Quantification of sleepiness through principal component analysis of the electroencephalographic spectrum

Although circadian and sleep research has made extraordinary progress in the recent years, one remaining challenge is the objective quantification of sleepiness in individuals suffering from sleep deprivation, sleep restriction, and excessive somnolence. The major goal of the present study was to apply principal component analysis to the wake electroencephalographic (EEG) spectrum in order to establish an objective measure of sleepiness. The present analysis was led by the hypothesis that in sleep-deprived individuals, the time course of self-rated sleepiness correlates with the time course score on the 2nd principal component of the EEG spectrum. The resting EEG of 15 young subjects was recorded at 2-h intervals for 32-50 h. Principal component analysis was performed on the sets of 16 single-Hz log-transformed EEG powers (1-16 Hz frequency range). The time course of self-perceived sleepiness correlated strongly with the time course of the 2nd principal component score, irrespective of derivation (frontal or occipital) and of analyzed section of the 7-min EEG record (2-min section with eyes open or any of the five 1-min sections with eyes closed). This result indicates the possibility of deriving an objective index of physiological sleepiness by applying principal component analysis to the wake EEG spectrum.     

Quantification of Sleepiness Through Principal Component Analysis of the Electroencephalographic Spectrum

Although circadian and sleep research has made extraordinary progress in the recent years, one remaining challenge is the objective quantification of sleepiness in individuals suffering from sleep deprivation, sleep restriction, and excessive somnolence. The major goal of the present study was to apply principal component analysis to the wake electroencephalographic (EEG) spectrum in order to establish an objective measure of sleepiness. The present analysis was led by the hypothesis that in sleep-deprived individuals, the time course of self-rated sleepiness correlates with the time course score on the 2nd principal component of the EEG spectrum. The resting EEG of 15 young subjects was recorded at 2-h intervals for 32–50?h. Principal component analysis was performed on the sets of 16 single-Hz log-transformed EEG powers (1–16?Hz frequency range). The time course of self-perceived sleepiness correlated strongly with the time course of the 2nd principal component score, irrespective of derivation (frontal or occipital) and of analyzed section of the 7-min EEG record (2-min section with eyes open or any of the five 1-min sections with eyes closed). This result indicates the possibility of deriving an objective index of physiological sleepiness by applying principal component analysis to the wake EEG spectrum. (Author correspondence: putilov@ngs.ru)     

Principal components of electroencephalographic spectrum as markers of opponent processes underlying ultradian sleep cycles

Sleep-wake regulation involves reciprocal interactions between sleep- and wake-promoting processes that inhibit one another. To uncover the signatures of the opponent processes underlying ultradian sleep cycles, principal component analysis was performed on the sets of 16 single-Hz log-transformed electroencephalographic (EEG) power densities (1-16?Hz frequency range). Data were collected during unrestricted night sleep followed by 9 20-min naps (14 women aged 17-55 yrs) and during 12 20-min naps after either restriction or deprivation of sleep (9 males and 9 males, respectively, aged 18-22 yrs). It was found that any subset of power spectra could be reduced to the invariant four-principal component structure. The time courses of scores on these four components might be interpreted as the spectral EEG markers of the kinetics of two pairs of opponent chronoregulatory processes. In a sequence of ultradian sleep cycles, the 1st and 2nd components represent the alternations between competing drives for sleep and wakefulness, respectively, whereas the 3rd and 4th components reflect the alternations between light and deep sleep, respectively. The results suggest that principal component structuring of EEG spectrum can be employed for derivation of the parameters of the quantitative models conceptualizing the three major aspects of sleep-wake regulation—homeostatic, circadian, and ultradian processes.     

Principal Components of Electroencephalographic Spectrum as Markers of Opponent Processes Underlying Ultradian Sleep Cycles

Sleep-wake regulation involves reciprocal interactions between sleep- and wake-promoting processes that inhibit one another. To uncover the signatures of the opponent processes underlying ultradian sleep cycles, principal component analysis was performed on the sets of 16 single-Hz log-transformed electroencephalographic (EEG) power densities (1–16?Hz frequency range). Data were collected during unrestricted night sleep followed by 9 20-min naps (14 women aged 17–55 yrs) and during 12 20-min naps after either restriction or deprivation of sleep (9 males and 9 males, respectively, aged 18–22 yrs). It was found that any subset of power spectra could be reduced to the invariant four–principal component structure. The time courses of scores on these four components might be interpreted as the spectral EEG markers of the kinetics of two pairs of opponent chronoregulatory processes. In a sequence of ultradian sleep cycles, the 1st and 2nd components represent the alternations between competing drives for sleep and wakefulness, respectively, whereas the 3rd and 4th components reflect the alternations between light and deep sleep, respectively. The results suggest that principal component structuring of EEG spectrum can be employed for derivation of the parameters of the quantitative models conceptualizing the three major aspects of sleep-wake regulation—homeostatic, circadian, and ultradian processes. (Author correspondence: putilov@ngs.ru)     

A new SWPAQ's scale predicts the effects of sleep deprivation on the segmental structure of alpha waves

The three-dimensional cube-in-globe model predicts the possibility to delineate six main adaptive abilities of the sleep - wake cycle (Putilov & Putilov, 2005). Such prediction led to the inclusion of a new sixth scale named “daytime wakeability” (Putilov & Putilov, 2006; Putilov, 2007) in the Sleep-Wake Pattern Assessment Questionnaire (SWPAQ; Putilov, 1990, 2000). This new scale permits self-assessment of the ability to keep waking at daytime in sleep provoking conditions. In the study reported here we applied the procedure of adaptive segmentation of the EEG record (SECTION 0.1®, Human Brain Research Group, Moscow State University) to determine whether sleep deprivation changes the temporal pattern of alpha activity and, if yes, whether this change is associated with state and trait self-measures of alertness/sleepiness including a score on “daytime wakeability” scale. In the course of sustained wakefulness, EEG was recorded nine times with three-hour intervals in frontal and occipital derivations in 39 healthy subjects. The procedure of adaptive segmentation allowed cutting EEG records on quasi-stationary segments and determining such characteristics of each segment as within-segmental amplitude, coefficient of its variation and segment length. These characteristics obtained in the morning hours on the first and second experimental days were compared. They were found to increase in the second morning, when eyes are open, but do not change when eyes are closed. It was also documented that “daytime wakeability” score along with other trait and state self-measures of alertness/sleepiness can serve as predictors of the response of alpha waves to sleep deprivation.     

Age-related changes in the circadian and homeostatic regulation of human sleep

The reduction of electroencephalographic (EEG) slow-wave activity (SWA) (EEG power density between 0.75-4.5 Hz) and spindle frequency activity, together with an increase in involuntary awakenings during sleep, represent the hallmarks of human sleep alterations with age. It has been assumed that this decrease in non-rapid eye movement (NREM) sleep consolidation reflects an age-related attenuation of the sleep homeostatic drive. To test this hypothesis, we measured sleep EEG characteristics (i.e., SWA, sleep spindles) in healthy older volunteers in response to high (sleep deprivation protocol) and low sleep pressure (nap protocol) conditions. Despite the fact that the older volunteers had impaired sleep consolidation and reduced SWA levels, their relative SWA response to both high and low sleep pressure conditions was similar to that of younger persons. Only in frontal brain regions did we find an age-related diminished SWA response to high sleep pressure. On the other hand, we have clear evidence that the circadian regulation of sleep during the 40 h nap protocol was changed such that the circadian arousal signal in the evening was weaker in the older study participants. More sleep occurred during the wake maintenance zone, and subjective sleepiness ratings in the late afternoon and evening were higher than in younger participants. In addition, we found a diminished melatonin secretion and a reduced circadian modulation of REM sleep and spindle frequency-the latter was phase-advanced relative to the circadian melatonin profile. Therefore, we favor the hypothesis that age-related changes in sleep are due to weaker circadian regulation of sleep and wakefulness. Our data suggest that manipulations of the circadian timing system, rather than the sleep homeostat, may offer a potential strategy to alleviate age-related decrements in sleep and daytime alertness levels.     

Principal component analysis of electroencephalographic activity during sleep and wakefulness in the spider monkey (Ateles geoffroyi)

The study of electroencephalographic (EEG) activity during sleep in the spider monkey has provided new insights into primitive arboreal sleep physiology and behavior in anthropoids. Nevertheless, studies conducted to date have maintained the frequency ranges of the EEG bands commonly used with humans. The aim of the present work was to determine the EEG broad bands that characterize sleep and wakefulness in the spider monkey using principal component analysis (PCA). The EEG activity was recorded from the occipital, central, and frontal EEG derivations of six young-adult male spider monkeys housed in a laboratory setting. To determine which frequencies covaried and which were orthogonally independent during sleep and wakefulness, the power EEG spectra and interhemispheric and intrahemispheric EEG correlations from 1 to 30 Hz were subjected to PCA. Findings show that the EEG bands detection differed from those reported previously in both spider monkeys and humans, and that the 1–3 and 2–13 Hz frequency ranges concur with the oscillatory activity elucidated by cellular recordings of subcortical regions. Results show that applying PCA to the EEG spectrum during sleep and wakefulness in the spider monkey led to the identification of frequencies that covaried with, and were orthogonally independent of, other frequencies in each behavioral vigilance state. The new EEG bands differ from those used previously with both spider monkeys and humans. The 1–3 and 2–13 Hz frequency ranges are in accordance with the oscillatory activity elucidated by cellular recordings of subcortical regions in other mammals.     

Spectral EEG indicator of pressure to enter into deep sleep: its responsiveness to closing the eyes for just a few minutes exhibits a pure exponential buildup during sleep deprivation

According to the two-process model of sleep–wake regulation, a homeostatic sleep pressure, i.e. a pressure to enter into deep non-rapid eyes movement (NREM) sleep, must exhibit a purely exponential buildup during prolonged wakefulness. However, this pressure is usually measured indirectly, i.e. during the following episode of actual deep NREM sleep. The purpose of this paper was to show that, despite a prominent circadian modulation of time course of any waking EEG index, the model-postulated purely exponential buildup of the homeostatic sleep pressure can be directly confirmed. During two days of sleep deprivation experiments, the EEG of healthy adults (N = 30) was recorded every other hour throughout 5-min eyes closed relaxation. Sixteen ln-transformed single-Hz power densities (from 1 to 16 Hz) were computed for each of 5 one-min intervals. Differences between these densities obtained for the first and the following intervals were calculated and averaged. The obtained 16 values were used as the frequency weighting curve for weighting densities of each set of 16 single-Hz power densities. Summing-up of these weighted densities provided a single measure that was found to co-vary with self-rated sleepiness throughout two-day interval of sleep deprivation, thus reflecting the joint influence of the circadian and homeostatic processes. However, two-day time course of responsiveness of this measure to closing the eyes for just a few minutes exhibited a purely exponential buildup. It was concluded that this result provided a direct experimental confirmation of the model-predicted exponential buildup of the homeostatic sleep pressure across prolonged episode of wakefulness.     

Age‐related Changes in the Circadian and Homeostatic Regulation of Human Sleep

The reduction of electroencephalographic (EEG) slow‐wave activity (SWA) (EEG power density between 0.75–4.5 Hz) and spindle frequency activity, together with an increase in involuntary awakenings during sleep, represent the hallmarks of human sleep alterations with age. It has been assumed that this decrease in non‐rapid eye movement (NREM) sleep consolidation reflects an age‐related attenuation of the sleep homeostatic drive. To test this hypothesis, we measured sleep EEG characteristics (i.e., SWA, sleep spindles) in healthy older volunteers in response to high (sleep deprivation protocol) and low sleep pressure (nap protocol) conditions. Despite the fact that the older volunteers had impaired sleep consolidation and reduced SWA levels, their relative SWA response to both high and low sleep pressure conditions was similar to that of younger persons. Only in frontal brain regions did we find an age‐related diminished SWA response to high sleep pressure. On the other hand, we have clear evidence that the circadian regulation of sleep during the 40 h nap protocol was changed such that the circadian arousal signal in the evening was weaker in the older study participants. More sleep occurred during the wake maintenance zone, and subjective sleepiness ratings in the late afternoon and evening were higher than in younger participants. In addition, we found a diminished melatonin secretion and a reduced circadian modulation of REM sleep and spindle frequency—the latter was phase‐advanced relative to the circadian melatonin profile. Therefore, we favor the hypothesis that age‐related changes in sleep are due to weaker circadian regulation of sleep and wakefulness. Our data suggest that manipulations of the circadian timing system, rather than the sleep homeostat, may offer a potential strategy to alleviate age‐related decrements in sleep and daytime alertness levels.     

Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages

ABSTRACT

We compared performance in deriving sleep variables by both Fitbit Charge 2?, which couples body movement (accelerometry) and heart rate variability (HRV) in combination with its proprietary interpretative algorithm (IA), and standard actigraphy (Motionlogger® Micro Watch Actigraph: MMWA), which relies solely on accelerometry in combination with its best performing ‘Sadeh’ IA, to electroencephalography (EEG: Zmachine® Insight+ and its proprietary IA) used as reference. We conducted home sleep studies on 35 healthy adults, 33 of whom provided complete datasets of the three simultaneously assessed technologies. Relative to the Zmachine EEG method, Fitbit showed an overall Kappa agreement of 54% in distinguishing wake/sleep epochs and sensitivity of 95% and specificity of 57% in detecting sleep epochs. Fitbit, relative to EEG, underestimated sleep onset latency (SOL) by ~11 min and overestimated sleep efficiency (SE) by ~4%. There was no statistically significant difference between Fitbit and EEG methods in measuring wake after sleep onset (WASO) and total sleep time (TST). Fitbit showed substantial agreement with EEG in detecting rapid eye movement and deep sleep, but only moderate agreement in detecting light sleep. The MMWA method showed 51% overall Kappa agreement with the EEG one in detecting wake/sleep epochs, with sensitivity of 94% and specificity of 53% in detecting sleep epochs. MMWA, relative to EEG, underestimated SOL by ~10 min. There was no significant difference between Fitbit and MMWA methods in amount of bias in estimating SOL, WASO, TST, and SE; however, the minimum detectable change (MDC) per sleep variable with Fitbit was better (smaller) than with MMWA, respectively, by ~10 min, ~16 min, ~22 min, and ~8%. Overall, performance of Fitbit accelerometry and HRV technology in conjunction with its proprietary IA to detect sleep vs. wake episodes is slightly better than wrist actigraphy that relies solely on accelerometry and best performing Sadeh IA. Moreover, the smaller MDC of Fitbit technology in deriving sleep parameters in comparison to wrist actigraphy makes it a suitable option for assessing changes in sleep quality over time, longitudinally, and/or in response to interventions.     

基于奇异值第一主成分的睡眠脑电分期方法研究

目的:脑电信号含多种噪声和伪迹,信噪比较低,特征提取前必须进行复杂的预处理,严重影响睡眠分期的速度。鉴于此,本文提出一种基于奇异值第一主成分的睡眠脑电分期方法,该方法抗噪性能较强,可省去预处理过程,减少计算量,提高睡眠分期的效率。方法:对未经过预处理的睡眠脑电进行奇异系统分析,研究奇异谱曲线,提取奇异值第一主成分,探索其随睡眠状态变化的规律。并通过支持向量机利用奇异值第一主成分对睡眠分期。结果:奇异值第一主成分不仅能表征脑电信号主体,而且可以抑制噪声、降低维数。随着睡眠的深入,奇异值第一主成分的值逐渐增大,但在REM期处于S1期和S2期之间。经MIT-BIH睡眠数据库中5例同导联位置的脑电数据测试(仅1导脑电数据),睡眠脑电分期的准确率达到86.4%。结论:在未对脑电信号进行预处理的情况下,提取的睡眠脑电的奇异值第一主成分能有效表征睡眠状态,是一种有效的睡眠分期依据。本文运用提出的方法仅采用1导脑电数据,就能得到较为满意的睡眠分期结果。该方法有较强的分类性能,且抗噪能力强,不需要对脑电作复杂的预处理,计算量小,方法简单,很大程度上提高了睡眠分期的效率。     

Reliability and validity of the Korean version of Morningness–Eveningness Questionnaire in adults aged 20–39 years

Morningness–Eveningness (ME) can be defined by the difference in individual diurnal preference observed from general behavioral patterns including sleep habits. The Horne & Östberg Morningness–Eveningness Questionnaire (MEQ) has been used for classifying ME types. We examined the reliability of a Korean version of the MEQ (Korean MEQ) and verified its validity by comparing responses on the Korean MEQ to objectively-recorded sleep–wake rhythms. After translating and back translating the MEQ from English into Korean, we examined the internal consistency of 19 items of the Korean MEQ in 91 subjects, and the test–retest reliability in 21 subjects who took the Korean MEQ twice, 4 weeks apart. The Korean MEQ was then administered to 1022 young adult subjects. A subset of 46 morning, neither, and evening type subjects took part in a validation study in which their rest-activity timing was collected by actigraphy for 7 days. Cosinor analyses on these data were done to obtain the acrophase and amplitude of the sleep–wake rhythm. Cronbach’s alpha of the total scores from the Korean MEQ was 0.77, and the test–retest reliability intra-class correlation coefficient was 0.90 (p?<?0.0001). There was a significant negative correlation between Korean MEQ score and reported sleep–wake timing among the entire cohort (p?<?0.0001). There was a significant difference in bedtime and wake time (on both work and free days), and in the mean sleep–wake rhythm acrophase, between ME types (p?<?0.01). In this study, the validity of the Korean MEQ was verified by illustrating the difference in acrophases of the sleep–wake rhythm between the ME types in young adults.     

Criterion validity of the Pittsburgh Sleep Quality Index: Investigation in a non-clinical sample

The objective of this study was to investigate the reliability and validity of the Pittsburgh Sleep Quality Index (PSQI) in a non-clinical sample consisting of younger and older adults. There has been little research validating the PSQI with respect to multinight recording as with actigraphy, and more validation is needed in samples not specifically selected for clinical disturbance. Also, the degree to which the PSQI scores may reflect depressive symptoms versus actual sleep disturbance remains unclear. One-hundred and twelve volunteers (53 younger and 59 older) were screened for their ability to perform treadmill exercises; inclusion was not based on sleep disturbance or depression. Internal homogeneity was evaluated by correlating PSQI component scores with the global score. Global and component scores were correlated with a sleep diary, actigraphy, and centers for epidemiological studies - depression scale scores to investigate criterion validity. Results showed high internal homogeneity. PSQI global score correlated appreciably with sleep diary variables and the depression scale, but not with any actigraphic sleep variables. These results suggest that the PSQI has good internal homogeneity, but may be less reflective of actual sleep parameters than a negative cognitive viewpoint or pessimistic thinking. The sleep complaints measured may often be more indicative of general dissatisfaction than of any specifically sleep-related disturbance.     

MCH levels in the CSF,brain preproMCH and MCHR1 gene expression during paradoxical sleep deprivation,sleep rebound and chronic sleep restriction

Neurons that utilize melanin-concentrating hormone (MCH) as neuromodulator are located in the lateral hypothalamus and incerto-hypothalamic area. These neurons project throughout the central nervous system and play a role in sleep regulation. With the hypothesis that the MCHergic system function would be modified by the time of the day as well as by disruptions of the sleep-wake cycle, we quantified in rats the concentration of MCH in the cerebrospinal fluid (CSF), the expression of the MCH precursor (Pmch) gene in the hypothalamus, and the expression of the MCH receptor 1 (Mchr1) gene in the frontal cortex and hippocampus. These analyses were performed during paradoxical sleep deprivation (by a modified multiple platform technique), paradoxical sleep rebound and chronic sleep restriction, both at the end of the active (dark) phase (lights were turned on at Zeitgeber time zero, ZT0) and during the inactive (light) phase (ZT8).We observed that in control condition (waking and sleep ad libitum), Mchr1 gene expression was larger at ZT8 (when sleep predominates) than at ZT0, both in frontal cortex and hippocampus.In addition, compared to control, disturbances of the sleep–wake cycle produced the following effects: paradoxical sleep deprivation for 96 and 120 h reduced the expression of Mchr1 gene in frontal cortex at ZT0. Sleep rebound that followed 96 h of paradoxical sleep deprivation increased the MCH concentration in the CSF also at ZT0. Twenty-one days of sleep restriction produced a significant increment in MCH CSF levels at ZT8. Finally, sleep disruptions unveiled day/night differences in MCH CSF levels and in Pmch gene expression that were not observed in control (undisturbed) conditions.In conclusion, the time of the day and sleep disruptions produced subtle modifications in the physiology of the MCHergic system.     

The time course of adenosine, nitric oxide (NO) and inducible NO synthase changes in the brain with sleep loss and their role in the non-rapid eye movement sleep homeostatic cascade

Both adenosine and nitric oxide (NO) are known for their role in sleep homeostasis, with the basal forebrain (BF) wakefulness center as an important site of action. Previously, we reported a cascade of homeostatic events, wherein sleep deprivation (SD) induces the production of inducible nitric oxide synthase (iNOS)-dependent NO in BF, leading to enhanced release of extracellular adenosine. In turn, increased BF adenosine leads to enhanced sleep intensity, as measured by increased non-rapid eye movement sleep EEG delta activity. However, the presence and time course of similar events in cortex has not been studied, although a frontal cortical role for the increase in non-rapid eye movement recovery sleep EEG delta power is known. Accordingly, we performed simultaneous hourly microdialysis sample collection from BF and frontal cortex (FC) during 11 h SD. We observed that both areas showed sequential increases in iNOS and NO, followed by increases in adenosine. BF increases began at 1 h SD, whereas FC increases began at 5 h SD. iNOS and Fos-double labeling indicated that iNOS induction occurred in BF and FC wake-active neurons. These data support the role of BF adenosine and NO in sleep homeostasis and indicate the temporal and spatial sequence of sleep homeostatic cascade for NO and adenosine.     

Aging in Mice Reduces the Ability to Sustain Sleep/Wake States

One of the most significant problems facing older individuals is difficulty staying asleep at night and awake during the day. Understanding the mechanisms by which the regulation of sleep/wake goes awry with age is a critical step in identifying novel therapeutic strategies to improve quality of life for the elderly. We measured wake, non-rapid eye movement (NREM) and rapid-eye movement (REM) sleep in young (2–4 months-old) and aged (22–24 months-old) C57BL6/NIA mice. We used both conventional measures (i.e., bout number and bout duration) and an innovative spike-and-slab statistical approach to characterize age-related fragmentation of sleep/wake. The short (spike) and long (slab) components of the spike-and-slab mixture model capture the distribution of bouts for each behavioral state in mice. Using this novel analytical approach, we found that aged animals are less able to sustain long episodes of wakefulness or NREM sleep. Additionally, spectral analysis of EEG recordings revealed that aging slows theta peak frequency, a correlate of arousal. These combined analyses provide a window into the mechanisms underlying the destabilization of long periods of sleep and wake and reduced vigilance that develop with aging.     

Dim Light at Night Does Not Disrupt Timing or Quality of Sleep in Mice

Artificial nighttime illumination has recently become commonplace throughout the world; however, in common with other animals, humans have not evolved in the ecological context of chronic light at night. With prevailing evidence linking the circadian, endocrine, immune, and metabolic systems, understanding these relationships is important to understanding the etiology and progression of several diseases. To eliminate the covariate of sleep disruption in light at night studies, researchers often use nocturnal animals. However, the assumption that light at night does not affect sleep in nocturnal animals remains unspecified. To test the effects of light at night on sleep, we maintained Swiss-Webster mice in standard light/dark (LD) or dim light at night (DLAN) conditions for 8–10 wks and then measured electroencephalogram (EEG) and electromyogram (EMG) biopotentials via wireless telemetry over the course of two consecutive days to determine differences in sleep timing and homeostasis. Results show no statistical differences in total percent time, number of episodes, maximum or average episode durations in wake, slow-wave sleep (SWS), or rapid eye movement (REM) sleep. No differences were evident in SWS delta power, an index of sleep drive, between groups. Mice kept in DLAN conditions showed a relative increase in REM sleep during the first few hours after the dark/light transition. Both groups displayed normal 24-h circadian rhythms as measured by voluntary running wheel activity. Groups did not differ in body mass, but a marked negative correlation of body mass with percent time spent awake and a positive correlation of body mass with time spent in SWS was evident. Elevated body mass was also associated with shorter maximum wake episode durations, indicating heavier animals had more trouble remaining in the wake vigilance state for extended periods of time. Body mass did not correlate with activity levels, nor did activity levels correlate with time spent in different sleep states. These data indicate that heavier animals tend to sleep more, potentially contributing to further weight gain. We conclude that chronic DLAN exposure does not significantly affect sleep timing or homeostasis in mice, supporting the use of dim light with nocturnal rodents in chronobiology research to eliminate the possible covariate of sleep disruption.     

丁苯酞对睡眠剥夺大鼠额叶小胶质细胞活化的影响*

目的: 探讨丁苯酞对慢性睡眠剥夺后大鼠脑部额叶小胶质细胞活化及炎症因子的影响。方法: 本实验共分为4组(n＝8):空白对照组、大平台对照组、慢性睡眠剥夺组、丁苯酞干预组。慢性睡眠剥夺组和丁苯酞干预组采用改良多平台睡眠剥夺法建立大鼠慢性睡眠剥夺模型,对大鼠进行每日18 h,连续28 d的睡眠剥夺。在这28 d内,空白对照组大鼠不进行睡眠干预,大平台对照组大鼠放于大平台箱内。丁苯酞干预组在睡眠剥夺28 d结束后按100 mg/kg腹腔注射丁苯酞针剂,每日1次,共14 d,其他组大鼠在这14 d内腹腔注射同样剂量的生理盐水。腹腔注射结束后各组大鼠取脑组织,免疫组化检测额叶皮质离子钙接头分子(Iba-1)阳性细胞并计数,Western blot检测额叶诱导型一氧化氮合成酶(iNOS)、精氨酸酶1(Arg1)表达,实时定量PCR检测额叶白介素-1(IL-1)mRNA、IL-6 mRNA、肿瘤坏死因子-α(TNF-α) mRNA。结果: 与空白对照组、大平台对照组比较,慢性睡眠剥夺组额叶Iba-1阳性细胞体积增大伴细胞突起增多,且细胞数增加(P均＜0.05),iNOS和IL-1 mRNA、IL-6 mRNA、TNF-α mRNA表达增加,而Arg1表达减少(P均＜0.05);与慢性睡眠剥夺组比较,丁苯酞干预组额叶Iba-1细胞数减少(P＜ 0.05),iNOS和IL-1 mRNA、IL-6 mRNA、TNF-α mRNA表达减少(P均＜0.05)而Arg1表达无明显改变。结论: 丁苯酞可抑制慢性睡眠剥夺导致的大鼠额叶小胶质细胞活化、减少慢性睡眠剥夺后的炎症因子表达。     

EEG Correlates of Dream Recall in Depressed Outpatients and Healthy Controls

The present study explored EEG correlates of dream recall in 17 symptomatic, unmedicated depressed patients and in 19 healthy adults. EEG segments from the last 30 minutes of sleep, from the five minutes following morning awakening, and the absolute difference between sleep and waking EEG were contrasted between the two groups of participants during successful dream recall and during no recall. Period amplitude analysis was used to quantify EEG frequencies. Increased high-frequency beta incidence in the right hemisphere and amplitude in both hemispheres during sleep were associated with dream recall in both patient and control groups. Depressed patients also showed higher delta amplitude in both hemispheres during sleep associated with recall, but this effect did not reach significance. With regard to the changes between sleep and wakefiilness, a smaller change in right hemisphere beta and delta incidence characterized successful recall in healthy controls. By contrast, those with depression showed recall success when the sleep/wake shifts in right hemisphere beta and delta incidence were larger. Recall failure was characterized by small EEG shifts from sleep to wakefulness in the depressed group. The same effects were observed for beta and delta amplitude measures, except that healthy controls showed a large shift in delta amplitude in the sleep-wake transition during successful recall but not during recall failure. Recall in those with depression was associated with a dramatic shift in left hemisphere delta amplitude. These findings provide support for Koukkou and Lehmann's (1983, 1993) state-shift hypothesis of dream recall in healthy controls (except for left hemisphere delta amplitude) but not in the depressed. It appears that in order to recall a dream, depressed patients must undergo larger shifts in brain activity and perhaps a different pattern of reorganization of EEG frequencies than controls. This finding may account for the low rates of recall reported previously in this clinical group.     

Antidepressant effects of combination of sleep deprivation and early evening treatment with melatonin or placebo for winter depression

Patients with winter depression (seasonal affective disorder) respond beneficially to sleep deprivation and bright light, but the mechanisms of these responses remain unknown. The study was designed to test whether afternoon/evening melatonin can prevent further relapse after sleep deprivation (presumably due to a pharmacologically induced advance shift of circadian phase). Compared to phase advancing by alteration of sleep - wake schedule or by bright light exposure, the melatonin intake is a more tolerated treatment procedure, and it provides a possibility of blind comparison between chronotherapeutic and placebo treatments. The depression was scored in 16 female patients with winter depression and 17 age-matched female controls before and after total night sleep deprivation and after subsequent six-day administration of melatonin (0.5 mg) or placebo under double blind conditions. The melatonin intake was scheduled at 17:00 in order to produce a phase advance of circadian rhythms. Sleep deprivation resulted in 38% reduction of depression score in patients, but it did not reduce depression score in controls. After subsequent treatment with placebo or melatonin, slight but significant improvement of mood was found in controls. These treatments also stabilized the antidepressant response to sleep deprivation in patients. However, neither differential effect of melatonin and placebo on depression score nor alteration of habitual sleep timing was found in patients and controls. Thus, the study results do not provide evidence for the antidepressant potential of melatonin in patients with winter depression under realistic clinical conditions. The finding of stabilization of mood in patients with placebo points to the contribution of psychological factors to the therapeutic action of this and other types of innovative treatments for winter depression. To include psychosocial aspects in the theoretical framework of seasonal depression, we conceptualized depression as an evolved feature of emotional response to psychosocial rather than physical environment. The seasonality of depression might be explained by cumulative effects of aperiodical psychosocial factors and periodical physical factors on one of the mechanisms of brain neurotransmission.