disorders were lower than those of normal people of the same age. However, after treatment, the lower range would become smaller and smaller and tend to the normal level. In addition, the patients’ FSS score was at a high level, indicating that fatigue was common in patients with MCI accompanied by sleep disorders. The scores of PSQI, sleep time, sleep efficiency, nocturnal sleep disturbance, and daytime dysfunction of patients were high, but the scores of subjective sleep quality and total sleep time were low, indicating that although patients with MCI accompanied by sleep disorders showed obvious sleep disorders in general, they did not show disturbances in the two dimensions of subjective sleep quality and total sleep time. It can be seen that the assessment of sleep status in patients with MCI needs a joint reference from the overall dimension. In terms of specific value assessment, patients with MCI accompanied by sleep disorders had lower sleep efficiency, higher awakening frequency, rem sleep latency period, total sleep time and oxygen desaturation index, which were consistent with the basic characteristics of sleep disorders and consistent with previous research results[23]. Our study also found that patients with MCI accompanied by sleep disorders had low Oxy-Hb concentration during the task period at T0, T1, and T2, and high peak time. However, the degree of low or high decreased with the time after treatment, indicating that patients’ near-infrared brain function had a trend of improvement after treatment.

To further explore the near-infrared brain functional characteristics of patients with MCI accompanied by sleep disorders, we used Pearson or Spearmen correlation analysis to analyze the relationship between Oxy-Hb concentration and sleep quality, parameters and fatigue index of patients during the task period. We found that Oxy-Hb concentration during T0 was negatively correlated with PSQI score and its dimensions such as sleep time, total sleep time and sleep efficiency scores, positively correlated with sleep efficiency and total sleep time, and negatively correlated with oxygen desaturation index and FSS scores. Among them, the Oxy-Hb concentration during the task period was closely related to PQSI score, sleep efficiency and total sleep time (R-value ranked the top 3, respectively -0.611, 0.754, and 0.650). Based on the matching mechanism between nerves and blood vessels, during the cognitive activity of the brain, the blood flow and oxygen carried in the brain area increase, and the concentration of Oxy-Hb in the blood increases accordingly[24]. Li et al[25] found that compared with patients with major depressive disorder who did not have insomnia, the dorsolateral prefrontal cortex of patients with insomnia, Oxy-Hb concentration was lower in dorsolateral prefrontal cortex and bilateral medial prefrontal cortex. It can be seen that cognitive function and sleep disorders simultaneously affect the concentration of Oxy-Hb in the brain and reduce the activity of brain function. In addition, lack of sleep also exacerbates cognitive dysfunction. Sleep deprivation (lack of sleep or sleep disorders) can cause a variety of physical and psychological changes that can lead to further cognitive decline. The process of sleep deprivation may be accompanied by the production of oxygen free radicals, thus inducing DNA damage, resulting in cell apoptosis or mitochondrial function damage, and finally leading to nerve cell function impairment, thus leading to cognitive function decline, which may be the possible mechanism of sleep deprivation causing cognitive function decline[26]. Miyata et al[27] showed that insufficient sleep at night would affect cognitive function during the day, accompanied by changes in cortical oxygenation response (decreased peak oxy-Hb levels in the left and right frontal lobes). Gao et al[28] found that the concentration of oxy-Hb in the 8^th channel of the dorsolateral prefrontal cortex in patients with sleep disorders was significantly reduced, revealing that sleep disorders caused changes in brain function. These findings are basically consistent with the results of this study. However, at present, the specific mechanism of sleep disorders on near-infrared brain functional characteristics cannot be determined, and more in-depth and reliable studies are needed to further explore.

Oxygen desaturation index refers to the number of times during sleep that blood oxygen saturation drops by more than 4% per hour. Obstructive sleep apnea shows brain damage in areas responsible for autonomic, cognitive, and respiratory function. Sahib et al[29] showed that the degree of brain damage of obstructive sleep apnea partly depended on the apnea-hypopnea index and oxygen desaturation, which affected its continuous development from acute to chronic. Therefore, we speculate that the higher the hypoxic index means that the more times the blood oxygen saturation drops, the greater the brain damage, and the lower the brain function activity. FSS is an indicator of fatigue level. Cao et al[30] confirmed that fatigue is related to sleep disorders (changes in sleep structure). In addition, Dardin et al[31] proved that poor sleep quality would aggravate fatigue in patients with primary Sjogren’s syndrome. We hypothesize that fatigue in patients with MCI and sleep disorders may alter brain function (oxy-Hb concentration) by affecting sleep quality. But more research is needed. Our study analyzed near-infrared brain functional properties and their correlation with sleep quality in patients with MCI and sleep disorders, but there are limitations.

We propose some specific intervention measures and compliance for patients with MCI accompanied by sleep disorders. In terms of intervention measures, patients should establish a regular sleep schedule, improve sleep quality, and alleviate the impact of sleep disorders on cognitive function. In terms of diet, patients should also have a balanced diet to maintain brain health. Moderate exercise can promote blood circulation in the brain, increase the volume and quantity of dendritic cells, and enhance memory function. Participate in more brain stimulating learning activities to enhance memory function. In terms of compliance, patients’ awareness of MCI and sleep disorders can be improved through health education and other means, making them aware of the importance and necessity of intervention measures, thereby enhancing treatment compliance. At the same time, personalized intervention plans can be developed based on the specific situation of the patient, including intervention content, frequency, intensity, etc., to improve the pertinence and feasibility of the plan, thereby enhancing patient compliance.

The implications of our findings for clinical practice including: (1) Employing fNIRS to monitor brain function changes in MCI patients offers robust support for early diagnosis and intervention. Physicians can tailor personalized treatment plans based on brain function status to slow cognitive decline and enhance quality of life; (2) This study underscores the detrimental impact of sleep disorders on the brain function of MCI patients. Consequently, clinical practice should prioritize the management of sleep disorders in MCI patients by enhancing sleep quality through medication and psychological therapy; and (3) Fatigue is a common symptom in MCI patients, effectively managing it can alleviate cognitive load and enhance quality of life. Physicians can advise patients on organizing their schedules and engaging in moderate physical activities to alleviate fatigue. Future research directions include: (1) Multimodal integration research: Integrating fNIRS with other neuroimaging techniques, such as fMRI and electroencephalography, to conduct multimodal integration research that more thoroughly reveals brain function changes in MCI patients and their relationships with sleep quality and fatigue; (2) Longitudinal follow-up studies: Conducting longitudinal follow-up studies to explore the dynamic interplay between brain function, sleep quality, and fatigue in MCI patients and how they evolve over time; and (3) Evaluating intervention effectiveness: Employing randomized controlled trials and other study designs to assess the effectiveness of different interventions in enhancing brain function, sleep quality, and fatigue in MCI patients, thereby providing robust evidence to support clinical practice.

We used a retrospective analysis method, which may cause selection bias in the study. In addition, this study focused on statistical analysis of the changes in near-infrared brain function and its relationship with sleep quality in patients with MCI accompanied by sleep disorders, ignoring the effects of other factors such as hypertension and treatment methods on near-infrared brain function. Future prospective studies and data expansion are needed to verify the conclusions of this study. Although we have established a correlation, further research is needed to determine the causal relationship and whether these near-infrared brain functional features are unique to MCI. Therefore, we need to expand the sample size and in-depth analysis of patient clinical data in the future.

CONCLUSION

In summary, patients with MCI and sleep disorders have lower near-infrared brain function than normal people, which is related to sleep quality. Clinically, a comprehensive assessment of the near-infrared brain function of patients should be carried out to guide targeted treatment and improve curative effect.