Etude expérimentale des dynamiques temporelles du comportement normal et pathologique chez le rat et la souris

155 p.Modern neuroscience highlights the need for designing sophisticated behavioral readout of internal cognitive states. From a thorough analysis of classical behavioral test, my results supports the hypothesis that sensory ypersensitivity might be the cause of other behavioural deficits, and confirm the potassium channel BKCa as a potentially relevant molecular target for the development of drug medication against Fragile X Syndrome/Autism Spectrum Disorders. I have also used an innovative device, based on pressure sensors that can non-invasively detect the slightest animal movement with unprecedented sensitivity and time resolution, during spontaneous behaviour. Analysing this signal with sophisticated computational tools, I could demonstrate the outstanding potential of this methodology for behavioural phenotyping in general, and more specifically for the investigation of pain, fear or locomotion in normal mice and models of neurodevelopmental and neurodegenerative disorders

SUPPORT VECTOR MACHINE FOR HIGH THROUGHPUT RODENT SLEEP BEHAVIOR CLASSIFICATION

This thesis examines the application of a Support Vector Machine (SVM) classifier to automatically detect sleep and quiet wake (rest) behavior in mice from pressure signals on their cage floor. Previous work employed Neural Networks (NN) and Linear Discriminant Analysis (LDA) to successfully detect sleep and wake behaviors in mice. Although the LDA was successful in distinguishing between the sleep and wake behaviors, it has several limitations, which include the need to select a threshold and difficulty separating additional behaviors with subtle differences, such as sleep and rest. The SVM has advantages in that it offers greater degrees of freedom than the LDA for working with complex data sets. In addition, the SVM has direct methods to limit overfitting for the training sets (unlike the NN method). This thesis develops an SVM classifier to characterize the linearly non separable sleep and rest behaviors using a variety of features extracted from the power spectrum, autocorrelation function, and generalized spectrum (autocorrelation of complex spectrum). A genetic algorithm (GA) optimizes the SVM parameters and determines a combination of 5 best features. Experimental results from over 9 hours of data scored by human observation indicate 75% classification accuracy for SVM compared to 68% accuracy for LDA

Assessment of a non-invasive high-throughput classifier for behaviours associated with sleep and wake in mice

This work presents a non-invasive high-throughput system for automatically detecting characteristic behaviours in mice over extended periods of time, useful for phenotyping experiments. The system classifies time intervals on the order of 2 to 4 seconds as corresponding to motions consistent with either active wake or inactivity associated with sleep. A single Polyvinylidine Difluoride (PVDF) sensor on the cage floor generates signals from motion resulting in pressure. This paper develops a linear classifier based on robust features extracted from normalized power spectra and autocorrelation functions, as well as novel features from the collapsed average (autocorrelation of complex spectrum), which characterize transient and periodic properties of the signal envelope. Performance is analyzed through an experiment comparing results from direct human observation and classification of the different behaviours with an automatic classifier used in conjunction with this system. Experimental results from over 28.5 hours of data from 4 mice indicate a 94% classification rate relative to the human observations. Examples of sequential classifications (2 second increments) over transition regions between sleep and wake behaviour are also presented to demonstrate robust performance to signal variation and explain performance limitations

A METHOD FOR NON-INVASIVE, AUTOMATED BEHAVIOR CLASSIFICATION IN MICE, USING PIEZOELECTRIC PRESSURE SENSORS

While all mammals sleep, the functions and implications of sleep are not well understood, and are a strong area of investigation in the research community. Mice are utilized in many sleep studies, with electroencephalography (EEG) signals widely used for data acquisition and analysis. However, since EEG electrodes must be surgically implanted in the mice, the method is high cost and time intensive. This work presents an extension of a previously researched high throughput, low cost, non-invasive method for mouse behavior detection and classification. A novel hierarchical classifier is presented that classifies behavior states including NREM and REM sleep, as well as active behavior states, using data acquired from a Signal Solutions (Lexington, KY) piezoelectric cage floor system. The NREM/REM classification system presented an 81% agreement with human EEG scorers, indicating a useful, high throughput alternative to the widely used EEG acquisition method

EXPERIMENTAL-COMPUTATIONAL ANALYSIS OF VIGILANCE DYNAMICS FOR APPLICATIONS IN SLEEP AND EPILEPSY

Epilepsy is a neurological disorder characterized by recurrent seizures. Sleep problems can cooccur with epilepsy, and adversely affect seizure diagnosis and treatment. In fact, the relationship between sleep and seizures in individuals with epilepsy is a complex one. Seizures disturb sleep and sleep deprivation aggravates seizures. Antiepileptic drugs may also impair sleep quality at the cost of controlling seizures. In general, particular vigilance states may inhibit or facilitate seizure generation, and changes in vigilance state can affect the predictability of seizures. A clear understanding of sleep-seizure interactions will therefore benefit epilepsy care providers and improve quality of life in patients. Notable progress in neuroscience research—and particularly sleep and epilepsy—has been achieved through experimentation on animals. Experimental models of epilepsy provide us with the opportunity to explore or even manipulate the sleep-seizure relationship in order to decipher different aspects of their interactions. Important in this process is the development of techniques for modeling and tracking sleep dynamics using electrophysiological measurements. In this dissertation experimental and computational approaches are proposed for modeling vigilance dynamics and their utility demonstrated in nonepileptic control mice. The general framework of hidden Markov models is used to automatically model and track sleep state and dynamics from electrophysiological as well as novel motion measurements. In addition, a closed-loop sensory stimulation technique is proposed that, in conjunction with this model, provides the means to concurrently track and modulate 3 vigilance dynamics in animals. The feasibility of the proposed techniques for modeling and altering sleep are demonstrated for experimental applications related to epilepsy. Finally, preliminary data from a mouse model of temporal lobe epilepsy are employed to suggest applications of these techniques and directions for future research. The methodologies developed here have clear implications the design of intelligent neuromodulation strategies for clinical epilepsy therapy

SLEEP ALTERATIONS IN MOUSE GENETIC MODELS OF HUMAN DISEASE

Sleep is a process essential for the well-being of an animal and in humans as much as one-third of our life is spent in sleep. Yet, the biological need for sleep still remains a conundrum. Our knowledge of the genes influencing sleep and the mechanisms regulating the process can be advanced with the utilization of genetic and genomic approaches which, in turn, may inform us about the functions of sleep as well. With this goal, I have investigated and examined sleep-wake phenotypes for a variety of transgenic and knock out animals. For the first part of my research (Chapter 3), I examined mouse models of Alzheimer’s disease, and a combined model of Alzheimer’s disease (AD) and Diabetes. Sleep disturbances in case of AD are evident long before the onset of cognitive decline. I investigated sleep-wake alterations in 5XFAD, a double transgenic mouse model of AD which displays an early onset of AD pathology and cognitive impairments. We found that these mice have shorter bout lengths under baseline conditions. This was true for both sexes, however, the effect was more prominent in females. Additionally, females also had a shorter duration of sleep compared to control animals. These overall bout length reductions are indicative of increased sleep fragmentation similar to the ones seen in human AD patients. Inadequate sleep is associated with increased risk for metabolic disorders such as diabetes besides neurodegenerative diseases such as AD. There is also growing evidence that type 2 diabetes mellitus (T2DM) poses an increased risk of AD. To understand how the two conditions interact, we studied a combined mouse model of AD and diabetes (db/AD) which was generated by crossing of db/db (diabetic obese mice) and APP-PS1 (knock-in AD mouse model). The resulting mice showed profound cerebrovascular as well as AD pathology. Both females and males, diabetic AD animals had longer sleep duration compared to non-diabetic AD animals. They also exhibited attenuated sleep-wake rhythms. Females were found to have shorter sleep bouts than males. In addition, significant two way interactions were found for the age and db/AD genotype. Our findings suggest that db genotype and not cerebrovascular pathologies affect sleep in our mouse model. For the last part of my research, we analyzed over 300 single gene knock out mouse lines generated on a C57BL6/NJ background, monitored at The Jackson Laboratory. With this unbiased approach where the knockouts were chosen at random, we identified 55 novel genes affecting various sleep traits, utilizing a variety of statistical approaches. Sex differences were found for a number of knockouts as well as controls. Control females were found to have shorter bout lengths and less sleep duration compared to male littermates

Genetic variation regulates opioid-induced respiratory depression in mice.

In the U.S., opioid prescription for treatment of pain nearly quadrupled from 1999 to 2014. The diversion and misuse of prescription opioids along with increased use of drugs like heroin and fentanyl, has led to an epidemic in addiction and overdose deaths. The most common cause of opioid overdose and death is opioid-induced respiratory depression (OIRD), a life-threatening depression in respiratory rate thought to be caused by stimulation of opioid receptors in the inspiratory-generating regions of the brain. Studies in mice have revealed that variation in opiate lethality is associated with strain differences, suggesting that sensitivity to OIRD is genetically determined. We first tested the hypothesis that genetic variation in inbred strains of mice influences the innate variability in opioid-induced responses in respiratory depression, recovery time and survival time. Using the founders of the advanced, high-diversity mouse population, the Diversity Outbred (DO), we found substantial sex and genetic effects on respiratory sensitivity and opiate lethality. We used DO mice treated with morphine to map quantitative trait loci for respiratory depression, recovery time and survival time. Trait mapping and integrative functional genomic analysis in GeneWeaver has allowed us to implicate Galnt11, an N-acetylgalactosaminyltransferase, as a gene that regulates OIRD

A Deep Learning-Based Approach for the Recognition of Sleep Disorders in Patients with Cognitive Diseases: A Case Study

Alzheimer's disease is the most common type of dementia. Patients suffer from of this kind of disease could show symptoms such as sleep disturbances, muscle rigidity or other typical Alzheimer's movement irregularities. In our work, we have focused on those types of disturbances related to sleep disorders. Due to their not well-known nature, it is difficult to develop software able to identify sleep disorders. In this work, we have addressed the problem of the automatic recognition of sleep disorders in patients with Alzheimer's disease by using deep learning algorithm

IDENTIFICATION OF NOVEL SLEEP RELATED GENES FROM LARGE SCALE PHENOTYPING EXPERIMENTS IN MICE

Humans spend a third of their lives sleeping but very little is known about the physiological and genetic mechanisms controlling sleep. Increased data from sleep phenotyping studies in mouse and other species, genetic crosses, and gene expression databases can all help improve our understanding of the process. Here, we present analysis of our own sleep data from the large-scale phenotyping program at The Jackson Laboratory (JAX), to identify the best gene candidates and phenotype predictors for influencing sleep traits. The original knockout mouse project (KOMP) was a worldwide collaborative effort to produce embryonic stem (ES) cell lines with one of mouse’s 21,000 protein coding genes knocked out. The objective of KOMP2 is to phenotype as many as of these lines as feasible, with each mouse studied over a ten-week period (www.mousephenotype.org). The phenotyping for sleep behavior is done using our non-invasive Piezo system for mouse activity monitoring. Thus far, sleep behavior has been recorded in more than 6000 mice representing 343 knockout lines and nearly 2000 control mice. Control and KO mice have been compared using multivariate statistical approaches to identify genes that exhibit significant effects on sleep variables from Piezo data. Using these statistical approaches, significant genes affecting sleep have been identified. Genes affecting sleep in a specific sex and that specifically affect sleep during daytime and/or night have also been identified and reported. The KOMP2 consists of a broad-based phenotyping pipeline that consists of collection of physiological and biochemical parameters through a variety of assays. Mice enter the pipeline at 4 weeks of age and leave at 18 weeks. Currently, the IMPC (International Mouse Phenotyping Consortium) database consists of more than 33 million observations. Our final dataset prepared by extracting biological sample data for whom sleep recordings are available consists of nearly 1.5 million observations from multitude of phenotyping assays. Through big data analytics and sophisticated machine learning approaches, we have been able to identify predictor phenotypes that affect sleep in mice. The phenotypes thus identified can play a key role in developing our understanding of mechanism of sleep regulation