Precise Measurement of Individual Rapid Eye Movements REM Sleep of Humans

Transcription

1 Sleep, 20(9): American Sleep Disorders Association and Sleep Research Society Sleep and Sleep States Precise Measurement of Individual Rapid Eye Movements REM Sleep of Humans. In Kazumi Takahashi and Yoshikata Atsumi Department of Neuropsychiatry, Tokyo Medical and Dental University, Tokyo, Japan Summary: An automated analyzer for individual eye movements (EMs) has been developed that enables precise analyses of their incidence. Three new parameters for each EM are obtained: EM magnitude, the angle and speed of eyeball rotation, and the energy of each EM. All rapid eye movement (REM) sleep EMs from 40 nights of polysomnography for 20 healthy young men were analyzed. The mean frequency of eye movement (EM frequency) was 15.9 per minute. Compared to conventionally analyzed rapid eye movement (REM) density, EM frequency was more sensitive to differences among sleep cycles, nights, and individuals. The mean EM rotation was 6.27 :!: degrees, the mean speed of rotation was :!: 0.18 degrees/second, and mean energy was :!: 3.82 degrees'/second. The distribution of changes in these new parameters differed from conventional measures across REM episodes. The conventional measures, REM episode duration, and REM density increased progressively in successive REM episodes in an ascent-to-right pattern. However, the new parameters peaked in the second, followed by relatively low values, producing an inverted V pattern. This discrepancy could indicate physiological mechanisms of EM that are not revealed in conventional measures of REM sleep intensity. Key Words: Rapid eye movements REM sleep-rem density-rem burst-rem sleep intensity. Aserinsky and Kleitman (1,2) first described the rapid eye movements that occur during sleep periods characterized by low-voltage fast electroencephalography (EEG). The proliferation of subsequent investigations of rapid eye movement (REM) and non-rem (NREM) sleep has revealed much about the physiology of sleep and its disorders and about the pathophysiology of mental illness. The existing parameters of REM sleep can be grouped into two categories. Tonic parameters include the timing and duration of REM sleep, such as the percentage of REM in total sleep, REM latency, and the duration of each REM sleep episode. Phasic REM sleep parameters include the number (REM activity) and incidence (REM density) of rapid eye movements (EMs; hereafter, EM represents a single rapid eye movement in a REM period). In this paper we focus on phasic REM sleep. The main purpose of the present study was to extract additional physiological information from more precise measurements of REM sleep. To extract more information from the electrooculogram (EOG), we developed a new automated analysis system for EMs that is also fit for clinical use. This system enabled us to investigate three aspects of EMs in REM sleep. First, to improve the accuracy of two conventional parameters, REM activity and REM density, the exact number and timing of individual EMs was determined. Second, on the basis of the measurement of the size of individual EMs, three new parameters of REM sleep were measured, which represent the magnitude of each EM. Finally, the simultaneous measurement of exact EM incidence and magnitude enabled us to study the characteristics of EM bursts in REM sleep. For comparison in future studies, we present standard values of these new parameters for young healthy males. We also discuss the physiological basis of both the new and older conventional parameters. Subjects and recordings METHODS Accepted for publication July Address correspondence and reprint requests to Kazumi Takahashi, M.D., Tokyo Metropolitan Matsuzawa Hospital, Kamikitazawa Setagaya-ku, Tokyo 156, Japan. 743 Twenty healthy normal male volunteers (24-28 years old) were polygraphically recorded for three consecutive nights. Lights were turned off between

2 744 K. TAKAHASHI AND Y ATSUMI a. b. Raw Data ~....../ t A First step; The raw data are digitized at 80Hz and filtered with a 7-point weighted moving average. Second step; A saccadic eye movement is defined by two obtuse angles at its onset and termination (point A and B). The duration of the EM is time(b) - time (A) To identify point A and B, the data are differentiated twice. The first differential increases near apex A and decreases near apex B. The second differential identifies the peak values of the apices. c. duration B Third step; If segment AB exceeds three thresholds for amplitude, duration and slope an EM is registered. The amplitude, duration and the time of onset of EM are stored for analysis. FIG. 1. Detection of an eye movement (EM) mimics the human recognition process in three steps. Detection occurs in the second step, when the electroculogram signal is differentiated twice. First step: Thc raw data are digitized at 80 Hz and filtered with a seven-point weighted moving average. Second step: A saccade eye movement is defined by two obtuse angles at its onset and termination (points A and B). The duration of the EM is time (B) - time (A). To identify points A and B, the data are differentiated twice. The first differential increases near apex A and decreases near apex B. The second differential identifies the peak values of the apices. Third step: If segment AB exceeds three thresholds for amplitude, duration, and slope, and EM is registered. The amplitude, duration, and time of onset of EM are stored for analysis and 0100 hours in accordance with each subject's daily routine. Subjects were awakened 7.5 hours after lights were turned off. EEG (FpI, Fp2, C3, C4, 01, and 02 referenced to linked Al + A2), electromyographic (EMG, mentalis), and EOG activity were monitored. Excluding the first night's records, 40 nights' data were obtained for analysis. Horizontal and vertical EMs were recorded using four electrodes: two at the outer canthi for horizontal movements, and two placed periorbital1y for vertical movements. Only the horizontal bipolar EOG was used in the present automatic analysis. EOG was amplified by an amplifier coupled with alternating current (AC), with a long time constant (TC) of 3.2 seconds. Calibration of EM amplitude was performed each night, just before the subjects were told to go to sleep, as follows: Three small green LED targets were arranged horizontally on the ceiling above the subject. Each subject lay on his back in bed and was instructed to move his gaze to the single lighted target as each target was lit. The distance between the left, middle, and right LEDs was designed to obtain 10 degrees of eye movement. EM detection and measurement We devised an algorithm for the detection of EOG deflections that mimics human pattern recognition. Long TC recordings of EOG produce deflections that have an abrupt beginning and sudden termination. Humans are able to extract these characteristics, ignoring electrical noise and maintaining a subjective threshold for identifying EMs. We simulated this process by decomposing it into three steps (Fig. la-c). Horizontal EOG records were digitized at a sampling rate of 80 Hz. The first step filtered the signal using a seven-point weighted moving average to eliminate jiggling electrical noise (Fig. Ia). The second step contained the main identification process (Fig. I b). Candidate EMs are defined by an abrupt beginning (A) and sudden termination (B) of activity. To detect apices A and B, differential calculations were used. Figure I b shows the

3 PRECISE MEASURES OF RAPID EYE MOVEMENTS 745 corresponding effects of the first- and second-order differentiations. Threshold criteria for identifying apices A and B were determined empirically. In the third step (Fig. Ic), the actual detection of EMs was performed by applying criteria to each EM candidate identified in step two. EMs were required to fulfill three criteria of amplitude (>30 microvolts = 1.05 mm on paper recordings), duration «0.5 second), and slope (>248.3 microvolt/second = 30 degrees on paper recordings). These criteria were established on the basis of our visual inspection of the long TC EOG., The amplitude, time of occurrence, and duration of all suprathreshold EMs were measured on the raw waves (not on the differentiated data) and then stored on a computer disk. Finally, after the on-line analyses were complete, each EM amplitude was converted into degrees of eyeball rotation on the basis of the prerecording IO-degree calibration of eyeball shift. The system was implemented on a PC-980I NEC personal computer with analog-to-digital converter board. Data analysis Visual sleep stage scoring was performed for each 20-second epoch according to the criteria of Rechtschaffen and Kales (3). Only the first four REM episodes were analyzed in the present study. To define NREM-REM cycles, a IS-minute rule was applied that defined two successive REM periods separated by more than 15 minutes as distinct REM episodes (4-6). Epochs in which the EOG channels contained EMG or artifacts were visually identified and eliminated before the statistical analyses. Four first REM episodes from three subjects were judged to be missed. In one of two recordings of a subject there were only three REM episodes; the subject awoke in the fourth NREM period. Thus, a total of 155 REM episodes were analyzed over 40 nights. Definition of EM measurements Three basic measures were obtained from computer analysis: the time of onset, duration, and amplitude of each EM. Four refined measures of conventional parameters, and three new measures of the magnitude of EM, were derived from these basic measurements as follows: 1) EM count, the total number of EMs in a period of time (e.g. each epoch, each REM episode, an entire night); 2) EM frequency, the number of EMs per minute; 3) EM interval, the time between two successive EMs, i.e. the interval between the end of an EM and the beginning of the next EM (the EM interval between REM episodes was not computed; 4) EM duration, the time between the onset and the termination of each EM; 5) EM rotation, degrees of eyeball rotation; 6) EM velocity, the angular velocity (degrees! second) of eyeball rotation; 7) EM power, defined as (EM rotation)2lem duration; EM power is an estimate of the total energy expended during an EM (7,8). Large and quick EMs consume more energy than small and slow EMs. REM burst analyses We tried to automatically detect bursts of REM activity in which the time between any two successive eye movements was less than a critical value (9). We defined a "burst" as a group of EMs in which the time between any two successive EMs was less than a critical value (Tl) and which contained more than a critical number of EMs (Cl). We determined Tl and Cl empirically by visually scoring 93 minutes of REM sleep EOG tracings from three subjects. Working independently, two human scorers visually detected EM bursts. Then, Tl and Cl were defined to produce the best agreement between the resulting computer analyses and the human scoring. With TI = 3 seconds and Cl = 7, the sensitivity of the computer analysis to testers A and B was 85.2 and 87.8%, respectively. Specificity, computed in the same manner, was 80.4 and 73.8%, respectively. Statistics Two methods were used to assess the significance of differences between REM episodes. First, differences in the durations of REM episodes were assessed by analysis of variance (ANOVA) using Fisher's protected least significant difference (PLSD) for post-hoc tests. Second, because EM frequency, EM rotation, EM velocity, and EM power each showed large interindividual variations, each night's values were standardized into a normal distribution by z transfonnation before being assessed by ANOVA using Fisher's PLSD for post-hoc tests. System validation RESULTS To assess the reliability of the system, 93 minutes of REM sleep from three subjects other than those used to determine thresholds Tl and Cl were analyzed by computer and two human scorers, and the results were compared. The two scorers were psychiatrists experienced in sleep research and the visual inspection of polysomnograms. They were instructed to independently mark each EM. (The criteria for the detection of EM are the same as the described computer algorithm: EOG amplitude> 1.05 mm, EOG duration <0.5

4 746 K. TAKAHASHI AND Y. ATSUMI EM-frequency (count/min) o~ subject FIG. 2. Interindividual and intraindividual variations in eye movement (EM) frequency, with each subject's second and third night values connected. Variability of EM frequency was much less within individuals than among individuals. second, and slope >30 degrees on paper recordings.) The same EOG data were analyzed by the computer system. Scorers A and B and the computer counted 2,002, 2,223 and 2,303 EMs, respectively. The concordance between the computer and scorers A and B was 87.0 and 84.2%, and between A and B it was 84.8%. The concordance was calculated as the percentage of EMs that both computer and human recognized of the total EMs that either of them recognized. For EMs counted per 20-second epoch in the 279 epochs, the linear correlation coefficients between the computer and scorer A (r = 0.942, p < ), computer and scorer B (r = 0.955, P < ), and scorers A and B (r = 0.983, p < ) were all highly significant. Basic REM sleep parameters Visual sleep stage scoring confirmed the expected amount and distribution of REM sleep. The mean total sleep time for the first four NREM-REM cycles was (±41.6) minutes, with a mean of 94.4 (±24.6) minutes of total REM sleep. The mean percentage of REM sleep in the total sleep of four NREM-REM cycles was 24.4%. The mean durations of the first four REM periods were 16.2 (± 13.3), 26.4 (± 15.5), 34.4 (± 18.7), and 33.6 (± 19.9) minutes, respectively. REM episodes became progressively longer during the night. Ourations of the first REM episodes were significantly shorter than the second, third, and fourth REM episodes, and second REM episodes were shorter than the third and fourth (ANOV A, P < 0.05). Incidence of EMs REM density and EM frequency After excluding artifact epochs, the total duration of the first four REM episodes (REMPs 1-4) for the 40 night recordings was 3,368.3 minutes (84.2 minutes/ night), and the total EM count was 53,562. The EM count for each night varied widely; mean 1,342.9, range 258 to 3,727, and standard deviation (SO) The grand mean EM frequency was 15.9 counts/minute. The mean EM frequency for each night ranged from 5.54 to counts/minute (SO = 8.11). Figure 2 shows interindividual and intraindividual variations in EM frequency, with each subject's second and third night values connected. Variability of EM frequency was much less within individuals (SO = 2.75) than among individuals (SO = 48.73). For 14 of the 20 subjects, the SO differed between the second and the third nights by less than 1 second. Table 1 compares the results from our automatic analyzer to conventional REM parameters. Because it was time consuming and practically impossible to count the exact number of EMs by visual inspection, the values of REM activity and REM density have been substituted for the true number and incidence of EMs (9); true values are thought to be best represented by EM count and EM frequency. To calculate REM density from the computer analyzed data, the total of 155 REM periods were divided into 20, 10, 5, and 1 second epochs, and each was examined for the presence of at least one EM. Comparing REM density to EM frequency, the correlation coefficients were 0.466,

5 PRECISE MEASURES OF RAPID EYE MOVEMENTS 747 TABLE 1. REM activity and REM density Analysis epoch duration (seconds) EM countlem frequency REM activity ,833 (EM count) REM density count/minute Correlation of REM density vs. EM frequency (EM frequency) REM. rapid eye movement; EM, eye movement ,0.824, and for analysis epochs of 20, 10, 5, and 1 second, respectively. Sensitivity of new measure EM frequency to REM density VA, P < 0.05). Only EM frequency increased significantly from the second to third, second to fourth, and third to fourth REM episodes (ANOVA, p < 0.05). New measure EM frequency can detect fine changes between REM episodes. EM interval The grand mean EM interval was 3.74 seconds (range seconds). Figure 4 shows the frequency distribution for EM interval values between 0 and 12 seconds. The modal interval was seconds; 80% of the EM intervals were less than 2.6 seconds long, and 90% were less than 7.0 seconds long. Magnitude of single eye movements Table 2 presents the mean characteristics of computer-analyzed EMs for 40 nights. The mean EM duration was second. The modal EM duration fell into the microsecond analysis bin (46.9% of To assess the sensitivity of our new measure EM frequency to conventional REM density, relative values were calculated for each measure by dividing each value by the corresponding value from REM period one. Figure 3 presents an example of the relative changes in REM density and EM frequency across REM periods from a representative 25-year-old male (subject 14). All calculations of REM density increased significantly between the second and third REM episodes (ANOVA, p < 0.05). REM density calculated for 20-second intervals did not increase significantly between the second and fourth REM episodes. However, when computed for epochs shorter than 10 seconds, REM density showed significant increments between REM periods two and four (ANOrelative value II EM-frequency REM density calculated for I-sec analysis epoch.. REM density calculated for 5-sec analysis epoch o REM density calculated for IO-sec analysis epoch ~ REM density calculated for 20-sec analysis epoch 2 4 REM period FIG. 3. An example of the relative changes in rapid eye movement (REM) density and eye movement (EM) frequency across REM periods from a representative 25-year-old male. All values of REM density increase during the night but differ from EM frequency. New measure EM frequency can detect fine changes between REM episodes. Sleep, Vol. 20. No.9, 1997

6 l 748 K. TAKAHASHI AND Y. ATSUMI % rotation, EM velocity, and EM power in bursts were significantly larger than were isolated EMs (ANOV A, p < 0.001). That is, EMs in bursts were larger, rotated more quickly, and had greater power than isolated EMs sec FIG. 4. Frequency distribution for eye movement (EM) interval values between 0 and 12 seconds. The modal interval was second; 80% of the EM intervals were less than 2.6 seconds. The distribution of EM intervals lacks a bimodal distribution. EMs were in this bin). The mean EM rotation was 6.27 degrees. The modal EM rotation was 2-3 degrees (18.9% of EMs were in this bin), with 80% of the values less than 9.0 degrees and 91 % less than 13.0 degrees. The mean EM velocity was degrees/ second. The mode was between 30 and 40 degrees/ second (17.7% of EMs were in this bin, with 83% less than 90 degrees/second and 91 % less than 120 degrees/second. Mean EM power was degrees 2 / second. For EM power values, the mode was between o and 100 degrees 2 /second (26.4% of EMs were in this bin), with 82% less than 800 degrees 2 /second and 90% less than 1,400 degrees 2 /second. REM bursts Table 3 compares the characteristics of isolated EMs with those in bursts. Of the 53,562 EMs detected in 40 nights, 37,383 (69.8%) belonged to REM "bursts" and 16,179 (30.2%) were isolated EMs. The mean EM interval in a burst was 1.40 seconds; for isolated EMs the mean interval was 9.16 seconds. The mean number of EMs that formed a burst was 16.5 (ranging from 7, defined by C1, to 206). There was a mean of 0.70 bursts per minute in REM sleep over 40 nights, ranging from 0.14 to 1.74 counts/minute per night. EM Changes in REM measures across REM episodes Figure 5 shows the relative changes in the duration of REM episode, EM frequency, and EM rotation across REM episodes for all 40 recordings. REM episode duration and EM frequency increased significantly from the first to fourth REM episodes in an ascent-to-right pattern. By contrast, although EM rotation increased significantly from the first REM episode to its peak value in the second REM episode, it then decreased in the third REM episode, making an inverted V pattern. EM velocity and EM power had the same pattern as EM rotation. Thus, the most vigorous rapid eye movements occurred in the middle of the sleep period. DISCUSSION Automated EM analyzer Our computer system was designed for clinical use and relied upon three essential features to accurately count and measure each EM: the use of horizontal EOG, amplification of the EOG with a long TC, and pattern recognition methods. The importance of these features was learned by investigating many previous studies of automated EOG devices (10-19). Horizontal EOG has two advantages over vertical or oblique EOG recordings. In clinical use, horizontal EOG is rarely affected by artifacts such as high-voltage EEG or eyelid movements. Also, more than one-half of all EMs occur in the horizontal direction (14,15). Long TC amplification is essential for the accurate measurement of single EM amplitudes, because short TCs produce large distortions of the EOG waveform. Compared to direct-current (DC) recording, the AC method cannot detect relatively slow frequency. However, a TC longer than 3 seconds is enough for rapid EMs in REM sleep (13,14). Pattern recognition methods can easily and effectively recognize the waveforms characteristic of TABLE 2. Measurements of single EMs (n = 53,562) EM duration (seconds) EM rotation (degrees) EM velocity (degrees/second) EM power (degrees squared/second) EM(s), eye movement(s); SD, standard deviation. a Threshold. Mean ~ SD Minimum Maximum ::': ::': ::': ::': " ,014.97

7 PRECISE MEASURES OF RAPID EYE MOVEMENTS 749 Count (% of total) EM interval (seconds) EM rotation (degrees) EM velocity (degrees/second) EM power (degrees squared/second) EM(s), eye movement(s)... p < TABLE 3. Comparison of burst EMs with isolated EMs EMs in burst Isolated EMs All 37,383 (69.8%) lao:!:: :!:: 5.23** :!:: 44.13** :!:: ** 16,179 (30.2%) 9.16 :!:: :!:: :!:: :!:: ,562 (100%) 3.74 :!:: 13A :!:: :!:: :!:: EMs; all measurements of individual EMs depend on precise recognition of these waveforms. Thus, the values of individual EM measurements depend critically on both recording and analysis methods. Future studies should clearly describe TC settings, EOG direction, and analysis thresholds to allow comparisons between the results obtained using different methods. Of the eight studies described in Table 4 (9,15,20-25), EM frequency ranged from 5.4/minute to 25.2/ minute in the studies of young adults. Aserinsky (9) and Ehlers and Kupfer (24) reported much smaller EM frequency values than we found in the present study. This could be due to their use of short TC recordings, which could not detect relatively slow and small amplitude EMs. Schneider's method (15) may have counted more EMs than ours because of its use of long 35 min count/min o * * REM episode duration * EM-frequency.08 p<o.05 EM-rotation REM episode FIG. 5. Changes in the duration of rapid eye movement (REM) episode, eye movement (EM) frequency, and EM rotation across REM episodes for all 40 recordings. REM episode duration and EM frequency increased in an ascent-to-right pattern. By contrast, EM rotation had its peak value in the second REM episode, making an inverted V pattern. TC and vectrooculogram (VOG) recordings, which counted both horizontal and vertical EOGs. Not all prior studies reported the validity of automated systems. Correlation coefficients between human scorings and the automatic devices were 0.91 (19), (26), and (23). In our system, the correlations between two scorers and the computer system ranged from to We also calculated concordances between each human scorer and the computer system to be 84.2 and 87.0%, which are very similar to that between the two human scorers (84.8%). Precise measurement of EM incidence The density of eye movements during REM sleep, a rough estimate of EM incidence, has long been used to evaluate mental characteristics such as intelligence and mental development (27-29), depressive illness (22,25,30,3 I}, schizophrenia (32), and aging (24,33). Computer analysis can improve the accuracy of EM incidence measures. In the present study, EM frequency was more sensitive to changes in EM incidence across REM episodes than was REM density measured in (the typical) 20-second and longer epochs. It is therefore possible that reassessment of past studies that did not indicate significant differences in REM density could reveal significant changes in EM characteristics. For example, McPartland et al. (22) measured EM frequency and studied primary depression. They reported differences in EM frequency between normals and depressives, as well as fine changes in EM frequency that correlated with the quantity of medication and severity of symptoms. It has also been reported that use of cholinergic agonists and antagonists alters the amount of REM sleep and number of EMs (34). EM frequency has a clear potential for application in such studies. Besides whole-night measurements of REM density, many studies have reported that patterns of REM density change across REM episodes. In healthy adults, REM density usually increases progressively across successive REM episodes in an ascent-to-right pattern. This pattern reportedly differs for different mental states; in an inverted V pattern during childhood that changes to the ascent-to-right pattern in adolescence

8 750 K. TAKAHASHI AND Y. ATSUMI TABLE 4. Prior studies of the frequency of EMs EM frequency Study (year) (count/minute) Time constant Detection threshold EOG Subjects (age) Aserinsky (1971) 8.4 Analog filter H 10 (student) Ornitz et al. (1973) 15.0 AC filtered 3-4 degrees H, V 8 (child) Benoit et al. (1974) H, V, 0 10 (19.5 years) Schneider (I 978b ) degrees VOG 12 (36.7 years) McPartland et al. (1979) flv H 23 (40.6 years, depressed) Coble et al. (1987) flv H 17 (14-15 years) Ehlers and Kupfer (1989) flv H 8 (21-30 years) Reynolds et al. (1990) flv H 15 (72.8 years) EM(s), eye movement(s); EOG, electrooculogram; AC, alternating current; H, horizontal; V, vertical; 0, oblique; VOG, vectrooculogram. (23), in a V-shaped pattern in depressive illness (25,35) that is partially normalized by the administration of antidepressant medication (22), in an inverted V pattern reflecting the severity of Alzheimer's disease (36), and in a flat pattern in narcoleptic patients (37). More detailed classifications of these patterns could be obtained based on EM frequency. Measuring the magnitude of single EMs Rotation, velocity, and power are new parameters representing the magnitude of individual EMs. Although EOG amplitude directly expresses the changes in polarity of the corneoretinal potential associated with shifts of eyeball position, that potential changes over time, especially during sleep. Thus, the relationship between the potential and the angle of rotation is imperfect. However, the deviation is small, and the linearity between the potential and the angle is maintained for movements less than 30 degrees (38,39). Aserinsky et al. (40) measured 12 students (with TC = 2.2 seconds) and reported that the mode of EM rotation was 4 degrees, with 90% of EMs less than 11.5 degrees. In the present study 90% of EM rotations were smaller than 13.5 degrees, supporting our simple conversion of EOG amplitude to angle of rotation based on each night's calibration. EM velocity expresses the angular velocity of eyeball rotation. Fukuda et al. (41,42) studied five healthy young males (horizontal EOG with TC = 0.3 second) and reported that 80% of EM velocity was distributed in the range greater than 60 degrees/second. These values are larger than ours, probably because they could not detect small EMs with the short TC used in their recordings. Ornitz et al. (20) measured EMs of year-old children and reported that most were between degrees/second. That finding suggests there may be age related differences in EM velocity. The variation in EM rotation and EM velocity showed much larger differences among individuals than within individuals. Mean EM rotation ranged from 4.57 to 8.60 degrees (mean = 6.30, SD = 1.22), and EM velocity ranged from to degrees/ second (mean = 58.55, SD = 10.51). However, 14 of 20 subjects had very similar values in repeated nights; differences between the second and third nights were only as high as 0.81 degree of EM rotation and 7.1 degrees/second of EM velocity. These 14 subjects also had very stable EM frequency, as seen in Fig. 2. Thus, it appears the frequency and magnitude of EMs have a tendency to maintain constant values within a given subject. These results suggest it is necessary to assess both intraindi vidual and interindi vidual variances when analyzing precise measures of EM frequency and magnitude. Measurements of REM bursts Aserinsky (9) reported the distribution of intervals of successive EMs to be bimodal, with one population of shorter intervals (less than 4-8 seconds) and a second of longer intervals. He suggested the first population represented the intervals within REM "bursts" and the second those of isolated EMs. In the present study, we did not find a bimodal distribution (Fig. 4). Our result does not differentiate the group of EMs conventionally called "bursts". On the other hand, on the basis of visual inspection we defined another condition that differentiated isolated EMs from those within bursts. EMs within bursts not only had shorter intervals but larger rotations and greater velocity. As discussed below, this discrepancy could be studied in detail to relate the physiological properties underlying the frequency of EMs and the intensity of REM sleep. Intensities of REM sleep Automated analyses of the amount of delta wave activity have for many years been used to reflect the "intensity of NREM sleep". The integrated amplitude of delta waves measured by period-amplitude analysis [advocated by Feinberg (4)] and the power density of the delta band measured by fast Fourier transform analysis [advocated by BorMy (43)] have mainly been used. It is well established that however measured, maximum delta wave activity occurs in the first

9 PRECISE MEASURES OF RAPID EYE MOVEMENTS 751 NREM sleep episodes, typically followed by a gradual decline across subsequent NREM sleep periods in a descent-to-right pattern. On the other hand, the "intensity of REM sleep" has usually been expressed by two parameters, the duration of REM sleep and the density of REM eye movements (REM density). As shown in Fig. 3, these conventional parameters usually increase progressively across the REM periods in an ascent-to-right pattern (21,23,33,36,37,44,45). This pattern implies that the intensity of REM sleep increases across the night and is highest in the early morning. In the present study we measured new parameters of REM sleep, the rotation and velocity of individual eye movements. In relation to REM sleep intensity, what do these new measures represent? Do they represent the intensity of REM sleep in ways comparable to conventional REM density? If so, these new measures should parallel conventional measures with an ascent-to-right increase across REM episodes. Alternatively, the magnitude of EMs might reflect mechanisms or processes different from conventional measures of REM sleep intensity. This seems to be the case, given the discrepancies between our new measures and conventional parameters. Our data show that the magnitude of EMs did not change during the night in the same manner as conventional REM intensity. Compared to EM frequency (REM density), which has an ascent-to-right pattern, the magnitude of EM had its peak values in the middle of the sleep period, producing an inverted-v pattern (Fig. 5). This suggests the magnitude of EM reflects physiological mechanisms that are not described by the parameters conventionally used to describe REM sleep intensity. Examination of REM bursts may help clarify the physiological meaning of the magnitude of EM in the concept of REM sleep intensity. Within a REM episode, EM frequency and magnitude always change in the same direction; during a REM burst both have high values, whereas EMs outside of bursts have low values. During REM bursts EOG activity may reflect particular patterns of brain neuron activity. For example, within REM bursts recorded from animals, pontogeniculo-occipital (PGO) frequency closely corresponds to EM frequency and is highly correlated with the intensity of neuronal activity. Comparisons of the relationships between precise EM measurements and findings such as PGO wave activity might strengthen comparisons of animal studies and human polysornnography (46,47). Combinations of these measures could be used as parameters for investigations of mental activity during REM sleep. It is known that REM intensity can reflect different mental disorders such as schizophrenia (48) and depressive illness (25,30,35,49). Automated measurement of the magnitude of single EMs in these mental disorders could provide much additional information in such studies of REM sleep in the future. New brain imaging technologies such as positron emission tomography, functional magnetic resonance imaging, and near-infrared spectroscopy will soon reveal images of neuronal activity during REM sleep (50,51). Precise EM measures could be used as concurrent measures of REM intensity. REFERENCES I. Aserinsky E, Kleitman N. Regularly occurring periods of eye motility during sleep. Science 1953;118: Aserinsky E, Kleitman N. Two types of ocular motility occurring in sleep. J Appl Physiol 1955;8: Rechtschaffen A, Kales A, eds. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Los Angeles: Brain Information ServicelBrain Research Institute, University of California, Feinberg I. Changes in sleep cycle patterns with age. J Psychiatr Res 1974; 10: Webb WB, Dreblow LM. The REM cycle, combining rules, and age. Sleep 1982;5: Merica H, Gaillard JM. A study of interrupted REM episodes. Physiol Behav 1991 ;50: Kobayashi T, Okuno H, Endo S, Tsuji Y. Computer analysis of rapid eye movements in REM sleep period. lyodenshi-to-seitaikogaku 1979; 17: Kobayashi T. Fluctuation of REM activity as analyzed by REM moment. Sleep 1980; Aserinsky E. Rapid eye movement density and pattern in the sleep of normal young adults. Psychophysiology 1971;8: Okuma T, Fukuma E, Hata N. "Dream detector" and automatization of REMP-awaking technique for the study of dreaming. Psychophysiology 1971;7: II. Minard JG, Krausman D. Rapid eye movement definition and count: an on-line detector. Electroencephalogr Clin NeurophysioI1971;31: McPartland RJ, Kupfer OJ, Foster G. Rapid eye movement analyzer. Electroencephalogr Clin Neurophysiol 1973;34: Ktonas PY, Smith JR. Automatic REM detection: modifications on an existing system and preliminary normative data. Int J Biomed Comput 1978;9: Schneider O. Spatio-temporal properties of rapid eye movements in human REM sleep: I. Qualitative analysis. Waking Sleeping 1978;2: Schneider D. Spatio-temporal properties of rapid eye movements in human REM sleep: II. Quantitative analysis. Waking Sleeping 1978;2: McPartland RJ, Kupfer OJ. Computerised measures of electrooculographic activity during sleep. Int J Biomed Comput 1978; 9: Martinerie J, Joseph Jp, Naillon M. Computerized detection of rapid eye movements during paradoxical sleep. Int J Biomed Comput 1980;11: Gath I, Bar-on E. Computerized method for scoring of polygraphic sleep recordings. Comput Methods Programs Biomed 1980;11 : Gopal IS, Haddad GG. Automatic detection of eye movements in REM sleep using the electrooculogram. Am J Physiol 1981; 241:R Ornitz EM, Forsythe AB, Pena A. The effect of vestibular and auditory stimulation on the rapid eye movements of REM sleep in normal children. Electroencephalogr Clin Neurophysiol 1973;34: Sleep, Vol. 20. No

10 752 K. TAKAHASHI AND Y. ATSUMI 21. Benoit 0, Parot S, Garma L. Evolution during the night of REM sleep in man. Electroencephalogr Clin Neurophysiol 1974;36: McPartland RJ, Kupfer OJ, Coble P, Shaw 0, Spiker OG. An automated analysis of REM sleep in primary depression. Bioi Psychiatry 1979; 14: Coble PA, Reynolds CF, Kupfer DJ, Houck P. Electroencephalographic sleep of healthy children. Part II: Findings using automated delta and REM sleep measurement methods. Sleep 1987; 1 0: Ehlers CL, Kupfer OJ. Effects of age on delta and REM sleep parameters. Electroencephalogr Clin Neurophysiol 1989;72: Reynolds CF III, Buysse OJ, Kupfer OJ, et al. Rapid eye movement sleep deprivation as a probe in elderly subjects. Arch Gen Psychiatry 1990;47: Kupfer OJ, Ulrich RF, Coble PA, et al. Application of automated REM and slow wave sleep analysis I: normal and depressed subjects. Psychiatry Res 1984; 13: Feinberg I. EEG sleep patterns as a function of normal and pathological aging in man. J Psychiatr Res 1967;5: Feinberg I. EEG sleep pattern in mental retardation. Electroencephalogr Clin Neurophysiol 1969;27: Becker PT, Thoman EB. Rapid eye movement storms in infants: rate of occurrence at 6 months predicts mental development at 1 year. Science 1981;2 I 2: Foster FG, Kupfer OJ, Coble P, McPartland RJ. Rapid eye movement sleep density. Arch Gen Psychiatry 1976;33: Lauer CJ, Riemann 0, Wiegand M, Berger M. From early to late adulthood changes in EEG sleep of depressed patients and healthy volunteers. Bioi Psychiatry 1991 ;29: Benson KL, Zarcone VP. Rapid eye movement sleep eye movements in schizophrenia and depression. Arch Gen Psychiatry 1993;50: Reynolds CF, Kupfer OJ, Taska LS, Hoch CC, Sewitch DE, Spiker OG. Sleep of healthy seniors: a revisit. Sleep 1985;8: Velazquez-Moctezuma J, Shiromani PJ, Gillin Je. Acetylcholine and acetylcholine receptor SUbtypes in REM: sleep generation. Prog Brain Res 1990;84: Kupfer OJ. Application of EEG sleep for the differential diagnosis and treatment of affective disorders. Pharmakopsychiatr Neuropsychopharmakol 1978; II: Vitiello MV, Bokan JA, Kukull WA, Muniz RL, Smallwood RG, Prinz PN. Rapid eye movement sleep measures of Alzheimer's- type dementia patients and optimally healthy aged individuals. Bioi Psychiatry 1984;19: Geisler P, Meier-Ewert K, Matsubashi K. Rapid eye movement, muscle twitches and sawtooth waves in the sleep of narcoleptic patients and controls. Electroencephalogr Clin Neurophysiol 1987 ;67: R. Boukadoum AM. Ktonas PY. EOG-based recording and automated detection of sleep rapid eye movement: a critical review, and some recommendations. Psychophysiology 1986;23 : Schackel B. A pilot study in electro-oculography. Br J Ophthalmol 1960;44: Aserinsky E, Lynch JA, Mack ME, Tzankoff Sp, Hurn E. Comparison of eye motion in wakefulness and REM sleep. Psychophysiology 1985;22: Fukuda T, Wakakura M, Ishikawa S. Comparative study of eye movements in the alert state and rapid eye movcment sleep. Neurol Ophthalmology 1980; I : Fukuda T. A study of eye movement during sleep: analysis of rapid eye movements in normal subjects. Nippon Ganka Gakkai Zasshi 1981 ;85: Borbey AA. A two process model of sleep regulation. Hum Neurobiol 1982; I: Aserinsky E. The maximal capacity for sleep: rapid eye movement density as an index of sleep satiety. Bioi Psychiatry 1969; 1: Zimmermann JC, Czeisler CA, Weitzman ED. REM density is dissociated from REM sleep timing during free-running sleep episodes. Sleep 1980;2: McCarley RW, Winkelman JW, Duffy FH. Human cerebral potentials associated with REM sleep rapid eye movements: links to PGO waves and waking potentials. Brain Res 1983;274: Atsumi Y, Tanaka K, Shiraishi H, Kojima T, Takahashi R. Human REMs related potentials in REM sleep and lambda complex. Clin Electroencephalogr 1987;29: Reich L, Weiss BL, Coble P, McPartland R, Kupfer OJ. Sleep disturbance in schizophrenia. Arch Gen Psychiatry 1985;32: Hudson n, Pope HG, Jonas JM, et aj. Sleep EEG in bulimia. Bioi Psychiatry 1987;22: Hoshi Y, Mizukami S, Tamura M. Dynamic features of hemodynamic and metabolic changes in the human brain during allnight sleep as revealed by near-infrared spectroscopy. Brain Res 1994;652: Maquet P, Peters J, Aerts J, et aj. Functional neuroanatomy of human rapid-eye movement sleep and dreaming. Nature 1996; 383:163-6.