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Sleep EEG characteristics associated with total sleep time misperception in young adults: an exploratory study

Sleep EEG characteristics associated with total sleep time misperception in young adults: an... Background: Power spectral analysis (PSA) is one of the most commonly-used EEG markers of cortical hyperarousal, and can help to understand subjective–objective sleep discrepancy (SOD). Age is associated with decreased sleep EEG activity; however, the PSA of young adults is currently limited. Thus, this study aimed to examine the correlation of spectral EEG power with total sleep time ( TST ) misperception in young patients. Methods: Forty-seven young adults were recruited and underwent a polysomnography recording in a sleep labora- tory. Clinical records and self-report questionnaires of all patients were collected, and were used to categorize patients into a good sleeper (GS) group (n = 10), insomnia with a low mismatch group (IWLM, n = 19) or participant with a high mismatch group (IWHM, n = 18). PSA was applied to the first 6 h of sleep. Results: IWHM patients exhibited a higher absolute power and relative beta/delta ratio in the frontal region com- pared to the GS group. No significant difference was observed between the IWLM and GS groups. No significant difference in the above parameters was observed between the IWHM and IWLM groups. Moreover, The SOD of TST was positively correlated with frontal absolute power and the relative beta/delta ratio (r = 0.363, P = 0.012; r = 0.363, P = 0.012), and absolute beta EEG spectral power (r = 0.313, P = 0.032) as well as the number of arousals. Conclusions: Increased frontal beta/delta ratio EEG power was found in young patients with a high mismatch but not in those with a low mismatch, compared with good sleepers. This suggests that there exists increased cortical activity in IWHM patients. In addition, the frontal beta/delta ratio and the number of arousals was positively correlated with the SOD of TST. Keywords: Young adult, Misperception, EEG, Power spectral analysis, Cortical activation Introduction healthcare and medical costs, higher chances of absen- Insomnia is a common disease in modern society with teeism, traffic accidents, falling and a poor quality-of-life a prevalence rate ranging from 12 to 20% [1]. Patients [4–7]. with insomnia have a higher risk of mental illness [2] Subjective–objective sleep discrepancy (SOD), also and physical diseases [3, 4], which may lead to higher called sleep misperception, refers to the underestima- tion of total sleep time (TST) and overestimation of sleep onset latency (SOL). The SOD of TST is obtained by sub - tracting self-reported TST (sTST) values from objective *Correspondence: cloudxby@163.com TST (oTST) values detected by polysomnography (PSG) Department of Fangcun Sleep-Disorder, the Second Clinical College [8]. SOD is very common in insomnia patients [9, 10] of Guangzhou University of Chinese Medicine (Guangdong Provincial Hopsital of Chinese Medicine), Guangzhou 510120, China and is extremely common in paradoxical insomnia which Full list of author information is available at the end of the article was listed in the International Classification of Sleep © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Xu et al. Behavioral and Brain Functions (2022) 18:2 Page 2 of 13 Disorders-2nd Edition (ICSD-2), but cancelled in ICSD-3 of which is the difference in various subtypes of insom - mainly due to a lack of consensus on its precise defini - nia. Many of the diseases studied fall under the umbrella tion. Although it was cancelled, the symptom is worth term of PI, which is a broad term that includes paradoxi- studying [11] since it is an important aspect for under- cal insomnia (ParI) and psychophysiological insomnia standing the mechanism of insomnia. (PsyI), as well as idiopathic insomnia. However, some Several studies have attempted to explain the possible subtypes, for example [9] individuals with and without mechanisms of SOD in insomnias and have accumulated sleep misperception, are not considered in most stud- 13 possible mechanisms thereof that are supported by ies. Therefore, it has been suggested that future studies good-quality evidence [9]. One of these potential mecha- on EEG spectral features should patients from different nisms is related to cortical hyperarousal. Power spectral insomnia subtypes. To our knowledge, only several stud- analysis (PSA) is one of the most commonly-used EEG ies aimed to investigate SOD to understand the underly- markers of cortical hyperarousal [12]. PSA can show ing mechanism of insomnia. Krystal et  al. [19], St-Jean whether information processing is in an enhanced state, et  al. [20] and Lecci et  al. [21] categorized their patients which may cause the misperception of sleeping state [13]. according to SOD, but there was no consistency in the The power of each waveform is defined as the area under spectral power analyses in these studies and only elderly the waveform, where a high amplitude represents high patients (40–80 years old vs. 40.21 + 9.38 vs 40–85 years power [9]. old) were included. Krystal et al. [19] reported that lower Previous studies have shown that patients with primary delta and greater alpha, sigma and beta NREM EEG insomnia (PI) have more high-frequency EEG activity, activity was found in patients with subjective insomnia especially beta-band activity, during non-rapid eye move- but not in those with objective insomnia, compared with ment (NREM) sleep [14–18] than healthy controls. This normal subjects. St-Jean et al. [20] reported that patients finding is also consistent with the hyperarousal theory of with ParI exhibited higher absolute delta activity at the insomnia. However, there is still no consensus on other standardized F3, C3, and P3 electrodes compared to power bands during NREM sleep. For example, a meta- those with PsyI. analysis including 532 patients with insomnia disorder It has also shown that there is a relationship between (ID) and 445 good sleepers performed by Zhao et al. [18] age and s decrease of sleep EEG activity [22, 23]. Com- found that patients with ID exhibited increased theta, pared to young individuals, elderly patients exhibit less alpha, and sigma power during NREM sleep. Riedner N3 and spindle activity during non-rapid eye move- et  al. [17] found that ID patients exhibited no difference ment (NREM) sleep, and a smaller proportion of in slow wave (specifically < 5 Hz) and sigma (spindle) fre- rapid eye movement (REM) sleep [24]. It has also been quencies (specifically 11–16 Hz) compared with GS. This shown that absolute power in the delta, theta and sigma difference could be explained by a number of reasons, one bands decline with age for both females and males [23]. Table 1 Demographic characteristics of all participants Variables GS (n = 10) IWHM (n = 18) IWLM (n = 19) Statistics Age, years 26.0 [25.0, 32.0] 30.5 [25.8, 38.2] 25.0 [23.0, 38.0] H = 3.378, p = 0.185 Sex (F/M) 5/5 9/9 16/3 χ = 5.616, p = 0.060 Race χ = 0.000, p = 1.000 Han 10 18 19 Non-Han 0 0 0 Place of residence χ = 1.233, p = 0.540 Downtown 9 13 15 Suburb 1 4 3 Village 0 1 1 Marriage χ = 2.798, p = 0.247 Unmarried 7 7 10 Married 3 10 9 Bereavement/divorce 0 1 0 Family history of insomnia (Y/N) 1/9 3/15 1/18 χ = 1.243, p = 0.537 Family history of psychosis (Y/N) 0/10 0/18 1/18 χ = 1.474, p = 0.479 GS: good sleeper; IWLM: insomnias with a low mismatch; IWHM: insomnias with a high mismatch Xu  et al. Behavioral and Brain Functions (2022) 18:2 Page 3 of 13 Table 2 PSQI scores, SCL90 scores, and PSG characteristics of all participants Variable GS (n = 10) IWHM(n = 18) IWLM (n = 19) Statistics a a PSQI total score 3.5 [2.0, 6.2] 13.5 [11.5,16.3] 11.0 [9.0, 14.0] H = 18.882, P < 0.001 a a SCL-90 total score 109.5 [103.5, 129.3] 174.5 [155.8, 223.8] 148.0 [127.5, 180.0] H = 16.037, P < 0.001 TST (min) 394.25 ± 45.25 415.06 ± 40.20 381.95 ± 39.85 F = 3.026, P = 0.059 SPT (min) 418.60 ± 47.39 459.03 ± 42.65 431.84 ± 49.77 F = 2.837, P = 0.069 SE, % 89.27 ± 3.92 87.84 ± 7.00 86.24 ± 8.39 F = 0.626, P = 0.540 SOL (min) 11.50 [7.13, 23.38] 8.25 [4.00, 12.63] 5.50 [3.00, 10.00] H = 2.697, P = 0.260 %NREM stage1 5.00 [3.00, 6.25] 5.00 [4.00, 8.50] 4.00 [3.00, 7.00] H = 1.158, P = 0.560 %NREM stage2 59.40 ± 8.51 61.17 ± 8.61 62.16 ± 7.63 F = 0.371, P = 0.692 % SWS 13.60 ± 4.79 10.06 ± 6.49 12.37 ± 4.82 F = 1.528, P = 0.228 %REM 21.90 ± 6.61 22.61 ± 3.27 19.31 ± 5.14 F = 2.259, P = 0.116 Number of awakenings 23.50 [19.50, 29.50] 24.00 [16.00, 31.00] 19.00 [13.00, 27.00] H = 2.147, P = 0.342 Number of arousals 17.00 [8.25,20.50] 24.00 [12.75, 47.50] 26.00 [17.00, 47.00] H = 3. 730, P = 0.155 Arousal index 2.69 [1.22, 3.21] 3.71 [1.77,7.42] 4.17 [2.28,7.55] H = 3. 611, P = 0.164 Number of arousals of NREM 15.00 [8.25, 20.50] 19.50 [10.00, 46.50] 22.00 [17.00, 41.00] H = 3.233, P = 0.199 Number of arousals of REM 0.00 [0.00, 3.00] 1.00 [0.00, 3.25] 1.00 [0.00, 5.00] H = 1.339, P = 0.512 GS: good sleeper; IWLM: insomnias with a low mismatch; IWHM: insomnias with a high mismatch PSQI: Pittsburgh Sleep Quality Index; TST: total sleep time; SPT: sleep period time; SE: sleep efficiency; SOL: sleep onset latency; NREM: non-rapid eye movement; SWS: slow wave sleep; REM: rapid eye movement The statistical value H represents the use of a Kruskal–Wallis non-parametric analysis among three group using a Bonferroni correction for post-hoc analysis, while the statistical value F represents the use of a one-way ANOVA using the least significant difference test for post-hoc analysis P < 0.05 versus GS. There was no difference between the IWHW and IWLW groups Table 3 Comparison of absolute EEG spectral power among the experimental groups (μν ) Variable GS (n = 10) IWHM (n = 18) IWLM (n = 19) Statistics Frontal derivation Delta (1–4 Hz) 46.47 ± 21.05 37.61 ± 15.30 46.65 ± 25.95 F = 0.981, P = 0.383 Theta (4–8 Hz 2.90 [2.29, 4.22] 3.23 [2.67, 3.99] 4.91 [2.59, 5.83] H = 3.481, P = 0.175 Alpha (8–12 Hz) 1.87 [1.16, 3.37] 2.53 [1.55, 2.99] 2.78 [1.97, 3.89] H = 2.693, P = 0.260 Sigma (12–16 Hz) 0.80 [0.55, 1.27] 1.09 [0.69, 1.56] 1.40 [0.88, 1.76] H = 4.137, P = 0.126 Beta (16–32 Hz) 0.71 [0.57, 0.99] 1.08 [0.69, 1.80] 1.06 [0.79, 1.50] H = 4.062, P = 0.131 Beta /Delta 0.01 [0.01, 0.03] 0.03 [0.02, 0.04] 0.03 [0.02, 0.04] H = 6.904, P = 0.032 Beta/Theta 0.27 [0.17, 0.37] 0.34 [0.23, 0.53] 0.25 [0.18, 0.36] H = 1.907, P = 0.385 Alpha/Delta 0.04 [0.03, 0.08] 0.07 [0.04, 0.09] 0.07 [0.05, 0.11] H = 3.086, P = 0.214 Alpha/Theta 64.00 ± 16.43 76.60 ± 41.44 74.33 ± 30.73 F = 0.487, P = 0.618 Central derivation Delta (1–4 Hz) 37.99 ± 12.36 33.89 ± 19.01 46.48 ± 20.94 F = 2.154, P = 0.128 Theta (4–8 Hz 4.77 [3.37, 4.99] 4.11 [3.21, 4.63] 4.93 [2.98, 8.85] H = 2.872, P = 0.238 Alpha (8–12 Hz) 2.51 [1.66, 3.24] 2.79 [1.89, 3.52] 2.70 [1.83, 4.02] H = 0.710, P = 0.701 Sigma (12–16 Hz) 1.43 [1.04, 1.84] 1.66 [1.21, 2.76] 1.75 [1.37, 2.51] H = 2.625, P = 0.269 Beta (16–32 Hz) 0.81 [0.66, 1.25] 1.29 [0.87, 1.62] 1.16 [0.72, 1.65] H = 2.139, P = 0.343 Beta/Delta 0.02 [0.02, 0.04] 0.04 [0.02, 0.06] 0.03 [0.02, 0.04] H = 3.087, P = 0.214 Beta/Theta 0.18 [015, 0.31] 0.28 [0.20, 0.43] 0.20 [0.15, 0.35] H = 2.385, P = 0.303 Alpha/Delta 0.07 [0.04, 0.09] 0.08 [0.05, 0.14] 0.06 [0.05, 0.09] H = 2.632, P = 0.268 Alpha/Theta 0.57 [0.47, 0.66] 0.58 [0.51, 0.92] 0.52 [0.42, 0.75] H = 1.774, P = 0.412 GS: good sleeper; IWLM: insomnias with a low mismatch; IWHM: insomnias with a high mismatch. The statistical value H represents the use of a Kruskal–Wallis non- parametric analysis among three group using a Bonferroni correction for post-hoc analysis, while the statistical value F represents the use of a one-way ANOVA using the least significant difference test for post-hoc analysis P < 0.05 versus GS. There was no difference between the IWHW and IWLW groups Xu et al. Behavioral and Brain Functions (2022) 18:2 Page 4 of 13 Fig. 1 Absolute and relative beta in the frontal and central region. GS: good sleeper; IWLM: insomnias with a low mismatch; IWHM: insomnias with a high mismatch Importantly, previous research has failed to combine age the Pittsburgh Sleep Quality Index (PSQI) [25] and the data with insomnia subtype information to study the PSA Symptom Checklist 90 (SCL-90), were given to each par- of SOD. ticipant. Subjective sleep quality was determined by self- In this study, we aimed to compare the PSA of three reported TST after PSG. The subjects were asked two groups of young patients: (1) patients who overestimated questions about their perceived sleep within 2  h after their total sleep time by at least 2 h; (2) patients who cor- PSG completion: (1) “How long did you sleep last night?” rectly estimated their sleep; (3): good sleepers (GS). Our and (2) “Did you sleep as usual?”. In this way, the sTST findings may be important to clinical and public health as of the patient was obtained. For example, if the patient well as the treatment and management of insomnia [9]. replied that they slept for 6 h during the previous night, 360 min was his/her sTST. Materials and methods Subjects were categorized as GS according to the Participants following criteria: (1) reported no difficulty in sleep Seventy participants aged between 18 and 40  years-old according to the two-week sleep diary (i.e. sleep onset were recruited from the Guangdong Provincial Hospital (SO) < 30  min, wake after sleep onset (WASO) < 40  min, of Chinese Medicine through posters from May 2016 to TST between 6.0 and 8.0  h, or sleep efficiency November 2017. All subjects were asked to complete a (SE) ≥ 85%); (2) had a PSQI score < 7 [25], SE > 85% or two-week sleep diary followed by a single all-night PSG TST > 6 h. recording in a sleep laboratory. Personal information was Participants were categorized as insomnia patients obtained from all subjects, including age, sex, race, place if they met the following criteria: (1) diagnosed with of residence, marital status, family history of insom- chronic insomnia disorder (International Classification nia and psychosis. Two self-reported questionnaires, of Sleep Disorders, 3rd edition); (2) reported at least Xu  et al. Behavioral and Brain Functions (2022) 18:2 Page 5 of 13 Fig. 2 Absolute power and relative beta/delta in the frontal and central regions. GS: good sleeper; IWLM: insomnias with a low mismatch; IWHM: insomnias with a high mismatch three nights per week of sleep difficulty (i.e.SO > 30 min, as an apnea–hypopnea index of more than five events per WASO > 40  min, sTST < 6.0  h, or SE < 85%); (3) had a hour using PSG, or restless leg syndrome); (2) affected PSQI score of > 7; (4) had difficulty sleeping for more than by other external factors that might affect insomnia (e.g. 3 months; (5) did not have other medical, psychological, physical pain caused by medical diseases, drugs affect - or sleeping disorders and did not take any medications ing sleeping structure, alcohol consumption, other treat- that would affect sleep (e.g. sedative and hypnotic drugs, ments, etc.); (3) go to sleep later than 0:00 am or wake up antidepressants, anti-schizophrenia drugs, etc.). before 6:00 am, or had irregular sleeping schedules. Insomnia patients were further categorized into two Based on the inclusion and exclusion criteria, 47 par- subgroups based on their SOD of TST. These two sub - ticipants were included in the study: GS group (n = 10; groups comprised patients with low mismatch (IWLM) 5 males, 5 females), IWHM group (n = 18; 9 males, 9 and patients with high mismatch (IWHM). The SOD of females), and IWLM group (n = 19; 3 males, 16 females). TST was operationalized as the values of the differences between subjective and objective measures (i.e. sTST– PSQI and SCL‑90 oTST value) [8]. IWLM patients were those individuals The PSQI is a questionnaire consisting of 21 items and who met the criteria of chronic insomnia disorder and has been commonly used to evaluate subjective sleep had an SOD < 60  min in TST. IWHM were defined as quality. The higher the score, the greater the severity of patients who met the criteria of chronic insomnia dis- insomnia. A score > 7 indicates abnormal sleeping (severe order and had normal PSG parameters (i.e. SE > 85% difficulty in at least two areas or moderate difficulty in and TST > 6.5 h) and SOD > 120 min in TST. In both the more than three areas). IWHM and IWLM subgroups, patients were excluded if The SCL-90 is one of the most widely used mental they met one of the following criteria: (1) diagnosed with health scales in the field of psychiatry. It is a 90-item, self- another Axis I disorder or any other sleeping disorder reported symptom inventory. The score for each item is (e.g. idiopathic insomnia, sleep apnea, which was defined summed, yielding a total score that covers ten aspects. Xu et al. Behavioral and Brain Functions (2022) 18:2 Page 6 of 13 Table 4 Comparison of relative EEG spectral power among the experimental groups Variable GS (n = 10) IWHM (n = 18) IWLM (n = 19) Statistics Frontal derivation Delta (1–4 Hz) 87.96 [79.34, 90.48] 80.74 [75.23, 83.99] 80.81 [76.26, 85.81] H = 5.046, P = 0.080 Theta (4–8 Hz 5.85 [4.54, 8.54] 7.39 [6.24, 9.56] 7.20 [6.61, 10.78] H = 3.820, P = 0.148 Alpha (8–12 Hz) 3.77 [2.49, 6.48] 5.08 [3.56, 6.84] 5.10 [3.80, 8.26] H = 2.767, P = 0.251 Sigma (12–16 Hz) 1.31 [1.16, 2.71] 2.51 [1.75, 3.35] 2.15 [1.62, 4.32] H = 3.498, P = 0.174 Beta (16–32 Hz) 1.26 [1.09, 2.00] 2.50 [1.54, 3.49] 1.94 [1.65, 2.99] H = 5.756, P = 0.056 Beta /Delta 0.01 [0.01, 0.03] 0.03 [0.02,0.04] 0.03 [0.02, 0.04] H = 6.904, P = 0.032 Beta/Theta 0.27 [0.17, 0.37] 0.34 [0.23, 0.53] 0.25 [0.18, 0.36] H = 1.907, P = 0.385 Alpha/Delta 0.04 [0.03, 0.08] 0.07 [0.04, 0.09] 0.07 [0.04, 0.11] H = 3.183, P = 0.204 Alpha/Theta 64.00 ± 16.42 76.60 ± 41.44 74.32 ± 30.73 F = 0.487, P = 0.618 Central derivation Delta (1–4 Hz) 78.42 [74.84, 86.28] 74.67 [66.94, 82.17] 77.34 [74.20, 82.11] H = 3.930, P = 0.140 Theta (4–8 Hz 10.04 [6.57, 10.67] 9.98 [8.57, 11.37] 9.54 [8.07, 12.22] H = 0.281, P = 0.869 Alpha (8–12 Hz) 5.74 [3.51, 7.13] 5.94 [4.59, 9.21] 4.84 [4.01, 7.00] H = 2.483, P = 0.289 Sigma (12–16 Hz) 3.11 [2.35, 4.39] 4.39 [3.15, 6.77] 3.29 [2.62, 4.55] H = 5.447, P = 0.066 Beta (16–32 Hz) 1.72 [1.43, 3.05] 2.79 [1.51, 4.42] 2.00 [1.45, 3.38] H = 3.121, P = 0.210 Beta /Delta 0.02 [0.02, 0.04] 0.04 [0.02, 0.06] 0.03 [0.02, 0.04] H = 3.087, P = 0.214 Beta/Theta 0.18 [0.15, 0.31] 0.28 [0.20, 0.43] 0.20 [0.15, 0.35] H = 2.385, P = 0.303 Alpha/Delta 0.07 [0.04, 0.09] 0.08 [0.05, 0.14] 0.06 [0.05, 0.09] H = 2.632, P = 0.268 Alpha/Theta 0.57 [0.47, 0.66] 0.58 [0.51, 0.92] 0.52 [0.42, 0.75] H = 1.774, P = 0.412 GS: good sleeper; IWLM: insomnias with a low mismatch; IWHM: insomnias with a high mismatch The statistical value H represents the use of a Kruskal–Wallis non-parametric analysis among three group using a Bonferroni correction for post-hoc analysis, while the statistical value F represents the use of a one-way ANOVA using the least significant difference test for post-hoc analysis. P < 0.05 versus GS. There was no difference between the IWHW and IWLW groups The higher the total score, the greater the risk of develop - Sleep records were reviewed and scored by a registered ing psychological distress [26]. PSG technician according to the revised AASM 2.5 sleep- ing scoring criteria [27]. The sleeping continuity param - PSG recordings eters, including TST, SPT, SE (ratio of TST to time in In conventional PSG (Nicolet, ONE, EEG 32, USA), the bed × 100%), and SOL, and sleeping architecture param- international 10–20 system was used to record EEG. In eters, including the number of awakenings, the number this study, the grounding electrode was placed on the of arousals, arousal index, percentage of NREM stage frontal pole midline point and the bilateral ear electrodes 1 and 2, slow wave sleep (SWS) or NREM stage 3, and were used as the reference. All electrographic electrodes REM sleep of TST were analyzed. were placed according to the AASM 2.6 recommended guidelines. The impedance was kept below 5  kΩ for all Spectral analysis electrodes. The surface electrodes included six EEG (two Normal sleep time is 6.0 to 8.0  h, and therefore we ana- central electrodes [C3, C4], two frontal EEG electrodes lyzed the first 6  h of the PSG recordings. The data from [F3, F4], and two occipital EEG electrode [O1, O2)]), two the central and frontal EEG electrode (averaged C3-A2 electro-oculogram (E1, E2), submental electromyogram and C4-A1 channels, averaged F3-A2 and F4-A1 chan- (EMG: Chin1-Chin2), electrocardiogram (ECG), and two nels) were generated using software of Nicolet EEG band reference electrodes (A1, A2). In addition, tibialis EMG width tools. and respiration were used to exclude periodic limb move- Most of the common artifacts were due to improper ments (a PLMSI > 15) and sleep apnea (an apnea–hypo- click placements (such as electrode popping, ECG or pnea index > 5), respectively. Participants were asked to pulse artifact), body movement (muscle artifact, eye sleep at their usual time (before 0:00 am) and wake up at movement artifact or major body movement) or environ- 7:00 am. The sampling rate of EEG was 500  Hz and the mental factors (overheated which lead to slow-frequency filter settings were as follows: notch frequency at 60 Hz; artifacts). We optimized the mastoid electrodes so that low pass filter at 35 Hz; high pass filter at 0.3 Hz. ECG and pulse artifacts could be minimized. Secondly, Xu  et al. Behavioral and Brain Functions (2022) 18:2 Page 7 of 13 Table 5 Correlation between absolute EEG spectral power and absolute power of each frequency band by the power of SOD of TST the total power spectrum. Variables SOD of TST Statistical analysis r P Statistical analysis was performed using the SPSS soft- ware (ver. 24.0) and with an unpaired two-tailed test of Frontal derivation significance. A normality test and Levene’s test were used Delta (1–4 Hz) − 0.202 0.173 to check whether the data followed a normal distribu- Theta (4–8 Hz) − 0.032 0.833 tion. A Chi-square test was used for demographic char- Alpha (8–12 Hz) 0.058 0.698 acteristics except for age. Normally distributed data with Sigma (12–16 Hz) − 0.013 0.931 homogeneous variance were compared using a one-way Beta (16–32 Hz) 0.200 0.179 ANOVA, while others were compared using a non-par- Beta/Delta 0.363 0.012 ametric analysis (Kruskal–wallis) with post-hoc analysis. Beta/Theta 0.208 0.161 The statistical value H represents the use of non-par - Alpha/Delta 0.171 0.249 ametric analysis, while the statistical value F represents Alpha/Theta 0.182 0.221 the use of a one-way ANOVA. In addition, we used pair- Central derivation wise least significant difference post-hoc tests after a one- Delta (1–4 Hz) − 0.109 0.467 way ANOVAs and a Bonferroni correction for multiple Theta (4–8 Hz − 0.063 0.673 comparison after a Kruskal–Wallis test. Spearman’s or Alpha (8–12 Hz) 0.188 0.205 Pearson’s correlation analysis was used to determine the Sigma(12–16 Hz) 0.156 0.295 correlation between the EEG spectral power (absolute Beta (16–32 Hz) 0.216 0.145 and relative) and the SOD of TST (after data normality Beta/Delta 0.249 0.091 was confirmed). A P- value < 0.05 was considered statisti- Beta/Theta 0.188 0.256 cally significant. Alpha/Delta 0.256 0.082 Alpha/Theta 0.169 0.257 Results SOD: Subjective–objective sleep discrepancy; TST: total sleep time Baseline characteristics There was no significant differences in age, sex, race, we kept impedance below 5  kΩ to avoid electrode pop- place of residence, marital status, family history of ping. At the same time, we maintained a temperature of insomnia, or family history of psychosis among the three 20  °C in the sleep laboratory which is the standard set- groups (Table 1). ting to ensure that the subjects completed the test in a comfortable environment, and avoided the influence of PSQI, SCL90, and PSG characteristics slow-frequency artifacts from sweat. A notch filter at The comparisons of the PSQI score, SCL-90 score, and 50  Hz was applied to avoid power line contamination of PSG among the three groups are shown in Table  2. The the electrical signals. Then, we set a high frequency filter IWHM and IWLM groups showed higher PSQI and to 35  Hz to reduce most of the interference from EMG. SCL-90 scores compared to the GS group. However, We chose this cutoff values as the frequency of EMG there was no significant difference in the PSQI or SCL-90 activity signal is generally contained in higher frequency scores between the IWHM and IWLM groups. The PSG bands and since the AASM recommend that EMG low parameters were not significantly different among the frequency and high frequency filter cutoffs should be at GS, IWHM, and IWLM groups. 10  Hz and 100  Hz, respectively, to capture the muscle activity. Finally, the data fragments that were displaced Absolute EEG spectral power or cut off due to movements or that were obviously dif - Post-hoc analysis (Bonferroni correction) revealed that ferent from the background were to excluded by visual the IWHM group exhibited a significantly higher frontal inspection (e.g. due to the excessive loss of occipital EEG beta/delta ratio than the GS group. No significant differ - electrode signal, these data were not included in this ence was observed between the IWLM and GS groups. study). Therefore, artifacts in each recording were visu - There was no significant difference in these parameters ally inspected and removed accordingly. between the IWHM and IWLM groups (Table  3, Figs.  1, The beta (16–32  Hz), sigma (12–16  Hz), alpha 2). (8–12  Hz), delta (0.5–4  Hz), and theta (4–8  Hz) band activity was extracted for PSA analysis. The values of relative spectral power were calculated by dividing the Xu et al. Behavioral and Brain Functions (2022) 18:2 Page 8 of 13 Fig. 3 Correlation between absolute frontal beta/dela spectral power and SOD of TST. SOD: Subjective–objective sleep discrepancy; TST: total sleep time Relative EEG spectral power Correlation between number of arousals and frontal beta The average NREM activity for the beta/delta ratio in and SOD of TST the frontal area was significantly different among the Spearman’s correlation was performed on frontal beta three groups. Post-hoc analysis (Bonferroni correction) and the SOD due to the non-normality of the data. The showed that the frontal beta/delta ratio in the IWHM number of arousals was correlated with the SOD of group was higher than that in the GS group. No sig- TST (r = 0.532, P = 0.023) in the IWHW group (Table 7, nificant difference was observed between the IWLM Fig. 5). and GS groups and no significant difference in relative spectral power was observed between the IWHM and IWLM groups (Table 4, Figs. 1, 2). Correlation between number of arousals and central beta, SOD of TST Spearman’s correlation was performed on the SOD of Correlation between absolute EEG spectral power and SOD TSTS due to the non-normality of the data. The num - Spearman’s correlation was performed on the SOD due ber of arousals was correlated with the SOD of TST to the non-normality of the data. The SOD of TST was (r = 0.532, P = 0.023) in the IWHW group (Table 8). positively correlated with absolute frontal beta/delta ratio (r = 0.363, P = 0.012) (Table 5, Fig. 3). Discussion To the best of our knowledge, this is the first study that Correlation between relative EEG spectral power and SOD has investigated the absolute and relative spectral power Spearman’s correlation was performed on the SOD due of young adult patients (18–40  years old) with subtypes to the non-normality of the data. The SOD of TST was of subjective insomnia. Here, we categorized insomnia positively correlated with relative frontal beta/delta patients into IWLM and IWHM groups to maximize ratio (r = 0.363, P = 0.012) and the absolute beta EEG the difference in the SOD. Overall, compared to the GS spectral power (r = 0.313, P = 0.032) (Table 6, Fig. 4). Xu  et al. Behavioral and Brain Functions (2022) 18:2 Page 9 of 13 Table 6 Correlation between relative EEG spectral power and that sigma increase during NREM sleeps in PI exhib- SOD of TST ited moderate and high heterogeneity in the dispersion of effect sizes [18]. Here, we found that both the IWHM Variables SOD of TST and IWLM groups exhibited no increase in absolute r P and relative power of all frequency bands in the central and frontal regions. Our findings are similar to a previ - Frontal derivation ous EEG-based spectral investigation by Buysse et  al. Delta (1–4 Hz) − 0.248 0.092 [29] that failed to find significant differences in the fre - Theta (4–8 Hz 0.127 0.397 quency band activity between insomnia types and GS Alpha (8–12 Hz) 0.158 0.290 during NREM sleep. Nevertheless, unlike other previ- Sigma (8–12 Hz) 0.124 0.405 ous work, we failed to observe lower delta NREM EEG Beta (16–32 Hz) 0.313 0.032 activity, or greater alpha, theta, sigma, beta NREM EEG Beta/Delta 0.363 0.012 activity in patients with insomnia. This discrepancy was Beta/Theta 0.208 0.161 unclear but could be influenced by the difference in the Alpha/Delta 0.173 0.245 age, frequency band definitions and diagnostic criteria Alpha/Theta 0.182 0.221 of IWHM and IWLW patients in the two studies. To our Central derivation knowledge, there has been little research on the relation- Delta (1–4 Hz) − 0.268 0.069 ship between age and power spectra, leading to dissimi- Theta (4–8 Hz − 0.072 0.631 lar results. For example, Krystal et  al. [19] reported that Alpha (8–12 Hz) 0.247 0.094 older age (40–80  years-old) was associated with signifi - Sigma (8–12 Hz) 0.207 0.163 cantly lower sigma (12.5–16 Hz) relative power during Beta (16–32 Hz) 0.232 0.116 NREM in insomnia patients. Svetnik et  al. [23] demon- Beta/Delta 0.249 0.091 strated that the power of the delta, theta and sigma bands Beta/Theta 0.1888 0.205 significantly decreased with age whereas the slope in the Alpha/Delta 0.256 0.082 alpha, beta and gamma bands did not. Therefore, age may Alpha/Theta 0.169 0.257 be a potential influencing factor. SOD: Subjective–objective sleep discrepancy; TST: total sleep time Insomnia is associated with poorer cognitive perfor- mance both generally and across multiple specific cogni - tive domains, especially in terms of a decline of working group, patients with IWHM exhibited an increase in the memory and executive ability [30, 31]. A longer course absolute power and relative beta/delta ratio in the fron- of insomnia generally leads to a poorer cognitive impair- tal region during sleep. Moreover, the SOD of TST was ment, which manifests as slower EEG frequency, a higher positively correlated with the absolute power and relative proportion of alpha and beta band power, and a lower beta/delta ratio in the same frontal region. However, no proportion of theta and delta band power. It will be more significant difference was observed in the EEG power or conducive if the relationship between age and power beta/delta ratio in the central region and no significant spectrum could be studied in combination with the difference in the above parameters was observed between course of disease. To our knowledge, PSA studies catego- the IWHM and IWLM groups. rizing insomnia into subtypes are limited. Some studies Beta power is generally considered an indicator of determined if the PSG was normal as a basis for judg- cortical arousal. It has been shown that beta activity in ing subjective and objective insomnia [19], which may PI patients is higher than that in GS [14–17], which sug- have led to the inclusion of patients with different sub - gests that patients with subjective insomnia may experi- types of insomnia. Other studies have also explored SOD ence enhanced sensory processing during sleep. In fact, of SOL. In our research, insomnia patients were further this phenomenon may render them highly responsive categorized into two subgroups based on their SOD of and sensitive to external sounds and in turn may also TST. IWLM individuals exhibited a SOD < 60 min in TST lead to the mistaken perception of their sleep as wakeful- whereas IWHM individuals exhibited a SOD > 120  min ness [13]. Based on prior studies, sigma activity (sleep in TST. In our study, the PSG of patients with IWHM spindle) represents a marker of sleep stability, especially and IWLW was normal and the PSQI was higher than against noises [15]. Therefore, sigma activity may be able GS participants. This meant that all patients were of sub - to distort the transmission of auditory information to jective insomnia, but that the degree of SOD was differ - the cortex during sleep [28]. A study from Spiegelhalder ent. SOD of IWHW insomnia patients was greater than et  al. [15] proposed the concept of simultaneous activa- 120  min in TST, while that of IWLW insomnia patients tion of wake-promoting and sleep-protecting neural activity patterns in PI. However, a meta-analysis showed Xu et al. Behavioral and Brain Functions (2022) 18:2 Page 10 of 13 Fig. 4 Correlation between relative frontal beta/dela spectral power and SOD of TST. SOD: Subjective–objective sleep discrepancy; TST: total sleep time was less than 120 min. In addition, patients with subjec- single night PSG in Krystal’s paper, which did not clearly tive insomnia met persistent PI criteria and had a normal define SOD. Recently, the ratio of high-frequency to low-frequency EEG power has been recognized as a novel indicator of cortical arousal. Furthermore, individuals with a higher Table 7 Correlation between the number of arousals and frontal ratio of this sort may have more sleeping difficulties. beta and SOD of TST Meric et  al. [32] found that PsyI patients exhibited an increased beta/delta ratio in the temporal lobe dur- Variables Number of arousals ing the sleep onset period (SOP). Some studies have r P also reported that delta EEG activity is decreased in PI GS (n = 10) patients in the temporal and central brain regions during Absolute beta − 0.049 0.894 the SOP [33, 34]. Thus, such an activity index (beta/delta Relative beta − 0.482 0.159 ratio) may be a more appropriate indicator of cortical SOD of TST − 0.028 0.938 arousal in insomnia patients [17, 32, 35]. In the current IWHM (n = 18) study, IWHM patients showed increased absolute power Absolute beta 0.271 0.277 and relative beta/delta ratio in the frontal region com- Relative beta 0.253 0.311 pared with the GS group, suggesting hyperarousal in the SOD of TST 0.532 0.023 frontal portion of the brain. IWLM (n = 19) SOD in insomnia has been shown to arise due to sev- Absolute beta 0.395 0.094 eral possible mechanisms, which mainly focus on sen- Relative beta 0.167 0.495 sory perception, emotion and cognition [9]. Various SOD of TST − 0.190 0.435 characteristics of insomnia patients support these con- cepts, such as: (1) insomniacs will judge PSG measured SOD: Subjective–objective sleep discrepancy Xu  et al. Behavioral and Brain Functions (2022) 18:2 Page 11 of 13 Fig. 5 Correlation between the number of arousals and SOD of TST. SOD, Subjective–objective sleep discrepancy; TST: total sleep time; GS: good sleeper; IWLM: insomnias with a low mismatch; IWHM: insomnias with a high mismatch sleep as wakefulness; (2) insomniacs have anxiety and selective attention toward sleep-related threats. The pos - Table 8 Correlation between the number of arousals and sibility that anxiety serves to trigger the misperception central beta and SOD of TST of sleep is drawn from the robust finding in time percep - Variables Number of arousals tion literature in that time is perceived as longer when the number of units of information processed per unit r P of time increases. Other characteristics of insomnia GS (n = 10) patients include: (3) patients may simply be poor esti- Absolute beta 0.055 0.880 mators of time; and (4) insomniacs’ assessment of sleep Relative beta − 0.085 0.815 quality is influenced by a memory bias that is influenced SOD of TST − 0.028 0.938 by current symptoms and emotions, a confirmation bias/ IWHM (n = 18) belief bias or a recall bias linked to intensity. In many Absolute beta 0.413 0.089 other papers, central regions, mainly involving in sen- Relative beta − 0.030 0.906 sory perception, are considered good representations SOD of TST 0.532 0.023 of the whole brain activity (from EEG) and have been IWLM (n = 19) widely used in PSA. Frontal lobes are also related to Absolute beta 0.173 0.479 emotion, cognition, and behavioral management, which Relative beta − 0.155 0.527 is connected with the mechanism of SOD. Therefore, it SOD of TST 0.190 0.435 is necessary to assess frontal regions. Unfortunately, cor- tical activation at sites other than central areas, such as SOD: Subjective–objective sleep discrepancy Xu et al. Behavioral and Brain Functions (2022) 18:2 Page 12 of 13 Acknowledgements frontal regions, has been poorly explored. In our study, We want to thank Chen Wang and Yujiao Sun who helped in the statistical a higher beta/delta ratio was only observed in frontal analysis. regions in the IWHW group when comparison to GS Authors’ contributions group. This result seems to suggest that high cortical BX: data analysis and interpretation, manuscript drafting/revision. QC: major arousal occurs in the frontal lobe and not just in the cen- role in data acquisition. RM: manuscript revision. HL: study supervision, critical tral region. revision of the manuscript. JH: data collection, manuscript revision. ZY: study concept and design, critical revision of the manuscript. All authors read and We further showed that the SOD of TST was associ- approved the final manuscript. ated with the absolute and relative NREM beta/delta ratio (r = 0.363) and relative beta power (r = 0.313) in the Funding This study was supported by 2014 Science and Technology Projects of frontal area. All in all, these results indicate that a higher Guangdong Province [2014A020212557]; Zhimin Yang Guangdong famous the beta/delta ratio and beta power during NREM sleep Chinese Medicine Inheritance studio construction project [Guangdong may be an underestimation of TST. Our results are simi- Chinese Medicine (2020) 1]. lar to the findings by Perlis et al. [14] that showed a mod - Availability of data and materials erate correlation between the SOD of TST and NREM The datasets used and/or analysed during the current study are available beta activity (14–35  Hz) (r = − 0.46). The underestima - from the corresponding author on request. tion of TST may be explained by the insertion of high frequency EEG into low frequency EEG, which has been Declarations shown to enhance the information processing ability and Ethics approval and consent to participate to degrade sleep quality [36]. This clinical trial was approved by the Ethics Committee of the Guangdong To the best of our knowledge, few studies have Provincial Hospital of Chinese Medicine (number: B3016-075) and performed in accordance with the World Medical Association Declaration of Helsinki. reported the correlation between the SOD of TST and This study was registered on http:// www. chictr. org/ up (registration number: the number of arousals. Results from our study showed chiCTR-COC-16008530). Informed consent was obtained from all subjects that the number of arousals was correlated with the prior to participation. SOD of TST in the IWHW group, suggesting that fre- Consent for publication quent awakenings that lead to sleep fragmentation may Not applicable. in turn lead to poor perception of insomnia. This is simi - Competing interests lar to the previous study by Choi et al. [37] that showed The authors declare that they have no competing interests. that sleep perception was negatively related to the PSG arousal index. Author details Department of Fangcun Sleep-Disorder, the Second Clinical College There are various limitations to our study that must be of Guangzhou University of Chinese Medicine (Guangdong Provincial Hopsital noted. First, only one PSG recording was performed in of Chinese Medicine), Guangzhou 510120, China. Applicants for Doctoral each participant and thus, the results might be biased by Degree with an Equivalent Educational Level in Guangzhou University of Chi- nese Medicine, Guangzhou 510006, China. 111 Dade Road, Yuexiu District, the “first night” effect. 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Sleep EEG characteristics associated with total sleep time misperception in young adults: an exploratory study

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Abstract

Background: Power spectral analysis (PSA) is one of the most commonly-used EEG markers of cortical hyperarousal, and can help to understand subjective–objective sleep discrepancy (SOD). Age is associated with decreased sleep EEG activity; however, the PSA of young adults is currently limited. Thus, this study aimed to examine the correlation of spectral EEG power with total sleep time ( TST ) misperception in young patients. Methods: Forty-seven young adults were recruited and underwent a polysomnography recording in a sleep labora- tory. Clinical records and self-report questionnaires of all patients were collected, and were used to categorize patients into a good sleeper (GS) group (n = 10), insomnia with a low mismatch group (IWLM, n = 19) or participant with a high mismatch group (IWHM, n = 18). PSA was applied to the first 6 h of sleep. Results: IWHM patients exhibited a higher absolute power and relative beta/delta ratio in the frontal region com- pared to the GS group. No significant difference was observed between the IWLM and GS groups. No significant difference in the above parameters was observed between the IWHM and IWLM groups. Moreover, The SOD of TST was positively correlated with frontal absolute power and the relative beta/delta ratio (r = 0.363, P = 0.012; r = 0.363, P = 0.012), and absolute beta EEG spectral power (r = 0.313, P = 0.032) as well as the number of arousals. Conclusions: Increased frontal beta/delta ratio EEG power was found in young patients with a high mismatch but not in those with a low mismatch, compared with good sleepers. This suggests that there exists increased cortical activity in IWHM patients. In addition, the frontal beta/delta ratio and the number of arousals was positively correlated with the SOD of TST. Keywords: Young adult, Misperception, EEG, Power spectral analysis, Cortical activation Introduction healthcare and medical costs, higher chances of absen- Insomnia is a common disease in modern society with teeism, traffic accidents, falling and a poor quality-of-life a prevalence rate ranging from 12 to 20% [1]. Patients [4–7]. with insomnia have a higher risk of mental illness [2] Subjective–objective sleep discrepancy (SOD), also and physical diseases [3, 4], which may lead to higher called sleep misperception, refers to the underestima- tion of total sleep time (TST) and overestimation of sleep onset latency (SOL). The SOD of TST is obtained by sub - tracting self-reported TST (sTST) values from objective *Correspondence: cloudxby@163.com TST (oTST) values detected by polysomnography (PSG) Department of Fangcun Sleep-Disorder, the Second Clinical College [8]. SOD is very common in insomnia patients [9, 10] of Guangzhou University of Chinese Medicine (Guangdong Provincial Hopsital of Chinese Medicine), Guangzhou 510120, China and is extremely common in paradoxical insomnia which Full list of author information is available at the end of the article was listed in the International Classification of Sleep © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Xu et al. Behavioral and Brain Functions (2022) 18:2 Page 2 of 13 Disorders-2nd Edition (ICSD-2), but cancelled in ICSD-3 of which is the difference in various subtypes of insom - mainly due to a lack of consensus on its precise defini - nia. Many of the diseases studied fall under the umbrella tion. Although it was cancelled, the symptom is worth term of PI, which is a broad term that includes paradoxi- studying [11] since it is an important aspect for under- cal insomnia (ParI) and psychophysiological insomnia standing the mechanism of insomnia. (PsyI), as well as idiopathic insomnia. However, some Several studies have attempted to explain the possible subtypes, for example [9] individuals with and without mechanisms of SOD in insomnias and have accumulated sleep misperception, are not considered in most stud- 13 possible mechanisms thereof that are supported by ies. Therefore, it has been suggested that future studies good-quality evidence [9]. One of these potential mecha- on EEG spectral features should patients from different nisms is related to cortical hyperarousal. Power spectral insomnia subtypes. To our knowledge, only several stud- analysis (PSA) is one of the most commonly-used EEG ies aimed to investigate SOD to understand the underly- markers of cortical hyperarousal [12]. PSA can show ing mechanism of insomnia. Krystal et  al. [19], St-Jean whether information processing is in an enhanced state, et  al. [20] and Lecci et  al. [21] categorized their patients which may cause the misperception of sleeping state [13]. according to SOD, but there was no consistency in the The power of each waveform is defined as the area under spectral power analyses in these studies and only elderly the waveform, where a high amplitude represents high patients (40–80 years old vs. 40.21 + 9.38 vs 40–85 years power [9]. old) were included. Krystal et al. [19] reported that lower Previous studies have shown that patients with primary delta and greater alpha, sigma and beta NREM EEG insomnia (PI) have more high-frequency EEG activity, activity was found in patients with subjective insomnia especially beta-band activity, during non-rapid eye move- but not in those with objective insomnia, compared with ment (NREM) sleep [14–18] than healthy controls. This normal subjects. St-Jean et al. [20] reported that patients finding is also consistent with the hyperarousal theory of with ParI exhibited higher absolute delta activity at the insomnia. However, there is still no consensus on other standardized F3, C3, and P3 electrodes compared to power bands during NREM sleep. For example, a meta- those with PsyI. analysis including 532 patients with insomnia disorder It has also shown that there is a relationship between (ID) and 445 good sleepers performed by Zhao et al. [18] age and s decrease of sleep EEG activity [22, 23]. Com- found that patients with ID exhibited increased theta, pared to young individuals, elderly patients exhibit less alpha, and sigma power during NREM sleep. Riedner N3 and spindle activity during non-rapid eye move- et  al. [17] found that ID patients exhibited no difference ment (NREM) sleep, and a smaller proportion of in slow wave (specifically < 5 Hz) and sigma (spindle) fre- rapid eye movement (REM) sleep [24]. It has also been quencies (specifically 11–16 Hz) compared with GS. This shown that absolute power in the delta, theta and sigma difference could be explained by a number of reasons, one bands decline with age for both females and males [23]. Table 1 Demographic characteristics of all participants Variables GS (n = 10) IWHM (n = 18) IWLM (n = 19) Statistics Age, years 26.0 [25.0, 32.0] 30.5 [25.8, 38.2] 25.0 [23.0, 38.0] H = 3.378, p = 0.185 Sex (F/M) 5/5 9/9 16/3 χ = 5.616, p = 0.060 Race χ = 0.000, p = 1.000 Han 10 18 19 Non-Han 0 0 0 Place of residence χ = 1.233, p = 0.540 Downtown 9 13 15 Suburb 1 4 3 Village 0 1 1 Marriage χ = 2.798, p = 0.247 Unmarried 7 7 10 Married 3 10 9 Bereavement/divorce 0 1 0 Family history of insomnia (Y/N) 1/9 3/15 1/18 χ = 1.243, p = 0.537 Family history of psychosis (Y/N) 0/10 0/18 1/18 χ = 1.474, p = 0.479 GS: good sleeper; IWLM: insomnias with a low mismatch; IWHM: insomnias with a high mismatch Xu  et al. Behavioral and Brain Functions (2022) 18:2 Page 3 of 13 Table 2 PSQI scores, SCL90 scores, and PSG characteristics of all participants Variable GS (n = 10) IWHM(n = 18) IWLM (n = 19) Statistics a a PSQI total score 3.5 [2.0, 6.2] 13.5 [11.5,16.3] 11.0 [9.0, 14.0] H = 18.882, P < 0.001 a a SCL-90 total score 109.5 [103.5, 129.3] 174.5 [155.8, 223.8] 148.0 [127.5, 180.0] H = 16.037, P < 0.001 TST (min) 394.25 ± 45.25 415.06 ± 40.20 381.95 ± 39.85 F = 3.026, P = 0.059 SPT (min) 418.60 ± 47.39 459.03 ± 42.65 431.84 ± 49.77 F = 2.837, P = 0.069 SE, % 89.27 ± 3.92 87.84 ± 7.00 86.24 ± 8.39 F = 0.626, P = 0.540 SOL (min) 11.50 [7.13, 23.38] 8.25 [4.00, 12.63] 5.50 [3.00, 10.00] H = 2.697, P = 0.260 %NREM stage1 5.00 [3.00, 6.25] 5.00 [4.00, 8.50] 4.00 [3.00, 7.00] H = 1.158, P = 0.560 %NREM stage2 59.40 ± 8.51 61.17 ± 8.61 62.16 ± 7.63 F = 0.371, P = 0.692 % SWS 13.60 ± 4.79 10.06 ± 6.49 12.37 ± 4.82 F = 1.528, P = 0.228 %REM 21.90 ± 6.61 22.61 ± 3.27 19.31 ± 5.14 F = 2.259, P = 0.116 Number of awakenings 23.50 [19.50, 29.50] 24.00 [16.00, 31.00] 19.00 [13.00, 27.00] H = 2.147, P = 0.342 Number of arousals 17.00 [8.25,20.50] 24.00 [12.75, 47.50] 26.00 [17.00, 47.00] H = 3. 730, P = 0.155 Arousal index 2.69 [1.22, 3.21] 3.71 [1.77,7.42] 4.17 [2.28,7.55] H = 3. 611, P = 0.164 Number of arousals of NREM 15.00 [8.25, 20.50] 19.50 [10.00, 46.50] 22.00 [17.00, 41.00] H = 3.233, P = 0.199 Number of arousals of REM 0.00 [0.00, 3.00] 1.00 [0.00, 3.25] 1.00 [0.00, 5.00] H = 1.339, P = 0.512 GS: good sleeper; IWLM: insomnias with a low mismatch; IWHM: insomnias with a high mismatch PSQI: Pittsburgh Sleep Quality Index; TST: total sleep time; SPT: sleep period time; SE: sleep efficiency; SOL: sleep onset latency; NREM: non-rapid eye movement; SWS: slow wave sleep; REM: rapid eye movement The statistical value H represents the use of a Kruskal–Wallis non-parametric analysis among three group using a Bonferroni correction for post-hoc analysis, while the statistical value F represents the use of a one-way ANOVA using the least significant difference test for post-hoc analysis P < 0.05 versus GS. There was no difference between the IWHW and IWLW groups Table 3 Comparison of absolute EEG spectral power among the experimental groups (μν ) Variable GS (n = 10) IWHM (n = 18) IWLM (n = 19) Statistics Frontal derivation Delta (1–4 Hz) 46.47 ± 21.05 37.61 ± 15.30 46.65 ± 25.95 F = 0.981, P = 0.383 Theta (4–8 Hz 2.90 [2.29, 4.22] 3.23 [2.67, 3.99] 4.91 [2.59, 5.83] H = 3.481, P = 0.175 Alpha (8–12 Hz) 1.87 [1.16, 3.37] 2.53 [1.55, 2.99] 2.78 [1.97, 3.89] H = 2.693, P = 0.260 Sigma (12–16 Hz) 0.80 [0.55, 1.27] 1.09 [0.69, 1.56] 1.40 [0.88, 1.76] H = 4.137, P = 0.126 Beta (16–32 Hz) 0.71 [0.57, 0.99] 1.08 [0.69, 1.80] 1.06 [0.79, 1.50] H = 4.062, P = 0.131 Beta /Delta 0.01 [0.01, 0.03] 0.03 [0.02, 0.04] 0.03 [0.02, 0.04] H = 6.904, P = 0.032 Beta/Theta 0.27 [0.17, 0.37] 0.34 [0.23, 0.53] 0.25 [0.18, 0.36] H = 1.907, P = 0.385 Alpha/Delta 0.04 [0.03, 0.08] 0.07 [0.04, 0.09] 0.07 [0.05, 0.11] H = 3.086, P = 0.214 Alpha/Theta 64.00 ± 16.43 76.60 ± 41.44 74.33 ± 30.73 F = 0.487, P = 0.618 Central derivation Delta (1–4 Hz) 37.99 ± 12.36 33.89 ± 19.01 46.48 ± 20.94 F = 2.154, P = 0.128 Theta (4–8 Hz 4.77 [3.37, 4.99] 4.11 [3.21, 4.63] 4.93 [2.98, 8.85] H = 2.872, P = 0.238 Alpha (8–12 Hz) 2.51 [1.66, 3.24] 2.79 [1.89, 3.52] 2.70 [1.83, 4.02] H = 0.710, P = 0.701 Sigma (12–16 Hz) 1.43 [1.04, 1.84] 1.66 [1.21, 2.76] 1.75 [1.37, 2.51] H = 2.625, P = 0.269 Beta (16–32 Hz) 0.81 [0.66, 1.25] 1.29 [0.87, 1.62] 1.16 [0.72, 1.65] H = 2.139, P = 0.343 Beta/Delta 0.02 [0.02, 0.04] 0.04 [0.02, 0.06] 0.03 [0.02, 0.04] H = 3.087, P = 0.214 Beta/Theta 0.18 [015, 0.31] 0.28 [0.20, 0.43] 0.20 [0.15, 0.35] H = 2.385, P = 0.303 Alpha/Delta 0.07 [0.04, 0.09] 0.08 [0.05, 0.14] 0.06 [0.05, 0.09] H = 2.632, P = 0.268 Alpha/Theta 0.57 [0.47, 0.66] 0.58 [0.51, 0.92] 0.52 [0.42, 0.75] H = 1.774, P = 0.412 GS: good sleeper; IWLM: insomnias with a low mismatch; IWHM: insomnias with a high mismatch. The statistical value H represents the use of a Kruskal–Wallis non- parametric analysis among three group using a Bonferroni correction for post-hoc analysis, while the statistical value F represents the use of a one-way ANOVA using the least significant difference test for post-hoc analysis P < 0.05 versus GS. There was no difference between the IWHW and IWLW groups Xu et al. Behavioral and Brain Functions (2022) 18:2 Page 4 of 13 Fig. 1 Absolute and relative beta in the frontal and central region. GS: good sleeper; IWLM: insomnias with a low mismatch; IWHM: insomnias with a high mismatch Importantly, previous research has failed to combine age the Pittsburgh Sleep Quality Index (PSQI) [25] and the data with insomnia subtype information to study the PSA Symptom Checklist 90 (SCL-90), were given to each par- of SOD. ticipant. Subjective sleep quality was determined by self- In this study, we aimed to compare the PSA of three reported TST after PSG. The subjects were asked two groups of young patients: (1) patients who overestimated questions about their perceived sleep within 2  h after their total sleep time by at least 2 h; (2) patients who cor- PSG completion: (1) “How long did you sleep last night?” rectly estimated their sleep; (3): good sleepers (GS). Our and (2) “Did you sleep as usual?”. In this way, the sTST findings may be important to clinical and public health as of the patient was obtained. For example, if the patient well as the treatment and management of insomnia [9]. replied that they slept for 6 h during the previous night, 360 min was his/her sTST. Materials and methods Subjects were categorized as GS according to the Participants following criteria: (1) reported no difficulty in sleep Seventy participants aged between 18 and 40  years-old according to the two-week sleep diary (i.e. sleep onset were recruited from the Guangdong Provincial Hospital (SO) < 30  min, wake after sleep onset (WASO) < 40  min, of Chinese Medicine through posters from May 2016 to TST between 6.0 and 8.0  h, or sleep efficiency November 2017. All subjects were asked to complete a (SE) ≥ 85%); (2) had a PSQI score < 7 [25], SE > 85% or two-week sleep diary followed by a single all-night PSG TST > 6 h. recording in a sleep laboratory. Personal information was Participants were categorized as insomnia patients obtained from all subjects, including age, sex, race, place if they met the following criteria: (1) diagnosed with of residence, marital status, family history of insom- chronic insomnia disorder (International Classification nia and psychosis. Two self-reported questionnaires, of Sleep Disorders, 3rd edition); (2) reported at least Xu  et al. Behavioral and Brain Functions (2022) 18:2 Page 5 of 13 Fig. 2 Absolute power and relative beta/delta in the frontal and central regions. GS: good sleeper; IWLM: insomnias with a low mismatch; IWHM: insomnias with a high mismatch three nights per week of sleep difficulty (i.e.SO > 30 min, as an apnea–hypopnea index of more than five events per WASO > 40  min, sTST < 6.0  h, or SE < 85%); (3) had a hour using PSG, or restless leg syndrome); (2) affected PSQI score of > 7; (4) had difficulty sleeping for more than by other external factors that might affect insomnia (e.g. 3 months; (5) did not have other medical, psychological, physical pain caused by medical diseases, drugs affect - or sleeping disorders and did not take any medications ing sleeping structure, alcohol consumption, other treat- that would affect sleep (e.g. sedative and hypnotic drugs, ments, etc.); (3) go to sleep later than 0:00 am or wake up antidepressants, anti-schizophrenia drugs, etc.). before 6:00 am, or had irregular sleeping schedules. Insomnia patients were further categorized into two Based on the inclusion and exclusion criteria, 47 par- subgroups based on their SOD of TST. These two sub - ticipants were included in the study: GS group (n = 10; groups comprised patients with low mismatch (IWLM) 5 males, 5 females), IWHM group (n = 18; 9 males, 9 and patients with high mismatch (IWHM). The SOD of females), and IWLM group (n = 19; 3 males, 16 females). TST was operationalized as the values of the differences between subjective and objective measures (i.e. sTST– PSQI and SCL‑90 oTST value) [8]. IWLM patients were those individuals The PSQI is a questionnaire consisting of 21 items and who met the criteria of chronic insomnia disorder and has been commonly used to evaluate subjective sleep had an SOD < 60  min in TST. IWHM were defined as quality. The higher the score, the greater the severity of patients who met the criteria of chronic insomnia dis- insomnia. A score > 7 indicates abnormal sleeping (severe order and had normal PSG parameters (i.e. SE > 85% difficulty in at least two areas or moderate difficulty in and TST > 6.5 h) and SOD > 120 min in TST. In both the more than three areas). IWHM and IWLM subgroups, patients were excluded if The SCL-90 is one of the most widely used mental they met one of the following criteria: (1) diagnosed with health scales in the field of psychiatry. It is a 90-item, self- another Axis I disorder or any other sleeping disorder reported symptom inventory. The score for each item is (e.g. idiopathic insomnia, sleep apnea, which was defined summed, yielding a total score that covers ten aspects. Xu et al. Behavioral and Brain Functions (2022) 18:2 Page 6 of 13 Table 4 Comparison of relative EEG spectral power among the experimental groups Variable GS (n = 10) IWHM (n = 18) IWLM (n = 19) Statistics Frontal derivation Delta (1–4 Hz) 87.96 [79.34, 90.48] 80.74 [75.23, 83.99] 80.81 [76.26, 85.81] H = 5.046, P = 0.080 Theta (4–8 Hz 5.85 [4.54, 8.54] 7.39 [6.24, 9.56] 7.20 [6.61, 10.78] H = 3.820, P = 0.148 Alpha (8–12 Hz) 3.77 [2.49, 6.48] 5.08 [3.56, 6.84] 5.10 [3.80, 8.26] H = 2.767, P = 0.251 Sigma (12–16 Hz) 1.31 [1.16, 2.71] 2.51 [1.75, 3.35] 2.15 [1.62, 4.32] H = 3.498, P = 0.174 Beta (16–32 Hz) 1.26 [1.09, 2.00] 2.50 [1.54, 3.49] 1.94 [1.65, 2.99] H = 5.756, P = 0.056 Beta /Delta 0.01 [0.01, 0.03] 0.03 [0.02,0.04] 0.03 [0.02, 0.04] H = 6.904, P = 0.032 Beta/Theta 0.27 [0.17, 0.37] 0.34 [0.23, 0.53] 0.25 [0.18, 0.36] H = 1.907, P = 0.385 Alpha/Delta 0.04 [0.03, 0.08] 0.07 [0.04, 0.09] 0.07 [0.04, 0.11] H = 3.183, P = 0.204 Alpha/Theta 64.00 ± 16.42 76.60 ± 41.44 74.32 ± 30.73 F = 0.487, P = 0.618 Central derivation Delta (1–4 Hz) 78.42 [74.84, 86.28] 74.67 [66.94, 82.17] 77.34 [74.20, 82.11] H = 3.930, P = 0.140 Theta (4–8 Hz 10.04 [6.57, 10.67] 9.98 [8.57, 11.37] 9.54 [8.07, 12.22] H = 0.281, P = 0.869 Alpha (8–12 Hz) 5.74 [3.51, 7.13] 5.94 [4.59, 9.21] 4.84 [4.01, 7.00] H = 2.483, P = 0.289 Sigma (12–16 Hz) 3.11 [2.35, 4.39] 4.39 [3.15, 6.77] 3.29 [2.62, 4.55] H = 5.447, P = 0.066 Beta (16–32 Hz) 1.72 [1.43, 3.05] 2.79 [1.51, 4.42] 2.00 [1.45, 3.38] H = 3.121, P = 0.210 Beta /Delta 0.02 [0.02, 0.04] 0.04 [0.02, 0.06] 0.03 [0.02, 0.04] H = 3.087, P = 0.214 Beta/Theta 0.18 [0.15, 0.31] 0.28 [0.20, 0.43] 0.20 [0.15, 0.35] H = 2.385, P = 0.303 Alpha/Delta 0.07 [0.04, 0.09] 0.08 [0.05, 0.14] 0.06 [0.05, 0.09] H = 2.632, P = 0.268 Alpha/Theta 0.57 [0.47, 0.66] 0.58 [0.51, 0.92] 0.52 [0.42, 0.75] H = 1.774, P = 0.412 GS: good sleeper; IWLM: insomnias with a low mismatch; IWHM: insomnias with a high mismatch The statistical value H represents the use of a Kruskal–Wallis non-parametric analysis among three group using a Bonferroni correction for post-hoc analysis, while the statistical value F represents the use of a one-way ANOVA using the least significant difference test for post-hoc analysis. P < 0.05 versus GS. There was no difference between the IWHW and IWLW groups The higher the total score, the greater the risk of develop - Sleep records were reviewed and scored by a registered ing psychological distress [26]. PSG technician according to the revised AASM 2.5 sleep- ing scoring criteria [27]. The sleeping continuity param - PSG recordings eters, including TST, SPT, SE (ratio of TST to time in In conventional PSG (Nicolet, ONE, EEG 32, USA), the bed × 100%), and SOL, and sleeping architecture param- international 10–20 system was used to record EEG. In eters, including the number of awakenings, the number this study, the grounding electrode was placed on the of arousals, arousal index, percentage of NREM stage frontal pole midline point and the bilateral ear electrodes 1 and 2, slow wave sleep (SWS) or NREM stage 3, and were used as the reference. All electrographic electrodes REM sleep of TST were analyzed. were placed according to the AASM 2.6 recommended guidelines. The impedance was kept below 5  kΩ for all Spectral analysis electrodes. The surface electrodes included six EEG (two Normal sleep time is 6.0 to 8.0  h, and therefore we ana- central electrodes [C3, C4], two frontal EEG electrodes lyzed the first 6  h of the PSG recordings. The data from [F3, F4], and two occipital EEG electrode [O1, O2)]), two the central and frontal EEG electrode (averaged C3-A2 electro-oculogram (E1, E2), submental electromyogram and C4-A1 channels, averaged F3-A2 and F4-A1 chan- (EMG: Chin1-Chin2), electrocardiogram (ECG), and two nels) were generated using software of Nicolet EEG band reference electrodes (A1, A2). In addition, tibialis EMG width tools. and respiration were used to exclude periodic limb move- Most of the common artifacts were due to improper ments (a PLMSI > 15) and sleep apnea (an apnea–hypo- click placements (such as electrode popping, ECG or pnea index > 5), respectively. Participants were asked to pulse artifact), body movement (muscle artifact, eye sleep at their usual time (before 0:00 am) and wake up at movement artifact or major body movement) or environ- 7:00 am. The sampling rate of EEG was 500  Hz and the mental factors (overheated which lead to slow-frequency filter settings were as follows: notch frequency at 60 Hz; artifacts). We optimized the mastoid electrodes so that low pass filter at 35 Hz; high pass filter at 0.3 Hz. ECG and pulse artifacts could be minimized. Secondly, Xu  et al. Behavioral and Brain Functions (2022) 18:2 Page 7 of 13 Table 5 Correlation between absolute EEG spectral power and absolute power of each frequency band by the power of SOD of TST the total power spectrum. Variables SOD of TST Statistical analysis r P Statistical analysis was performed using the SPSS soft- ware (ver. 24.0) and with an unpaired two-tailed test of Frontal derivation significance. A normality test and Levene’s test were used Delta (1–4 Hz) − 0.202 0.173 to check whether the data followed a normal distribu- Theta (4–8 Hz) − 0.032 0.833 tion. A Chi-square test was used for demographic char- Alpha (8–12 Hz) 0.058 0.698 acteristics except for age. Normally distributed data with Sigma (12–16 Hz) − 0.013 0.931 homogeneous variance were compared using a one-way Beta (16–32 Hz) 0.200 0.179 ANOVA, while others were compared using a non-par- Beta/Delta 0.363 0.012 ametric analysis (Kruskal–wallis) with post-hoc analysis. Beta/Theta 0.208 0.161 The statistical value H represents the use of non-par - Alpha/Delta 0.171 0.249 ametric analysis, while the statistical value F represents Alpha/Theta 0.182 0.221 the use of a one-way ANOVA. In addition, we used pair- Central derivation wise least significant difference post-hoc tests after a one- Delta (1–4 Hz) − 0.109 0.467 way ANOVAs and a Bonferroni correction for multiple Theta (4–8 Hz − 0.063 0.673 comparison after a Kruskal–Wallis test. Spearman’s or Alpha (8–12 Hz) 0.188 0.205 Pearson’s correlation analysis was used to determine the Sigma(12–16 Hz) 0.156 0.295 correlation between the EEG spectral power (absolute Beta (16–32 Hz) 0.216 0.145 and relative) and the SOD of TST (after data normality Beta/Delta 0.249 0.091 was confirmed). A P- value < 0.05 was considered statisti- Beta/Theta 0.188 0.256 cally significant. Alpha/Delta 0.256 0.082 Alpha/Theta 0.169 0.257 Results SOD: Subjective–objective sleep discrepancy; TST: total sleep time Baseline characteristics There was no significant differences in age, sex, race, we kept impedance below 5  kΩ to avoid electrode pop- place of residence, marital status, family history of ping. At the same time, we maintained a temperature of insomnia, or family history of psychosis among the three 20  °C in the sleep laboratory which is the standard set- groups (Table 1). ting to ensure that the subjects completed the test in a comfortable environment, and avoided the influence of PSQI, SCL90, and PSG characteristics slow-frequency artifacts from sweat. A notch filter at The comparisons of the PSQI score, SCL-90 score, and 50  Hz was applied to avoid power line contamination of PSG among the three groups are shown in Table  2. The the electrical signals. Then, we set a high frequency filter IWHM and IWLM groups showed higher PSQI and to 35  Hz to reduce most of the interference from EMG. SCL-90 scores compared to the GS group. However, We chose this cutoff values as the frequency of EMG there was no significant difference in the PSQI or SCL-90 activity signal is generally contained in higher frequency scores between the IWHM and IWLM groups. The PSG bands and since the AASM recommend that EMG low parameters were not significantly different among the frequency and high frequency filter cutoffs should be at GS, IWHM, and IWLM groups. 10  Hz and 100  Hz, respectively, to capture the muscle activity. Finally, the data fragments that were displaced Absolute EEG spectral power or cut off due to movements or that were obviously dif - Post-hoc analysis (Bonferroni correction) revealed that ferent from the background were to excluded by visual the IWHM group exhibited a significantly higher frontal inspection (e.g. due to the excessive loss of occipital EEG beta/delta ratio than the GS group. No significant differ - electrode signal, these data were not included in this ence was observed between the IWLM and GS groups. study). Therefore, artifacts in each recording were visu - There was no significant difference in these parameters ally inspected and removed accordingly. between the IWHM and IWLM groups (Table  3, Figs.  1, The beta (16–32  Hz), sigma (12–16  Hz), alpha 2). (8–12  Hz), delta (0.5–4  Hz), and theta (4–8  Hz) band activity was extracted for PSA analysis. The values of relative spectral power were calculated by dividing the Xu et al. Behavioral and Brain Functions (2022) 18:2 Page 8 of 13 Fig. 3 Correlation between absolute frontal beta/dela spectral power and SOD of TST. SOD: Subjective–objective sleep discrepancy; TST: total sleep time Relative EEG spectral power Correlation between number of arousals and frontal beta The average NREM activity for the beta/delta ratio in and SOD of TST the frontal area was significantly different among the Spearman’s correlation was performed on frontal beta three groups. Post-hoc analysis (Bonferroni correction) and the SOD due to the non-normality of the data. The showed that the frontal beta/delta ratio in the IWHM number of arousals was correlated with the SOD of group was higher than that in the GS group. No sig- TST (r = 0.532, P = 0.023) in the IWHW group (Table 7, nificant difference was observed between the IWLM Fig. 5). and GS groups and no significant difference in relative spectral power was observed between the IWHM and IWLM groups (Table 4, Figs. 1, 2). Correlation between number of arousals and central beta, SOD of TST Spearman’s correlation was performed on the SOD of Correlation between absolute EEG spectral power and SOD TSTS due to the non-normality of the data. The num - Spearman’s correlation was performed on the SOD due ber of arousals was correlated with the SOD of TST to the non-normality of the data. The SOD of TST was (r = 0.532, P = 0.023) in the IWHW group (Table 8). positively correlated with absolute frontal beta/delta ratio (r = 0.363, P = 0.012) (Table 5, Fig. 3). Discussion To the best of our knowledge, this is the first study that Correlation between relative EEG spectral power and SOD has investigated the absolute and relative spectral power Spearman’s correlation was performed on the SOD due of young adult patients (18–40  years old) with subtypes to the non-normality of the data. The SOD of TST was of subjective insomnia. Here, we categorized insomnia positively correlated with relative frontal beta/delta patients into IWLM and IWHM groups to maximize ratio (r = 0.363, P = 0.012) and the absolute beta EEG the difference in the SOD. Overall, compared to the GS spectral power (r = 0.313, P = 0.032) (Table 6, Fig. 4). Xu  et al. Behavioral and Brain Functions (2022) 18:2 Page 9 of 13 Table 6 Correlation between relative EEG spectral power and that sigma increase during NREM sleeps in PI exhib- SOD of TST ited moderate and high heterogeneity in the dispersion of effect sizes [18]. Here, we found that both the IWHM Variables SOD of TST and IWLM groups exhibited no increase in absolute r P and relative power of all frequency bands in the central and frontal regions. Our findings are similar to a previ - Frontal derivation ous EEG-based spectral investigation by Buysse et  al. Delta (1–4 Hz) − 0.248 0.092 [29] that failed to find significant differences in the fre - Theta (4–8 Hz 0.127 0.397 quency band activity between insomnia types and GS Alpha (8–12 Hz) 0.158 0.290 during NREM sleep. Nevertheless, unlike other previ- Sigma (8–12 Hz) 0.124 0.405 ous work, we failed to observe lower delta NREM EEG Beta (16–32 Hz) 0.313 0.032 activity, or greater alpha, theta, sigma, beta NREM EEG Beta/Delta 0.363 0.012 activity in patients with insomnia. This discrepancy was Beta/Theta 0.208 0.161 unclear but could be influenced by the difference in the Alpha/Delta 0.173 0.245 age, frequency band definitions and diagnostic criteria Alpha/Theta 0.182 0.221 of IWHM and IWLW patients in the two studies. To our Central derivation knowledge, there has been little research on the relation- Delta (1–4 Hz) − 0.268 0.069 ship between age and power spectra, leading to dissimi- Theta (4–8 Hz − 0.072 0.631 lar results. For example, Krystal et  al. [19] reported that Alpha (8–12 Hz) 0.247 0.094 older age (40–80  years-old) was associated with signifi - Sigma (8–12 Hz) 0.207 0.163 cantly lower sigma (12.5–16 Hz) relative power during Beta (16–32 Hz) 0.232 0.116 NREM in insomnia patients. Svetnik et  al. [23] demon- Beta/Delta 0.249 0.091 strated that the power of the delta, theta and sigma bands Beta/Theta 0.1888 0.205 significantly decreased with age whereas the slope in the Alpha/Delta 0.256 0.082 alpha, beta and gamma bands did not. Therefore, age may Alpha/Theta 0.169 0.257 be a potential influencing factor. SOD: Subjective–objective sleep discrepancy; TST: total sleep time Insomnia is associated with poorer cognitive perfor- mance both generally and across multiple specific cogni - tive domains, especially in terms of a decline of working group, patients with IWHM exhibited an increase in the memory and executive ability [30, 31]. A longer course absolute power and relative beta/delta ratio in the fron- of insomnia generally leads to a poorer cognitive impair- tal region during sleep. Moreover, the SOD of TST was ment, which manifests as slower EEG frequency, a higher positively correlated with the absolute power and relative proportion of alpha and beta band power, and a lower beta/delta ratio in the same frontal region. However, no proportion of theta and delta band power. It will be more significant difference was observed in the EEG power or conducive if the relationship between age and power beta/delta ratio in the central region and no significant spectrum could be studied in combination with the difference in the above parameters was observed between course of disease. To our knowledge, PSA studies catego- the IWHM and IWLM groups. rizing insomnia into subtypes are limited. Some studies Beta power is generally considered an indicator of determined if the PSG was normal as a basis for judg- cortical arousal. It has been shown that beta activity in ing subjective and objective insomnia [19], which may PI patients is higher than that in GS [14–17], which sug- have led to the inclusion of patients with different sub - gests that patients with subjective insomnia may experi- types of insomnia. Other studies have also explored SOD ence enhanced sensory processing during sleep. In fact, of SOL. In our research, insomnia patients were further this phenomenon may render them highly responsive categorized into two subgroups based on their SOD of and sensitive to external sounds and in turn may also TST. IWLM individuals exhibited a SOD < 60 min in TST lead to the mistaken perception of their sleep as wakeful- whereas IWHM individuals exhibited a SOD > 120  min ness [13]. Based on prior studies, sigma activity (sleep in TST. In our study, the PSG of patients with IWHM spindle) represents a marker of sleep stability, especially and IWLW was normal and the PSQI was higher than against noises [15]. Therefore, sigma activity may be able GS participants. This meant that all patients were of sub - to distort the transmission of auditory information to jective insomnia, but that the degree of SOD was differ - the cortex during sleep [28]. A study from Spiegelhalder ent. SOD of IWHW insomnia patients was greater than et  al. [15] proposed the concept of simultaneous activa- 120  min in TST, while that of IWLW insomnia patients tion of wake-promoting and sleep-protecting neural activity patterns in PI. However, a meta-analysis showed Xu et al. Behavioral and Brain Functions (2022) 18:2 Page 10 of 13 Fig. 4 Correlation between relative frontal beta/dela spectral power and SOD of TST. SOD: Subjective–objective sleep discrepancy; TST: total sleep time was less than 120 min. In addition, patients with subjec- single night PSG in Krystal’s paper, which did not clearly tive insomnia met persistent PI criteria and had a normal define SOD. Recently, the ratio of high-frequency to low-frequency EEG power has been recognized as a novel indicator of cortical arousal. Furthermore, individuals with a higher Table 7 Correlation between the number of arousals and frontal ratio of this sort may have more sleeping difficulties. beta and SOD of TST Meric et  al. [32] found that PsyI patients exhibited an increased beta/delta ratio in the temporal lobe dur- Variables Number of arousals ing the sleep onset period (SOP). Some studies have r P also reported that delta EEG activity is decreased in PI GS (n = 10) patients in the temporal and central brain regions during Absolute beta − 0.049 0.894 the SOP [33, 34]. Thus, such an activity index (beta/delta Relative beta − 0.482 0.159 ratio) may be a more appropriate indicator of cortical SOD of TST − 0.028 0.938 arousal in insomnia patients [17, 32, 35]. In the current IWHM (n = 18) study, IWHM patients showed increased absolute power Absolute beta 0.271 0.277 and relative beta/delta ratio in the frontal region com- Relative beta 0.253 0.311 pared with the GS group, suggesting hyperarousal in the SOD of TST 0.532 0.023 frontal portion of the brain. IWLM (n = 19) SOD in insomnia has been shown to arise due to sev- Absolute beta 0.395 0.094 eral possible mechanisms, which mainly focus on sen- Relative beta 0.167 0.495 sory perception, emotion and cognition [9]. Various SOD of TST − 0.190 0.435 characteristics of insomnia patients support these con- cepts, such as: (1) insomniacs will judge PSG measured SOD: Subjective–objective sleep discrepancy Xu  et al. Behavioral and Brain Functions (2022) 18:2 Page 11 of 13 Fig. 5 Correlation between the number of arousals and SOD of TST. SOD, Subjective–objective sleep discrepancy; TST: total sleep time; GS: good sleeper; IWLM: insomnias with a low mismatch; IWHM: insomnias with a high mismatch sleep as wakefulness; (2) insomniacs have anxiety and selective attention toward sleep-related threats. The pos - Table 8 Correlation between the number of arousals and sibility that anxiety serves to trigger the misperception central beta and SOD of TST of sleep is drawn from the robust finding in time percep - Variables Number of arousals tion literature in that time is perceived as longer when the number of units of information processed per unit r P of time increases. Other characteristics of insomnia GS (n = 10) patients include: (3) patients may simply be poor esti- Absolute beta 0.055 0.880 mators of time; and (4) insomniacs’ assessment of sleep Relative beta − 0.085 0.815 quality is influenced by a memory bias that is influenced SOD of TST − 0.028 0.938 by current symptoms and emotions, a confirmation bias/ IWHM (n = 18) belief bias or a recall bias linked to intensity. In many Absolute beta 0.413 0.089 other papers, central regions, mainly involving in sen- Relative beta − 0.030 0.906 sory perception, are considered good representations SOD of TST 0.532 0.023 of the whole brain activity (from EEG) and have been IWLM (n = 19) widely used in PSA. Frontal lobes are also related to Absolute beta 0.173 0.479 emotion, cognition, and behavioral management, which Relative beta − 0.155 0.527 is connected with the mechanism of SOD. Therefore, it SOD of TST 0.190 0.435 is necessary to assess frontal regions. Unfortunately, cor- tical activation at sites other than central areas, such as SOD: Subjective–objective sleep discrepancy Xu et al. Behavioral and Brain Functions (2022) 18:2 Page 12 of 13 Acknowledgements frontal regions, has been poorly explored. In our study, We want to thank Chen Wang and Yujiao Sun who helped in the statistical a higher beta/delta ratio was only observed in frontal analysis. regions in the IWHW group when comparison to GS Authors’ contributions group. This result seems to suggest that high cortical BX: data analysis and interpretation, manuscript drafting/revision. QC: major arousal occurs in the frontal lobe and not just in the cen- role in data acquisition. RM: manuscript revision. HL: study supervision, critical tral region. revision of the manuscript. JH: data collection, manuscript revision. ZY: study concept and design, critical revision of the manuscript. All authors read and We further showed that the SOD of TST was associ- approved the final manuscript. ated with the absolute and relative NREM beta/delta ratio (r = 0.363) and relative beta power (r = 0.313) in the Funding This study was supported by 2014 Science and Technology Projects of frontal area. All in all, these results indicate that a higher Guangdong Province [2014A020212557]; Zhimin Yang Guangdong famous the beta/delta ratio and beta power during NREM sleep Chinese Medicine Inheritance studio construction project [Guangdong may be an underestimation of TST. Our results are simi- Chinese Medicine (2020) 1]. lar to the findings by Perlis et al. [14] that showed a mod - Availability of data and materials erate correlation between the SOD of TST and NREM The datasets used and/or analysed during the current study are available beta activity (14–35  Hz) (r = − 0.46). The underestima - from the corresponding author on request. tion of TST may be explained by the insertion of high frequency EEG into low frequency EEG, which has been Declarations shown to enhance the information processing ability and Ethics approval and consent to participate to degrade sleep quality [36]. This clinical trial was approved by the Ethics Committee of the Guangdong To the best of our knowledge, few studies have Provincial Hospital of Chinese Medicine (number: B3016-075) and performed in accordance with the World Medical Association Declaration of Helsinki. reported the correlation between the SOD of TST and This study was registered on http:// www. chictr. org/ up (registration number: the number of arousals. Results from our study showed chiCTR-COC-16008530). Informed consent was obtained from all subjects that the number of arousals was correlated with the prior to participation. SOD of TST in the IWHW group, suggesting that fre- Consent for publication quent awakenings that lead to sleep fragmentation may Not applicable. in turn lead to poor perception of insomnia. This is simi - Competing interests lar to the previous study by Choi et al. [37] that showed The authors declare that they have no competing interests. that sleep perception was negatively related to the PSG arousal index. Author details Department of Fangcun Sleep-Disorder, the Second Clinical College There are various limitations to our study that must be of Guangzhou University of Chinese Medicine (Guangdong Provincial Hopsital noted. First, only one PSG recording was performed in of Chinese Medicine), Guangzhou 510120, China. Applicants for Doctoral each participant and thus, the results might be biased by Degree with an Equivalent Educational Level in Guangzhou University of Chi- nese Medicine, Guangzhou 510006, China. 111 Dade Road, Yuexiu District, the “first night” effect. 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Behavioral and Brain FunctionsSpringer Journals

Published: Jan 24, 2022

Keywords: Young adult; Misperception; EEG; Power spectral analysis; Cortical activation

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