Sleep Disruption in Epilepsy 公开
Harvey, Brandon (Fall 2025)
Abstract
People with epilepsy are known to experience comorbid sleep disorders, including insomnia and sleep fragmentation, which bi-directionally interact with the occurrence of both seizures and interictal epileptiform discharges (IEDs). However, the limitations of clinical research in patient populations have precluded thorough mechanistic investigations of these phenomena. In this dissertation, I describe both methodological development for the evaluation of the sleep-wake phenotype of the murine intra-amygdalar kainic acid (IAKA) model of medial temporal lobe epilepsy (MTLE) and the use of these methods to characterize the sleep phenotype of this model in relation to the frequency and timing of IED and seizure events.
First, we used a large dataset of chronically-recorded electrophysiological data from both IAKA model mice and saline controls, along with manually labeled sleep-wake and seizure states, to train and validate the Sleep-Wake and Ictal State Classifier (SWISC). This is the first classifier capable of scoring wake, non-rapid-eye-movement (NREM) sleep, REM sleep, and seizure states in both epileptic and non-epileptic mice, achieving greater than 96% accuracy.
This classifier was used to evaluate full four-week chronic recordings from 23 epileptic mice, as well as 9 saline controls, revealing that epileptic mice present with insomnia, leading to a continuous accrual of sleep debt. This sleep loss was not found to be related to specific ictal or interictal phenomena, suggesting homeostatic alterations from status epilepticus. Additionally, bouts of sleep were found to be shortened after the induction of epilepsy, demonstrating sleep fragmentation as seen in people with epilepsy. This sleep fragmentation was found to be primarily attributable to the induction of the epileptic state as well as IED frequency, and the associated increased arousal frequency from both NREM and REM sleep stages.
We then employed probabilistic analysis, demonstrating that the post-IAKA increase in arousal from NREM sleep was partially attributable to IED events detected in both the left hippocampus, the cortical EEG, or the simultaneous detection in both areas. However, the primary contributor to this increase in arousal is not attributable to the occurrence of IEDs, suggesting potential alterations to the homeostatic regulation of sleep pressure as a driver of epilepsy-related sleep instability.
Table of Contents
Table of Contents
1. Introduction 1
1.1. Overview 1
1.2. Epilepsy 2
1.2.1. Terminology Related to Epilepsy 2
1.2.1.1. Seizure 2
1.2.1.2. Epilepsy 2
1.2.1.3. Epilepsy Syndromes and Subtypes 4
1.2.1.4. Seizure-Related Phenomena 5
1.2.1.4.1. Semiology 5
1.2.1.4.2. The Post-Ictal State 5
1.2.1.4.3. Electrographic Phenomena 6
1.2.2. History of Epilepsy Studies 7
1.2.2.1. Epilepsy Studies in the Ancient World - The Age of Demonology 7
1.2.2.2. Epilepsy Studies During the Enlightenment - The Age of Semiology 9
1.2.2.3. Epilepsy Studies in Modern Medicine - The Age of Electrophysiology 10
1.2.2.4. Anatomo-Electroclinical Correlations 12
1.2. Sleep 12
1.2.1. Terminology Related to Sleep 12
1.2.1.1. Sleep and Sleep Medicine 12
1.2.1.2. Sleep Staging 13
1.2.1.3. Types of Sleep Disruption 15
1.2.2. History of Sleep Studies 15
1.2.2.1. Nascent Reports of the Electrophysiological Method 15
1.2.2.2. Rapidly Escalating Momentum 16
1.3. Epilepsy and Sleep Disruption 19
1.3.1. Overview 19
1.3.2. Influence of Sleep on Epilepsy 19
1.3.3. Influence of Epilepsy on Sleep 21
1.3.5. Mechanisms of the Sleep-Epilepsy Relationship 22
1.4. Bridging the Knowledge Gap 22
2. Methodological Development to Study Sleep in A Mouse Model of Epilepsy 25
2.1. Overview 25
2.2.1. The Intra-Amygdalar Kainic Acid Model of Epilepsy 25
2.2.2. Four-Channel Electrophysiological Headplate 26
2.2.2.1. Headplate Design and Instrumentation 26
2.2.2.2. Surgical Procedure 27
2.2.3. Recording Equipment and Software 27
2.2.4. Intra-Amygdalar Kainic Acid Administration 28
2.4. Preliminary Results of Manual Evaluation of Sleep-Wake and Ictal States 32
2.5. Discussion 33
3. Automated Classification of Sleep-Wake States and Seizures in Mice 36
3.1. Overview 36
3.2. Introduction 37
3.3. Methods and Materials 39
3.3.1. Mice 39
3.3.2. Surgery 39
3.3.3. Mouse Recording 40
3.3.4. Kainic Acid Injection 41
3.3.5. Manual Sleep and Seizure Scoring 41
3.3.6. Dataset Composition 44
3.3.7. Computational Resources 44
3.3.8. Preprocessing 45
3.3.9. Feature Extraction 46
3.3.10. Training, Validation, and Test Dataset Creation 50
3.3.11. Model Architectures 51
3.3.11.1. AccuSleep 51
3.3.11.2. Support Vector Machine 52
3.3.11.3. Multi-Layer Architectures 53
3.3.12. Grid Search Paradigm for Multi-Layer Architectures 54
3.3.13. Statistics and Classification Metrics 55
3.3.14. Classification Performance of Trained Classifier with Shorter Epochs 59
3.4 Results 60
3.4.3. Performance of Existing Sleep-Wake Classifier (AccuSleep) in Epileptic Mice 61
3.4.4. Support Vector Machine 65
3.4.5. Multi-Layer Architectures 66
3.4.6. Channel Dropping and Applicability to Other Recording Configurations 73
3.4.7. Scoring Results Comparison 76
3.4.8. Performance on Shorter Epochs 77
3.5. Discussion 78
3.6. Data Sharing and Availability 81
4. Determinants of Sleep Disruption in a Mouse Model of Medial Temporal Lobe Epilepsy 83
4.1. Summary 83
4.2. Introduction 83
4.3. Methods and Materials 85
4.3.1. Mice 85
4.3.2. Surgery 86
4.3.3. Mouse Recording 86
4.3.4. Kainic Acid Injection 87
4.3.5. Perfusion and Histology 87
4.3.6. Data Analysis 88
4.3.6.1. Sleep-Wake and Seizure Measures 88
4.3.6.2. Sleep Fragmentation Measures 88
4.3.6.3. Behavioral Scoring of Seizures 89
4.3.6.4. Interictal Epileptiform Discharge Detection 89
4.3.6.5. Summary Data and Statistical Analysis. 89
4.3.6.6. Linear Mixed-Effects Modeling 90
4.3.6.7. Markov Chains 93
4.4. Results 94
4.4.1. Epileptic Phenotype 94
4.4.2. Histology 96
4.4.3.1. Sleep Disruption 97
4.4.3.2. Sleep Loss 98
4.4.3.3. Vigilance State and Seizure Relationships 101
4.4.3.4. Sleep Fragmentation 104
4.4.3.5. Interictal Epileptiform Discharges and Sleep Fragmentation 108
4.4.3.6. Modeling of Sleep Disruption in Relation to Epileptiform Events 114
4.5. Discussion 121
5. The Role of Interictal Epileptiform Discharges in Sleep Fragmentation 124
5.1. Overview 124
5.2. Introduction 124
5.3. Methods 125
5.3.2. Sleep-Wake, Seizure, and Interictal Discharge Measures 125
5.3.3. Probability Decomposition 126
5.3.3.1. Conditional Probability 126
5.3.3.2. Total Probability 126
5.3.3.3. Kitagawa Decomposition 128
5.3.3.4. Computing Resources 130
5.4. Results 130
5.4.1. Overall Arousal Probability 130
5.4.2. Discharge Distributions 132
5.4.3. Arousal Rate by Discharge Category 134
5.4.4. Decomposition of Arousal Rate and Discharge Prevalence 137
5.4.4.1. Decomposition of NREM Arousals 139
5.4.4.2. Decomposition of REM Arousals 140
5.5. Discussion 141
6. Conclusions and Future Directions 145
6.1. Summary 145
6.2. Automated Classification of Sleep-Wake States and Seizures in Mice 146
6.3. Determinants of Sleep Disruption in a Mouse Model of Medial Temporal Lobe Epilepsy 147
6.4. Probabilistic Analysis to Determine Interictal Epileptiform Discharge Influence on Sleep 149
6.5 Contextualization of the Findings 150
6.6. Future Directions 151
6.7. Overall Conclusions 152
References 154
Appendix 167
Supplementary Chapter 7. The Multisite Recording Headplate 172
7.1. Methods and Materials 172
7.1.1.2. Presurgical Assembly 177
7.1.2. Preparation of Depth Electrodes 177
7.1.3. Surgical Technique 178
7.1.3.1. Surgical Preparation 178
7.1.3.2. Craniotomy and Headplate Application 178
7.1.3.3. Electrode Implantation 179
7.1.3.4. Electrode Wiring 179
7.1.4. Recovery 180
7.1.5. Recording 180
7.1.6. Intrahippocampal Electrical Kindling 181
7.1.6.1. Identification of Afterdischarge Threshold 181
7.1.6.2. Cortico-Cortical Evoked Potentials 181
7.1.6.3. Kindling Stimulation 181
7.1.7. Analysis 182
7.2. Discussion 183
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