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. Author manuscript; available in PMC: 2016 Jun 1.
Published in final edited form as: J Clin Neurophysiol. 2015 Jun;32(3):188–193. doi: 10.1097/WNP.0000000000000133

Seizures and brain regulatory systems: Consciousness, sleep, and autonomic systems

Madineh Sedigh-Sarvestani 1,*, Hal Blumenfeld 2, Tobias Loddenkemper 3, Lisa M Bateman 4
PMCID: PMC4362811  NIHMSID: NIHMS625820  PMID: 25233249

Abstract

Research into the physiological underpinnings of epilepsy has revealed reciprocal relationships between seizures and the activity of several regulatory systems in the brain, including those governing sleep, consciousness and autonomic functions. This review highlights recent progress in understanding and utilizing the relationships between seizures and the arousal or consciousness system, the sleep-wake and associated circadian system, and the central autonomic network.

1 Introduction

Individuals with pharmacologically intractable epilepsy face challenges in everyday activities due to the anticipation and consequences of their unpredictable seizures. Progress in understanding how various brain networks affect, and are affected by, epilepsy has led to improvements in epilepsy care (Segal et al., 2012; Seyal et al., 2013). Regulatory systems known to interact with epileptic networks include the consciousness system, the sleep-wake regulatory system, and the autonomic nervous system, among others. This review highlights recent research into the roles of these systems in understanding and managing epilepsy.

2 The consciousness system

Impairment of consciousness (IoC) during seizures puts patients at a risk for social embarrassment and stigmatization as well as physical dangers, including injury and death, due to falls, drowning, and automobile accidents, among others. Generalized seizures typically lead to IoC. Complex partial seizures also lead to IoC, while simple partial seizures do not. Improved understanding of the short-lived and reversible IoC seen during seizures could also impact progress in the study of other disorders of consciousness such as coma.

The cortical and subcortical structures implicated in regulating consciousness, including the basal forebrain, hypothalamus, thalamus, upper brainstem, medial frontal, anterior and posterior cingulate, and frontal and temporal-parietal association cortices, are collectively termed the ‘consciousness system’ (Blumenfeld, 2012). This system subsumes portions of the reticular activating system involved in sleep and arousal, as well as the recently categorized ‘default mode network’ (Raichle et al., 2001) involved in self-awareness and internal processing during the resting-state. Seizures that result in IoC typically affect brain regions within this system. Understanding how spatially restricted partial seizures can cause IoC remains an unresolved challenge.

2.1 Effect of seizures on consciousness in epilepsy syndromes

Consciousness involves subjective first-hand experiences, referred to as content of consciousness, and observed behavioral manifestations, referred to as level of consciousness (Cavanna and Ali, 2011). While various questionnaires have been designed to quantify IoC based on subjective and objective phenomena, IoC can be difficult to define using patient reports (Sanders et al., 2012).

Imaging studies have been used to determine the involvement of various brain structures in seizure-related IoC. Early studies used positron emission tomography (PET) and single-photon emission computed tomography (SPECT) to map metabolism and blood flow-dependent measures of neural activity. More recent studies utilize functional magnetic resonance imaging (fMRI). However, both metabolism and blood flow measures provide indirect estimates of neuronal activity with poor temporal resolution. On the other hand, EEG has high temporal resolution but low spatial resolution. Simultaneous fMRI/EEG measurements (Formaggio et al., 2011; Flanagan et al., 2014) alleviate some concerns with single imaging modalities. However, some caution is required in interpreting even these dual-mode neuroimaging results and novel techniques that more accurately reflect neuronal activity patterns are necessary (Schwartz and Bonhoeffer, 2001).

2.1.1 Impairment of consciousness (IoC) during generalized seizures

Generalized tonic-clonic (GTCS) and absence seizures are the seizure types in which ictal IoC has been most rigorously studied (Seri et al., 2011; Blumenfeld, 2012). There is some debate as to whether these seizures are truly generalized in the sense of homogeneous brain involvement or whether involvement is biased towards corticothalamic networks (Meeren et al., 2005; Schindler et al., 2007). This distinction is important as it can help to identify and separate brain structures involved in IoC.

Both level and content of consciousness are altered during GTCS, rendering the patient unresponsive to external stimuli. SPECT imaging of secondarily generalized (Shin et al., 2002; Blumenfeld et al., 2009) and electroconvulsive stimulation-induced seizures (Enev et al., 2007) has revealed a pattern of activation of subcortical structures, including the upper brainstem and the midline, mediodorsal and intralaminar thalamic nuclei, with deactivation of cortical structures, including the prefrontal cortex and the anterior/posterior cingulate.

IoC in absence seizures is less severe than in GTCS and patients may respond to simple tasks. EEG/fMRI studies in humans and animals indicate a similar pattern to GTCS, with increased activity in the thalamus, insula, and cerebellum and decreased activity in frontal and parietal association cortices, posterior cingulate, and other cortical areas (Archer et al., 2003; Moeller et al., 2010). This pattern has also been associated with interruption of the default mode network (Gotman et al., 2005). Similar patterns of activation and deactivation have been observed during interictal spike-wave discharges without IoC (Li et al., 2009). It is suggested that the magnitude of spike-wave discharges during seizures is related to the degree of IoC in absence epilepsy (Bai et al., 2010; Berman et al., 2010).

2.1.2 IoC during partial seizures

There may be changes in the content of consciousness in simple partial seizures and in both the content and level of consciousness in complex partial seizures. IoC in complex partial seizures is less severe than in GTCS, and some responsiveness to simple tasks may be preserved.

A seizure originating in and spatially constrained to the temporal lobe should theoretically lead to deficits in functions such as memory and emotional salience rather than consciousness. However, human and animal studies have confirmed IoC during complex partial seizures restricted to one hemisphere (Van Paesschen et al., 2003; Mueller et al., 2004; Englot et al., 2008), suggesting that focal seizures can lead to secondary effects on larger brain networks.

IoC in temporal lobe epilepsy (TLE) has been correlated with increased amplitude of frontal delta activity on intracranial EEG recordings (Englot et al., 2010). SPECT imaging shows similar patterns of blood flow changes (Lee et al., 2002; Blumenfeld et al., 2004; Tae et al., 2005) in regions with altered EEG/fMRI activity: increased blood flow to the thalamus, basal ganglia, and upper brainstem and decreased blood flow to bilateral orbitofrontal cortices, anterior/posterior cingulate cortex and frontal and parietal association cortices. As with GTCS and absence seizures, these regions overlap the consciousness system and the default mode network. Enhanced thalamocortical EEG synchrony during these seizures has also been observed (Arthuis et al., 2009).

2.2 Mechanisms of seizure-induced IoC

Two main hypotheses exist for IoC during seizures. The network inhibition hypothesis proposes that focal limbic seizures propagate to and alter function of midline subcortical structures involved in the activating and arousal systems. This leads to decreased activity in the fronto-parietal association cortices (Englot et al., 2009; Blumenfeld, 2012), possibly due to an increase in effective inhibition or a decrease in effective excitation in these networks. This hypothesis is similar to a proposed default mode network deactivation hypothesis (Fahoum et al., 2013).

The global workspace hypothesis (Bartolomei et al., 2014) assumes a similar indirect deactivation of frontal and parietal regions due to altered functioning of the thalamus. In this model, excessive seizure-induced synchronization between thalamus and parietal cortex is responsible for breakdown of normal processing leading to deactivation of other cortical areas.

3 Sleep and the circadian system

Several biological rhythms are thought to influence seizure frequency, including sleep-wake, day/night, circadian, menstrual, and lunar cycles (Ramgopal et al., 2013). Sleep-wake, day/night and circadian cycles are related and often defined ambiguously. Sleep-wake cycles require at a minimum a measurement of cortical activity and motion, muscle, or eye activity. Day/night cycles are defined by light on/off cycles in controlled conditions or time of day in natural conditions. Circadian cycles refer to the body’s endogenous light-entrained oscillations, with a period of just over 24 hours. Without a direct measure of endogenous activity, daily rhythms are restricted to day/night specifications. While sleep/wake and day/night cycles frequently overlap with the circadian cycle, they can also change independently.

The sleep and circadian system consists mainly of subcortical brain structures, including the thalamus, hypothalamus, and nuclei of the pons and brainstem (Saper et al., 2005). The suprachiasmatic nuclei in the hypothalamus contain neurons with synchronized sub-cellular gene expression ‘clocks’ that regulate circadian activity (Yamaguchi et al., 2003). These clocks are modulated by both endogenous and extrinsic variables - including food-intake, body temperature, and light input.

Different sleep states engage different portions of the sleep and circadian network. Non-rapid-eye-movement (NREM) or slow-wave sleep is correlated with reduced activity of certain brainstem nuclei, thalamus and medial prefrontal cortex. REM sleep is associated with enhanced activity of the pons, thalamus, limbic areas, and temporo-occipital cortices and reduced activity of prefrontal cortices (Dang-Vu, 2012).

3.1 Effect of seizures on sleep

Epilepsy often co-occurs with sleep disturbances, including frequent arousals, circadian dysregulation, and obstructive sleep apnea (OSA) (Malow, 2007). OSA affects 10–20% of all patients with epilepsy and successful treatment may reduce seizure frequency in some patients (Segal et al., 2012). Anti-convulsant drugs can affect sleep, both beneficially and detrimentally (Legros and Bazil, 2003; Shvarts and Chung, 2013).

3.2 Effect of sleep rhythms on seizure susceptibility

Many syndromes exhibit a correlation between sleep-wake stage and seizure occurrence. This effect is dependent on the epilepsy syndrome and the location and pathophysiology of the seizure focus (Derry and Duncan, 2013; Ramgopal et al., 2013). NREM sleep has been associated with increased seizure susceptibility, whereas REM sleep is usually regarded as the least seizure-susceptible state. Due to the lack of circadian markers in most studies, the true effect of circadian cycles on seizure frequency is poorly understood (Hofstra and de Weerd, 2009; Mirzoev et al., 2012).

Childhood epilepsy syndromes are frequently associated with increased epileptogenicity during NREM sleep, as seen in continuous spike-waves during sleep (Sanchez Fernandez et al., 2013). Absence seizures preferentially occur during the first phases of NREM sleep (Matos et al., 2010). Seizures in inherited and other frontal lobe epilepsies also occur predominately during NREM sleep. In contrast, seizures in TLE most often occur during wakefulness, though more partial seizures secondarily generalize during NREM (Sinha, 2011). In juvenile myoclonic epilepsy, seizures predominate at the transition of sleep to wakefulness and seizure frequency increases following nights of sleep deprivation. The modulatory role of sleep deprivation on seizure susceptibility is poorly understood (Malow, 2004; Huber et al., 2013).

3.3 Animal models

An association between sleep and seizure frequency has been established in several animal models, particularly in electrochemical rodent models of TLE. Although these models (pilocarpine, kainic acid, tetanus toxin) all produce seizures originating from the temporal lobes, the anatomical and neurochemical deficits that produce the seizures are different (Sharma et al., 2007). As different patterns of structural damage can have differing effects on the sleep network, direct comparison of sleep and seizure associations across models requires caution. Rodent and human sleep systems are homologous and many important sleep-related findings were discovered in rodent models. However, these systems differ in two important ways: rats are nocturnal and have punctuated sleep-wake cycles, whereas humans are diurnal and sleep in consolidated bouts. Thus, for rats, day/night and sleep-wake cycles do not overlap. This may confound analyses that average over long time periods or do not differentiate between the two rhythms.

Despite these differences, the basic effect of sleep on seizures in humans is also observed in rats (Matos et al., 2013). In models of TLE and absence epilepsy, seizures are more frequent during the day, when rats spend a greater amount of time in sleep, and are more frequent in NREM than REM sleep. However, measures required for accurate disambiguation between sleep states are not routinely incorporated. A recent study (Sedigh-Sarvestani et al., 2014) incorporating head motion activity found that REM sleep preceded a disproportionately large fraction of seizures in the tetanus toxin model of TLE, providing counterevidence for the presumed anti-epileptic role of REM sleep (Shouse et al., 2000; Colom et al., 2006).

3.4 Mechanisms of sleep and seizure co-regulation

Several hypotheses exist to explain the role of sleep-wake state on seizure susceptibility (Sinha, 2011). The prevailing theory is that cortical synchrony during NREM facilitates seizure initiation and spread, while cortical desynchrony during REM prohibits seizure initiation. Systemic neurotransmitters that covary with sleep-wake cycles are also being investigated for their role in seizure susceptibility (Loddenkemper et al., 2011). Adenosine, which rises and falls in concentration during sleep and wakefulness, may have anti-epileptic properties (Boison, 2010). The roles of melatonin and serotonin are also being investigated in relation to sleep (Gholipour et al., 2010; Richerson, 2013).

4 The central autonomic network (CAN)

The CAN is involved in regulation of visceral functions including blood pressure, heart rate, respiration, thermoregulation, and digestion (Benarroch, 1993). Brain structures constituting the CAN include the insula, anterior cingulate, hippocampus, amygdala, hypothalamus, and regions of the pons and medulla. These areas are reciprocally connected with, and modified by, sleep-wake and circadian cycles.

The observation of autonomic disturbances, such as cardiac arrhythmias and respiratory arrest, accompanying seizures indicates that epileptic activity can disrupt normal functioning of the CAN (Moseley et al., 2013). Most reports indicate an increase in sympathetic and decrease in parasympathetic activity. Animal studies have shown ictal increases in blood pressure and heart rate and increased epinephrine and norepinephrine levels, indicative of increased sympathetic activity (Sevcencu and Struijk, 2010). An imbalance of these systems can be detrimental to overall health, and has been recognized as a potential mechanism contributing to increased risk for sudden unexpected death in epilepsy (Hirsch et al., 2011). Improved understanding of the involvement of the CAN in epilepsy has potential to mitigate adverse epilepsy outcomes resulting from autonomic dysfunction.

4.1 Effect of seizures on CAN

Ictal autonomic disturbances depend on the localization and lateralization of seizure onset and vary greatly between childhood and adult epilepsy syndromes. Autonomic symptoms can be more visible than non-autonomic and localization related symptoms of epileptic activity (Moseley et al., 2013). One of the most notable autonomic epilepsy syndromes is Panayiotopoulos syndrome, a partial seizure disorder occurring in children with ictal vomiting and tachycardia. Autonomic disturbances are also prominent in TLE in both children and adults (Jansen et al., 2013).

Given the variety of potential ictal autonomic disturbances, multiple measures are required to develop a profile of ictal or interictal autonomic impairment. Testing methods employed in clinical and research settings include electrocardiography (EKG), pulse oximetry and respiratory monitors, and galvanic skin response testing (Moseley et al., 2013).

4.1.1 Cardiac function

About one third of epilepsy patients have an abnormal interictal EKG. Ictal tachycardia occurs in a majority of seizures while bradycardia, asystole and other findings, such as ventricular repolarization changes, are noted less frequently (Sevcencu and Struijk, 2010; Moghimi and Lhatoo, 2013). Reduced heart rate variability, an indicator of impaired autonomic integrity and an identified risk factor for mortality in other medical conditions, is seen in some epilepsy patients, particularly those with uncontrolled seizures, and with GTCS (Lotufo et al., 2012).

4.1.2 Respiratory function

Respiratory function is rarely monitored in epilepsy patients, even in epilepsy monitoring units, though this practice is changing. Seizures may be associated with respiratory abnormalities, including apneas, hypopneas, excessive oral and bronchial secretions, laryngospasm or neurogenic pulmonary edema. Hypoxemia and hypercapnia occur commonly in patients with localization related epilepsy, and may be related to seizure spread and generalization (Bateman et al., 2008; Moseley et al., 2011). Both may influence cardiac conduction, thus certain cardiac impairments may be secondary effects of ictal respiratory dysfunction (Seyal et al., 2011). However, the administration of exogenous carbon dioxide reduces seizure duration (Tolner et al., 2011), thus hypercapnia may contribute to seizure termination processes and serve a protective function.

4.1.3 Sudden unexpected death in epilepsy (SUDEP) and its mechanisms

SUDEP is defined as a “sudden, unexpected, witnessed or unwitnessed, non-traumatic, and non-drowning death in a patient with epilepsy, with or without evidence of a seizure and excluding documented status epilepticus” (Nashef and Brown, 1996). While the definitive pathophysiological mechanisms of SUDEP are not known, leading mechanistic hypotheses include respiratory dysfunction, cardiac arrhythmias, and cerebral electrical shutdown (Surges et al., 2009; Shorvon et al., 2011).

Animal studies provide conflicting data on possible SUDEP mechanisms. In certain channelopathy mouse models, cardiac arrhythmias have been reported in association with fatal seizures (Goldman et al., 2009). Alternately, in mice with fatal audiogenic seizures, and in a sheep model of epileptic death, hypoventilation and central apnea were identified as the primary contributors (Johnston et al., 1995; Faingold et al., 2010). Cases of sudden death have been observed in a colony of captive baboons with naturally-occurring epilepsy, which may provide further insights into human SUDEP (Szabo et al., 2009).

A recent review of SUDEP cases reported from international epilepsy centers (the MORTality in Epilepsy Monitoring Units Study, MORTEMUS) demonstrated a common pattern of post-ictal autonomic dysfunction leading to sudden death. Following a GTCS, a brief period of tachypnea with generalized suppression of the EEG was followed by terminal or transient and subsequently recurring cardiorespiratory failure within three minutes. Apnea always preceded asystole and primary cardiac arrhythmias were not seen (Ryvlin et al., 2013a).

5 Future Directions: Utilizing regulatory system relationships to improve epilepsy care

5.1 Prediction and detection of seizure onset

Accurate prediction of seizures would reduce the anxiety of anticipating their occurrence and allow for improvements in lifestyle, such as driving. It could also facilitate the development of closed-loop brain stimulation devices, which have the advantage of reduced current delivery and increased battery life over existing open-loop devices.

Efforts to predict seizures based on features extracted from the EEG span at least five decades, but to date no accurate and robust prediction algorithm has been developed. At the recent International Workshop on Seizure Prediction (IWSP6), identification and implementation of features based on physiologically relevant modulators of epileptic activity was recommended. Sleep-wake state is the best known modulator of seizure onset but has not been incorporated into any prediction algorithm. In fact, failure to account for sleep-wake state can reduce the accuracy and efficiency of prediction algorithms (Schelter et al., 2006; Mormann et al., 2007). Basic differentiation between sleep and wakefulness can be done from extra or intracranial EEG. Thus it is currently feasible to incorporate instantaneous sleep-wake state determinations into implantable seizure warning devices (Cook et al., 2013).

Detection of seizure onset, compared to its prediction, is a relatively easier task. Many accurate seizure detection algorithms have been developed but their reliance on EEG measurements currently restricts their use. Seizure detection based on measurement of ictal autonomic changes has been demonstrated (Poh et al., 2012; Osorio, 2014) and offers a potential, economically feasible detection strategy for home use. A benefit of such methods in the outpatient setting has yet to be demonstrated, however.

5.2 Reducing seizure frequency with chronoepileptology

Although many patients have seizures influenced by sleep-wake or day/night cycles, these patterns are not routinely used in epilepsy management. Profiling individual seizure susceptibility cycles with the use of seizure diaries or ambulatory EEG, could allow for a medication dosing strategy that matches peaks of seizure susceptibility to peaks of serum levels. This strategy, referred to as chronoepileptology (Ramgopal et al., 2013), may also be used in stimulation devices (Loddenkemper et al., 2011).

The circadian kinetics of anti-convulsant drug absorption and depletion and the effects of these drugs on sleep and circadian cycles must also be considered when developing individualized dosing efforts (Ramgopal et al., 2013). A chronoepileptology approach demonstrated improved seizure control in a recent pilot study (Guilhoto et al., 2011), and additional trials are underway. Continued progress in our understanding of systems that regulate seizure susceptibility could lead to improved seizure prediction algorithms, and thus increase the population of patients who may benefit from chronoepileptology.

5.3 Reducing incidence of SUDEP

Ictal involvement of the CAN likely plays a role in SUDEP. However, the factors that determine whether ictal autonomic dysfunction will be transient with full recovery or terminal leading to SUDEP require further investigation. IoC and failure of normal arousal mechanisms have been linked to SUDEP (Richerson, 2013). SUDEP is thought to occur more frequently during sleep, suggesting a possible causal association. Sleep-related ictal phenomena, such as preferential seizure occurrence and secondary generalization in some populations could favor the pathophysiological mechanisms leading to SUDEP (Nobili et al., 2011; Ramgopal et al., 2012), Sleep-related environmental factors, such as lack of supervision at night, may also play a role (Ryvlin et al., 2013a; Lamberts et al., 2012).

In the MORTEMUS study, patients were successfully rescued from cardiorespiratory arrest if resuscitative efforts were administered within three minutes. SUDEP cases involved delayed or absent resuscitative measures (Ryvlin et al., 2013a). This suggests, albeit with a small sample size, that the incidence of SUDEP may be reduced by mechanisms used to detect seizures or autonomic disturbances and alert caregivers so that potentially life-saving interventions may occur (Poh et al., 2012; Ryvlin et al., 2013b; Seyal et al., 2013).

Conclusions

Incorporation of recent findings on the relationships between sleep, consciousness, and autonomic regulatory networks in epilepsy can be utilized to develop new therapies and improve existing protocols in epilepsy management. Improved understanding of the functional role of these systems in epilepsy will lead to advances in the prediction, prevention, and management of seizures and their consequences and in turn improve both the quality, and in some cases, the duration of life of patients living with epilepsy.

Footnotes

Disclosure of Funding: NIH R01 NS055829, R01 NS066974 (HB); NIH 1 P20 NS076916-01 (LMB)

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