Methodology: Scoring and Computerized Methods

Transcription

1 Sleep, 17(1): American Sleep Disorders Association and Sleep Research Society Methodology: Scoring and Computerized Methods Scoring Transitions to REM Sleep in Rats Based on the EEG Phenomena of Pre-REM Sleep: An Improved Analysis of Sleep Structure Joel H. Benington, Susheel K. Kodali and H. Craig Heller Department of Biological Sciences, Stanford University, Stanford, California, US.A, Summary: Algorithms for scoring sleep/waking states and transitions to REM sleep (NRTs) in rats are presented and validated. Both algorithms are based on electroencephalographic (EEG) power in delta (.5-4. Hz), theta (6-9 Hz) and sigma (1-14 Hz) frequency bands, and electromyogram (EMG) intensity. Waking is scored when EMG intensity is high or (sigma powerhtheta power) is low. Nonrapid eye movement (NREM) sleep is scored in nonwaking epochs having high (delta power)/(theta power). Rapid eye movement (REM) sleep is scored in nonwaking epochs having low (delta power)/(theta power). NRTs are identified by the EEG phenomena of the pre-rem sleep phase of NREM sleep. Algorithms are validated by comparison with records scored independently by two investigators based on visual examination ofeegs and EMGs. The sleep/waking-state scoring algorithm produces greater than 9% agreement with visual scoring. The NRT-scoring algorithm produces 88-92% agreement with visual scoring. Scoring NRTs based on the phenomena of the pre-rem sleep phase of NREM sleep, instead of relying solely on REM sleep expression for identification of REM sleep onset, reveals a significant population of brief REM sleep episodes that are ignored by most sleep cycle analyses and allows independent quantification of REM sleep timing and maintenance. Key words: Automated sleep scoring-fourier analysis-rem sleep maintenance-rem sleep timing-sleep cycle. Precise characterization of rapid eye movement (REM) sleep timing is essential in physiological studies of REM sleep regulation. REM sleep timing has been assumed to be controlled by an oscillator simply because REM sleep episodes occur at fairly regular intervals (1,2). However, the actual temporal distribution of epochs scored as REM sleep is more complex, necessitating the imposition of criteria for delimiting sleep cycles. For example, in some studies in humans, a segment of REM sleep is not scored as an REM sleep episode (for purposes of sleep cycle identification) unless there is a minimum duration of continuous REM sleep (3,4). Two segments of REM sleep are not recognized as distinct REM sleep episodes unless they are separated by a minimum duration of continuous nonrapid eye movement (NREM) sleep (5). Skipped first REM sleep episodes are recognized in some studies (1), but not others (6). Similar criteria are imposed in sleep cycle analyses in nonhuman mammals (7-9). Such cri- Accepted for publication September Address correspondence and reprint requests to Joel Benington, Neurobiology Research (lsia3), VA Medical Center, Plummer Street, Sepulveda, CA 91343, U.S.A. 28 teria may be valid, but they are based on the theoretical preconception that REM sleep timing is cyclical, thus they necessarily reinforce that preconception. The sleep cycle in the rat is reported to be ca. 1 minutes long (1,11). Most studies of sleep structure in rats score sleep/waking states in 3-6-second epochs, so that REM sleep is not scored unless it lasts at least 15-3 seconds. When states are scored in 4-second epochs, many sleep cycles are less than 2 minutes long and the mean sleep cycle length is considerably shorter than 1 minutes (12). Using 4-second epochs, a sleep cycle length distribution with a mode at ca. 1 minutes is produced only if criteria are imposed such that the REM sleep phase of a sleep cycle is identified only when at least eight consecutive epochs (32 seconds) are scored as REM sleep (12). Rapid eye movement sleep episodes in the rat are often aborted shortly after REM sleep onset. In such cases, the classic electrographic markers of REM sleep (hippocampal theta activity in the surface electroencephalogram (EEG), electromyogram (EMG) atonia and increased ponto-geniculo-occipital- (PGO-) wave activity in cerebellar or pontine depth EEG) are present for a brief interval (5-2 seconds), followed by a tran-

2 SCORING TRANSITIONS TO REM SLEEP 29 sient arousal and, most often, a return to NREM sleep. Both brief and sustained REM sleep episodes are preceded by a period of transitional NREM sleep that has been identified in both cats and rats and is called pre REM sleep or intermediate stage (13-16). The pre REM sleep phase of NREM sleep lasts ca. 3 seconds in the rat and is characterized by decreased EEG slowwave activity, increased EEG activity in the theta (6-9 Hz) and sigma (1-14 Hz) frequency bands, progressive decrease in EMG tonus and the occurrence of single, large, PGO spikes (14,17). Brief REM sleep episodes are readily identifiable as true REM sleep episodes because, as in the case of sustained REM sleep episodes, the characteristic signs of the transition from NREM to REM,sleep culminate in a state displaying the classical electrographic markers of REM sleep. Brief REM sleep episodes are not, however, counted for purposes of scoring sleep cycles either when sleep is scored in 3-6-second epochs or when sleep is scored in 4-second epochs and a 32- second criterion is used, as described above. In other words, the analyses according to which the rat is said to have a 1 O-minute sleep cycle ignore these brief REM sleep episodes. Ifan appreciable number of brief REM sleep episodes occur, these analyses do not accurately reflect REM sleep timing. A more exact quantification of REM sleep timing can be achieved by using the electrographic indices of pre-rem sleep to identify transitions from NREM to REM sleep (NRTs). These indices distinguish NRTs from NREM sleep/waking transitions (16, 17). Using NRTs as markers of REM sleep onset, one can identify the occurrence of an REM sleep episode independently of its duration. This allows REM sleep timing to be quantified independently of REM sleep maintenance. In this paper, we present and validate algorithms for scoring both sleep/waking states and NRTs in rats offline, using Fourier-analyzed EEG recordings and integrated EMG activity. These algorithms have been refined through application to recordings from over 1 rats in a wide range of experimental protocols. MATERIALS AND METHODS Animals and surgical procedures Adult male Wistar rats (25-31 g) were anesthetized (ketamine 8 mg/kg, xylazine 8 mg/kg and acepromazine maleate 1.6 mg/kg administered intraperitoneally) at least 3 minutes before surgery. EEG electrodes were implanted bilaterally over frontal cortex (AP + 1. mm, ML ±2.5 mm from bregma) and parietal cortex (AP -S.O mm, ML ±3.S mm from bregma). These coordinates were selected to maximize recording of hippocampal theta activity and sleep spindling (18). EMG electrodes were threaded through nuchal muscles. PGO waves were recorded in some animals using a 125-J,Lm, teflon-coated, stainless-steel, twisted-bipolar depth electrode implanted in the anterior lobe of the cerebellum (AP -1.3, ML 1., DV +4.7 from ear-bar zero). This placement in rats has been demonstrated to allow recording of spiky waves that have many of the same characteristics as PGO waves recorded from the lateral geniculate nucleus in cats (19). Wound areas were perfused with lidocaine repeatedly during surgery. Gentamicin (.25 ml) was administered intramuscularly after surgery, nitrofurazone was applied to wound areas, and animals were monitored until recovery from anesthesia. Recording procedures and EEG analysis Animals were allowed at least 7 days to recover from surgery and were then cabled and acclimated to the recording environment for at least 72 hours. Animals were housed individually in transparent Plexiglas cages within grounded Faraday cages. One EEG electrode was used to ground the animal. This setup minimizes movement artifact in EEG recordings. The recording room was dimly lit, sound-attenuated and maintained at C. Lights were on 12 hours per day, between 8 and 2 hours. Animals were given food and water ad libitum and were monitored for the entire recording period for complications from the implant or behavioral abnormalities. Animals were cabled via a commutator to a Grass 7 polygraph. Unihemispheric, frontal-parietal EEG data were filtered at.3 Hz and 35 Hz (1/2 max, 6 db/octave), digitized at 1 Hz and stored in 1 O-second epochs on a personal computer. EMG data were full-wave rectified, integrated and stored as one value (-1) per epoch. Data were collected 24 hours per day except for 1 hour every 4-8 days for backing up data from the computer's hard disk. Electroencephalograms were Fourier-analyzed in 1- second epochs using a Fast Hartley Transform [adapted to Turbo C with corrections by Lorenz Trachsel from Bracewell's Basic program (2)]. Power spectra were averaged in three frequency bands: delta (.5-4. Hz), theta (6-9 Hz) and sigma (1-14 Hz). The delta and theta bands encompass the frequency ranges that show the greatest increase in power in NREM and REM sleep, respectively. The sigma band encompasses the frequency range of sleep-spindle activity. Sleep spindles in the rat are most prevalent during light NREM sleep, as in cats and humans. They differ from those seen in cats and humans only in that high-amplitude spindle-frequency oscillations often persist for more than 2 seconds at a time. Sleep. Vol. 17. No

3 3 J. H. BENINGTON ET AL. NS-RS Transition (NRT) EEG L ST ATE NS R W---I---NS---- NS-Waking Transition EMG ",.ItII:.11 11,",j,',[, 111,,,1.",,', '.: 1,1', 11",il 1,1.. 11"111 11'1," II "II!III.. I :, ,: ":111:11 n',1 1I':'1'~~f_illt II ""lllill,it.it"ii' I. ", PGO ~""''WI<wo... " ~.'~'"."14>,~'.. " "~',~., *-'''";::L- L 5 Sec STATE NS W~I ---NS---- FIG. 1. Examples of an NRT and an arousal in one animal. Traces represent frontal-parietal EEG, nuchal EMG and PGO waves recorded from a bipolar, cerebellar depth electrode. Top panel shows an NRT. Bottom panel shows an arousal. W = waking; NS = NREM sleep; RS = REM sleep. Algorithm validation Sleep/waking-state and NR T -scoring algorithms were validated by epoch-by-epoch comparison with sleep/ waking states and NRTs scored visually from polygraph records. Two investigators independently scored recordings from three rats (a total of 14 and 11.5 hours, respectively). Recordings comprised frontal-parietal EEG, nuchal EMG and, in one animal, a PGO-wave depth electrode. Polygraph paper was run out at 5 mm/ second and synchronized to the computer by time marks. Each 1 O-second epoch was identified as waking, NREM sleep or REM sleep according to standard criteria. NRTs were scored when NREM sleep with highamplitude slow waves was succeeded by frequent highamplitude sleep spindles with an increasing theta-wave component over a period of 3-6 seconds, and EMG tonus remained low or decreased (see Fig. 1). This sequence generally culminates in a desynchronized EEG with a visually prominent theta rhythm. In animals instrumented for recording PGO waves, one or two large PGO waves generally anticipate the desynchronized EEG by 15-2 seconds and background activity in the PGO electrode increases as high-amplitude slow waves and sleep spindles disappear from the EEG. By contrast, in NREM sleep/waking transitions, there are no progressive changes in the EEG preceding an increase in EMG tonus, no theta rhythm in the initial period of de synchronized EEG and no anticipatory PGO waves. NRTs were scored only when the above suite of phenomena allowed unambiguous identification in contrast to an NREM sleep/waking transition. NRTs were assigned either to the first epoch scored as REM sleep or waking at the culmination of the tran- Sleep. Vol. 17, No

4 SCORING TRANSITIONS TO REM SLEEP 31. o Delta / Theta o Delta / Theta FIG. 2. Scatter plots for determining sleep/waking-state scoring thresholds. Each point represents one \ O-second epoch from a \ 2- hour record in one animal. Dashed lines represent thresholds for each variable. Figure 2A plots integrated EMG activity versus DP/ TP. Sleep/waking states scored for epochs in each quadrant are indicated in the upper-right comer of the quadrant. Figure 2B plots SP TP versus DP/TP. Epochs scored as waking based on the EMG threshold in Fig. 2A are plotted as open circles. All other epochs are plotted as filled circles. Epochs having SP. TP values below threshold are scored as waking. All other epochs are scored as determined by thresholds in Fig. 2A. The data in this and succeeding figures are taken from the same representative animal. The EMG axis plots arbitrary units. The units on the SP TP axis are,",v'/hz. NS = NREM sleep; RS = REM sleep. sitional period, or to the last epoch of mixed sleep spindles and theta activity, before the EEG became transiently desynchronized. Thresholds for scoring sleep/waking states and NRTs algorithmically were set by one of the investigators, based on an examination of a series of recording days from each animal, adjacent to but not including the time period that was scored visually. Sleep/waking states from both investigators and the algorithm were tabulated. NRTs were identified as equivalent when scored within 3 seconds of the same time by two scorers. For calculating percentage agreement between scorers by sleep/waking state, the total number of epochs of each state was calculated as the average number of epochs scored as that state by the two scorers. Source code (in Turbo C, v2.) for all software used in this application is available upon request. RESULTS Sleep/waking states are scored algorithmically by generating scatter plots for each animal as shown in Fig. 2. Each epoch is scored as waking, NREM sleep or REM sleep according to the values for that epoch of delta power/theta power (DP/TP), EMG, and sigma power' theta power (SP' TP), relative to animal-specific thresholds. Recordings are plotted first for EMG versus DP/TP (Fig. 2A) and then for SP TP versus DP/TP (Fig. 2B). Populations of epochs representing waking, NREM sleep and REM sleep can be distinguished in both scatter plots. In Fig. 2A, epochs having high EMG values represent waking. (These epochs express a range of DP/TP values because both DP and TP are low in waking.) Epochs having low EMG values and high DP/TP values represent NREM sleep. Epochs having low EMG values and low DP/TP values represent REM sleep. Waking is initially discriminated from sleep by an EMG threshold. Among non waking epochs, NREM and REM sleep are discriminated by a DP/TP threshold. The NREM/REM sleep threshold is placed in the region of minimal point density between high-dp/tp and low-dp/tp populations. In the low-emg population (excluding REM sleep), high DP/TP is most frequently associated with low EMG, whereas there is no relationship between DP/TP and EMG in the high-emg population. The threshold for scoring waking versus sleep is placed at the inferior margin of this high-emg population in which there is no observed relation between DP/TP and EMG, which generally coincides with the region of minimal point density between high EMG and low-emg populations. An EMG threshold that is low enough to discriminate all waking epochs will also score as waking some NREM sleep epochs early in sleep periods, when EMG is intermediate. We have solved that problem by placing the EMG threshold high enough to correctly score sleep and then overdetermining waking using the SP TP threshold (see Fig. 2B). The SP TP threshold discriminates waking from sleep because both TP and SP are low during quiet waking, whereas TP is high in REM sleep and SP is high in NREM sleep. The threshold for scoring waking versus sleep in Fig. 2B is placed at the superior margin of the region of high point density in the low SP TP population. Epochs having SP TP values below the threshold shown in Fig. 2B are scored as waking. Epochs having SP TP values above the threshold are scored as waking, NREM sleep or REM sleep, based on EMG and DP/TP thresholds. Consequently, waking is scored for epochs having either high EMG (active waking) or slow Sp TP (quiet waking). Use of the SP TP threshold to overdetermine waking enables epochs with low EMG activity but a de synchronized EEG to be scored as waking. These epochs are distinct from REM sleep epochs in that TP is at a very low amplitude during quiet waking, hence SP TP is low. Sleep. Vol. 17. No

5 32 J. H. BENINGTON ET AL. Assignment of sleep/waking state scoring thresholds is verified by comparing sleep/waking states as scored by a given set of thresholds with a time-course display ofdp, TP and SP, as shown in the top three panels in Fig. 3. Threshold placement is judged optimal when the following conditions are met: 1) WakingiNREM sleep transitions coincide with simultaneously increased DP, TP and SP. Increased SP is the surest indicator of NREM sleep onset, as it is not affected by movement artifact (like DP) or increased during motor activity (like TP). Ifwaking is scored for some time after DP, TP and SP have increased, the EMG threshold should be increased. 2) Brief interruptions ofnrem sleep, scored as waking or REM sleep, coincide with steep decreases in DP. If waking is scored at other times in NREM sleep, the EMG threshold should be increased. 3) NREM/REM sleep transitions coincide with steep decreases in DP and increased TP. If these EEG indices occur without REM sleep being scored, the DP/TP threshold should be increased. 4) REM sleep/waking transitions coincide with decreased TP. 5) Extended periods oflow DP, TP and SP are scored as consolidated waking. DP is higher during waking in animals that have more movement artifact, but TP and SP remain low. Brief sleep periods are scored when DP, TP and SP all increase transiently. If NREM sleep is scored at other times during waking, the EMG threshold should be decreased and/or the SP TP threshold should be increased. 6) REM sleep episodes are consolidated with few isolated epochs scored as waking in the middle. Occasional isolated waking epochs are unavoidable in animals that have intense phasic motor activity in REM sleep. Excessive scoring of waking within REM sleep indicates that the SP TP threshold should be decreased. Isolated epochs scored as NREM sleep within REM sleep episodes should occur only when DP and SP transiently increase within REM sleep. Any other occurrence of isolated NREM sleep epochs indicates that the DP/TP threshold should be increased. We arrived at these criteria for determining sleep/waking state scores visually based on the DP-TP-SP display through extensive comparison of DP-TP-SP displayed data with polygraph records. When algorithmic sleep/ waking state scores do not meet these criteria, EMG, DP/TP and/or SP TP thresholds are adjusted and reverified versus the DP-TP-SP display. Transitions from NREM to REM sleep are scored using the EEG phenomena of the pre-rem sleep period ofnrem sleep. Figure 3 demonstrates that peaks in TP and SP precede both sustained and brief REM Sleep. Vol. 17. No C ' C 2 (j) Ci3 8 4 ~ a... 2 CJ w W Detta o RJ ~j"~lihfulli"il::: ~ R 9: 9:15 9:3 9:45 1: Recording Time FIG. 3. Time-course display of DP, TP, SP, NIV, sleep/waking states and NRTs. The top three panels represent values for DP, TP and SP (/tv2/hz) in IO-second epochs. The fourth panel represents the NIV assigned to each candidate NRT region according to the method shown in Fig. 4. The horizontal dashed line represents threshold for scoring NRTs based on NIV. The bottom panel is a sleep/waking-state histogram in IO-second epochs as scored algorithmically. The top line represents waking, the middle NREM sleep and the bottom REM sleep. NRTs are shown as vertical dashed lines. sleep episodes. By contrast, most instances in which NREM sleep is interrupted by waking are not attended by increased TP and SP. NRTs are scored by dividing an EEG recording into segments and calculating for each segment an NRT indicator value (NIV) that is maximal for segments that comprise the pre-rem sleep phase ofnrem sleep (see Fig. 4). As the termination of an NREM sleep episode necessarily involves a decrease in DP, all segments of the EEG recording during which DP is decreasing are identified as candidate NRT regions. Segments of decreasing DP are identified by calculating a-dp = (DP at time t - 4 seconds)/(dp at time t). a-dp is greater than 1 when DP is decreasing. A 4- second interval is used for calculating o-dp because that time span best discriminates the large, progressive decreases in DP that occur in association with the transition to REM sleep. For each candidate NRT, the NIV is calculated as max(tp) max(sp) max(ya-dp). Max(TP), max(sp), and max(a-dp) represent the highest TP, SP and o-dp values that occur within the seg-

6 SCORING TRANSITIONS TO REM SLEEP 33 C 'iii c (j)... ~.. (') W W ~~--~~~~--~~~--~W N ~ _-----,------_,------~R 12:45 12:5 12:55 13: 13:5 Recording Time Delta Theta Sigma FIG. 4. Illustration of the NRT-scoring method. The top three panels represent DP, TP and SP in lo-second epochs. The bottom panel is a sleep/waking-state histogram in which the top line represents waking, the middle NREM sleep and the bottom REM sleep. Vertical dashed lines connected by bars delimit candidate NRT regions (intervals in which a-dp is greater than 1). Numbers below bars represent maximal a-dp values for each candidate NRT region. Arrows identify maximal TP and SP values for each candidate NRT region. NRT indicator value (NIV) is calculated as max(tp) max(sp) max(ya-dp) (see text). ment of decreasing DP. The above formula was selected to combine in one variable the EEG phenomena of the pre-rem sleep phase ofnrem sleep. The NIV is maximal when DP decreases substantially over a 4- second interval simultaneously with peaks in TP and SP. Most candidate NRT regions have low NIVs-as during waking-when TP and SP are low and DP fluctuates in a narrow range. Segments of decreasing DP associated with arousals have higher NIV s because the decrease in DP is greater, but o-dp in these cases is multiplied by relatively low values for TP and SP. NIVs are greatest for actual NRTs, when all three variables are high. In calculating NIVs, the square root of o-dp is used to de-emphasize o-dp relative to TP and SP. When simply o-dp is used, the algorithm may erroneously score arousals as NRTs on the strength of large decreases in DP, even though TP and SP are low. Deemphasizing o-dp relative to TP and SP, on the other hand, increases the tendency of the algorithm to score NR Ts in association with relatively small fluctuations in DP within REM sleep episodes. This, however, is easily corrected for as described below. Actual NR Ts are identified from the large pool of candidate NRT regions by determining a threshold level for the NIV, so that a candidate NRT region having an NIV above that level is scored as an actual NRT. That threshold is determined as follows. A histogram is generated by collecting candidate NR T regions in bins according to NIV s. The percentage of 1 Q) ~ 8 C (j) 2 6 (j).. ill C/) 4 c Cf) a: 2 o NIV (% of Threshold) FIG. 5. Percentage of candidate NRT regions in which REM sleep onset occurs, as a function ofniv. Each circle represents REM sleep onset percentage for one NIV bin. Solid circles show values for one animal; open circles show values for 11 additional animals. Bins are sized so that each bin represents greater than 1 % of the data set. NIVs for each animal are expressed as percent of the NRT-scoring threshold for that animal. REM sleep onset is scored when an epoch scored as REM sleep is preceded by at least 3 seconds of continuous NREM sleep. An average of 177 hours of EEG were recorded per animal. candidate NRT regions associated with REM sleep onset is calculated for each bin. REM sleep onset is defined as the occurrence in a candidate NRT region of an REM sleep epoch preceded by at least 3 seconds of NREM sleep. Figure 5 displays one such histogram (solid circles) superimposed on the combined histograms for 12 animals. The histogram of REM sleep onset percentage versus NIV describes a sigmoid curve, high NIV s being associated with high REM sleep onset percentage. The clear and consistent relationship between NIV and the occurrence of REM sleep onset confirms that the NIV formula discriminates NRTs from other EEG events. The threshold for NRT determination is set at the NIV level at which the slope of the REM sleep onset percentage versus NIV curve is greatest. This criterion has been determined by experience to score NRTs most faithfully and is further justified in that the point of greatest slope represents a boundary between two fairly homogeneous populations (high vs. low REM sleep onset percentage). Assignment of the NRT threshold is verified by comparing NRTs as scored algorithmically with the DP-TP-SP display (Fig. 3). The NR T threshold is judged optimal when NRTs are scored for brief or sustained REM sleep episodes but not for arousals. Two additional criteria are used to eliminate falsepositive NRTs. Candidate NRT regions in which EMG activity is high in one of the last two epochs before o-dp becomes < 1. are eliminated from the pool. This screens out instances in which the NIV is high during waking because of movement artifact. NR Ts preceded by less than 3 seconds of NREM sleep since the last NRT are rejected, because in all cases they result from Sleep, Vol. 17, No.1, 1994

7 34 J. H. BENINGTON ET AL. TABLE 1. Correspondence between sleep/waking-state scoring by algorithm and each human scorer Scorer I a Scorer 2 W NS RS Total % W NS RS Total % Algorithm Waking 1, , , , NS 141 2, , , , RS Total 1,396 2, ,189 2, % Scorer 2 Waking 1, , NS 72 2, , RS Total 1,21 2, % a W, waking; NS, NREM sleep; RS, REM sleep. very brief intrusions of high-amplitude EEG activity within an REM sleep episode. Because other factors (e.g. persistence oflow EMG with phasic twitches) indicate that REM sleep is continuous over this interval, it is not meaningful to identify these events as NRTs. The above-described procedures for establishing sleep/waking-state and NRT-scoring thresholds off-line are applied to the entire set of data from each animal. In the case of both sleep/waking states and NR Ts, threshold verification seldom causes thresholds to be adjusted by more than 15% of the originally determined value. The same thresholds are used for all recordings from a given animal unless identifiable changes in the recording equipment require that different thresholds be used for the periods before and after the changes. The combination of algorithmic scoring with invariant animal-specific thresholds allows both flexibility in adapting the scoring methods to each animal and objectivity in comparing different conditions in one animal. These procedures have been refined through repeated verification of the accuracy of the algorithms versus visual examination of EEG recordings. In addition to this extensive verification process, a formal validation of the algorithms was performed on recordings from three animals, scored independently by two investigators. The agreement matrices between visual and algorithmic scoring and between visual scorers are shown in Table 1. Overall agreement between visual and algorithmic scoring of sleep/waking states is 91.6% for scorer 1 and 91.% for scorer 2. Interscorer agreement is 95.2%. Visual/algorithmic agreement is high in all three animals (range % for scorer 1, % for scorer 2), and for all sleep/waking states [minimum is 86.1% for REM sleep (scorer 1), maximum is 93.8% for NREM sleep (scorer 1)]. As shown in Table 1, disagreements between visual and algorithmic scoring are fairly symmetrical. The algorithm scores only slightly less waking and slightly more REM sleep than either scorer. Visual/algorithmic agreement in NRT scoring ranges from 87.8% to 91.5%. The percentages of NRTs scored visually that are also scored by the algorithm are 9.5% and 91.5% for scorers 1 and 2, respectively. The percentages ofnrts scored by the algorithm that are also scored visually are 87.8% and 88.2% for scorers I and 2, respectively. DISCUSSION We have presented and validated algorithms for scoring sleep/waking states and NREM/REM sleep transitions in the rat. The sleep/waking-state scoring algorithm is similar to others in the literature (e.g. 21). DP/TP, for example, has been used in other algorithms to distinguish REM sleep from NREM sleep (for review, see 16). Our algorithm is unique in using Sp TP as a measure of the amplitude of higher frequency EEG waveforms to distinguish waking from both NREM and REM sleep. The level of agreement between algorithm and visual scoring is comparable to other algorithms that have been validated inn rats (21-23). Of importance for analysis of sleep structure is the fact that this algorithm scores all states, including REM sleep, with a high degree of accuracy. One advantage of this algorithm is that it is based on three simple variables that measure universally recognized electrographic characteristics of sleep/waking states. EMG activity distinguishes waking from sleep because motor activity is high in waking and low in both NREM and REM sleep. SP TP distinguishes waking from sleep because, in the rat, there are no prominent, high-amplitude EEG waveforms in waking, whereas in NREM sleep the EEG is of higher amp1itude generally and in REM sleep the hippocampal Sleep, Vol. 17, No.1, 1994

8 SCORING TRANSITIONS TO REM SLEEP 35 theta rhythm predominates. DP/TP distinguishes NREM from REM sleep because high-amplitude slow waves predominate in NREM sleep and the theta rhythm predominates in REM sleep. Scoring NR Ts based on the EEG phenomena of the pre-rem sleep phase of NREM sleep allows more reliable quantification of REM sleep regulation. The pre REM sleep phase of NREM sleep typically lasts 3-6 seconds and can be readily identified both visually and algorithmically by the conspicuous EEG phenomena that characterize it. These EEG phenomena are present in every transition from a consolidated NREM sleep episode to REM sleep and not present when REM sleep is momentarily interrupted by high-amplitude EEG activity. Therefore, NRTs can be used as markers of REM sleep onset. The sleep cycle can be defined as the interval between two NRTs. REM sleep episode length in each cycle can be calculated as total REM sleep duration within each such interval, and NREM sleep episode length can be calculated as total NREM sleep duration. This technique differs from the use of ad hoc criteria in that REM sleep timing is scored based on an event, the NRT, that can be identified unambiguously by visual scoring. The EEG indices by which the NRT is scored are concomitants of neurophysiological processes that appear to be necessary for initiation of REM sleep. Consequently, NRTs always precede REM sleep episodes. The parameters by which NRTs are scored algorithmically produce a high rate of agreement with visual scoring (ca. 9%). We have argued that the presence of the EEG phenomena of the NRT in all transitions from consolidated NREM sleep episodes to REM sleep, but not in association with transient high-amplitude EEG activity within REM sleep, makes it an effective tool for distinguishing separate REM sleep episodes from fragmentation of one REM sleep episode. However, transient high-amplitude EEG activity within REM sleep is scored as NREM sleep by most algorithms and many visual scoring conventions (including ours). One could argue that such an event is a bona fide NREM sleep episode to the same extent as a consolidated NREM sleep episode that culminates in an NRT and, consequently, that the periods of REM sleep before and after it are two distinct REM sleep episodes. This position cannot be definitively refuted, but in our opinion it obscures the fact that NREM and REM sleep tend to occur in consolidated episodes. Our behavioral observations indicate that when REM sleep is disrupted to the extent that the animal arouses briefly (raising its head and opening its eyes), an NRT always precedes the resumption of REM sleep. The absence of an arousal and the persistence of other component phenomena of REM sleep (notably EMG atonia and phasic twitches) during transient EEG synchronization within REM sleep suggest that such events represent fluctuations in neuronal activity within one REM sleep episode. We further argue that an emphasis on alternation between consolidated episodes of NREM and REM sleep is justified on the basis of the physiology of the two states. Consolidated NREM sleep episodes are characterized by an exponentially saturating increase in EEG slow-wave activity that begins at NREM sleep onset and, in rats, has a time constant of ca. SO seconds (24). The progressive increase in EEG slow-wave activity during NREM sleep probably reflects progressively increased membrane hyperpolarization of neurons in the cerebral cortex and thalamus (25). This hyperpolarization must be reversed in the transition to REM sleep, as neurons in these regions are tonically depolarized in REM sleep (26). The EEG phenomena that characterize the pre-rem sleep phase of NREM sleep presumably reflect this process, and are paralleled by a progressive increase in neuronal activity in many brain regions (27-3). Thus, NRTs represent the boundary between two brain states with very different neuronal properties. Some hyperpolarization of neuronal membrane potential presumably occurs in association with transient high-amplitude EEG activity within REM sleep, but two points suggest that this hyperpolarization is marginal. First, EEG slow-wave activity is low relative to the level reached in deep NREM sleep. Second, sleep spindles rather than slow waves dominate the EEG, and sleep-spindle activity has been shown to represent an intermediate level of neuronal membrane hyperpolarization (31). Thus, the absence of an NRT in the return to consolidated REM sleep after such events probably reflects the absence of a major reversal of membrane hyperpolarization. We feel that the fundamental question in the study of sleep structure is what determines when NREM sleep episodes are to be interrupted by a transition to REM sleep. Treating transient fluctuations between EEG desynchronization and EEG synchronization within a predominantly REM sleep interval as bona fide NREM sleep episodes in a sense trivializes this question. To summarize, we have presented algorithms for scoring both sleep/waking states and NR Ts in rats. The NR T -scoring algorithm is of particular interest in that it permits analysis of sleep structure based on an event, the transition from NREM to REM sleep, rather than on somewhat arbitrary criteria. Because this analysis is independent of the duration of REM sleep following each NRT, it allows REM sleep timing to be analyzed independently of REM sleep maintenance. Acknowledgements: We thank Lorenz Trachsel for the use of his adaptation of Bracewell's FHT algorithm and for ongoing assistance during the development of the algorithms. This research was supported by The Upjohn Company. Sleep, Vol. 17, No.1, 1994

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