Resting after learning facilitates memory consolidation and reverses spatial reorientation impairments in female 3xTg-AD mice

Animals

Across two experiments, we assessed the effects of 60-min (Experiment 1) and 50-min (Experiment 2) rest sessions on spatial reorientation ability. In the first experiment, sleep was manipulated under conditions nearly identical to those in which we previously observed cognitive deficits in this spatial learning and memory task with a small brain stimulation implant. ¹⁵ In the second experiment, we directly assessed sleep with a larger recording-array implant. Overall, for both experiments, 6-month female 3xTg-AD (n = 19) and age-matched non-Tg control mice from the same background strain (129X1SvJ/C57Bl6 hybrid; n = 11) were group-housed (2–4/cage) in 12:12 h light/dark cycles until the beginning of the experiment. We used only female mice because we found impaired cognition in 6-month 3xTg-AD females, but not males.^15,46 These mice underwent behavioral testing during the 12 h light phase of the light/dark cycle in a dimly lit room. For Experiment 1, mice (3xTg-AD, n = 14; non-Tg, n = 6) did not have a recording-array implant to ensure that the effects we observed in Experiment 2 were not a consequence of the recording-array. The 3xTg-AD mice in Experiment 1 were subdivided into no-sleep (n = 7) and sleep (n = 7) groups. For Experiment 2, mice (n = 10) had a recording-array implant and for these mice some data from a different later training phase was published previously. ⁵¹ The recording array allowed us to measure the quantity of SWS and rapid eye movement (REM) sleep. The groups in Experiment 2 were 3xTg-AD (n = 5) and non-Tg controls (n = 5). Both groups in this experiment had pre- and post-task rest sessions. For simplicity, all mice which underwent rest sessions are in the 'sleep' group. All experimental procedures were carried out in accordance with the NIH Guide for the Care and Use of Laboratory Animals and approved by the Florida State University Animal Care and Use Committee.

Behavioral assessment

The behavioral assessments for pre-training, stimulation parameters, spatial reorientation task, and probe testing were performed using the same methods as in Stimmell et al. ¹⁵ so that two groups in this study were a replication of our previous work (3xTg-AD no-sleep mice and age-matched non-Tg controls). The protocols for these training phases are described in the respective sections below.

Pre-training

Mice were water deprived to no less than 80% of their initial body weight before starting water restriction and given food ad libitum. They were then trained to shuttle to a black barrier at the end of a linear track and back for a water reward (pre-training; see Figure 1(a) and (b) for experimental timeline). This barrier was positioned so that there was a black background behind it (from the view of the mouse) on the wall. The entire track was moved to different locations in the room leading to different starting positions, so the length of the track varied (Figure 1(c)). The starting position was randomly selected from 9 possible start locations (spaced over a range of 56–76 cm from the center of the goal zone). Note, all calculations were performed in pixel coordinates. The distances listed in cm throughout the paper were estimated for visualization purposes. After reaching criterion (either asymptote, ± 6 total runs, 3 out of 4 days, or a total of 50 or more runs down the track and back), the mouse was scheduled for surgery to implant stimulating electrodes and continued with pre-training every other day until the day before surgery, when water deprivation ceased. We report the number of days mice in each group were maintained on pre-training every other day (Mean ± SD; Experiment 1 No Recording-Array: Non-Tg 4.5 ± 1.0 days; 3xTg-AD no-Sleep 4.1 ± 1.7 days; 3xTg-AD Sleep 3.0 ± 1.4 days; Experiment 2 Recording-Array; Non-Tg Sleep 5.8 ± 2.5 days; 3xTg-AD Sleep 5.6 ± 1.9 days) and found no significant differences between groups for either experiment (Experiment 1: F_{(2, 17)} = 2.03, p = 0.16; Experiment 2: t₍₈₎ = 0.14, p = 0.89).

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Figure 1.

Rest sessions ameliorate impaired spatial reorientation in 6-month 3xTg-AD female mice. (a) Illustration of the timeline for the full experiment. 1-week of pre-training was followed by surgery, 1–2 weeks of recovery, and then 1–2 weeks of operant conditioning to determine optimal MFB stimulation settings. Sleep (rest) or home cage sessions began with the onset of the spatial reorientation task and lasted approximately 1 month. (b) From the onset of the spatial reorientation task forward each daily session consisted of a 50–60 min pre-sleep or home cage session, followed by a 20 min spatial reorientation task session, then a 50–60 min post-sleep or home cage session. (c) The maze consists of a linear track with a start box at one end and an unmarked reward zone near the opposite end (blue bar marked as ′Reward′ under the track). The reward zone remains fixed in the room; however, the start box and track move between trials. As the mouse traverses the track, it uses stable distal cues in the room to locate the reward zone and slow down to obtain a MFB stimulation reward. (d) Mean (± SEM) Z-scored speed from the first half of the reward zone (left 1/2 of the blue bar in panel e) for each reward delay (0.5–2.5 s) in 6-month female non-Tg (blue/dark circle) and 3xTg-AD (red/light circle) mice, as well as 6-month female 3xTg-AD sleep mice (yellow triangle). 3xTg-AD sleep and non-Tg mice slow significantly more than 3xTg-AD mice with no-sleep sessions (*ps < 0.05). (e) Mean (± SEM) Z-scored velocity plotted along the distance of the track during the 1 s reward delay for 6-month female non-Tg, 3xTg-AD, and 3xTg-AD sleep mice. Both non-Tg and 3xTg-AD sleep mice slowed in the reward zone (blue bar above 0 pixels). (f) Same as panel b, but for the 1.5 s reward delay. * 3xTg-AD no-sleep versus sleep mice, ps <0.05; # 3xTg-AD no-sleep versus non-Tg no-sleep mice ps <0.05; $ 3xTg-AD sleep versus non-Tg no-sleep mice ps < 0.05.

Stimulating electrode only implantation

The surgical procedure for Experiment 1 was described previously. ¹⁵ Briefly, once criteria were met on the pre-training task, mice underwent surgery during which bilateral stimulating electrodes were implanted targeting the left and right medial forebrain bundle (MFB; 1.9 mm posterior to bregma, ±0.8 mm lateral, 4.8 mm below dura). The implantation of these stimulating electrodes allowed for intracranial stimulation of the MFB.

Recording-array implantation

The surgical procedure for the subset of animals with a recording-array in Experiment 2 was described previously. ¹⁶ For this subset, two bipolar stimulating electrodes were implanted unilaterally to accommodate the recording-array in the contralateral hemisphere, targeting the left MFB pathway (1.9 mm & 1.4 mm posterior to bregma, ±0.8 mm lateral, 4.8 mm below dura). The implantation of these stimulating electrodes allowed for intracranial stimulation of the MFB. A 16-tetrode recording-array ⁵² was implanted, targeting the parietal cortex and dorsal hippocampus (2.2 mm posterior to bregma, 2.0 mm lateral). Animals recovered for one week, during which tetrodes targeting the hippocampus were lowered approximately 124 µm daily for the first three days, and then 31 µm every other day, until reaching the hippocampus. Positioning was indicated by depth records, as well as the characteristic hippocampal LFP. Data from tetrodes targeting the parietal cortex are not reported here. We selected hippocampal tetrodes due to their more robust local field potential (LFP) features for sleep classification (e.g., theta power). Tetrode locations were histologically verified postmortem­, based on the presence of damage tracks and/or an electrolytic lesion corresponding to the tetrode tip. Mice implanted with recording-arrays underwent extensive habituation via daily tetrode adjustment over 7 days, following the lowering rate schedule described above. Tetrode lowering continued during 7–10 days of stim-box training, which included rest sessions on at least the final few days. This was followed by an additional day of pre-training for further habituation.

Stimulation parameters

Following a 1-week recovery period, mice were placed in a custom 44 × 44 × 44 cm box with a circular nose poke port (Med Associates) mounted on the bottom left half of one wall of the chamber. Using manual stimulations, mice were first shaped to approach the nose poke port, then to touch the port, and finally to poke their nose into the port to break an infrared beam. Once trained to nose poke, the animal automatically triggered each brain stimulation reward by breaking the infrared beam. Brain stimulation rewards lasting 500 ms were delivered using a custom-written MATLAB (The MathWorks Inc.) program that triggered a Stimulus Isolator (World Precision Instruments). Over the course of one week, settings were adjusted (171–201 Hz frequency, 30–70 μA current, & electrode wire combinations) to achieve maximal response rate. No attempt was made to balance responding across genotype; however, responding (nose poke) rate was compared across genotype to ensure that differences in reward strength were not likely to contribute to the observed effects_. The ideal measure of stimulation effectiveness is use of a running wheel to trigger brain stimulation because operant conditioning is less effective in mice. ⁵³ However, since our task requires mice to stop running for a reward, we did not want to confound the study by training mice to run for brain stimulation and then training them on the reverse (stopping for brain stimulation, i.e., reversal learning). Thus, we first assessed brain stimulation responding with an operant task, and this worked well for nearly all the mice in the study. For low responding mice, we assessed stimulation responsiveness with a task that required alternating for stimulations at either end of the track. This was assessed after spatial reorientation training, to prevent a confound of reversal learning. We then combined responses for stimulation across these two tasks, which produced a continuous distribution with all stim values falling below operant training values.

Spatial reorientation training

Once mice were responding maximally, one additional refresher pre-training session was conducted, followed by spatial reorientation training on a mouse version of a task we published previously (as in ¹⁵ and ¹⁶ ) and adapted from the Rosenzweig et al. ⁴⁵ task for rats. Throughout the remainder of training and testing, mice shuttled to the end of the track and back to the start box for a water reward, which they consumed while the entire track was moved slowly and smoothly to the next randomly selected starting location. The running for water reward is designed to create a baseline of regular motion and to increase confidence that the key spatial test (stopping) is due to intentional stopping by the mouse. There were 9 possible start locations, spaced over a range of 56–76 cm from the center of the goal zone, selected using random lists generated by Random.org (random with repeats). Next, an unmarked reward zone was added to the task (28 cm from the barrier at the end of the track) that could automatically trigger a single brain stimulation reward lasting 500 ms. This reward was only marked in the closed-loop software which automatically delivered a reward if the mouse remained in the zone for a sufficient period of time before progressing to the end of the track (Figure 1(c)). This zone was fixed in relation to the room with cues positioned around the periphery of the room and was marked in camera coordinates only. Thus, there were no visible markings on the track indicating the location of the reward zone. Further, since the track was moved to new positions following each trial, any olfactory or other cues on the track could not signal the location of the reward zone. Thus, the reward zone occurred at a variety of physical locations on the track, but always in a fixed location within the room. If the mouse remained in the zone for the duration of a delay period (starting at 0.5 s), a brain stimulation reward was delivered. The delay was fixed for a given day and was increased by 0.5 s (up to 2.5 s) each time the mouse met a criterion of similar percent correct (i.e., ±15%), for 3 out of 4 days. This means that the mouse must use the distal cues distributed around the room to figure out the location of this unmarked reward zone on each trial. Distal cues consisted of black poster-board polygons—stars, rectangles, squares, arrows, and other random shapes—each about 1–2 ft tall or wide. They were affixed to the walls around the maze at or above maze level, within 4 ft of the track on both sides, with 4 in to 2 ft spacing between cues. Cabinets and a bench along one wall served as additional salient landmarks. The use of two different rewards, brain stimulation and water, is necessary because we are teaching mice to do two opposite behaviors, to run back and forth and also to stop. To get mice to perform both behaviors, it is necessary to use different reinforcers for each: water reward for running back and forth and brain stimulation to stop in an unmarked reward location. Custom MATLAB software was used for timestamping the key events of each trial (start, reward zone, and end of track), automatically delivering the brain stimulation reward after the reward delay and generating a record of integrated events and the tracked position of the mouse for offline analysis. All behavior sessions were timed to last 20 min. The dependent variable is the velocity during the approach to the reward zone which we have found to be most sensitive to performance. This is the most sensitive measure, likely because the mouse can accidentally obtain rewards for the smaller reward delays. We have previously made the software for running this task freely available. ⁵⁴

There were pre- and post-task rest sessions before and after each daily session, excluding pre-training. These sessions were included while determining optimal stimulation parameters, and throughout the spatial reorientation task and probe testing. Each day consisted of a 50–60 min pre-task rest session, followed by 20 min of task, and then a 50–60 min post-task rest session. Sleep was induced by placing mice in a 17 × 16.5 × 16 cm box while connected to the recording system, as in. ¹⁶ As we have done previously, e.g., ¹⁵ we assessed the average stimulation response rate to verify that a particular group was not responding better than the other. We found no significant effects of group in Experiment 1 (3xTg-AD rest versus non-Tg versus 3xTg-AD no-sleep) on response rate (F_{(2, 17)} = 1.65, p = 0.22). Though unlikely to influence the results given that responding did not differ across groups, it is possible that greater MFB stimulation was needed to produce equivalent responding in some mice. Therefore, we also performed the same comparison for the two brain stimulation parameters that we adjusted: frequency and current. Non-recording-array mice showed no variation across genotype for frequency (F_{(2, 17)} = 0.43, p = 0.66) or current (F_{(2, 17)} = 0.85, p = 0.44). Similarly, the two recording-array mice groups from Experiment 2 (3xTg-AD versus non-Tg) showed no differences across genotype in response rate (t₍₁₀₎ = 0.69, p = 0.51). There was also no difference in frequency or current across genotype (t₍₁₀₎ = 0.52, p = 0.61; t₍₁₀₎ = 0.15, p = 0.88; as reported in ¹⁶ ).

Sleep recording procedures

As described in Cushing et al., ¹⁶ an electrode interface board (EIB-72-QC-Small or EIB-36-16TT, Neuralynx) was attached with a custom adapter to the recording-array utilizing independently drivable tetrodes ⁵² connected via a pair of unity-gain headstages (HS-36, Neuralynx) to the recording system (Digital Lynx SX, Neuralynx). Tetrodes were referenced to a tetrode wire in the corpus callosum and one wire from each tetrode was selected for LFP recording. Adjustments were made after each day's recording to allow for stabilization overnight. A continuous trace was collected for processing as LFP from one of the tetrode wires (bandpass-filtered 0.1–1000 Hz and digitized at 6400 Hz). Mouse position was tracked using a colored dome of reflective tape for the spatial reorientation task, and online position information was used to trigger MFB stimulation rewards. Video-tracking data were collected at 30 Hz and co-registered with LFPs and event timestamps. LFP analyses were performed using custom-written MATLAB code or Freely Moving Animal (FMA) Toolbox (https://fmatoolbox.sourceforge.net/). The LFP signal was collected at 6400 Hz and subsequently resampled to 2000 Hz for further analysis using the MATLAB resample function.

Sleep quantification

First, still periods for both recording-array mice and non-recording-array sleep mice were extracted from the rest sessions.^10,13,16 Rest sessions lasted 50–60 min and occurred immediately before and after the 20 min behavioral testing session (Figure 1(b)). Only mice in sleep groups underwent these rest sessions. Rest sessions consisted of placing the mouse inside a small black enclosure with tall walls, so that the room is almost entirely obscured except for a small portion of the ceiling while tethered to a commutator via either the brain stimulation cables or the recording-array cables. We have found that placing mice in this “boring” enclosure encourages them to sleep. ¹⁶ The raw position data from each video frame were smoothed by convolution of both x and y position data with a normalized Gaussian function (standard deviation of 120 video frames). After smoothing, the instantaneous velocity was calculated by taking the difference in position between successive video frames. An epoch during which the velocity was constantly below 0.78 pixels/s (∼0.19 cm/s) for more than 2 min was considered a stillness period. We find that this definition of sleep has the advantage of being very reliable for identifying true sleep periods (based on manual sleep classification of LFP) and in fact find that this criterion has the opposite problem of excluding short periods of “true sleep”. We found that 7% of sleep is excluded, which is comprised of these short sleep periods excluded by the measure used here, based on combined LFP/EEG/Accelerometer recordings in other mice (not included in this manuscript) where a shorter 4 s minimum still period was used. Note, in the literature, 4 s and 10 s minimum still periods are frequently used so this is a “worst case” value.^55–58 All analyses of rest sessions were limited to these 2 min still periods. Still data were also collected in an identical manner for Experiment 1. For Experiment 2 only, SWS and REM sleep were identified during these still periods using K-means clustering of the theta/delta power ratio extracted from the CA1 pyramidal layer LFP recorded during stillness,^59–64 which has been validated. ⁶⁵

Cross-frequency analyses

Phase-amplitude coupling (PAC)

To assess the degree to which gamma amplitude was modulated by theta phase throughout SWS, we considered 6 phase-frequency bands (theta, 7–10 Hz; 0.6-Hz increments, 1.2-Hz bandwidth) and 91 amplitude-frequency bands (gamma, 30–120 Hz; 1-Hz increments, 2-Hz bandwidth). Hippocampal LFP extracted from all SWS or REM sleep epochs (assessed separately) were bandpass-filtered, and the corresponding phase ( φ ) and amplitude envelopes ( A ) were obtained using the Hilbert transform. As described in prior literature,^66,67 PAC was calculated as

P A C = | 1 n ∑ t = 1 n A * e i φ |

for each cross-frequency bin. PAC matrices were z-scored to control for variation (PAC_z). Representative comodulograms were generated from the average PAC_z matrix across all SWS sessions collected from each experimental task.

Histology and imaging

After the conclusion of the experiment, mice (3xTg-AD no-sleep, n = 7; 3xTg-AD sleep, n = 7) were euthanized by administering an intraperitoneal injection of Euthasol and transcardially perfused with 1X phosphate-buffered saline (PBS), followed by 4% paraformaldehyde (PFA) in 1X PBS. Whole heads were post-fixed in PFA for 24 h to identify the placement of stimulating electrodes in the medial forebrain bundle and tetrode tracks when appropriate. After brain extraction, an additional 24 h post-fixation period was carried out, followed by cryoprotection of the brain in a 30% sucrose solution. Coronal frozen sections were cut at a thickness of 40 µm using a sliding microtome. Sections were split into 6 evenly spaced series for histological analysis. One series was processed for c-Fos as part of a separate, larger study not reported here. Whole slides were imaged using a Zeiss Axio Imager M2 with a 10x objective (∼100x magnification) and stitched together using Zeiss ZEN Blue software. Histology procedures were conducted as previously described.^15,16,46 In summary:

Region of interest analyses

The density of positive cells for M22, M78, pTau (AT8), and 6E10 intracellular pathology was assessed in four predefined regions of interest: retrosplenial cortex (RSC), CA1 field of the hippocampus, subiculum (SUB), and parietal cortex. These regions were further subdivided into dorsal (RSCd and SUBd) and ventral (RSCv and SUBv) parts for the retrosplenial cortex and SUB. Dorsal CA1 (CA1d) encompassed all sections rostral to a point 2.55 mm posterior to bregma, while ventral CA1 (CA1v) included all sections caudal to that same point. Seven regions were examined in total. Using the Allen Brain Atlas, regions of interest (ROI) were manually drawn based on cytoarchitecture to locate regional boundaries. Automated cell counts were completed using Zeiss Zen 3.6 (Blue edition). Images were taken with a 10x objective (approximately 100x magnification) using a Zeiss Axio Imager M2 microscope. Outlines were manually drawn around the ROI in a 1:12 evenly spaced series throughout the brain using manual selection tool in Zen. The experimenter remained blind to groups. The number of M22, M78, pTau (AT8), and 6E10 labeled cells that were also NeuN positive were then expressed as the proportion per unit of area.

Statistics

Following tests confirming that the data did not significantly deviate from a normal distribution (Shapiro-Wilk test, which we selected in part because it did not violate the sample size rules for the range of sample sizes used here), statistical comparisons for recording-array mice (Experiment 2) were performed using two-way repeated measures ANOVAs (genotype × delay or genotype × position on the track) and unpaired t-tests (for two-group or planned comparisons). A similar statistical approach was applied to Experiment 1 groups (non-Tg, 3xTg-AD, and 3xTg-AD sleep mice). Statistical comparisons of sleep data were conducted using one-way ANOVA and unpaired t-test, if it passed Shapiro-Wilk test, or Kruskal-Wallis and Mann-Whitney tests if it did not. Box plots instead of bar plots were used for all data on a given figure if there was at least one data set that significantly deviated from a normal distribution for consistency. Additionally, statistical comparisons of histology data between groups were performed for each set of brain regions with similar cell density (CA1, SUB, and remaining cortical regions) using two-way repeated measures ANOVAs (group × ROI), followed by post hoc testing when appropriate (e.g., comparing sleep and no-sleep groups for each brain region). For some data, the two-way ANOVA residuals deviated from normal distribution; therefore, we plotted all data using box and whisker plots. We explored transformations to improve these normality violating data sets and successfully improved two data sets. Finally, for the remaining three data sets, because non-parametric two-way tests have low power and ANOVA is robust against non-normality, ⁷¹ and ultimately, we found a lack of differences we did not want to be attributed to a lower power test, we retained ANOVA analyses for the remaining non-normal data. p < 0.05 was considered significant. Statistical analyses were performed using StatView, MATLAB, GraphPad Prism, and R.

6-month 3xTg-AD no-sleep mice are impaired at spatial reorientation compared to the 3xTg-AD sleep group

6-month female 3xTg-AD sleep mice are still for a significant portion of the daily rest sessions

We use a “boring” black box to encourage mice to sleep, and our previous studies suggest that mice do spend significant time sleeping under these conditions. ¹⁶ However, those mice have a larger implant to allow for recording of brain activity patterns, which may further encourage immobility and sleep. Therefore, we next assessed stillness, defined as the amount of time the mouse remained motionless for periods lasting at least 2 min, as a proxy for sleep in 3xTg-AD sleep mice (Figure 2). We found that these mice were still ¹⁶ approximately 40% of the time they were in the sleep box (i.e., about 48 min per day). This data also did not deviate from a normal distribution (Ws>0.98, ps>0.72). Furthermore, there was no significant difference in the proportion of stillness during pre-task and post-task rest sessions (F_{(1, 12)} = 2.04, p = 0.18). There was also no significant effect of reward delay (F_{(4, 48)} = 1.80, p = 0.15), and no significant interaction between the rest session and reward delay (F_{(4, 48)} = 1.02, p = 0.41) on sleep duration, suggesting that sleep did not change for the more difficult phases of the task. These data show that the mice were still for the same proportions during both rest sessions and thus likely spent a significant amount of time sleeping during these sessions. One hour may be a critical time point for sleep-dependent memory consolidation. ⁷² In both rest sessions, we found that these mice were still for approximately 80% of the last ten minutes of the one hour sleep session for the 1.0 s and 1.5 s reward delays of the task (1.0 s Average = 0.81, SEM = 0.04; 1.5 s Average = 0.84, SEM = 0.03).

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Figure 2.

Sleep in 3xTg-AD mice is increased during rest sessions immediately before and after a task. (a) 3xTg-AD non-recording-array mice spend significantly less time still during sleep sessions than 3xTg-AD mice with a recording-array. Box and whiskers (min to max) reflect the proportion of stillness during the rest session for non-Tg mice with a recording-array (dark blue), 3xTg-AD mice with a recording-array (light green), and 3xTg-AD non-recording-array mice (light purple). (b) 3xTg-AD mice with a recording-array had significantly fewer episodes of sleep but significantly higher proportions of sleep. Box and whiskers (min to max) still episodes, sleep episodes, proportion of stillness, and sleep proportion for non-Tg (dark blue) and 3xTg-AD (light green) mice. (c) Time to sleep onset and wakefulness is reduced in 3xTg-AD mice. The time spent in SWS is increased in 3xTg-AD mice, but REM sleep is unchanged. (d) Same as panel c but for proportions instead of time. (e) The number of episodes of REM and SWS did not differ between groups. (f) The SWS, but not REM, episode length was greater in 3xTg-AD mice. *p < 0.05, **p < 0.01, ***p < 0.001.

6-month 3xTg-AD sleep mice without a recording-array spend less time still than those with a recording-array

Next, we compared results across experiments (Experiment 1 mice without recording-arrays versus Experiment 2 mice with recording-arrays) to explore potential impacts of the different experimental conditions (presence or absence of a recording-array). First, we compared stillness in mice with recording-arrays (Experiment 2; 3xTg-AD and non-Tg mice) to 3xTg-AD sleep mice without recording-arrays from Experiment 1 (Figure 2(a)). This data for two groups (3xTg-AD sleep and non-Tg mice both with recording arrays) did not deviate from a normal distribution (Ws>0.84, ps>0.17); however, 3xTg-AD sleep mice without a recording array did (W=0.64, ps<0.01). Stillness varied significantly across groups (H = 13.66, p < 0.0001). Planned comparisons between each group showed that 3xTg-AD sleep mice with no recording-array were still significantly less than 3xTg-AD recording-array mice (Z = 3.67, p < 0.001). These data suggest that the larger recording-array leads to more sleep, but that even the lower amount of sleep induced in 3xTg-AD sleep mice without a recording-array is sufficient to reverse impairments in spatial reorientation learning and memory.

6-month female 3xTg-AD mice sleep more than non-Tg mice

6-month female 3xTg-AD sleep mice with a recording-array are not impaired at spatial reorientation learning and memory

As a next step, we assessed spatial reorientation ability of mice implanted with a recording-array for Experiment 2, in which rest sessions took place just before and after the behavioral session for all mice. We assessed spatial reorientation ability in the 3xTg-AD sleep mice with a recording-array compared to non-Tg sleep mice with a recording-array. As in Experiment 1, we isolated Z-scored velocity data for the first half of the reward zone (Figure 3(a)). We again found that the data did not significantly deviate from a normal distribution (W = 0.99, p = 0.95). We found that velocity for 6-month female mice varied significantly across delays (Figure 3(a); F_{(4, 32)} = 5.63, p < 0.05), indicating that task performance changed with increasing difficulty. However, velocity did not vary across genotype, and there was no significant interaction between delay and genotype (Fs_{(1−4, 32)} < 1.71, ps > 0.17). These findings together strongly suggest that the addition of rest sessions helped reverse the impairment previously observed in 3xTg-AD mice.

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Figure 3.

6-month 3xTg-AD female recording-array sleep mice are not impaired at spatial reorientation. (a) Mean (± SEM) Z-scored velocity from the first half of the reward zone (blue bar in b, just above the 0 pixel mark) for each reward delay (0.5–2.5 s) for 6-month female non-Tg (green) and 3xTg-AD (purple) recording-array mice. 3xTg-AD recording-array sleep mice did not perform significantly different than non-Tg recording-array sleep mice. (b) Mean (± SEM) Z-scored velocity plotted for position on the track during the 1 s reward delay for non-Tg and 3xTg-AD recording-array sleep mice. Both non-Tg and 3xTg-AD recording-array sleep mice slowed into the reward zone (blue bar). (c) Same as panel b, but for the 1.5 s reward delay. 3xTg-AD recording-array sleep mice did not significantly differ from non-Tg sleep mice.

Next, we examined the mean Z-scored velocities for the full length of the track individually for two reward delays where we had observed group differences in Experiment 1 (the 1 and 1.5 s delays; Figure 1(b)). During the 1 s delay, velocity did not vary across the length of the track, genotype, or as a function of genotype and position in the room (Figure 3(b); Fs_{(1−53, 424)} < 4.20, ps > 0.07). During the 1.5 s delay, velocity varied across the length of the track (Figure 3(c); F_{(61, 488)} = 2.29, p < 0.0001), but it did not vary across genotype or as a function of genotype and position in the room (Fs_{(1−61, 488)} < 0.95, ps > 0.59). This pattern of data further suggests that 6-month non-Tg and 3xTg-AD female sleep mice with recording-arrays were also performing similarly.

6-month non-Tg mice perform similarly with or without rest sessions

Next, we again compared results across experiments (Experiment 1 mice without recording-arrays versus Experiment 2 mice with recording-arrays) to better understand the pattern of these data across experiments. To assess whether sleep benefited 6-month female non-Tg mice with a recording-array in Experiment 2, we compared their velocity to that of 6-month female non-Tg mice without a recording-array from Experiment 1, who did not experience pre- and post-task rest sessions. We first compared the Z-scored velocity isolated from the first half of the reward zone between the two groups during each reward delay. Velocity did not significantly vary across groups and delays, and there was no interaction present between delay and group (Fs_{(1−4, 36)} < 2.51, ps > 0.06). Although no significant difference was observed in the velocity in the first half of the reward zone, we also evaluated the velocity profile along the entire track during the two reward delays (1 and 1.5 s delays), where group differences were previously observed in Experiment 1. For both delays, velocity varied across the length of the track (Fs_{(54−59, 531)} > 1.43, ps < 0.05), but did not vary significantly across groups nor was there an interaction between group and position on the track (Fs_{(1−59,498−531)} < 0.66, ps > 0.68). Finally, to ensure that differences in movement velocity (i.e., behavior) did not account for the lack of sleep-related improvement in non-Tg mice, we assessed velocity on the portion of the track covered in the shortest track configuration (ensuring even sampling across trials). We then focused on the half of this segment furthest from the reward zone (i.e., when task dependent velocity should not vary). Non-Tg sleep and no-sleep groups did not differ in velocity for this portion of the track (t₍₉₎ = 0.96, p = 0.36), suggesting that behavioral differences are unlikely to explain the lack of sleep-related benefits in non-Tg mice. Overall, this suggests that while 3xTg-AD mice did benefit from rest, non-Tg mice did not.

Further, to ensure that differences in the number of trials to criterion were not likely to account for the results, we compared these values across groups. For the 3xTg-AD sleep mice with recording-arrays in Experiment 2, the average number of trials on the three days when the mice met criteria at 0.5, 2.0, and 2.5 s reward delays was 9 trials; at the 1.0 s delay and 1.5 s delay the average was 8 and 10 trials respectively. For the non-Tg mice with recording-arrays in this experiment, the average number of trials on the days that met criteria for the 0.5 and 2.5 s reward delays was nine trials; at the 1.5 and 2.0 s reward delays the average was 11; and at the 1.0 s reward delay it was 10 trials.

3xTg-AD sleep mice with recording-arrays (Experiment 2) exhibited comparable spatial learning and memory to sleep mice without arrays from Experiment 1, while outperforming no-sleep 3xTg-AD mice lacking arrays

To ensure that these groups did not differ because the sleep mice had a recording-array while the no-sleep mice did not, we performed two additional comparisons: 1) 3xTg-AD sleep mice with recording-array versus without a recording-array; 2) 3xTg-AD no-sleep mice without a recording-array versus 3xTg-AD sleep mice with a recording-array. If the presence of a recording-array does not impact these comparisons significantly, then comparison 1 should not differ but comparison 2 should. For comparison 1, velocity did not vary across groups, and there was also no significant interaction between delay and group (Fs_(1−4,40) < 1.63, ps > 0.19). We found that the velocity varied significantly across delay (F_(4,40) = 4.012, p < 0.01). We did the same comparison between the 3xTg-AD recording-array sleep mice from Experiment 2 and the 3xTg-AD non-recording-array mice without rest from Experiment 1. We found a significant effect between groups (F_(1,48) = 5.03, p < 0.05), with the 3xTg-AD sleep mice from Experiment 2 slowing more to obtain the reward stimulation. Velocity did not vary across delay, and there was also no interaction between delay and group (Fs_(4,48) < 1.45, ps > 0.23). Together this pattern suggests that the presence of a recording-array was not a key factor in the results we observed.

Theta-gamma coupling during SWS may explain the recovered cognition in 3xTg-AD sleep mice

Finally, we had previously characterized sleep and memory-related activity patterns during sleep in these mice, including delta waves (DW) in parietal cortex and interactions with sharp-wave ripples in the hippocampus (SWRs). ¹⁶ Thus, we looked for evidence of whether these measures might explain variability in the improved performance of 3xTg-AD sleep mice. First, we examined the relationship between SWR × DW coupling during sleep and spatial reorientation performance the next day (slowing in the reward zone). SWR × DW coupling did not predict performance during the reward delays highlighted in Figure 1 (1.0 s; r₍₂₆₎=-0.16, p = 0.430). This lack of correlation is not surprising because SWR-DW coupling is impaired in 3xTg-AD mice at the 1.0 s reward delay. Specifically coupling increases in post-task sleep in non-Tg mice (mixed models to account for animal identity with data set as the sample: F(_1,3) = 14.793, p = 0.035) but not 3xTg-AD mice (F_(1,28) = 1.587, p = 0.218). Thus this coupling is impaired at this early timepoint and remains impaired in the same mice one month later as we reported previously. ¹⁶

Given that SWR-DW coupling is not a likely mechanism, we sought a different mechanism underlying the restored cognition. Theta-gamma coupling is thought to reflect synchrony between long-range communication and local processing, ⁷³ so we next explored theta-gamma phase-amplitude coupling in the hippocampus. Prior work has focused on theta-gamma coupling during REM sleep, when theta power is high, ⁷⁴ therefore, we examined theta-gamma cross-frequency coupling during REM sleep. We found that theta-gamma comodulation was apparent in all mice (Supplemental Figure 1, Left & Middle); however, none of the correlations between theta-gamma comodulation and spatial reorientation task performance were significant (multilevel correlation accounting for animal identity: rs₍₈₋₉₎< |-0.39|, ps > 0.166) for the 1.0 s and 1.5 s reward delays. REM sleep was relatively rare, and several datasets lacked REM entirely. Because extensive prior work (including our own) has implicated SWS in learning and memory, including spatial learning and memory,^13,75–79 we looked to see if theta-gamma cross-frequency coupling was also apparent during SWS. Although theta power was lower during SWS (which is classified by a theta-delta ratio reflecting higher delta and lower theta relative to REM), theta power remained relatively strong (Supplemental Figure 1, Right), which made it less surprising that cross-frequency coupling was strong during SWS in non-Tg mice (Figure 4 Left Column). 3xTg-AD sleep mice also exhibited strong theta-gamma comodulation during SWS that did not differ from control mice (Figure 4(a); ps > 0.05). However, approximately 1 month later–when the same mice were impaired on a virtual version of the task-theta-gamma coupling during SWS was now significantly reduced in 3xTg-AD sleep mice compared to Non-Tg sleep mice (Figure 4(b)). Thus, we looked for a relationship between theta-gamma comodulation during SWS and spatial reorientation performance the next day. Maximum theta-gamma comodulation during SWS approached, but did not reach significance, for predicting performance in 3xTg-AD sleep mice at the 1.0 s delay (multilevel correlation accounting for animal identity: r₍₂₈₎=-0.34 p = 0.067). Comodulation also was not predictive at the 1.5 s reward delay (r₍₃₉₎=-0.10 p = 0.530).

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Figure 4.

Theta-gamma comodulation during SWS is preserved in 3xTg-AD sleep mice and is marginally predictive of spatial reorientation performance the following day. (a) During SWS adjacent to the spatial reorientation task, cross-frequency (phase-amplitude) coupling between theta phase and low-gamma amplitude in HPC was strong and did not significantly differ between non-Tg sleep mice (Left) and 3xTg-AD sleep mice (Right). (b) In the same mice one month later, during a virtual version of the same task (behavioral data previously published¹⁶; comodulation data shown here for the first time), theta–gamma comodulation was reduced in 3xTg-AD sleep mice and was significantly lower than in non-Tg sleep mice (see contour on the 3xTg-AD comodulation heatmap).

Pathology is not reduced in female 6-month 3xTg-AD mice with rest sessions

The mice in Experiment 2 were derived from a previous publication that included an additional virtual maze experiment following the spatial reorientation task data; thus, pathology was assessed 1 month after behavioral data collection. In contrast, the mice in Experiment 1 were euthanized immediately following the spatial reorientation task, therefore we only examined pathology in these animals (Figure 5). The age at perfusion for the two groups of 6-month female 3xTg-AD mice (sleep versus no-sleep; t₍₁₂₎ = 1.11, p = 0.29; sleep mean = 8.8 months; no-sleep mean = 8.2 months) did not differ significantly. We found few, if any, extracellular plaques present at this age. Therefore, we quantified the density of the proportion of neurons (NeuN positive cells) that were also positive for intracellular accumulation of amyloid or pTau (AT8), measures we have previously shown to be associated with behavioral task performance. ⁴⁶ We quantified pathology in the regions previously associated with task performance and shown to be important for spatial navigation, as well as in several control regions: the CA1 field of the hippocampus, parietal cortex (PC), and RSC.^80,81 The dorsal region of the CA1 field (CA1d) was counted separately from the ventral region (CA1v) because the dorsal region is thought to be critical for precise spatial navigation.^82–85 Finally, because we were measuring density of the proportion of pathology positive NeuN labeled cells, we analyzed sets of regions with similar packing density separately: CA1, SUB, and cortex (RSC and PC).

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Figure 5.

Histological markers of AD pathology do not show a significant decrease of pathology in the sleep groups. Box and whisker plots (Min to Max) of cell density for cells positive for NeuN and also: (a) Aβ 1–16 specific antibody (6E10); (b) amyloid fibril conformation-specific antibody (mOC78; M78); (c) amyloid fibril specific antibody (mOC22; M22), (d) antibody specific to hyperphosphorylated isoforms of tau protein (AT8, pTau). While several brain regions significantly differed from each other, only three group differences emerged in the sleep group: reduced Aβ staining (M78 in SUBv), increased Aβ staining (6E10 in SUBv), and increased pTau (AT8 in dCA1). Otherwise, rest did not significantly impact tau or Aβ aggregation. (e) Representative image showing 6E10 (green) and NeuN (red) staining in retrosplenial cortex dorsal and ventral (RSCd/v), motor cortex secondary and primary (MOs/p), dorsal subiculum (Sub), and the CA1 field of the dorsal hippocampus (CA1). (f) Same as panel e, but for M78 (green). (g) Same as panel f, but for M22, showing parietal cortex posterior (PTLp) instead of MO. (h) Same as panel e, but for pTau, also showing agranular RSC (RSCagl). The DAPI (blue) channel is turned off in panels e-h for visualization purposes. Additional abbreviations not shown on histological sections (e-h): ventral CA1 (CA1v). ventral subiculum (SUBv). Scale bar = 100 μm. *p < 0.05, **p < 0.01, *** p < 0.001.