Investigation of the Effects of Cannabidiol on Sleep-Like States and Memory-Associated Brain Events

DEGREE PROJECT IN MEDICAL ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2020

Investigation of the effects of Cannabidiol on sleep-like states and memory-associated brain events

TUGDUAL ADAM

KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ENGINEERING SCIENCES IN CHEMISTRY, BIOTECHNOLOGY AND HEALTH

Investigation of the eﬀects of Cannabidiol on sleep-like states and memory-associated brain events

TUGDUAL ADAM

Master in Medical Engineering Date: September 24, 2020 Supervisor: Lisa Genzel Examiner: Arvind Kumar School of Engineering Sciences in Chemistry, Biotechnology and Health Host company: Genzel Lab Swedish title: Undersökning av eﬀekten av Cannabidiol på sömnliknande tillstånd och minnesassocierade hjärnhändelser

iii

Abstract

A growing interest for Cannabidiol (CBD), a component of Cannabis Sativa, has occurred over the past years. The medical potential of the component is yet to be better characterized, as its effects on sleep, and in particular memory, are to date not well understood or consistently characterized. This master thesis project focuses on analysing the effect of CBD on an anaesthesia-induced sleep-like state in rats, and its effects on the hippocampal sharp-wave-ripples, which have been shown to be associated with memory replay during sleep, and hence system consolidation. The hippocampus and prefrontal cortex, the two structures involved in memory consolidation, were recorded in 19 rats, split in two groups (CBD and vehicle). From these recordings, an automated sleep scorer using principal component analysis was developed to obtain the animals’ hypnograms, which were analysed to study sleep-like structure. From the recordings of the hippocampal pyramidal layer, and an additionnal layer deeper under it, respectively ripples and sharp waves were detected in all animals, and characterized for each group. We observed and demonstrated that CBD changes the sleep-like structure by shortening both REM and NREM bouts, resulting in an increase in transitions between both states. Addition- ally, we observed that, although ripples are not significantly different between both groups, sharp waves tend to be smaller among CBD animals. Lastly we noticed that both sharp wave and ripple activity, after increasing upon transition to NREM, decreases as the bout last. This finding suggests that vehicle animals, who have longer bouts and less transitions, would display less sharp wave and ripple activity, although we found no significant difference in the amount of both brain events. This paradox suggests that there is still more to characterize in order to understand if CBD enhances or not memory consolidation. In sum, CBD changes anaesthesia-induced sleep by shortening the duration of both NREM and REM bouts, resulting in an increase in transitions between both state. As for sleep events, sharp waves appeared shorter among CBD animals, although the same difference was not observed for ripples. Fi- nally, sharp wave and ripple activity appear to peak upon transition from REM to NREM sleep, and decreases as the NREM bout lasts longer, however, no effect of CBD on this observation was highlighted. iv

Sammanfattning

Under de senaste åren har det förekommit ett växande för intresse för Canna- bidiol (CBD), en del av textit Cannabis Sativa. Ämnets medicinska potential är ännu inte väl kartlagt, då dess effekter på sömn, och minne i synnerhet, hittills inte är väl förstått eller konsekvent karaktäriserade. Det här examens- arbetet fokuserar på att analysera effekten av CBD på ett anestesi-inducerad sömnliknande tillstånd hos råttor, och dess effekter på det hippocampala skarp- vågkrusningkomplex, som har visat sig vara förknippade med minnesuppspel- ning under sömn, och därmed systemkonsolidering. De två strukturerna invol- verade i minneskonsolidering, hippocampus och prefrontalcortex, registrera- des i 19 råttor, uppdelade i två grupper (CBD och kontroll). Från dessa re- gistreringar utvecklades ett automatiserat sömnbetyg med hjälp av principal- komponentanalys för att erhålla djurens hypnogram, som analyserades för att studera sömnliknande uppbyggnad. Från registreringarna av det hippocampala pyramidala lagret, och ett ytterligare lager djupare under det, upptäcktes krusningar och skarpa vågor hos alla djur och kännetecknades för varje grupp. Vi observerade och demonstrerade att CBD förändrar den sömnliknande struktu- ren genom att förkorta både REM- och NREM-anfall, vilket resulterade i ett utbrott i övergångar mellan dem båda tillstånden. Dessutom observerade vi att även om krusningar inte signifikant skiljer sig mellan båda grupperna, tende- rar skarpa vågor att vara mindre bland CBD-djur. Slutligen märkte vi att både skarp våg- och krusningsaktivitet, efter utbrott vid övergången till NREM, av- tar under anfallets gång. Denna upptäckt tyder på att kontrolldjur, som slår längre och har färre övergångar, skulle uppvisa mindre skarp våg- och krusningsaktivitet, även om vi inte hittade någon signifikant skillnad i mängden av båda hjärnhändelserna. Denna paradox antyder på att det fortfarande finns mer att karaktärisera för att förstå om CBD förbättrar minneskonsolidering eller inte. Sammanfattningsvis förändrar CBD anestesi-inducerad sömn genom att förkorta varaktigheten av både NREM- och REM-anfall, vilket resulterar i ett utbrott i övergångar mellan båda tillstånden. När det kommer till sömn- aktiviteter förefaller det att skarpa vågor är kortare bland CBD-djur, även om samma skillnad inte observerades för krusningar. Slutligen verkar skarp våg- och krusningsaktivitet toppas vid övergången från REM till NREM-sömn, och avta när NREM-anfallet varar längre, däremot framhävs ingen effekt av CBD i denna observation. Preface

The following document constitutes my master research project as the conclusion of my Master in Medical Engineering at the Royal Institute of Technology (KTH), Stockholm, Sweden. The project was conducted at the Genzel Lab, Radboud University, Nijmegen, Netherlands, from the 2nd of february 2020 to the 30th of June 2020, and was supervised by Lisa Genzel, PI of the laboratory, as well as Adrian Aleman, junior researcher. I chose, during my studies, to specialize in various ﬁelds, among which was neuroscience. With the precious help of Arvind Kumar, responsible for the course DD2401 Neuroscience, I contacted Lisa and found a project about the neuroscience of sleep, for which I have always felt a strong interest. The following report presents my research project at the Genzel Lab, the methods I developed for tackling the main issues, the results I obtained as well as a critical discussion regarding the latter.

Tugdual Adam

v Acknowledgement

I would first of all like to thank Lisa Genzel, PI of the Genzel Lab, for making this project possible, and for being patient and of precious help, especially when it came to providing neuroscientific background to the data and results, as I arrived with only little knowledge about the specifics of sleep and memory neuroscience. I would then like to thank Arvind Kumar, professor at KTH, and also my own reviewer, who made all of this possible by giving me the contact of Lisa Gen- zel. I moreover learned a lot about neuroscience thank to him during the course at KTH DD2401 Neuroscience. He gave me the basics to start this master thesis. I would also like to thank Adrian Aleman, junior researcher at Genzel Lab, who introduced me to the laboratory when I arrived, and was of precious help for the statistical aspect of the project, and Anumita Samanta, in charge of the project I am working on, for her enthusiasm and great help for the neuroscientific background of the project. More generally, I thank all of the researchers and students at the Genzel Lab for sharing their different experiences, back- grounds, for being of help and keeping each other distracted via our Slack platform during this covid-19 quarantine, which lasted most of my time in the Netherlands. Finally, a big thank to my family for being of moral support during my time away working from my student room as my workplace was closed due to the pandemic.

vi Contents

1 Introduction 2 1.1 Comprehensive framework ...... 2 1.1.1 Role of sleep in memory consolidation ...... 2 1.1.2 Sleep disorders and cannabis ...... 4 1.2 Project objectives and hypotheses ...... 5

2 Material and methods 7 2.1 Material and experiments ...... 7 2.2 Channel selection ...... 9 2.3 Data preprocessing ...... 10 2.4 Sleep classiﬁcation ...... 10 2.5 Automatic scoring assessment ...... 12 2.6 Event detection ...... 12 2.6.1 Ripples detection ...... 13 2.6.2 Sharp waves detection ...... 13 2.7 Statistical analysis ...... 13 2.7.1 Processing of the data ...... 13 2.7.2 Two sample Student’s test ...... 13 2.7.3 Repeated measure analysis of variance ...... 14

3 Results 15 3.1 Automated sleep scorer ...... 15 3.1.1 Analysis of the PCA weights ...... 17 3.1.2 Scorer accuracy ...... 17 3.2 Eﬀect of cannabidiol on sleep ...... 19 3.3 The eﬀect of cannabidiol on memory-associated events . . . . 25 3.3.1 Sharp waves and ripples characterization ...... 25 3.3.2 Sleep events in long bouts ...... 30

4 Discussion 32

5 Conclusion 38

vii viii CONTENTS

Bibliography 39

A Additional ﬁgures and tables 44

B Background chapter 51 Glossary and abbreviations

BW - Brief wake • CBD - Cannabidiol • EEG - Electroencephalogram • EMG - Electromyogram • HPC - Hippocampus • LFP - Local ﬁeld potential • NREM(S) - Non rapid eye movements (sleep) • OST - Object space task • PFC - Prefrontal cortex • PCA - Principal component analysis • PC1 (or PC2) - First (respectively second) principal component • (R)ANOVA - (Repeated measures) Analysis of variance • REM(S) - Rapid eye movements (sleep) • SW(s) - Sharp Wave(s) • SWR(s) - Sharp-wave-ripple(s) • SWS - Slow wave sleep • THC - Δ-9-tétrahydrocannabinol • VEH - Vehicle •

1 Chapter 1

Introduction

1.1 Comprehensive framework

Sleep is a vital function that can be found in most animals on earth. Its functions are numerous and it plays a crucial role in cognition, immunity, brain waste clearance, performance and energy conservation [1]. Sleep goes back as far as the appearance of a central nervous system in evolution, and evidence even show that sleep may have existed prior to the emergence of brains [2]. Humans sleep for about a third of their lifespan [3], and every night undergo several sleep cycles, characterized by repetitive patterns of rapid eye movement sleep (REMS) and non-rapid eye movement sleep (NREMS), each associated with their own brain activity patterns [4][5]. Rodents, and especially rats, are commonly used as an animal model in sleep research [6]. Contrary to humans, rats are polyphasic sleepers, meaning that they sleep several times over the course of a day. These episodes, in a study from Simasko and Mukherjee [6], were found to last between roughly 18 minutes in the dark period and around 120 minutes during the light period (which should not come as a surprise as rats are nocturnal animals). When sleeping, they cycle between states of brief wake (BW), NREM sleep, and REM sleep. However,contrary to humans, where a sleep cycle is usually measured as the time from an REM sleep period to the next one, rats can cycle through NREM sleep and BW without REM sleep (humans can also skip REM, especially amongst older individuals, early in the night).

1.1.1 Role of sleep in memory consolidation Sleep is intrinsically associated with memory consolidation: during sleep, memory is thought to be reprocessed, and shifts from an ephemeral form, or "episodic" form, to a long lasting one, or "semantic" form [7]. Two brain struc-

2 CHAPTER 1. INTRODUCTION 3

tures are involved in this system consolidation, the hippocampus (HPC), associated with short-term memory, and the neocortex, outer layer of the brain, associated with long-term memory [8]. To briefly explain: when learning something new during awake state, neurons in the cortex (sensory neurons associated with the experience) and in the hippocampus (acting as an index to the cortical neurons) are activated. These cells in the hippocampus are necessary for retrieval of these new memory, therefore during training, connections among the activated hippocampal cells are strengthened. However, at this point, we believe that synapses in the hippocampus are strong, but cortical synapses are weak. Therefore, a system consolidation is necessary, which happens during sleep. Note that this is the classical view of memory consolidation and recent studies have shown that episodic memories remain dependent on the hippocampus across time and do not undergo systems consolidation [9]. In NREM sleep, the prefrontal cortex (PFC) cycles between periods of elevated activity, UP states, and periods of low activity, DOWN states, resulting in characteristic slow oscillations in the LFP, whose DOWN states are often followed by sleep spindles [7]. A very characteristic brain pattern in the hippocampus, called sharp-wave-ripples (SWRs), occur shortly before the spindles. Appearance of the SWRs is associated with a burst of activity in the CA3 region of the HPC (caudal part of the hippocampus, see figure 1.1 for a schema of the HPC) that lasts for a few milliseconds [8]. The excitation spreads to the rest of the hippocampus, and causes in the CA1 field (supe- rior part of the hippocampus) a deflection of the local field potential, called sharp wave, and high frequency oscillations called ripples (150-200Hz). A phenomenon called neural replay occur during these SWRs, it consists in the reactivation of hippocampal memory, sending a signal back to neurons in the cortex. During this replay, PFC neurons are allocated in the semantic storage, and cortical synapses strengthen: as a result memory can now be retrieved from the PFC. Therefore, in theory, hippocampal cells are no longer activated, and their synapses weaken. Hence a form of ’transfer’ of information, called memory consolidation, from the hippocampus to the PFC happened.

To date, We only have a grasp of the function of SWRs, but the interest in these patterns has constantly grown over the last years. Peyrache et al. [11] showed that hippocampal SWR-activity is also associated with transient activity in many cortical areas, but mainly in PFC, highlighting the high connectivity between the structures and their mutual involvement in memory consolidation. In another study, Valero et al. [12] investigated the single-cell response to SWRs and showed that epileptic rats with distorted SWRs showed 4 CHAPTER 1. INTRODUCTION

Figure 1.1: Schema of a slice of hippocampus [10] lower memory performances in memory tasks. Similarly, Buzsáki [13] and Girardeau [14] explain that selective disruption of SWRs is associated with memory interference, but more characterization is still necessary. Beyond SWRs, HPC and cortex are coupled probably in more ways than we know today, cortical replays were for example found in 2002 [15], and evidence of a link with hippocampal replays was later discovered [16]. While early studies show that HPC cortical activity, recent evidence show that cortical states also module hippocampus, showing that it is a two-ways communication. Isomura et al. [17] reported that hippocampus activity was inﬂuenced by cortical UP- DOWN transitions, possibly triggering the hippocampal SWRs. More recently in 2016, Maingret et al. [18] proved the causal role of the dialog between both areas in memory consolidation, and in particular the coordination between SWRs, spindles and delta waves.

1.1.2 Sleep disorders and cannabis As previously said, sleep is a vital function, yet sleep disorders are common, and one of the most widespread health condition: a cross-national study [19] conducted in Europe in 2015 showed that one in four individuals reported having symptoms of sleep disorders over the past 6 months, and when it comes to the USA, 50 to 70 millions citizen have a sleep disorder, and around 30% of american adults report having insomnia with short term issues [20]. Typ- ical medications for sleep disorders are often associated with adverse effects [21], and a growing non-pharmalogical practise against insomnia is the use of cannabis [22], often without full knowledge of its actual effects, and potential negative ones, on sleep. That is why more research on the effects of cannabis and its different components on sleep is necessary. Cannabis comes from the plant Cannabis Sativa and is made up several CHAPTER 1. INTRODUCTION 5

components, the two major ones being Δ-9-tetrahydrocannabinol (THC), and Cannabidiol (CBD). These two phytocannabinoids have the particularity to be able to act on the receptors of the endocannabinoid system, a neuromodulatory system involved in several functions such as sleep, fertility, memory or appetite. Two specific receptors are involved in this system, CB1 and CB2. While the former is located mainly in the central nervous system, the latter is located majorly in the peripheral nervous system. CBD and THC mainly targets the CB1 receptors, as they are located in the brain, although studies have shown that CBD has a low affinity to these receptors comparatively to THC [23]. While THC has often been the focus of studies related to cannabis in the past [24][25], CBD was often set aside. However, studies showed lately that CBD has a therapeutic potential: some results show that CBD could lead to an increase in total sleep time with a reduced REM sleep time [26], or that it has a sedating effect and would promote sleep maintenance in rats [27]. Con- versely, Nicholson et al. [28] demonstrated that low dosage of CBD could significantly increase awake time in humans, hence highlighting the biphasic, dose-dependent effect of CBD. Other studies show that it acts on the synaptic plasticity in the hippocampus [29] and could thus have an effect of memory, as its consolidation involves hippocampal synapses. Pertwee et al [30] demonstrated that CBD displayed potential for antagonizing CB1 receptors agonist, and recent results showed that the activation of CB1 receptors were associated with a disruption of SWRs patterns [31], suggesting that CBD would promote SWRs, and hence memory consolidation, by preventing their potential disruption.

1.2 Project objectives and hypotheses

The effects of CBD on the structure of sleep and memory consolidation are still not well described, but the neuroscientific community believes that there is potential to be found. This project is an attempt at better characterizing the effects of acute intake of CBD on sleep and memory consolidation. The objectives are as follow: Analysing the effect of CBD on sleep stages under urethane anaesthesia, • especially on the structure of REM and NREM sleep and their transitions in rats.

Investigating the eﬀect of CBD on sleep-related brain events under ure- • thane anaesthesia, especially the occurrence of SWRs, associated with 6 CHAPTER 1. INTRODUCTION

memory consolidation.

We will test the prevailing assumption that the intake of CBD decreases the amount of REM sleep in animals (and an REM rebound as the drug washes oﬀ from their system), and improves the consolidation of memory by preventing the disruption of SWRs. Note that urethane anaesthesia mimics a sleep-like state, hence referring to the sleep stages under anaesthesia as REM-like and NREM-like would be more accurate, but for simpliﬁcation purposes, we will refer to these stages as REM and NREM. Chapter 2

Material and methods

2.1 Material and experiments

We used in this project data from 30 Lister hooded rats. They were first trained and studied in a behavior experiment, which is the object of a separate project which I did not work on. About an hour before surgery, animals were blindly fed with either CBD (120mg/kg in 300 μL flavoured oil) or a control food(300 μL flavoured oil). After roughly 30 minutes, they were given a fast-acting inhalation anaesthetic gas, isoflurane, and were intraperitoneally injected with urethane upon effect of isoflurane (1.4g/kg in a 0.28g/ml concentration saline. Rats were about 600g at the time of the surgery). Urethane is known to induce a sleep-like state, unlike most anaesthetic drugs [32]. While urethane became effective, the rats were applied an anaesthetic cream (Lido- caïne) in the ears, as they are head-fixed at the basis of the ears during the surgery, and on the scalp where the incisions for surgery are made. They were finally injected with an additional drug (Rymadyl) to reduce the bleeding and pain from the skin cut. Once the rats showed no reaction to external stimuli, meaning the urethane came into effect, they were placed in a stereotaxic frame in preparation for surgery. The surgery consisted of removing the scalp skin layers to expose the skull. Then two craniotomies were performed (locations of these were calculated as offset in millimeters from the bregma, intersection of the coronal suture and the sagittal suture of skull, on the medio-lateral -ML, left to right-, anterior- posterior -AP, head to feet- and dorso-ventral -DV, depth from dorsal dural surface- axes). The locations, in our case, were the CA1 of dorsal hippocampus and the prefrontal cortex (coordinates are 3.5 AP, 0.5 ML, 3.5 DV for PFC, 3.2 AP, 2 ML, 1.8 DV for HPC). Once the holes were drilled, silicone probes (32 channels, linear, 50m spacing thus 1.6mm recording from Atlas neuroengineering) were inserted into place, and coated in dye, before begin lowered to the desired calculated depth. The fluorescent dye enables to check

7 8 CHAPTER 2. MATERIAL AND METHODS

for correct probe placement later on during histology. The electrodes and the experimental setup can be observed on images 2.1 and 2.2. From then, the probes were connected to an Open Ephys acquisiton box [33] and the local field potential around the probes were recorded for about 6 hours. CBD is hypothesized to act roughly 5h after intake, as there is evidence in the literature that plasma levels of CBD are highest around 4-5h after oral administration [34]. By recording for 6 hours, we could theoretically observe the build up phase of the drug, the effect peak, and part of the flush out phase. Heat pads were in addition placed under the animals and replaced roughly every 1h30 to keep them warm.

Figure 2.1: Electrodes, by Atlas neuroengineering. Only the metal tip is inserted into the brain.

The experiments were separated in two batches, the first one was conducted in april/may 2019 and included 16 animals (of which 4 animals were operated later in july 2019), and the second one, including 14 animals, was conducted in april 2020. Note that the experiments were done at the rate of an animal a day, and surgeries were performed by expert experimentalists (Lisa Genzel and Anumita Samanta). Each probe includes 32 channels along its length, enabling to record different different local field potentials (50m between each channel, hence recording on roughly 1.6mm of length). After all recordings 11 animals were excluded of the study (2 animals died, 6 animals were missing a brain recording, the pilot animal was removed, 1 animal had abnormally short recordings and 1 animal showed abnormal reaction to the drug and abnormal brain recordings). In the end, a total of 19 animals were kept for the data analysis (10 CBD, 9 vehicles). A more extensive description of the animals can be found in table A.1 in Appendix A. CHAPTER 2. MATERIAL AND METHODS 9

Figure 2.2: Experimental setup. The rat is head-ﬁxed in the stereotaxic frame. The skull is exposed, and electrodes lowered into the drilled holes.

2.2 Channel selection

The first step was the channel selection. As previously mentioned, each probe recorded the LFP in 32 locations (or channels) along its length. Not every channel was relevant for the data analysis. Channel selection was especially important for the hippocampus because, being a small structure, different parts of the hippocampus were recorded simultaneously. The criteria for channel selection were the following. For the HPC the probe was applied in the CA1 region. The middle layer of this field is called the pyramidal layer, as it is composed of pyramidal cells (excitatory neurons). Ripples in the hippocampus are characterized by a fast oscillation in this layer. Above the pyramidal layer, a positive deflection of the LFP is observed, while a negative deflection is observed below it (See figure 2.3). Recordings were observed and checked for ripples, and a channel in the pyramidal layer was manually selected or each animal. For the PFC, the differences between the channels were not as substantial as for the hippocampus. A superficial channel showing slow oscillations of large amplitudes was selected. 10 CHAPTER 2. MATERIAL AND METHODS

Figure 2.3: Extracted signal from rat 209, CBD. A ripple can be observed in the pyramidal layer associated with the characteristic positive deﬂection above the pyramidal layer, and negative deﬂection below it.

2.3 Data preprocessing

The LFP data was acquired at a frequency of 30kHz, resulting in voluminous files. The data was first low-pass filtered with a zero-phase Butterworth filter of 3rd order to 300Hz and then downsampled by a factor 50. The data was then filtered for artifacts by applying an amplitude detector (outliers above the 99.5th percentile to reduce false positives, as artifacts are usually characterized by high amplitude, but are not so frequent).

2.4 Sleep classiﬁcation

The ﬁrst step of the project was to classify the sleep-like structure of the recordings. This process is called sleep scoring and is usually done manually, which is highly time-consuming. In rats, sleep is usually divided in REM sleep, NREM sleep and brief wake periods. Occasionally, intermediate sleep is added when the time window characterized show features of both REMS and NREMS. In our case, rats were anaesthetized, reducing the stages to REMS and NREMS (as there can be no wakeful state). In order to avoid the long process of manual scoring, an automatic scorer was set up. For each brain area, the spectral power of the signal was estimated with a multitaper ﬁlter with a time-bandwidth product of 4 in epochs of 10 seconds and for frequencies from 0-300Hz. The sleep scoring was based on Principal Component Analysis (PCA). PCA is a unsupervised dimension reduction method that transforms redundant variables into uncorrelated new CHAPTER 2. MATERIAL AND METHODS 11

ones. The motivation behind this is that the use of urethane anaesthesia is poorly characterized, and how it affects the structure of sleep is not well known, hence relevant parameters for sleep classification in natural sleep might not be as relevant in a urethane-induced sleep-like state. In addition to this, different papers in the literature use different parameters as a basis to discriminate sleep stages between REMS and NREMS ([35], ratio of delta to theta power, and high frequency to low frequency EEG power, [36], heuristic ratio, slow oscillations-delta power over slow oscillations-theta power, and ratio of low frequency to high frequency powers). By using PCA, one can input many parameters and let the algorithm find the ones that explain the most difference within the data, and hence differentiate the sleep stages. Various parameters were therefore used as input to the PCA: slow oscillations (0.1-1Hz), delta power (1-3Hz), theta power (3-6Hz), low beta power (10-20 Hz), low gamma power (30-45Hz), high gamma (55-80 Hz), ripples (90 - 300 Hz), ratio of theta power to slow oscillations as well as the amplitude of every epoch (an epoch being a 10 second signal window). Note that these frequency ranges are lower than the ones usually found in the literature: urethane anaesthesia was shown to slow down brain patterns [37], and a direct observation of the spectrograms confirmed this, hence the frequency ranges were set heuristically. The PCA returns a matrix of coefficients, or weights, for each of the parameters. Each column of the matrix represent a principal component and explains a given fraction of the variability between the datapoints (where each point is associated with the values of the parameters for a 10sec epoch). The two principal components that explained most of the variability were kept, and a k-means algorithm was performed in the PC1-PC2 2D space in order to separate the data into two or three clusters based on the squared Euclidean distance (REMS, NREMS and intermediate sleep when a third cluster would appear). The selection between either two or three clusters was based on a silhouette analysis [38]. A silhouette analysis computes how far the points from a cluster are from the other clusters, being assigned a value ranging within [-1 1], where 1 means that the point is far away from the other clusters, 0 means that it is onto another cluster, and negative values means that it is probably assigned to the wrong cluster. The mean of silhouettes value for each point was computed in both 2 clusters and 3 clusters scenario, and the situation with highest score was kept. 12 CHAPTER 2. MATERIAL AND METHODS

2.5 Automatic scoring assessment

To assess the accuracy of the automatic scoring, some animals from batch 1 were manually scored on a 1 second basis by an expert (Lisa Genzel), and each of their PCA-scoring was given an overall accuracy score based on the ground truths, as well as a REM and NREM score. If we use the notation from the below confusion matrix (Table 2.1):

Ground truth REM NREM PCA-scored REM A B NREM C D

Table 2.1: Example of a confusion matrix between ground truth and automatic scoring.

The overall score is computed as the fraction of correctly scored states, or: A + D Soverall = ; (2.1) A + B + C + D In addition, a REM and a NREM scores were computed to further investigate which state is more poorly characterized. A SREM = (2.2) A + B + C D SNREM = (2.3) B + C + D These scores take into account the false positive and the false negative rates, and equal 1 in an ideal case (without false positives and false negatives).

2.6 Event detection

The sleep events we are interested in here, as previously introduced, are the sharp-wave-ripples, as they are associated with memory consolidation. A SWR is the co-occurrence of two patterns: a short and acute deflection of the LFP in the CA3 field of the hippocampus (sharp wave), and a fast oscillation (80-200Hz) in the CA1 field (ripple) that lasts around 200 ms. As these complexes are yet not fully comprehended, it is only for now assumed that they CHAPTER 2. MATERIAL AND METHODS 13

co-occur. We chose to look into these events separately, in order to understand if there could be ripples occurring without sharp waves, or inversely, and the type of information that could be carried by these diﬀerent events.

2.6.1 Ripples detection The ripple detection is done using a script previously written by a colleague (Adrian Aleman). A pyramidal layer channel of the hippocampus is bandpass ﬁltered to 90-299Hz, then an amplitude threshold is set to 5 standard deviations of the signal amplitude around the mean value of the signal, and oscillations whose envelope exceeds the threshold for longer than 30 ms are counted as ripples.

2.6.2 Sharp waves detection The sharp wave (SW) detection is done using a channel below the pyramidal layer, in order to better observe a negative deflection of the LFP, as the deeper you are, the more of a deflection you can observe. The signal is filtered to 1-20 Hz, and patterns of amplitude lower than a threshold of 5 standard deviations below the signal’s average are counted as sharp waves. The local minimum of each SW is retrieved automatically.

2.7 Statistical analysis

2.7.1 Processing of the data In order to compare both vehicles versus CBD animals, a processing of the data was necessary. As explained earlier, we want to capture the general effect of cannabidiol, without it being influenced too much by the individual variability. First of all, the data was aligned on the feeding time, in order to synchronize the hypothetical effective time of the drug in the CBD animals. The data was then separated in bins of 45 minutes, as a trade-off between individual variability and general effect, and averaged over time. Statistical tests were then performed on the processed data.

2.7.2 Two sample Student’s test A two sample Student’s t-test tests if the diﬀerence between the means of two groups is signiﬁcant or due to random chance. The null hypothesis is here that 14 CHAPTER 2. MATERIAL AND METHODS

animals from both groups come from the same population, i.e. that there is no diﬀerence in mean between both groups for a given parameter (for example the amount of REM sleep). The test assumes that the data is normally distributed, and returns a p-value, probability of observing such or more extreme result given that the null hypothesis is true. A p-value will be considered signiﬁcant if it is lower than 0.05. Two sample t-tests were performed on time- and animal- averages in each group for every parameters (REM and NREM amounts, bouts counts and durations, transitions, number of ripples and sharp waves, theta and delta powers, ripples and sharp waves features). Data was normalized when the criteria for normal distribution was not met.

2.7.3 Repeated measure analysis of variance The limitation of the previous Student’s t-test is that it compares means, and hence does not take into account the time. However, the CBD drug has a build up phase to full effect, and a wash off phase, and therefore the time dimension plays a significant role. Hence, another model was needed. A repeated measure model is suitable in studies that investigates changes in a parameter over three or more time points, or under three or more conditions [39]. In the present situations, animals belong to either one or the other group, which is called the between-subjects factor, and measures are repeated as we have several successive 45-minute bins, which are called the within-subject factors. Therefore, a repeated measure ANOVA is suitable in this case. Such test is commonly used in analysis of drugs over time as it supposedly analyse the effect of drug while excluding the individual variability at the beginning of the trial. Chapter 3

Results

In this section will be presented the main results from the study, first regarding the effects of CBD on sleep-like stages, then the effect of CBD on memory-associated sleep events. These results will be later discussed.

3.1 Automated sleep scorer

A pipeline was developed to automatically score the data using a PCA based algorithm. The pipeline is adaptable depending on the data available, and the idea is that it can be used for various datasets. The script takes as an input a HPC channel and/or a PFC channel. When the rats are not anaesthetized, EMG or accelerometer data is also relevant to detect wakeful states and can be loaded as well. The scorer returns an interactive Matlab image with the following outputs.

Spectrograms The spectral power for a given frequency range is computed as the area under the multitaper power density curve for this frequency range. The spectrograms of HPC and/or PFC are shown (depending on the input channels), with the possibility to adjust the window to the desired frequency range (Fig- ure 3.1). Note that, for a given frequency range, here 0-300Hz, there is a trade off between temporal and frequency resolution. The finer the temporal resolution, the rougher the frequency resolution, and vice versa. In order to reach sufficient accuracy in low frequency, as low frequency bands regroup various different characteristic oscillations very close in frequency, epochs of 10 seconds had to be used.

15 16 CHAPTER 3. RESULTS

Figure 3.1: Spectrograms for the HPC and PFC of rat 209, CBD. Spectral power is color coded from blue (low power) to yellow (high power) for frequencies ranging from 0 to 300 Hz throughout the signal, in bins of 10 seconds.

Filtered signals and PCA scored sleep states The brain signals used in the PCA are shown in the output, as well as the PCA scored sleep states. The sleep states are aligned on the channel signals and the spectrograms to better visualize the correlation between sleep state, signal and spectral power (Figure 3.2).

Clustering output and PCA weights The result of the clustering in the 2D space composed of the two first components of the PCA is shown in the ouput. This enables us to visually assess that the number of clusters is correct, and that they are well defined. In addition, the weights of the parameters involved in the two first components of the the PCA are displayed and color coded. This, alongside with the previous cluster plot of the data, allows us to visualize which parameters are mainly involved in the discrimination between the different sleep-like stages (Figure 3.3). CHAPTER 3. RESULTS 17

Figure 3.2: From top to bottom: HPC signal, PFC signal and PCA-scored sleep stages (the plot showing the diﬀerent sleep stages and their duration is usually called a hypnogram) for rat 209, CBD. HPC and PFC signals were beforehand ﬁltered for artifacts. The hypnogram is on a 10 seconds basis.

3.1.1 Analysis of the PCA weights The weights obtained from the PCA are typically assigned a value between 1 and -1. The closer to 1 in absolute value the weight is, the more "important" the parameter when it comes to discriminating between REM sleep and NREM sleep. We looked at the weights in the first component of each animal, as they explained most of the variance (mean variance across all animals explained by the first components was 71.6 10.6%, so we can confidently assume that ± weights involved in the first component were the weights involved in most of the discrimination between the sleep stages). Analysis of these weights showed that the dominant parameters for distinguishing sleep stages were the raw (after pre-processing) signal amplitude of each 10 seconds epochs (weights 0.60 ± 0.09 for the PFC, 0.45 0.07 for the HPC), the slow oscillations power (weights ± 0.32 0.09 for the PFC, 0.33 0.05 for the HPC) and the ratio of theta power ± ± to slow oscillations power (weights 0.17 0.07 for the PFC, 0.24 0.07 − ± − ± for the HPC).

3.1.2 Scorer accuracy To assess the accuracy of the scorer, manual scoring was performed on parts of the recordings for 4 rats from batch 1 (rat 5 - CBD, rat 9 - VEH, rat 10 - CBD, rat 11 - CBD). Each manual scoring was made on a 1 second basis (while the PCA scorer is made on a 10 seconds basis), so the PCA-scored output was expanded ten times to ﬁt the manual scoring in size, and global accuracy, REM accuracy and NREM accuracy were computed for each manually scored animal. Results are found in Table 3.1 18 CHAPTER 3. RESULTS

Figure 3.3: Output of the PCA scorer for rat 209, CBD. Top: clustered data in the PC1-PC2 space. Bottom: weights of the parameters involved in PC1, PC2. Weights are values between -1 and 1, the closer to 1 in absolute value, the greater the weight of the associated parameter.

Global accuracy REM accuracy NREM accuracy Rat 5 0.896 0.668 0.868 Rat 9 0.995 0.977 0.994 Rat 10 0.927 0.826 0.889 Rat 11 0.932 0.808 0.906

Table 3.1: Accuracy scores of the PCA scorer for the 4 manually scored rats.

We could see that overall, we have very good accuracy scores, close to the one we usually ﬁnd in the literature ([35], interestingly, in this study, REM accuracy was also signiﬁcantly lower; [40], they obtain slightly better accu- CHAPTER 3. RESULTS 19

racy scores, around 96%). When looking closer to REM and NREM accuracy scores, it appeared that REM sleep has a lower accuracy. Looking at the individual confusion matrices (table A.2), we noticed that the false positive rate of REM is rather high (especially in rat 5), i.e. real NREMS was quite often scored as REMS. Interestingly, the rat showing almost perfect scores, rat 9, was the only vehicle animal of these four, while the CBD ones showed lower scores, but other manually scored vehicles would have been necessary to con- ﬁrm the trend.

3.2 Eﬀect of cannabidiol on sleep

As previously introduced, after scoring every rat, the data was aligned on the feeding time (either vehicle or CBD), separated in bins of 45 minutes and averaged over time for each group. Various parameters were observed and the introduced statistical tests were performed.

REM and NREM amounts are not significantly affected A quick look at the proportions of each stage in every animal (Figure A.2 in appendix A) did not show any trend between groups. The results are summa- rized in table 3.2. The very high standard deviations we could observe account for the diversity within groups, and the necessity to therefore average over animals in order to reduce this individual variability and reveal the underlying possible effect of cannabidiol.

NREM proportion REM proportion CBD 57.04(18.07) 42.96(18.07) VEH 58.00(18.97) 42.00(18.97)

Table 3.2: Proportion of NREMS and REMS in both groups. Results are presented as mean std. ±

Looking at figures 3.4 and 3.5 confirmed the absence of a significant effect of CBD on the amount of REMS and NREMS. When we examined the amount of REM or NREM sleep across time, we noticed that they both os- cillate over time. Interestingly, these oscillations appeared smaller among the CBD animals when the drug came into effect. Both the amounts of REMS and NREMS appeared significant across time in a RANOVA (F(9,153) = 3.232, p- value = 0.0163 for REM, F(9,153) = 8.059, p-value = 1.16e-5 for NREM. All 20 CHAPTER 3. RESULTS

Figure 3.4: Temporal evolution of the amount of REM sleep per group, with Standard Error of the Mean (SEM). Right barplot is the mean value in each group, with individual values per animal displayed as dots to visualize the distribution of individuals.

Figure 3.5: Temporal evolution of the amount of NREM sleep per group, with Standard Error of the Mean (SEM). Right barplot is the mean value in each group, with individual values per animal displayed as dots to visualize the distribution of individuals. CHAPTER 3. RESULTS 21

statistics can be found in table A.3), suggesting that these oscillations could be related to a "sleep cycle" under anaesthesia. Note that CBD did not significantly aﬀect the temporal evolution of REMS and NREMS in the RANOVA, and the amount of both REMS and NREMS was not signiﬁcant through a 2- sample t-test or an ANOVA.

VEH sleep bouts are longer but less numerous compared to CBD ones Next we examined the REM and NREM bouts, in terms of number and duration. First of all, ﬁgure 3.6 showed that the CBD animals have more bouts than vehicles ones, and notably more NREM bouts than REM bouts. A second observation was that these bouts are also shorter (less than 10 minutes), while vehicle animals showed in comparison notably longer bouts. In terms of average, CBD animals had a mean duration of 3min 48sec for NREM bouts and 4min 08sec for REM bouts, while vehicle animals had respectively 6min 11sec for NREM and 6min 39sec for REM bouts.

Figure 3.6: Distributions of the REM (left) and NREM (right) bouts durations for both groups. Only bouts longer than 20 seconds were taken into account. Rat 2 from batch 1 was not taken into account in the distribution, resulting in 9 animals in each group. The dashed line is the mean duration for each group. 22 CHAPTER 3. RESULTS

(a) Averaged number of NREM bouts in both groups.

(b) Averaged number of REM bouts in both groups

Figure 3.7: Temporal evolution of the number of sleep bouts longer than 20 seconds per group, with SEM. Signiﬁcance with t-test and ANOVA is represented with a star. Right barplot is the mean value in each group with individual values per animal displayed to visualize the individual distribution. CHAPTER 3. RESULTS 23

(a) Average duration of NREM bouts in both groups

(b) Average duration of REM bouts in both groups

Figure 3.8: Temporal evolution of the duration of sleep bouts per group, with SEM. Right barplot is the mean value in each group with individual values per animal displayed to visualize the individual distribution. 24 CHAPTER 3. RESULTS

When we looked at the number of bouts across time (figure 3.7) and their duration (figure 3.8), the first notable observation was the increase in both sleep-like bouts from 3h30 post feeding to 8h post feeding in the CBD, coinciding with the hypothesised effect window of the CBD (build-up, peak and wash off). Both the number of REM and NREM bouts showed a significant difference between CBD and VEH animals, through the t-test as well as the ANOVA (table A.3 For the number of REM bouts, T(17) = 2.813, p-value = 0.012 for Student’s test, F(1,17) = 6.539, p-value = 0.0204 for the ANOVA. For the number of NREM bouts, T(17) = 3.001, p-value = 0.008 for Student’s test, F(1,17) = 7.796, p-value = 0.0125 for the ANOVA). We could notice an interesting global transient decrease in both bouts for both groups around 6h to 7h30 post feeding. This period was also associated with notably longer NREM bouts (figure 3.8a). The duration of bouts appeared generally longer among vehicle animals, although statistical tests did not yield significance at the 5% threshold. These increases in duration coincides with (and explain) the REM-NREM oscillations we previously observed.

More transitions in between states in rats with CBD The most prominent effect of cannabidiol, which relates to the previous observation of shorter and more numerous bouts in CBD animals, is the great increase in transitions between REM and NREM stages. Indeed, in figure 3.9, the increase in transitions started around 4h post feeding and ended around 7h30 post feeding. This difference was moreover significant at the 5% threshold for the t-test and the ANOVA (table A.3. T(17) = 2.468, p-value = 0.0304 for Student’s test, F(1,17) = 5.923, p-value = 0.0461 for the ANOVA). The sleep-like state appeared more "disturbed" with periods of fast transitions between REM and NREM. Note that these periods of fast transitions can also be witnessed in VEH animals (figure A.1, animals 203, 210, 211), but they are more frequent and last longer in CBD animals. The underlying mechanism explaining this increase in transitions is not yet understood, but could be related to the underlying sleep associated events, or brain patterns, which is the focus of the second part of this project. CHAPTER 3. RESULTS 25

Figure 3.9: Temporal evolution of the number of transitions per group, with SEM. Right barplot is the mean value in each group with individual values per animal displayed to visualize the individual distribution. Signiﬁcance with t- test and ANOVA is represented with a star.

3.3 The eﬀect of cannabidiol on memory-associated events

The neural activity events we are interested in here are the sharp waves and ripples, as they form SWRs complexes, thought to be at the basis of memory consolidation during sleep.

3.3.1 Sharp waves and ripples characterization We first of all looked at the amount of sharp waves and ripples averaged across time in both groups (figure 3.10). No significant difference could be noticed between VEH and CBD animals, and two peaks could be observed (especially for the sharp waves - for the ripples, the first peak is much smaller) that coincide with the peaks of the NREM "oscillation". This is not a surprise as these activity patterns are mainly observed during NREM sleep, which is known to be the sleep stage during which most memory consolidation happen. This observation does not seem a priori to support the hypothesis that CBD improves memory consolidation throught the promotion of SWRs, and closer look needs to be brought to the individual rats. 26 CHAPTER 3. RESULTS

(a) Averaged number of ripples in both groups.

(b) Averaged number of sharp waves bouts in both groups

Figure 3.10: Bin-averaged number of sleep events per group, with SEM. Right barplot is the mean value in each group with individual values per animal displayed to visualize the individual distribution. CHAPTER 3. RESULTS 27

In figure 3.11 we show the number of sharp waves and ripples for rat 209 batch 2 (CBD animal, a vehicle can be observed in Appendix A, figure A.4, to notice that is it not an effect of CBD), the hypnogram is displayed as a background to better visualize the incidence of sleep events during the different stages. Several observations were made from this: first of all, some events surprisingly happened during periods scored as REM, this is most probably due to very short NREM-like bouts wrongly scored as REM (note that there is also mention in the literature of SWRs happening on rare occasions during REM [13]). Set aside this point, the most interesting observations were that, when NREM bouts last longer, the amount of sharp waves and ripples seems to gradually diminish across the bout (figure 3.11, one especially "long" NREM bout is highlighted in red, from 4h23 to 5h28 - time since beginning of recording). In addition, every transition into NREM is marked by a spiking activity in both ripples and SWs, but a better characterization needs to be done as the spike is not of consistent amplitude.

Figure 3.11: PCA scored sleep stages superimposed with the amount of ripples (top) and sharp waves (bottom) per bins of 30 seconds in rat 209, CBD. A notably long NREM bout is highlighted in red.

Looking at features of ripples (ﬁgure 3.12), we noticed that no signif- 28 CHAPTER 3. RESULTS

(a) Distribution of the normalized ripples’ amplitudes in both groups

(b) Distribution of the ripples’ duration in both groups

Figure 3.12: Histograms of ripples features (amplitude, duration, mean frequency) for both groups. Amplitude is computed as the peak to trough difference of the event, normalized by the interquantile gap (5th-95th quantiles) of the REM periods ofthe HPC signal from which the ripples are detected, to prevent bias due to inherent variability in the recorded signals amplitudes. CHAPTER 3. RESULTS 29

icant difference can be observed regarding their amplitude. As for their durations, both distributions are very much alike, although CBD ripples seem slightly shorter (T(21554) = -2.071, p-value = 0.0384 for Student’s test), but a more in depth analysis of this would be necessary to confirm this. The mean frequency of the ripples does not differ significantly between groups, but it is interesting to note the tendency of the parameter to follow a bimodal distribution, although this latter speculation was not confirmed through a statistical test.

As for the sharp waves, their amplitude is shown in figure 3.13, and we observed that sharp waves are overall smaller in amplitude amongst the CBD animals (T(18810) = -29.04, p-value = 2.105e-181 for Student’s test). Surprisingly, the same shift was not observed for the amplitude of the ripples, which are supposed to be the consequence of the propagation of sharp waves toward the CA1 field of the hippocampus, suggesting a possible decorrelation between sharp wave and ripple amplitude, or at least the existence of different subtypes of SWRs, which would not be equally affected by CBD.

Figure 3.13: Normalized sharp wave amplitude in both group. The amplitude is computed as the diﬀerence between the whole signal’s average, and the local minima of the sharp wave, and is normalized by the interquantile gap (5th- 95th quantiles) of the REM portion of the signal used for sharp wave detection (channel below the pyramidal layer), to prevent bias due to inherent variability in the recorded signals amplitudes. REM portions were used since it is a rather steady signal in amplitude, and reﬂects the overall recording’s amplitude. 30 CHAPTER 3. RESULTS

3.3.2 Sleep events in long bouts To better observe in all rats the observations made in rat 209, we decided to observe all the long NREM bouts, i.e. longer than 15 minutes, and compare the mean number of SWs/ripples on a given portion at the start and at the end of the bout. We chose here to take the first and the last 10% of each long bout, and to normalize over the resulting duration the number of SW and ripples. The results are displayed in figure 3.14. We can undeniably see a trend here: the amount of both sleep events are decreasing toward the end of the bout (T(55) = 6.012, p-value = 1.5401e-07 for Student’s test for ripples, T(55) = 6.489, p-value = 2.5814e-08 for Student’s test for sharp waves). In order to ensure that the difference is not due to one abnormal animal, animal-averaged amount of events at the start/end of long NREM bouts were computed, and significance was kept (figure A.5). There is however no clear effect of CBD on this difference in sleep events amount between start and end of long bouts, if not that CBD animals have less long bouts (56 NREM bouts longer than 15 minutes for vehicle animals, against 28 for CBD animals). CHAPTER 3. RESULTS 31

(a) Normalized amount of ripples at the start/end of long NREM bouts

(b) [Normalized amount of SW at the start/end of long NREM bouts

Figure 3.14: Each point represent one long NREM bout, color coded if they come from a CBD animal (green) or a vehicle one (grey). Left hand column is the normalized amount of the sleep event across the ﬁrst 10% of the bout, right hand column if across the last 10%. Pairs of data from the same bout are linked with a dotted line. Chapter 4

Discussion

This project aimed at analysing the effects of cannabidiol on a sleep-like state induced by urethane anaesthesia. Brain recordings from the PFC and HPC of 19 rats were acquired and analysed. Using PCA, we successfully obtained the hypnograms of our 19 animals. Analysis of these hypnograms revealed no significant effect of CBD on the overall amount of REMS or NREMS, but highlighted a substantial decrease in the duration of both REM bouts, resulting in more transitions between states in the CBD group. The study of hippocampal ripples and sharp waves revealed that CBD has no effect on the former but significantly reduces the amplitude of the latter, but didn’t alter the overall amount of either event. Besides, we highlighted that both sharp wave and ripple activity peaks upon transition from NREM to REM, and diminishes as the NREM bout lasts, although this effect is not affected by CBD. The PCA scorer separated the data in two clusters for all animals but 2. Interestingly, these 2 animals were from the vehicle group, and showed a distinct third cluster. This third cluster was manually assessed as either REM or NREM, and we did not investigate if this additional cluster was an artifact created by the PCA, or if it reveals a true distinct sleep-like state. Some studies distinguish two NREMS subtypes [41][42], or even add a transitional state between NREM and REM: further investigation could have highlighted that our occasionnal third cluster was one of these. The data was separated in epochs of 10 seconds for the PCA, as it is the window commonly used in manual scoring, and a smaller window caused the slowed down UPs and DOWNs in the PFC to be seen as two different oscillations. The PCA determined that the main discriminating factors between REMS and NREMS are the signal amplitude, the slow oscillation power, and the ratio of theta to slow oscillation powers. In a review by Claude Robert et al. [43], they analysed over 20 papers using automated sleep scoring. Most papers were using EEG and EMG, the latter being used for detecting wake. Most papers scored sleep in epochs of 10 seconds and based the discrimination between REMS and NREMS on a predominant

32 CHAPTER 4. DISCUSSION 33

theta-activity during REMS (6-12Hz), while NREMS was dominated by high delta activity (0.5-4Hz). In addition to this, 8 of the reviewed papers used EEG amplitude as a discrimination parameter. In our case, because of anaesthesia, no EMG is necessary as there is no wake. Apart from this, our parameters turn out to be similar: REMS is dominated by theta oscillations slowed down by the anaesthesia (3-6Hz), while NREMS shows a prevalence of “slow oscillations” (0.1-1Hz), which could be regarded as anaesthesia-slowed down delta oscillations, and higher amplitudes both in the HPC (due to the presence of sharp waves) and the PFC. Note that this review is quite old, and none of the papers included in it used LFP, but recent studies show that the same parameters are still used nowadays (Yaghouby et al.[35], 2016, EEG, Wei et al.[42], 2019, EEG and LFP), and are also used for LFP: Lacroix et al. [44] used LFP from both PFC and HPC, and parameters for discriminating REM from NREM were the same, i.e. theta power during REMS and delta power during NREMS. We assessed the performance of the scorer using manual scoring from 4 rats. We obtained high scores, comparable with PCA scorer found in the literature [45] [46]. Interestingly, we found REM scores to be lower than the NREM ones, a tendency observed as well in the literature [47][48][45][46]. In our case, REMS tends to be harder to classify, especially in CBD animals, as the fast transitions between REMS and NREMS causes the REM bouts to be noisy, without settlement of regular theta oscillations as can be seen during longer REM bouts. The subsequent analysis of the hypnograms revealed no major difference in the amount of REMS and NREMS between CBD and VEH animals, while, on average, animals from both groups display roughly as much REMS as NREMS. However, we observed a strong effect of CBD with a significant increase in the number of transitions between REMS and NREMS as well as a reduction in the duration of both REM and NREM bouts. These results are not in accordance with our hypotheses, as we were expecting to observe a deeper sleep, meaning more NREMS and less REMS, as can be found in the literature. Chagas et al. [49] for instance reported that a systemic administration of CBD causes an overall increase in sleep time and slow wave sleep, although they did not report any decrease in REMS, while Murillo-Rodriguez [50] highlighted that an icv (intracerebroventricular) injection of CBD in rats caused a reduction in REMS. There are not many studies investigating the effect of CBD on sleep in rodents or humans, and the conclusions vary as much as the experimental designs (CBD dose, location of the injection, pure CBD or combined with THC...). For example, Linares et al. [51] demonstrated that a systemic high 34 CHAPTER 4. DISCUSSION

dose administration of CBD did not significantly alter the ratio of REMS to NREMS, as we observed in our case, but did not report more frequent switches between states. Some studies support that CBD is sleep inducing: Hsiao et al. [52] reported that CBD suppresses anxiety-induced REM reduction, Chagas et al. [49], as previously mentioned, supported that it increases the amount of NREMS, and Carlini et al. [26] demonstrated that CBD in sleep inducing in insomnia patients. Converse to this, many studies support that CBD is wake-inducing. Nicholson et al. [28] for example highlighted that a greater amount of CBD in a THC/CBD mix was associated with a higher awake time during sleep in rodents, but didn’t notice any increase in the number of awaken- ings of transitions between states. Murillo-Rodriguez studied in several papers [50][53][54][55] the wake-inducing effects of CBD and exhibited increases in neural activity of wake-inducing brain areas during sleep in rodents under CBD, such as hypothalamus or dorsal raphe nucleus. This alertness inducing effect is supported by the fact that CBD works as a CB1 receptor antagonist [56][29], a receptor which has been shown to be sleep inducing. Antagonism of these receptors suppresses the sleep promoting effects of an acute accumulation of endocannabinoids [57]. The diversity of results and experimental designs suggest that CBD underlies a complex mechanism, with dose-dependent and location-dependent effects. Yet, no study of our knowledge has investigated the effect of CBD in an anaesthesia-induced sleep-like state, and neither did any study highlight a significant increase in transitions between stages, which is the main effect we notice in this project. These observations might suggest that it is a combined effect of CBD and anaesthesia. An hypothesis would be that CBD induces a wake pressure, as suggested by the literature, but since awakening is prohibited by anaesthesia, the animals would rapidly transition from NREM to REM, as REM, from a brain perspective, is closer to wake than NREM, which is why it is sometimes called paradoxical sleep. This would also explain why, in CBD animals, short REM bouts are very noisy, and not as regular as long REM bouts in vehicles, and would further support the reduced performance of the PCA-scorer for classifying REMS, since there would be two “types” of REM bouts, the ones created under urethane anaesthesia, and the ones driven by the wake pressure. This doesn’t however explain why CBD animals switch back to NREM so quickly. In a second phase, we studied hippocampal ripples and sharp waves. It is known that the reactivation of hippocampal place cells happen during ripples, making them suited for memory consolidation [58][59][60]. Further evidence states that the number of ripples following learning predicts the memory performance [61]. In this project, no significant difference was observed in terms CHAPTER 4. DISCUSSION 35

of quantity or temporal evolution of either event between CBD and VEH animals. This does not, a priori, support the hypothesis that CBD enhances memory performance through an increase in ripple activity, as no such increase was observed. However, in the present experimental design, the rats did not neces- sarily learn anything new the day prior to the surgeries, which could motivate why there would not be any particular increase in ripple activity to observe in the first place. We further characterized that hippocampal ripples are similar in both groups in terms of amplitude, mean frequency and duration. Interestingly, we highlighted a significant decrease in the sharp waves’ amplitude in the CBD group. It was shown that CBD is associated with an increase in levels of anandamide, the main endocannabinoid, and a CB1 receptors agonist: CBD indeed blocks its hydrolysis by the fatty acid amide hydrolase enzyme [49][62], leading to the accumulation of the endocannabinoid. Maier et al. [31] demonstrated in a study that the administration of a CB1 receptor agonist had for primary effect to reduce the amplitude and frequency of SWRs, leading to a form of SWRs disruption. Put together, these observations could explain the CBD-induced reduction in sharp waves’ amplitude that we notice. We also highlighted that this reduction in sharp waves’ amplitude was not associated with a significant alteration of the ripples’ amplitude: this finding suggests that there could be different subtypes of SWRs that could be differentially affected by CBD. Another interesting observation which could support the hypothesis of subtypes is the apparent bimodal distribution of the ripples’ durations, although, as we said, we did not confirm this through a statistical analysis. Logothetis et al. [63] already investigated this question: they discovered different SWRs subtypes associated with different brain activity, these subtypes were characterized mainly by the synchronisation between the sharp wave and the associated ripple, but exhibited variations in many other parameters, including sharp wave amplitude. The apparent reduction in sharp wave amplitude in CBD animals could be related to the predominance of a certain SWR subtype, associated with a certain brain activity, and a better characterization of this could help understand how CBD affects brain-wide dynamics. Lastly, we highlighted how both sharp wave and ripple activity peaks upon transition from REMS to NREMS, and their activity gradually decreases as the subsequent NREM bout lasts. Surprisingly, we would expect that, the more transitions there are, the more SWR activity there is, since this activity is “degraded” in long NREM bouts, but not in short bouts. This is however not the case, as CBD animals clearly exhibits more transitions, but the same amount of sharp waves or ripples. Ponomarenko et al. [64] showed using 36 CHAPTER 4. DISCUSSION

wake-inducing drugs that the occurrence of ripples during NREMS was correlated to the time of wake preceding sleep. They also demonstrated that, if the ripple activity reaches a baseline level at the end of a NREM bout, the duration of the subsequent REM bout will be positively correlated with the peak in ripple activity at the beginning of the next NREM bout. In other words, the longer the REM bout, the more ripple activity is observed at the start of the subsequent NREM bout. Although this study was conducted in natural sleep, their ﬁndings strongly support of our own observations, suggesting that CBD animals do not exhibit higher ripple activity, despite showing more transitions, because their REM bouts are particularly short, hence they do not trigger a ripple-activity peak upon transition as high as in VEH animals, which show longer REM bouts.

Limitation and future work This project has however some limitations. First, the whole study was based on brain recordings from rodents in an anaesthesia-induced sleep-like state. For obvious ethical reasons, the use of anaesthesia also permits to re- use the expensive probes, and to obtain cleaner recordings. Anaesthesia is not typically supposed to induce a sleep-like state, and even if urethane proved to reach a state close to natural sleep with low interactive potential with other drugs [32], it still includes a background effect which is difficult to account for. In other words, the rats were not in natural sleep, and we have to be aware of this: the sleep stages were not real REMS and NREMS, but sleep-like stages. The first issue with this was that our animals "slept" longer than they sleep on a regular basis: they indeed "slept" for 7 hours under anaesthesia, while, as polyphasic sleepers, they normally sleep up to 2 hours [6]. This resulted in a different sleep structure that is normally observed, also in terms of durations of REM and NREM bouts, as our typical bouts in VEH animals lasted longer than they do in natural sleep. Brief wakes were also suppressed by anaesthesia, yet they are an inherent part of rodents’ sleep cycle (Sismako et al. [6] reported that BW accounts for roughly 37% of a rat’s sleep cycle). Replicating the results in natural sleep would be an interesting continuation of the project and would help better understand the effect of CBD on features such as arousals, sleep latency, sleep and wake onset, etc. Secondly, regarding the detection of memory-associated events, sharp waves and ripples, we merely described them, and our results already open up for many possibilities of further research. For example, we only looked at sharp waves and ripples in the hippocampus. But evidence of cortical ripples has been made, and some studies seem to highlight a form of coupling between hippocampal and cortical ripples [65]. Furthermore, the PFC and the HPC are both involved in memory consolidation, but it is unclear how activity in the PFC reacts with ongoing activity in the HPC [66], especially with regards to hippocampal SWRs. We did not, in this project, consider specific memory- associated patterns in the PFC, but this could be a continuation of the work conducted here. It could also relate to the aforementioned SWRs subtypes that we only briefly highlighted. There is a lot to unravel regarding this HPC-PFC communication during sleep, but I believe one needs to better characterize it in controls before examining the effect of CBD on it. Therefore such investigation did not have its place in the current project, but could be the object of future research. Lastly, this study was conducted on brain recording from rats, and on the analysis of these only. However, to better characterize if CBD influences memory consolidation, the best way would be to submit CBD animals to memory exercises and analyse their results. Such study was conducted in parallel of mine. Prior to the surgeries, the animals were trained during several weeks before carrying out the Object Space Task (OST). The OST consist in expos- ing animals to multiple sample trials in which they explore objects under different conditions (objects are presented to the animals under successive trials, and objects as well as their location can change in each trial). Such task is meant to trigger memory as animals may retain the information of a certain object in a certain location, and hence spend less time exploring it. Analysis of the OST results was performed in a separate study, and evidence of long-term memory enhancement in the CBD group was exhibited, but more investigation needs to be done. Nevertheless, looking at SWRs for memory consolidation is still relevant, as these patterns could be involved in more than just memory, and the results obtained opened to more questions and lines of research for the future. Note that there are, in the literature, studies investigating the behavioral effects of CBD in rodents. Schleicher et al. [67] recently showed no significant improvement, in CBD animals, of spatial memory or motor performance in the Plus Maze, or memory in a Novel Object Recognition test, but showed reduction in anxiety, confirming the anxiolytic properties of CBD that were already highlighted in the past [68][69][70]. Another study [71] demonstrated, by place-conditioning rats with addictive drugs, that CBD prevented the consolidation of drug-related events, and rats did not subsequently show preference to places where the addictive drugs were presented, suggesting here a disruption of memory consolidation. The diversity of results reaffirms the aforementioned complexity of CBD, either for sleep or for memory, and support the idea that more research is still necessary to unravel its full potential.

37 38 CHAPTER 5. CONCLUSION

Chapter 5

Conclusion

In this master thesis project, we looked at brain recordings from rats in an anaesthesia-induced sleep-like state. The animals were separated in two groups: in one, animals were given CBD orally, and the other was a control group. Two brain areas were targeted: the hippocampus and the prefrontal cortex, as they are both involved in memory consolidation, and are coupled in probably more ways that we know of to date. We developed an automated sleep scorer based on PCA to score the animals’ sleep, and managed to obtain good accuracy scores when comparing the hypnograms with ground truths. Based on this automated scoring, we studied various sleep parameters and compared both groups with regard to these. The overall amount of both REMS and NREMS were not affected significantly, but we observed a striking reduction in the duration of REM and NREM bouts, resulting in more transitions between both states. We found no effect of CBD on the amount of either hippocampal sharp waves or ripples, but we highlighted a significant decrease in the sharp waves’ amplitude among CBD animals. Interestingly, we observed that, upon transition from REM to NREM, a peak in both sharp wave and ripple activity occur, and this activity decreases gradually as the NREM bout last, reaching low activity at the end of long NREM bouts. This paper is the first, to our knowledge, to investigate CBD in a sleep-like state, and we highlight an interesting novel effect never specifically reported in previous papers studying CBD in natural sleep. We did not obtain the expected results, but we did support that the mechanism of CBD on the brain is complex, and that more research needs to be done to fully understanding it. Yet we reaffirmed, in this project, that there is potential to be found, there is a clear effect of CBD to be observed, and whether or not this effect is beneficial - or more exactly how do we make it beneficial - is still blurry, but it is a promising path toward understanding sleep, memory, and how to treat the related disorders. Bibliography

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Additional ﬁgures and tables

Rat Batch Rat ID Included in Comments Condition Recording duration Time elapsed between number analysis (hh:mm:ss) feeding and start of recording 1 1 1 No Pilot 2 1 2 Yes CBD 06:00:37 02:00:00 3 1 3 Yes VEH 06:00:09 01:53:00 4 1 4 Yes VEH 05:59:46 02:00:00 5 1 5 Yes CBD 06:04:51 01:44:00 6 1 6 No Short recording, no HPC data 7 1 7 No Died during recording 8 1 8 No Short recording 9 1 9 Yes VEH 04:00:02 06:11:00 10 1 10 Yes CBD 04:00:16 05:00:00 11 1 11 Yes CBD 04:08:19 05:41:00 12 1 12 No No PFC data 13 1 13 No No HPC data 14 1 14 No No HPC data 15 1 15 No No HPC data 16 1 16 No No HPC data 17 2 201 Yes VEH 05:34:00 02:28:00 18 2 202 No Died during recording 19 2 203 Yes VEH 07:08:00 01:44:00 20 2 204 Yes CBD 07:15:00 01:45:00 21 2 205 Yes CBD 07:46:00 01:44:00 22 2 206 Yes VEH 07:53:00 01:37:00 23 2 207 Yes CBD 07:10:00 01:50:00 24 2 208 No Abnormal recordings 25 2 209 Yes CBD 07:17:00 01:43:00 26 2 210 Yes VEH 07:20:00 01:40:00 27 2 211 Yes VEH 07:20:00 01:40:00 28 2 212 Yes CBD 07:21:00 01:39:00 29 2 213 Yes VEH 07:25:00 01:35:00 30 2 214 Yes CBD 07:26:00 01:34:00

Table A.1: Table including all the animals involved in the experiments, including the motivation for the animals non included in the statistical analysis. Note, CBD = cannabidiol animals, VEH = vehicle animals.

44 APPENDIX A. ADDITIONAL FIGURES AND TABLES 45

(a) Hypnograms for the CBD animals (b) Hypnograms for the VEH animals

Figure A.1: PCA scored sleep of every animal, left column are CBD, right column are vehicles. Note that animals are not aligned here, and recording durations diﬀer across animals. 46 APPENDIX A. ADDITIONAL FIGURES AND TABLES

Figure A.2: Proportion of REM and NREM sleep in each animal, sorted by group (ﬁrst 10 animals are CBD, last 9 are VEH).

Rat 5 Ground truth Rat 9 Ground truth REM NREM REM NREM PCA-scored REM 3425 1637 PCA-scored REM 2795 55 NREM 63 1197 NREM 10 10643 (a) Confusion matrix for rat 5 (b) Confusion matrix for rat 9

Rat 10 Ground truth Rat 11 Ground truth REM NREM REM NREM PCA-scored REM 2283 395 PCA-scored REM 3350 350 NREM 86 3843 NREM 448 7664 (c) Confusion matrix for rat 10 (d) Confusion matrix for rat 11

Table A.2: Confusion matrices for the 4 manually scored animals. Values represent the number of REM/NREM epochs (10 seconds). APPENDIX A. ADDITIONAL FIGURES AND TABLES 47

(a) Theta power (3-6 Hz) per bins of 45 min in both groups

(b) [Slow oscillation - delta power (0-3 Hz) per bins of 45 min in both groups

Figure A.3: Bin-averaged theta (top) and slow oscillations-delta (bottom) powers, averaged across animals, with SEM. Right barplot is the mean value across time and rats with individual values per animal displayed to visualize the individual distribution. 48 APPENDIX A. ADDITIONAL FIGURES AND TABLES

Figure A.4: PCA scored sleep stages superimposed with the amount of ripples (top) and sharp waves (bottom) per bins of 30 seconds in rat 203, VEH. APPENDIX A. ADDITIONAL FIGURES AND TABLES 49

(a) Time-normalized animal-averaged amount of ripples at the start/end of long NREM bouts

(b) [Time-normalized animal-averaged amount of SW at the start/end of long NREM bouts

Figure A.5: Each point represent one animal, color coded for CBD (green) or vehicle (grey). Left hand column is the time-normalized mean amount of the sleep event across the ﬁrst 10% of the long NREM bouts, right hand column is across the last 10%. Pairs or data for the same animal are linked with a dotted line. 50 APPENDIX A. ADDITIONAL FIGURES AND TABLES

Feature P sphericity RANOVA F-stat P GG df1,df2 P ANOVA F-stat (df1,df2) Time Drug:Time Time Drug:Time Drug REM amount 0.0064 3.232 1.106 0.0163 0.361 9,153 0.893 0.0186(1,17) REM bouts count 0.00139 1.905 0.693 0.109 0.616 9,153 0.0204 6.539(1,17) REM bouts duration 1.989e-09 4.213 1.2529 0.00667 0.299 9,153 0.104 2.9458(1,17) NREM amount 0.00756 8.059 1.161 1.16e-05 0.336 9,153 0.914 0.0121(1,17) NREM bouts count 0.000295 1.93 0.649 0.107 0.646 9,153 0.0125 7.796(1,17) NREM bouts duration 0.0216 1.708 1.023 0.151 0.406 9,153 0.369 0.851(1,17) Transitions amount 1.84e-07 2.409 0.916 0.0292 0.468 9,153 0.0461 5.923(1,17) Ripple amount 9.95e-17 2.405 0.847 0.0971 0.449 9,153 0.675 0.181(1,17) SW amount 0.0286 5.506 1.363 0.000139 0.309 9,153 0.765 0.0787(1,17) Theta power 3.93e-14 4.215 0.522 0.0140 0.642 9,153 0.981 6.145e-4(1,17) Delta power 6.16e-09 5.937 0.518 0.000398 0.721 9,153 0.302 1.131(1,17) (a) Statistics for RANOVA and ANOVA

Feature Normalization P-value T stat DF REM amount Yes 0.873 0.1626 17 REM bouts count Yes 0.012 2.813 17 REM bouts duration Yes 0.0635 -1.985 17 NREM amount Yes 0.824 -0.226 17 NREM bouts count Yes 0.008 3.001 17 NREM bouts duration Yes 0.231 -1.243 17 REM/NREM transitions amount Yes 0.0304 2.468 16 Ripple amount Yes 0.684 0.413 17 SW amount Yes 0.0.775 -0.290 17 Theta power Yes 0.983 -0.0215 17 Delta power Yes 0.306 1.055 17 Ripple amplitude No 0.0534 1.932 21554 Ripple duration No 0.0384 -2.071 21554 Ripple frequency No 0.582 0.551 21554 SW amplitude No 2.105e-181 -29.04 18810 Ripples start/end long NREM bouts No 1.540e-07 6.012 55 SW start/end long NREM bouts No 2.581e-08 6.489 55 Ripples star/end long bouts, rat averaged No 9.930e-05 4.970 18 SW star/end long bouts, rat averaged No 3.238e-04 4.429 18 (b) Statistics for Student’s test

Table A.3: (Top) Statistics for RANOVA and ANOVA. P sphericity indicates the p-value for Mauchly’s sphericity test, as it is an assumption of the RA- NOVA. Since no p-value was above the 0.05 threshold, sphericity can’t be assumed, hence we did not show the statistics for sphericity assumed, and rather the p-values with Greenhouse-Geisser (GG) adjustment. For the analysis of the drug eﬀect only, a simple ANOVA was computed. (Bottom) Statistics for the Student’s test. For the analysis of ripple at the start/end of long bouts, a paired t-test was performed. For all other features, a 2-sample t-test was performed. Signiﬁcant p-values at the 5% threshold have been bolded. Appendix B

Background chapter

51 KTH Royal Institute of Technology

Investigation of the eﬀect of Cannabidiol on sleep-like states and memory-associated brain events

Background chapter - Neuroscience of sleep and memory

Author: Tugdual Adam Supervisor: Lisa Genzel

19-03-2020 Contents

1 Introduction 1

2 Basics - Neurons and neuroanatomy 1 2.1 Neurons and the nervous system ...... 1 2.2 The human brain ...... 2

3 Techniques for brain imaging 3

4 Introduction to sleep and memory 5 4.1 Sleep stages and brain activity ...... 6 4.2 Memory classiﬁcation and role of sleep ...... 8

5 Sleep scoring 9

6 Sleep and anesthesia 10

7 The endocannabinoid system and phyto-endocannabinoids 11

8 Conclusion 13

ii 1. Introduction

Brain research has developed exponentially in the last years, and the interest from the scientific community is even greater than before. Indeed, the more discoveries are made, the more we realize we don’t know: the human brain probably is among the things the least understood. This literature review gives an insight into the neuroscience research field, and in particular sleep and memory neuroscience, which are the main topics of my master thesis project. Enough content is provided for an uninformed reader to understand the main information and latest studies within the field.

Tugdual Adam

2. Basics - Neurons and neuroanatomy

The human brain is one of the most complex biological structure that can be found in the nature. It has been studied for a very long time, but a turning point has been the neuron doctrine: Santiago Ramón y Cajal discovered in 1887 that any animal brain is made up of discrete individual cells, later introduced as neurons by Waldeyer. These cells communicate together through chemical and electrical interactions, known as synapses, and form a very complex neural network. Neuroscience is the multidisciplinary science that studies the nervous system of any living thing in order to understand the biological basis for behavior [46]. It emcompasses various aspects, such as the chemical study of brain processes, the biological understanding of nerve cells, or the grasp of disordered behaviors, emotions or cognition.

2.1 Neurons and the nervous system

Let us ﬁrst explain brieﬂy the basic biology of neuroscience. Most of the brain cells aren’t actually neurons, as previously introduced, but glial cells. They make up 90% of the brain cells and, unlike neurons, do not carry an electric impulse [9]. Their function include supporting neurons, chemically and physically, digesting dead neurons or producing myelin (insulating layer around neurons’ axons which fastens the transmission of electric impulses) [9]. The remaining 10% of brain cells are the aforementioned neurons. Like almost every other cells, they have a nucleus, carrier of the DNA, located in the cell body (soma). The particularity of these cells is that they can physically communicate with each other. For this purpose, they collect information from other neurons via multiple branch-like cellular extensions called dendrites, and can send out their own information via one long slender ‘arm’ called axon. The latter connects with other neurons’ dendrites by forming synapses to pass on information in the form on electrical pulses, also usually referred to as action potentials (Figure 2.1a).

1 This big connectome forms the brain’s neural network, which for humans contains an approximation of 80 to 100 billions neurons [19]. The neural network can be seen as the biological computer processing the information collected by the body in its environment and sending out appropriate response signals to the rest of the body. In vertebrates, the brain alongside with the spinal cord, constitutes the central nervous system (CNS), which role is to process the collected information by the body and coordinate an appropriate response by inﬂuencing the activity of the whole body. To route the collected sensory information to the brain, the peripheral nervous system (PNS) acts as a relay. It consists of nerves and ganglia that run through every limb of the body (Figure 2.1b).

(a) (b)

Figure 2.1: 2.1a Schema of a neuron. The neuron receives input through his dendrites, and sends out an electric signal via its axon to other neurons’ dendrites. Note that, unlike suggested by the schema, a single neuron receives input from multiple neurons and pass on the information to multiple neurons as well [44]. 2.1b Overview of the central nervous system (CNS) and peripheral nervous system (PNS) in humans [3].

2.2 The human brain

The human brain is divided into three distincts structures (Figure 2.2a): the cerebrum, the cerebellum and the brainstems, each having diﬀerent functions.

The cerebellum, located at the back of the head, under the cerebrum, is involved in motion planning and coordination and balance. The brainstem connects the rest of the brain to the spinal cord. It is involved in numerous automatic functions, such as heartbeat, coughing, breathing, sneezing or swallowing. The high nerve density of this structure makes it critical to lesions, which often bring many complications. The cerebrum is the largest structure of the brain and performs higher functions. It consists of two hemispheres which are divided in several lobes (Figure 2.2b).

2 (a) Main brain structures (b) Architecture of the cerebrum

Figure 2.2: 2.2a The three main structures of the brain, cerebrum, cerebellum and brainsteam. 2.2b Cerebrum lobes architecture. The Broca’s area is responsible for the formulation of words, while the Wernicke’s area is involved in the understanding of these words. The motor strip, in the frontal lobe, is the primary motor control of the body and the sensory strip is involved in processing all of our sensory information from the environment [8].

The frontal lobe has many functions, but it is known to be the control center of our emotions. It regulates personality and behavior. It is also involved in attention skills and memory as well as motor control. One particular area of the lobe, Broca’s area, is related to the formulation of words [9]. The parietal lobe is mainly involved in the sense of touch and is important for the control of ﬁne motions, such as ﬁnger motions. It is also known for visuospatial processing, by which one is able to locate their body in relation to the objects around them [9]. The temporal lobe is located on the side of each hemisphere, it is mainly known to process the auditory information. It contains the hippocampus, which plays an important role in memory. It is also in this lobe that is located the Wernicke’s area, which is associated with the understanding of known words [9]. Finally the occipital lobe, located at the back of the brain, is responsible for visual perception, allowing us to make sense of what we see [9].

3. Techniques for brain imaging

There are currently many diﬀerent techniques that exist to record the activity of the human brain, from global dynamics to local activity of a group of neurons. The next sections go through the most used ones nowadays.

3 Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) These techniques are called hemodynamic techniques, as they measure neural activity by detecting changes in blood flow, which is commonly accepted as a relevant measure of brain activity [21]. In PET, glucose is marked by a radioactive tracer, forming a radionuclide and then injecting into the blood. Using a scanner, photons will break down the radionuclide, which result in the emission of a positron and gamma rays, which are detected by the scanner[11]. fMRI in contrast measures blood flow via changes in oxygenation. Upon activation of a brain area, the concentration of oxyhemoglobin (hemoglobin loaded with oxygen) increases, while the concentration of deoxyhemoglobin decreases, change that is detected via MRI[11]. PET is more accurate than fMRI as it measures activity based on glucose consumption, and active neurons use glucose as fuel, however PET scans are costly, invasive and quite slow [7] . fMRI on the other side is noninvasive and pose very little health risks, making it the most used brain imaging technique nowadays [7]. Leaders in clinical PET and MRI scanners include GE Healthcare, Philips Healthcare and Siemens Healthcare [49]. (See figure 3.1)

(a) PET scanner Discovery IQ Gen 2 by GE (b) fMRI scanner MAGNETOM Verio by Healthcare [15] Siemens Healthcare

Figure 3.1: Examples of clinically used PET/fMRI scanners.

Electroencephalography (EEG) This technique consists in placing electrodes on the subject’s scalp over the brain area of interest. Neu- rons communicating via electric impulses, resulting in changes in the extracellular concentration of ions. This change in ion concentration induces changes in the potential of the extracellular medium, change that can be captured by the electrodes. The resulting electroencephalogram consists in several electric signals, recording of the brain area of interest by each electrode[33]. The leaders in humans EEG hardware, in terms of number of publications, are NeuroScan, Brain Products and BioSemi [51]. A typical EEG hardware looks like a tissue helmet with already placed electrodes, that just needs to be put on the subject’s head. Note that is also usually includes reference electrodes usually placed over the masseter muscle (chewing muscle) or the mastoids (muscles located behind the ear) as it far enough away from any neurons, and is usually inactive during brain recording sessions (the masseter being especially used for sleep recordings) [27].

Local field potential (LFP) The local field potential refers to the potential of the extracellular space around the neurons. It is recorded by micro-electrodes inserted deep into the desired brain area (contrary to the scalp as in EEG)[14]. The type of signal obtained is similar to the one from EEG, but it enables to record the activity locally in a desired area, while EEG only record superficial activity. It was shown [14] that action potentials have little influence in the creation of LFP, it is rather the synchronicity of postsynaptic current in cortical neurons, which differ

4 (a) (b)

Figure 3.2: 3.2a EEG recording helmet 32-channels Quick Cap Neo Net by NeuroScan [1]. 3.2b Typical EEG recording, here of an epileptic patient [16]. Each channel corresponds to a recording from an electrode. depending on the brain state, that gives rise to LFP. In the end, EEG and LFP record the same extracellular potential, but EEG is more superﬁcial, and ﬁltered through the skull as the electrodes are external, while they are internal in the case of LFP. LFP recording is mostly only done in animals, and the hardware consists of:

Electrodes, that are placed into the brain to record the local field potential. There are several types • of electrodes, for example silicon probes, which have numerous acquisition channels along their length (see figure 3.3a). Tetrodes are also being used, and consist in a grouping of 4 silicon probes of adjustable depth. Such probes are being produced by companies like Atlas Neuroengineering, Doric Lenses or Open Ephys. A headstage that connect to the probes and allow the recording of their signal. As its name indicates, it • is placed and left on the head of the animal (see figure 3.3b). Companies like intan Technologies produce headstages. An acquisition box which acts an the interface between the headstage and a computer. A known • manufacturer for such acquisition boxes is Open Ephys.

4. Introduction to sleep and memory

Sleep is a biological process common to all the known animal species, which happen for most of them on a cyclical basis. In humans, it covers roughly a third of the lifespan, thus it is a crucial process. Sleep neuroscience is the understanding of the mechanisms of sleep and sleep-related processes. It is well known nowadays

5 (a) Schema of a silicon probe (b) Rat with a headstage

Figure 3.3 that sleep regulates a number of primordial biological functions, and is hence necessary for all animals. Studies dealing with sleep deprivation are numerous, and conclusions are often the same: sleep deprivation leads in humans as well as in other animals to cognitive impairment, namely deﬁcit in attention, impaired long-term memory, diﬃculty to make decisions and reduced vigilance [2]. However the brain is a complex organ that behaves in a particular and unique way during sleep, and there is still a lot to discover about sleep neuroscience.

4.1 Sleep stages and brain activity

Each night, during sleep, humans undergo several so-called sleep stages characterized with their own brain activity. These cycles, in humans, typically last around 90 minutes [30] and are divided into 4 main stages: non-rapid-eye movement (NREM) sleep make up the ﬁrst 3 stages, and rapid-eye movement (REM) sleep completes each cycle (Figure 4.1). NREM sleep is the succession of three phases: the two ﬁrst phases make up the light sleep, at the beginning of which (phase 1) the sleeper still has a sensation of awareness, and can move its body, although physiological functions such as heartbeat, breathing and eye movement, slow down. Upon reaching phase 2, eye-movement stop, hence the name non-rapid-eye movement, and consciousness is lost. Dreams can happen during light sleep, and it is usually easy to wake the sleeper in this stage. On an EEG, the light sleep is characterized by spindles, which are oscillations of frequency 12-16 Hz.

6 Figure 4.1: Typical hypnogram for a human adult’s night, note that the NREM sleep formerly had stages 3 and 4, which are now gathered as one by the neuroscientiﬁc community [32].

The next stage is called deep sleep, during this phase physiological functions are slowed down, eyes do not move and it is typically hard to wake up the sleeper. Brain activity is also slowed down, which is why this phase is also referred to as slow wave sleep (SWS). On an EEG, SWS is dominated by delta waves, which are oscillations characterized by frequencies around 0.5-4 Hz. SWS is physiologically characterized by the secretion of growth hormone. Note that all on NREM sleep is associated with memory consolidation (which will be detailed later on) which is why it is a crucial part of our sleep. The last stage of the night is the REM sleep, characterized, as its name suggest, by a lot of rapid eye motions, as well as an absence of muscle tone in the body (atonia), and the ability to dream vividly. One noticeable fact is that, throughout the night, REM sleep tends to last longer, while NREM, and especially deep sleep, tends to shorten (see ﬁgure 5). Brain-wise, REM sleep is characterized by fast and desynchronized oscillations, which makes is similar to the waking state. Notably, hippocampus EEG is dominated by 3-10Hz theta oscillations [47], while the cortex is usually subject to 40-60 Hz gamma waves. Energy-wise, the brain during REM sleep uses as much, if not more, energy as during the wake state, while the rate is 10-40% lower during NREM sleep [20]. REM atonia is the process by which motoneurons (neurons innervating muscle ﬁbers), by various mechanisms, hyperpolarize (meaning that their potential decreases), hence raising the threshold for their activation, and resulting in a global absence of muscle tone [10].

7 4.2 Memory classiﬁcation and role of sleep

As previously mentioned, sleep plays an important role in memory consolidation, and in particular the SWS. Memory is typically divided into two kind of memories. Procedural memory consists in implicit motor skills (such as biking), it cannot be told or taught orally. Declarative memory on the other side consists of recall of speciﬁc items, and can be told orally. It is further divided into semantic memory, which is associated with the memory of general facts (for example the rules of a certain sport, or knowing the capital of a certain country) and episodic memory, which is the memory of personal punctual events (for example one can recall what they have eaten the last day for dinner - episodic is often associated with short term events). Declarative memories are usually stored into the cerebrum. During daily life, one usually work with short-term memory, or working memory, it is items that are useful for the task currently being carried out. This memory is fast forgotten, so a step is required turn it into long- term memory, which is mainly stored in the neocortex. The hippocampus (HPC) plays an important role in this transfer of information to the neocortex, or more commonly called memory consolidation, therefore it is often referred to as the center for short-term memory. The term transfer does not mean that a memory “moves” within the brain, but rather that connections (synapses) between HPC and cortex strengthen.

The pre-frontal cortex (PFC)

The PFC (frontal part of the frontal lobe) plays an important role for memory, it is involved in the retrieval of remote memories, acting as a hub in the coordination of other brain areas. It is also particular in such that it has a direct pathway from the hippocampus, bypassing cortical relay areas, which explains the wealth of interactions between PFC and HPC, most likely important for memory consolidation.

The hippocampus during sleep

The hippocampus takes its name from its resemblance with the sea-horse (hippokampos in greek). It is located in the temporal lobe and is part of the limbic system. It is both organized laterally and longitudinally: it is divided in its length in four fields, named CA1-CA4 (figure 4.2) with CA4 closest to the dentate gyrus (DG) and CA1 to the opposite extremity, closest to the subiculum. Laterally, the hippocampus is also made up of 5 layers. The HPC is made up of both inhibitory cells and excitatory cells, the latter being called pyramidal cells which, like other neuronal cells, have dendrites and axons. The hippocampus mainly takes input from the entorhinal cortex (EC, brain area in the medial temporal lobe, acting as a relay between HPC and neocortex) and projects its axons to different part of the brain, including hypothalamus, EC and the aforementioned PFC. The HPC shows a temporally distinct activity, between periods when memory is encoded and used, and inactive periods, or sleep, when memory is being consolidated. This “two-stage” hippocampal activity is hence divided between active state (or on state), corresponding to retrieval of memory, and off state (or down state), when memory consolidation happens. The on states are dominated by theta oscillations (6-10Hz) that propagate to other cortical areas, in particular PFC, and are believed to carry information between HPC and other brain areas [5]. The off states are characterized by a peculiar event called sharp-wave: it is a burst of activity by the CA3 field of the hippocampus that last for a few milliseconds only [5]. The excitations generated spread in the rest of the hippocampus, and the sharp wave causes in CA1 a deflection in the LFP and high frequency oscillations (150-200Hz) called ripples. Thus ripples are usually used to detect sharp-waves, and the term sharp-wave ripples (SWR) is often used to refer to these events.

8 Figure 4.2: Organization of the hippocampus [13]. DG - Dentate Gyrus, brain structure close to the HPC, providing input to the latter. SUB - Subiculum, or subicular cortex, transitional area between the HPC and the EC.

Recent studies seem to agree on the fact that SWR are important for memory consolidation: SWR are associated with high synchronous neural ﬁring [24] from the hippocampus to many cortical structures, including directly cortical interneurons in the PFC, causing a feed-forward inhibition simultaneous to the excition, thus enabling the excitation to be focalized to only a subset of PFC neurons [50]. SWR activity has be shown to be simultaneous with many transient cortical activity spread throughout the brain, but prominent in the PFC [40], reﬂecting the high connectivity between both structures, and how they must both be involved in memory consolidation.

5. Sleep scoring

A crucial step in all neuroscience studies consist in relating the measured data (EEG,LFP...) to the different sleep stages the subject has been going through during the sleep period. This step is very important as it is the basis on which conclusions will be drawn from the observations, this is what is commonly referred to as sleep scoring. The common process consists in computing the ratio of the spectral power (power of the frequency components present in a signal, and that can be isolated via a Fourier transform for example) for theta and delta oscillations throughout the whole brain signal. Then, by going chronologically through the signal, usually in bins of roughly 10 seconds, a human scorer can differentiate REM sleep and NREM sleep by looking at the aforementioned ratio, which is typically high for REM stages and wake and low for NREM stages[26]. Micro-arousal are also common in natural sleep, and are usually detected by the addition of an accelerometer in the experimental design, which is normally of very low amplitude when the animal sleeps, and shows significant increase in amplitude during these arousals. Automated scoring of REM and NREM stages are becoming more common[25], usually having recourse to clustering algorithms or machine learning processes, but the neuroscientific community agrees on the fact that

9 hand scoring is the closest one can get to the ground truth.

6. Sleep and anesthesia

It is common in animal studies to use anesthesia, for example if the experimental design requires that the animal shouldn’t move, or for pain reasons. Anesthesia is a drug-induced state of unconsciousness that is revertible. Some drugs used for anesthesia have been shown to be similar in several ways to sleep, the brain showing characteristics REM and NREM phases [45]. But note that many anesthetic drugs do not have this characteristic, the brain showing no sign of activity at all. Usually, anesthesia used in humans for medical purposes does not put the patient into a sleep-like state, but in animal sleep studies, the drugs used imitate the natural sleep pattern, hence the focus will be made on these drugs. However, anesthesia-induced sleep, even with these drugs (isoflurane, ketamine and urethane, detailed later on) is not natural sleep, and this should be taken into account when analyzing the data. A further precaution that needs to be considered is that anesthesia can also interact with the experimental design or the goal the study: for example in studies investigating the effect of a specific drug on sleep, the experimenter needs to verify that this drug doesn’t produce a cocktail effect with anesthesia, nor that one of the drug cancels the effect of the other. Several anesthetic drugs are commonly used in sleep neuroscience for their similarity with natural sleep on various aspects, presented bellow are the most commonly used ones: isoflurane, ketamine and urethane anesthesia.

Isoflurane Isoflurane is an inhalation anesthesia that imitates sleep when administered in light doses. A study conducted on rats [23] showed no significant alteration of REM and NREM sleep durations in anesthetized rats, and a slight increase in the duration of SWS in sleep-deprived anesthetized rats. Similar results were achieved in humans, with no difference in subsequent REM and NREM sleeps, but a slight shift from SWS toward lighter sleep [36].

Ketamine Ketamine on the other side is an intravenous/subcutaneous drug. A study conducted on rabbits [48] showed that ketamine substantially increases NREM sleep and in particular SWS. Same conclusions could be reached in humans [45]. However studies showed substantial alterations of the brain recordings in ketamine-induced sleep due to psychotropic side eﬀects and reportedly unpleasant dreams [52][6].

Urethane Urethane is also in intravenous/subcutaneous anesthesia. A recent study [39] investigated urethane in rats. They reached the conclusion that urethane induced sleep is a “good enough” substitutes, in such that REM and NREM phases showed the same breath associated changes as in the natural sleep and urethane sleep correctly model the transitions between these stages. An older study [53] reported that urethane use was associated with globally reduced EEG patterns, with for instance ripples decreasing from 180-200 Hz to 100-120Hz in anesthetized rats. Because under urethane anesthesia, only two stages can be distinguished, resembling REM and NREM[54], it is common to refer to these stages as REM-like and NREM-like. Note that in rodents, urethane is toxic, hence when it is used as anesthesia in rats/mices sleep study, the latter are not woken up

10 (which would most likely cause them to have a seizure) and are instead killed after the study [43].

7. The endocannabinoid system and phyto-endocannabinoids

The endocannabinoid system (ECS) is a neuromodulatory system of the human body, which is involved in the regulation of numerous functions, such as sleep, memory, mood, appetite or fertility [18]. The system comprises endogenous cannabinoids, cannabinoid receptors (CB1 and CB2) and enzymes that synthesize and degrade endocannabinoids. Endogenous cannabinoids are any endogenous lipids that interact with the cannabinoid receptors. The two most known and characterized endocannabinoids are anandamide (AEA) and and 2-arachidonoyl glycerol (2-AG) [29]. They are enzymatically produced and released on “demand” by the body via fast enzymatic steps, contrary to regular neurotransmitters that are usually produced ahead of time and stored in vesicles. Because the body only produces them when needed, it is arduous to characterize their typical level in the body. The functions of the ECS are primarily influenced by the cannabinoid receptors CB1 and CB2 [29]. CB1 are found mainly in the central nervous system (CNS), especially in the cortex, cerebellum, hippocampus and basal ganglia [31]. CB2 are found mainly in the PNS, and are expressed at much lower levels in the CNS. The function of an endocannabinoid will depend on the endocannabinoid type, the type of the receptor and where it is located (Figure 7.1). Finally, enzymes are involved in the ECS both for producing the cannabinoids and for breaking them down once they have fulfilled their function. Enzymes involved in the production and degradation of AEA and 2-AG are very different, but, for simplicity reasons, no further detail will be made. They however share the characteristics that enzymatic steps to produce each endocannabinoid are rather fast, to respond to the need of the body. One characteristics of ECS is that is also accepts phytocannabinoids (plant based), among which, cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC), two components found in the cannabis. THC is the main component of cannabis, responsible for the “high” feeling associated with its use, it acts on the CB1 receptors. CBD is a non-intoxicating component of cannabis which interacts with the CB2 receptors. Studies showed that it could counter the effects of THC, thus cannabidiol has received a lot of attention for its potential therapeutic effects [4]. The role of ECS in the circadian cycle (all the biological processes cycling with a period of roughly 24 hours) has been recently studied and results show that it is involved in its regulation [41], and especially affects promotion of sleep [42]: ECS acts as a relay between the different circadian regulation systems, and the underlying behavioral and physiological processes, among which sleep. Different studies highlighted that wakefulness and sleeps stages durations were affected by manipulation of the ECS, for example rats treated with a CB1 antagonist would increase time spent in wakefulness and decrease SWS and REM sleep[42], or intracerebroventricular injection (directly into the cerebrospinal fluid in cerebral ventricles) of AEA would cause a decrease in wakefulness and an increase in SWS and REM sleep [37]. It then seems reasonable to assume that THC and CBD may also affect sleep, however their effects are various and hardlier characterized. For example, while low doses of THC have been reported to have a sedative effect [34], which is why cannabis is being used more and more as a non-pharmaceutical solution to insomnia (it is estimated by the National Institutes of Health that around 30% of the global population suffers from insomnia or sleep disruption [22]), high doses of THC seem to have more of an activating effect, resulting in a delayed sleep

11 Figure 7.1: Location of the cannabinoid receptors CB1 and CB2 and non-exhaustive list of target functions [17] onset [4], suggesting that THC would have a biphasic dose-dependent effect. As for CBD, studies have shown that it can counter the activating effect of THC and potentiate its sedative effect [35]: direct administration of CBD would have both sleep-inducing and sleep-maintenance effects [35] in rats. In humans however, results are yet not well defined: some studies suggest that it would mainly have an alerting effect in humans, increasing awake activity during sleep [38], especially when combined with THC. Older studies [12] show that acute intake of CBD is associated with an increased total sleep time and quality, with a reduced REM sleep, while in a more recent paper, Linares et al. show that CBD does not have a substantial effect on sleep [28]. The effect of CBD on sleep is yet to better determine, but the neuroscientific community do agree that there is a therapeutic

12 potential to be found.

8. Conclusion

Neuroscience is a vast and complex domain, which emcompasses various natural behavior and processes. Sleep and memory are widely studied and still poorly known, but late studies show that the hippocampus, and especially sharp wave ripples complexes play an important role in memory consolidation and the transfer of information to the neocortex. It is now known that such activity happens during sleep, which makes it a very crucial step of the circadian cycle. However, sleep disruption is nowadays a worldwide problem, which ultimately also affects memory. Researchers found some encouraging preliminary results that cannabidiol, a component of cannabis, could have beneficial effects on sleep, and hence memory, by acting on the endocannabinoid system, however further studies need to be conducted to further investigate this hypothesis. That is why the investigation the effect of cannabidiol on sleep stages and cortico-hippocampal communication is the main focus of this master thesis.

13 Bibliography

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