NREM Sleep EEG Characteristics Correlate to the Mild Cognitive Impairment in Patients with Parkinsonism.

Early identification and diagnosis of mild cognitive impairment (MCI) in patients with parkinsonism (PDS) are critical. The aim of this study was to identify biomarkers of MCI in PDS using conventional electroencephalogram (EEG) power spectral analysis and detrended fluctuation analysis (DFA). In this retrospective study, patients with PDS who underwent an overnight polysomnography (PSG) study in our hospital from 2019 to 2020 were enrolled. Patients with PDS assessed by clinical examination and questionnaires were divided into two groups: the PDS with normal cognitive function (PDS-NC) group and the PDS with MCI (PDS-MCI) group. Sleep EEG signals were extracted and purified from the PSG and subjected to a conventional power spectral analysis, as well as detrended fluctuation analysis (DFA) during wakefulness, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Forty patients with PDS were enrolled, including 25 with PDS-NC and 15 with PDS-MCI. Results revealed that compared with PDS-NC patients, patients with PDS-MCI had a reduced fast ratio ((alpha + beta)/(delta + theta)) and increased DFA during NREM sleep. DFA during NREM was diagnostic of PDS-MCI, with an area under the receiver operating characteristic curve of 0.753 (95% CI: 0.592-0.914) (p < 0.05). Mild cognitive dysfunction was positively correlated with NREM-DFA (r = 0.426, p = 0.007) and negatively correlated with an NREM-fast ratio (r = -0.524, p = 0.001). This suggested that altered EEG activity during NREM sleep is associated with MCI in patients with PDS. NREM sleep EEG characteristics of the power spectral analysis and DFA correlate to MCI. Slowing of EEG activity during NREM sleep may reflect contribution to the decline in NREM physiological function and is therefore a marker in patients with PDS-MCI.

1. Introduction

Parkinsonism (PDS) is a syndrome with various causes characterized by bradykinesia, rigidity, and rest tremor [1], usually seen in neurodegenerative disorders, such as Parkinson's disease (PD), multiple system atrophy, and Lewy body dementia. Apart from the motor symptoms, patients with PDS also present with several nonmotor symptoms, which at times can be more debilitating than motor dysfunction [2]. Cognitive impairment is a common and troublesome nonmotor complication frequently encountered by clinical practitioners. Approximately 30% of patients with Parkinson's disease experience mild cognitive impairment (MCI) [3].

The cumulative prevalence of PD dementia is approximately 75–90% in patients with a disease course exceeding 10 years. This increases the mortality rate and severely impacts the quality of life [4]. Similar conditions are seen in atypical parkinsonian syndromes [5-7]. Importantly, cognitive impairment without dementia can persist for years before the onset of dementia in parkinsonian patients [8], which provides a potential monitoring and therapeutic window. Therefore, the early detection of cognitive deficits is important.

Currently, in clinical practice, cognitive function is usually assessed using questionnaires or scales. These are well-established methods and easy to use; however, the specificity and sensitivity of many scales remain unsatisfactory [9] due to their subjective nature as well as the complexity of the cognitive profile in PDS. This is worse when it comes to MCI.

The quantitative analysis of electroencephalogram (EEG) signals provides a method for assessing cognitive function in various diseases. For example, numerous studies focusing on EEG have demonstrated correlations between quantitative EEG (qEEG) biomarkers and cognitive function in Alzheimer's disease [10]. Some studies have also shown that qEEG is a very promising predictor of PD-related cognitive decline [11].

However, previous studies have mainly focused on the association between resting awake EEG alterations and cognitive dysfunction in patients with PDS and rarely addressed sleep EEG. However, sleep EEG also provides a window of insight into cognitive dysfunction [12].

For example, previous studies have shown that alterations in sleep EEG are associated with brain function in the aging population [12]. Decreased slow-wave, theta, and sigma activities during nonrapid eye movement (NREM) sleep and changes in spindle characteristics predicted the very early onset of cognitive impairment [12]. It has also been shown that the power of alpha activity during NREM sleep is associated with intelligence in the general population [13].

It remains unclear, however, whether sleep EEG is associated with mild cognitive impairment in PDS patients.

Nowadays, power spectral analysis is the most common method used in these EEG studies.

In addition, due to the nonlinear dynamics and nonstationarity of EEG signals limiting the conventional power spectral analysis, a simpler alternative method, the detrended fluctuation analysis (DFA) method, has been used for various nonstationary physiological signals in recent years [14]. This is a method for calculating the long-term correlation of nonstationary time series proposed by Peng et al. in 1994 [15] in their study of DNA sequences. The advantage of the DFA method over many other analysis methods is that it can detect long-range correlations implicitly in unstable time series and can effectively minimize interference due to signal instability. Recent studies have applied DFA in the field of EEG and cognition, and it can be used as an alternative to conventional EEG spectral analysis as a marker of cognitive impairment in patients with obstructive sleep apnea (OSA) or Alzheimer's disease [14, 16].

Therefore, this study sought to determine sleep EEG biomarkers of polysomnography (PSG) related to MCI in PDS patients. We hypothesized that the power spectral analysis features of overnight sleep EEG are related to cognitive function in patients with PDS.

2. Materials and Methods

Patients with PDS who underwent overnight PSG monitoring were enrolled in this study, and their overnight EEG was analyzed using conventional spectral analysis and DFA. Differences between patients with normal cognitive function and those with MCI were compared to identify EEG markers reflecting cognitive impairment.

2.1. Study Population

A total of 45 consecutive patients who were admitted to our neurology department with PDS from August 2019 to August 2020 and underwent overnight PSG in our sleep lab were enrolled in this study.

This retrospective study was approved by the ethics committee of Peking University First Hospital, and the requirement for obtaining informed consent was waived (ethics approval no. 2019[181]). The study adhered to the Declaration of Helsinki, and patient confidentiality was maintained.

Inclusion criteria are as follows:

Compliance with the criteria for PDS includes bradykinesia, in combination with either resting tremor, rigidity, or both [1]

PDS was not secondary to identified structural, toxic, or metabolic disorders or conditions

Exclusion criteria are as follows:

Poor quality of PSG-EEG signals, high impedance, excessive electromyography (EMG), or presence of motor artifacts

Dementia cases where substantial impairment is present in one or more cognitive domains and the impairment must be sufficient to interfere with independence in everyday activities [1]

Other medical conditions that cause EEG changes, such as epilepsy

Other medical conditions that may affect cognition, such as stroke

Extremely severe motor symptoms affecting patients' cooperation with cognitive evaluations

Among the 45 patients with PDS, we excluded two patients with high signal impedance, excessive myoelectricity, or motion artifacts; moreover, three cases of PDS with dementia were excluded. A total of 40 patients were finally enrolled.

2.2. Cognitive Function Evaluation

All patients were interviewed, and a detailed medical and medication history was acquired. The cognitive function was evaluated based on clinical examination, the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). All cognitive evaluations were performed when PD patients were in the “on” state and when the other parkinsonian patients were in relatively better conditions. Subjects were stratified into two groups based on cognitive evaluations: PDS with MCI (PDS-MCI) group and PDS with normal cognition (PDS-NC) group. There are no diagnostic criteria for PDS-MCI. We defined the PDS-MCI group based on the criteria of mild neurocognitive disorders [17] and MCI in PD [17]: parkinsonism with modest impairment in one or more cognitive domains that are not sufficient to interfere significantly with functional independence, although subtle difficulties in complex functional tasks may be present, and cognitive deficits on either formal neuropsychological testing or a scale of global cognitive abilities. More specifically, the cutoff value of MoCA for PDS-MCI is 21–25 points. The MMSE was used to exclude dementia with a cutoff value of 23/24 points. The PDS-NC group was defined as having parkinsonism without dementia or MCI, as described above.

2.3. Overnight PSG

Sleep apneas were confirmed by an overnight PSG (Compumedics, E-Series, Australia). Six EEG signals (C3-M2, C4-M1, F3-M2, F4-M1, O1-M2, and O2-M1), two channels of electrooculography (EOG) signals (E1-M2, E2-M2), chin EMG (EMG1-EMG2, EMG1-EMG3), electrocardiography (ECG), respiration (nasal pressure, airflow), oxygen saturation (SpO2), abdominal and chest movement, and leg movements were recorded according to the American Academy of Sleep Medicine (AASM) guidelines [13, 14].

Sleep stage and respiratory events were analyzed according to the AASM 2.3 guidelines [13, 14]. Sleep stages were divided into the NREM (N1, N2, and N3) sleep, rapid eye movement (REM) sleep, and wake stages.

Sleep-related parameters (total sleep time (TST), sleep efficiency (SE), sleep latency (SL), wake time after sleep onset (WASO), and the proportion of each sleep period to total sleep (N1/TST, N2/TST, N3/TST)) were calculated and reported in the PSG study report.

2.4. Spectral Analysis and DFA of PSG-EEG Signals

2.4.1. EEG Spectral Analysis

We used noninvasive electrodes to record scalp EEG signals from sleeping participants, as described in the overnight PSG method. All EEG signals were recorded at a sampling rate greater than 200 Hz and stored in the EDF format. A band-pass filter of 0.3–35.0 Hz was applied to the original signals. The signals of two channels (C4-M1, O1-M2) were analyzed. MATLAB software (MATLAB 2018a) was used for EEG preprocessing, artifact removal, and data analysis. We first estimated the power spectrum in each frequency band (delta, 0.5–4.0 Hz; theta, 4.0–8.0 Hz; alpha, 8.0–13.0 Hz; sigma, 12.0–16.0 Hz; and beta, 13.0–30.0 Hz) for each 30 s segment using the Welch method [18]. This is a modification of the periodogram method that produces more reliable power spectrum estimates by applying windows and overlaps [19]. We used the Hamming window function with a window width of two full cycles of the lowest interest frequency (0.5 Hz) to avoid underestimation, and the overlap was set to half the window width. Next, the absolute power of the segmented signal bands was obtained by numerical integration of the estimated power spectrum by the Simpson method. The relative power and fast ratio were also calculated and defined as follows: (alpha + beta)/(delta + theta).

2.4.2. DFA

DFA is a method for calculating the long-term correlation of nonstationary time series proposed by Peng et al. in 1994 [15]. The obtained scaling coefficient α describes the relationship between the future and historical trends of the signal; for example, white noise α = 0.5 and fractal Brownian noise α = 1.5. DFA is suitable for EEG data analysis because of its nonstationarity. Recent studies have demonstrated its advantages in sleep EEG analysis, such as sleep staging [20] and sleep apnea detection [21]. In this study, we performed DFA of the segmented sleep EEG following preprocessing. First, we calculated the scaling coefficient α for all segments of the wake, NREM sleep, and REM sleep stages. We then averaged them as typical values of the corresponding stages for subsequent statistical analysis. The coefficient was obtained by a first-order fit to 50 logarithmically spaced scales of each EEG segment.

2.5. Statistical Methods

The normality of distributions was assessed by the Shapiro-Wilk test.

Data are expressed as mean ± standard deviation, and t-tests were used if the variables conformed to a normal distribution (alpha, beta, theta, sigma, delta, DFA, age, TST, WASO, R/TST, N1/TST, and N2/TST). Nonnormally distributed continuous variables (fast ratio, AHI, BMI, SE (%), sleep latency, and N3/TST (%)) were analyzed using a nonparametric test and presented as the median (25th-75th percentile).

A Spearman correlation was used to analyze the correlation between cognitive impairment and power spectral analysis and DFA. The values of DFA in NREM sleep were used to diagnose cognitive dysfunction using receiver operating characteristic (ROC) curve analysis. A p value of < 0.05 indicated a significant difference.

SPSS 17.0 software (SPSS Inc., Chicago, IL, USA) was used for statistical analysis.

3. Results

3.1. Demographic Characteristics and Comparison of Sleep Parameters Monitored by PSG

Forty patients with PDS (age: 46–84 years; mean ± standard deviation: 66.83 ± 9.18 years; and 25 men and 15 women) were included: 15 cases of PD, 13 cases of MSA, and 12 cases of PDS with unknown etiologies. Based on cognitive evaluations, participants were stratified into two groups: 25 patients with normal cognitive function (PDS-NC) and 15 with mild cognitive impairment (PDS-MCI).

No significant differences were revealed between the two groups in terms of age, sex, composition ratio, BMI, AHI, and sleep-related parameters (total sleep time (TST), sleep efficiency (SE), sleep latency (SL), wake time after sleep onset (WASO), and the proportion of each sleep period to total sleep) (Table 1).

Table 1

Demographic characteristics and sleep parameters monitored by PSG.

	PDS-NC (n = 25)	PDS-MCI (n = 15)
Age (years)	65.80 ± 7.89	68.53 ± 11.11
BMI (kg/m2)	23.59 (21.79, 26.68)	25.51 (20.70, 27.68)
AHI (/h)	11.90 (2.30, 17.80)	4.40 (2.80, 15.30)
TST (min)	279.70 ± 94.97	234.50 ± 91.25
SE (%)	58.70 (48.15, 76.10)	59.00 (39.30, 68.80)
Sleep latency (min)	22.50 (9.38, 48.13)	36.00 (13.00, 52.63)
WASO (min)	137.38 ± 76.52	158.97 ± 83.03
R/TST (%)	16.93 ± 7.68	15.77 ± 8.08
N1/TST (%)	13.10 ± 6.73	17.25 ± 10.73
N2/TST (%)	58.47 ± 10.87	53.01 ± 16.24
N3/TST (%)	10.20 (5.85, 15.70)	8.00 (3.90, 21.90)

p > 0.05 PDS-NC vs. PDS-MCI. Data representing age, TST, WASO, R/TST, N1/TST, and N2/TST are expressed as mean ± standard deviation, and t-tests were used for between-group comparison. Data representing AHI, BMI, SE, sleep latency, and N3/TST are expressed as the median (25th-75th percentile), and nonparametric tests were used for between-group comparison. Abbreviations: BMI: body mass index; AHI: apnea and hypopnea index; TST: total sleep time; SE: sleep efficacy; WASO: wake time after sleep onset; R: rapid eye movement sleep; N1: nonrapid eye movement 1 sleep; N1: nonrapid eye movement 2 sleep; N3: nonrapid eye movement 3 sleep.

Of the 40 patients evaluated, 18 had REM sleep without atonia (RWA) on PSG, and 22 had no RWA. In the PDS-NC group, 11 had RWA and 14 had no RWA; in the PDS-MCI group, 7 had RWA and 8 had no RWA. The composition ratio of RWA in the two groups was not significantly different (p > 0.05).

3.2. Comparisons of the Power Spectral Analysis and DFA between the PDS-MCI and PDS-NC Patients

3.2.1. General Summary of the Comparisons of the Power Spectral Analysis and DFA

The alpha activity and fast ratio ((alpha + beta)/(delta + theta)) were lower during wakefulness and NREM sleep in patients with PDS-MCI than in those with PDS-NC (p < 0.05).

The sigma and beta activities were lower during wakefulness in patients with PDS-MCI than in those with PDS-NC (p < 0.05).

DFA increased in patients with PDS-MCI during wakefulness and NREM sleep compared to those with PDS-NC (p < 0.05).

The other power spectral analysis characteristics were not significantly different in the two groups (p > 0.05).

The results of channels C4 and O1 were similar. The data of the power spectral analysis and DFA (those of p values < 0.05) are shown in Table 2 and Figure 1.

Table 2

The EEG power spectral analysis and DFA in PDS-NC and PDS-MCI patients.

	PDS-NC	PDS-MCI	P
alpha-NREM (C4)	0.08±0.032	0.05±0.015	0.032
alpha-NREM (O1)	0.08±0.034	0.05±0.023	0.022
alpha-wake (C4)	0.15±0.068	0.10±0.061	0.037
alpha-wake (O1)	0.17±0.094	0.11±0.041	0.016
sigma-wake (C4)	0.04±0.015	0.03±0.015	0.045
sigma-wake (O1)	0.04±0.017	0.03±0.012	0.038
beta-wake (C4)	0.13±0.050	0.08±0.031	0.005
beta-wake (O1)	0.12±0.041	0.08±0.032	0.003
fast ratio-NREM (C4)	1.12±0.60	0.70±0.32	0.022
fast ratio-NREM (O1)	1.24±0.55	0.65±0.24	0.001
fast ratio-wake (C4)	1.27±0.702	0.82±0.435	0.031
fast ratio-wake (O1)	1.40±0.64	0.73±0.28	0.001
DFA-NREM (C4)	0.76±0.12	0.87±0.14	0.011
DFA-NREM (O1)	0.80±0.12	0.90±0.12	0.005
DFA-wake (C4)	0.76±0.13	0.87±0.16	0.026
DFA-wake (O1)	0.80±0.12	0.90±0.11	0.012

Abbreviations: PDS-NC: parkinsonism with normal cognitive function; PDS-MCI: parkinsonism with mild cognitive impairment; NREM: nonrapid eye movement; DFA: detrended fluctuation analysis. Fast ratio: (alpha + beta)/(delta + theta).

Figure 1

The EEG power spectral analysis and DFA in PDS-NC and PDS-MCI patients (a summary of Table 2, p < 0.05).

All the variables analyzed in the study are summarized in the Supplementary Figure (see available here).

3.2.2. Power Spectral Analysis and DFA in the NREM Sleep, REM Sleep, and Wake Stages

The O1 lead was selected as the analysis lead. The p value was adjusted to 0.05/7 = 0.007 as there were seven indicators analyzed (alpha, beta, theta, sigma, delta, fast ratio, and DFA) in each sleep stage. p < 0.007 was considered significant.

(1) Comparisons of the Power Spectral Analysis and DFA in NREM Sleep between Both Groups. The results showed that PDS-MCI patients had a decreased EEG fast ratio (0.65 ± 0.24 vs. 1.24 ± 0.55, p = 0.001) and increased DFA (0.90 ± 0.12 vs. 0.80 ± 0.12, p = 0.005) during NREM compared to PDS-NC patients (p < 0.007) (Figure 1).

The alpha, beta, theta, sigma, and delta activities are not different between the PDS-MCI and PDS-NC groups (p > 0.007) (Figure 2).

Figure 2

Comparisons of the power spectral analysis and DFA in NREM sleep between the PDS-MCI and PDS-NC groups.

(2) Comparisons of the Power Spectral Analysis and DFA in REM Sleep between the PDS-MCI and PDS-NC Groups. The alpha, beta, theta, sigma, and delta activities, EEG fast ratio, and DFA of REM sleep are not different between the PDS-MCI and PDS-NC groups (p > 0.007) (Figure 3).

Figure 3

The comparisons of the power spectral analysis and DFA in REM sleep between the PDS-MCI and PDS-NC groups.

(3) Comparisons of the Power Spectral Analysis and DFA during Wakefulness between the PDS-MCI and PDS-NC Groups. The beta activity and fast ratio were significantly lower in PDS-MCI patients (0.08 ± 0.032; 0.73 ± 0.28) than in PDS-MCI patients (0.12 ± 0.041; 1.40 ± 0.64) (p = 0.003; p = 0.001, respectively).

The alpha, theta, sigma, and delta activities and DFA during wakefulness did not differ between the PDS-MCI and PDS-NC groups (p > 0.007) (Figure 4).

Figure 4

The comparisons of the power spectral analysis and DFA during wakefulness between the PDS-MCI and PDS-NC groups.

3.3. Correlations between DFA and Fast Ratio in NREM Sleep and MCI

Mild cognitive dysfunction was positively correlated with DFA in NREM (r = 0.426, p = 0.007) and negatively correlated with the fast ratio in NREM (r = −0.524, p = 0.001) in channel O1 during NREM sleep.

3.4. ROC Curve of DFA and Fast Ratio in NREM Sleep for the Diagnosis of PDS-MCI

The area under the curve was 0.753 (95% CI: 0.592-0.914) for DFA applied to diagnose MCI in channel O1 during NREM sleep (p = 0.009). The cutoff value was 0.92 for channel O1, with sensitivity (60.0%) and specificity (91.7%), respectively (Figure 5).

Figure 5

ROC curve of DFA in NREM sleep for the diagnosis of PDS-MCI with the AUC of 0.753 (95% CI: 0.592-0.914).

The area under the curve for the fast ratio was only 0.183 (p = 0.002).

4. Discussion

This study demonstrated that the power spectral analysis and DFA characteristics of NREM sleep EEG were related to MCI in patients with PDS, who showed a reduced fast ratio ((alpha + beta)/(delta + theta)) and increased DFA. DFA during NREM was diagnostic of PDS-MCI, with an area under the ROC curve (AUC) of 0.753 (95% CI: 0.592–0.914) (p < 0.05). Therefore, we believe that there are characteristic alterations of NREM sleep EEG in patients with MCI. DFA and the fast ratio can be used as biomarkers reflecting cognitive function.

Cognitive decline is common in the progression of parkinsonism [3]. Impaired cognition has a major impact on either quality of life or mortality in patients with PDS [3]. The early prediction of MCI in patients with PDS is important to intervene before the development of dementia.

Neuropsychological testing is undoubtedly an important method for determining the cognitive function of patients with PDS. However, it also has several limitations. For example, it is subjective; the repeated use of the scale can induce learning effects. Thus, various objective markers have been investigated for the early identification of PDS-MCI, such as epidermal and insulin-like growth factors in plasma/serum and β-amyloid in cerebrospinal fluid [22], and hippocampal atrophy on brain magnetic resonance imaging [3]. Despite the diversity of studies [23], no biomarker has yet been validated. Among these markers, qEEG has shown good potential in identifying cognitive decline in PDS [11]. For example, theta power and alpha power help to distinguish PD with MCI from PD with normal cognition, and delta power and beta power help to distinguish PD with MCI from PD with dementia. In fact, besides the resting awake EEG, sleep EEG activity also relates to cognitive performance [24] and even general intelligence [25]. For example, some sleep spindle characteristics and sigma EEG power are related to improved learning and memory consolidation [26]. In addition, reductions in slow-wave, theta, and sigma activities and sleep spindle characteristics during NREM sleep may be related to MCI in older adults [12, 27].

In addition to conventional power spectral analysis, DFA is a simpler alternative method to quantify EEG. It is more suitable for the analysis of nonlinear and nonstationary physiological data [28] such as EEG and more easily removes artifacts [29]. Previous studies showed that OSA patients had a higher DFA during wakefulness than controls, which may be a marker of impaired performance of these patients [14]. DFA of EEG also performs as well as conventional power spectral analysis as a marker of impaired performance and sleepiness resulting from sleep loss [14]. DFA has been used to analyze EEG as a new alternative biomarker of cognitive impairment [14, 16]. It has been applied to the analysis of the EEG background activity in Alzheimer's disease [16, 30].

The relationship between the characteristics of sleep EEG power spectral analysis and DFA and cognitive dysfunction in patients with PDS remains unclear. In this study, we extracted and analyzed the PSG-EEG signals in the wake, NREM sleep, and REM sleep stages throughout the night. We sought to observe the changes in sleep EEG in MCI patients from the perspective of sleep. Our study found that PDS-MCI patients had a tendency of reduced alpha activity during NREM sleep. Studies have shown that alpha activity in NREM sleep is associated with intelligence in the general population [25]. The reduction in alpha activity in NREM sleep may reflect a change in the electrophysiological basis for memory consolidation and other cognitive activities. In fact, some studies have already shown that changes in EEG activities during NREM sleep are associated with a higher risk of early decline in cognitive function in older adults [12].

Another finding with regard to the EEG power spectral analysis is a decreased fast ratio ((alpha + beta)/(delta + theta)) during NREM sleep in the PDS-MCI group. Generally, slowing of resting awake EEG frequencies, which can be manifested as a decrease in alpha and beta activities and an increase in delta and theta activities, correlates with PD-related cognitive decline [14]. EEG slowing during wakefulness was also found in OSA patients with a higher EEG slowing ratio ((delta + theta)/(alpha + beta)), which may be linked to the subjective sleepiness of OSA patients [25]. The present study found that during NREM sleep, patients with PDS-MCI showed slowing of sleep EEG activity, which is similar to that in wakefulness, as previously reported. Many studies have shown that sleep, especially NREM sleep, promotes long-term consolidation of memories connecting the hippocampus and neocortex and is important for the maintenance of brain function [25]. Slowing of EEG activity during NREM sleep may reflect the decline in NREM physiological function and is therefore a potential marker in patients with PDS-MCI.

In addition, we found that a new parameter, DFA, was related to cognition in patients with PDS. Previous studies have shown that DFA can reflect the brain activity and complexity of an EEG. It increases during the transition from wake to sleep and continues to increase with deeper stages of NREM sleep [31]. A higher DFA reflects a lower EEG complexity. Our study showed that DFA in NREM sleep increased in PDS-MCI patients, reflecting a decrease in the complexity of EEG. The complexity of EEG is related to neurodegeneration and brain function. Recent studies have demonstrated that the decreased complexity of EEG is a predictor of neurodegeneration in patients with idiopathic rapid eye movement behavior disorders [32].

The complexity of EEG also correlates to the mild cognitive impairment of Alzheimer's disease (AD) [33]. Compared to healthy individuals, the EEG signals from AD patients have less complexity, and this complexity is considered a potential biomarker for the functional abnormalities in AD [33]. Our result suggests that reduced EEG complexity may also be associated with cognitive decline seen in patients with PDS; DFA is a promising marker for it.

The main limitations of the study are as follows. (1) In this retrospective study, NREM sleep EEG characteristics were correlated with MCI, but it remains to be investigated whether there is a causal relationship and whether it can predict MCI. (2) To assess whether MCI is independently correlated with these EEG parameters, a regression analysis would be more appropriate. However, the sample size was too small to allow for a regression analysis. This study is a preliminary study, and the sample size can be increased for subsequent analysis.

The early prediction of MCI in patients with PDS is important for early intervention prior to the development of dementia. Recent studies suggest that EEG changes precede the onset of the clinical signs of cognitive impairment in PDS [11, 32]. This retrospective study points out that EEG characteristics and DFA parameters in NREM sleep are associated with MCI in patients with PDS. Future prospective studies are required to see whether DFA parameters during NREM sleep could predict early cognitive decline and thus provide a basis for early intervention.

5. Conclusion

In summary, this study demonstrated that the power spectral analysis and DFA characteristics of NREM sleep EEG were related to MCI in patients with PDS. DFA can be used as a biomarker reflective of cognitive function. Slowing of EEG activity during NREM sleep may reflect the decline in NREM physiological function and is therefore a marker in patients with PDS-MCI. The early prediction of MCI in patients with PDS is important for early interventions prior to the development of dementia.