SOCIETY FOR AUTONOMOUS NEURODYNAMICS (SAND) |
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PRINCIPLES OF AUTONOMOUS NEURODYNAMICS 2006
July 3rd and 4th, 2006
USING DIET TO TREAT SEIZURE DISORDERS: IMPLICATIONS FOR AUTONOMY It has been known for centuries that fasting is anticonvulsant. Fasting is limited in the treatment of chronic seizure disorders, so in the 1920s Wilder devised a "ketogenic diet" (KD) to mimic the physiological effects of fasting. The KD is a high fat, low carbohydrate and adequate protein diet that is highly efficacious in the treatment of drug-resistant seizures. The KD, however, is extremely rigorous. In terms of autonomy, the KD improves autonomy through its broad spectrum anticonvulsant actions. The KD is also associated with significant improvements in both the patient's mood and cognition. The KD, however, can have deleterious effects on the more protracted aspects of autonomy. Parents must oftentimes lock up foods. Preparation of the KD requires more time and energy as both a KD and a normal meal must be prepared at each mealtime. Preparation of the KD can also be very tedious as food must be weighed to a 100th of a gram. Recently, there has been a discovery that the more popular, less stringent Atkins and low-glycemic index diets share the KD's anticonvulsant efficacy. These diets are much less rigorous and thus have fewer negative effects on autonomy. The purpose of this talk is to introduce the various "anticonvulsant" diets, review their potential mechanisms of action, and discuss their impact on the autonomy of individuals with seizures and their family.
THE EFFECTS OF SEIZURES ON HORMONAL FEEDBACK SYSTEMS:
Introduction: Epileptic patients often suffer from menstrual dysfunction and/or obesity. In addition to seizures, these co-morbidities adversely impact the quality of life for an individual with epilepsy. A better understanding of the effects of seizure activity on hormonal reproductive and feeding feedback systems may lead to the development of novel treatment solutions for these co-morbidities, thereby improving the autonomy of patients living with epilepsy.
THE LONG-TERM EFFECTS OF SEIZURES ON REPRODUCTIVE CYCLES
People with epilepsy are more likely to suffer from reproductive dysfunction than the general population. Attributing the cause of this trend to seizure activity alone is difficult due to the use of antiepileptic medications in epileptics. Using an animal model of epilepsy, such as the amygdala-kindling model in rats, allows for the study of the effects of seizures on the reproductive system without the interference of antiepileptic medications. The amygdala-kindled female rat is a model for complex partial seizures of temporal lobe origin in women. Many of the seizure-related disorders in women, such as disruptions in reproductive cyclicity, have also been demonstrated in the amygdala-kindled female rat. The relationship between seizure occurrence and disruptions in estrous cyclicity were examined in the amygdala-kindled female rat during kindling and the cessation of kindling. The question of whether seizure-induced disruptions in reproductive cyclicity are permanent will be addressed.
NEURONAL DEPOLARIZATION ACCOMPANIES SEIZURE ACTIVITY INDUCED BY
For more than 80 years, type 1 insulin dependant diabetes mellitus has been treated by the exogenous application of synthetic insulin, which has considerable difficulty mimicking normal physiological fluctuations. When the actions of insulin in the peripheral nervous system are severe; hypoglycemia ensues, which has dire consequences in the central nervous system. End stage hypoglycemia results in seizures, coma and death. Repeated severe episodes are detrimental to cognition, especially in the developing brain. Previous intracellular recordings from hippocampal slices under hypoglycemic conditions in the absence of seizure activity suggest neuronal hyperpolarization mediated by K+. However, using in vitro seizure models of epilepsy, it has been demonstrated that neurons depolarize during seizure activity. We have recently characterized a model of hypoglycemic seizures in the isolated intact hippocampus of the mouse, in vitro. Using this model, we have demonstrated that neurons, both pyramidal cells and interneurons, depolarize during seizure activity induced by transient hypoglycemia. Remarkably, when the seizure activity is inhibited by the GABAA agonist midazolam (50 nM), pyramidal cells and interneurons still depolarize during hypoglycemia. Regular spiking cells of the neocortex also depolarize during hypoglycemia in the absence of seizure activity. Hypoglycemic neuronal depolarization in the hippocampus was also observed in the presence of CNQX/APV/gabazine, suggesting that the depolarization is not synaptically mediated. Together, our data suggest that neurons depolarize during hypoglycemia, both in the presence and absence of seizure activity, which may be a cause of the excitotoxic cell death associated with hypoglycemia. Surprisingly there was no clear relationship between the neuronal spiking activity and the epileptiform fields. Supported by the JDRF.
FINDING A SPACE FOR SAM
The life my family changed drastically on September 29th 2005, when our then two year old son Sam had his first seizure. This started the long task of uncovering the cause, prognosis and treatment of his myoclonic epilepsy. This has greatly affected the autonomy of Sam, our family and our greater family. Sam loses his autonomy when he is seizing as he has no contact with the world around him. We hold him, protect him and talk to him as though he can hear us, it makes us feel better. My husband and I have lost autonomy as we wouldn't leave Sam alone for 6 months after his first seizure- not that being there changes the seizure-but we were unsure of how others would handle it. We followed him around like he was going to fall apart, which really cramps the style of a three year old boy. Our extended family has also lost autonomy. Our family has moved time and space to help with our other 3 kids so that my husband and I can be with Sam for various tests. Sam gets unexplained hugs and some extra presents, all because he is "sick." We sometimes worry about Sam's quality of life as he grows and wants to become more independent. We worry about his development. We worry about how he will be accepted when he goes to school and how the teachers will handle it if he does have a seizure at school. Mostly we worry.
COBALT INDUCED EPILEPTIFORM DISCHARGES IN MOUSE HIPPOCAMPUS IN VITRO
Animal models of cobalt-induced epileptic seizures have been frequently used since 1960s, but the underlying cellular mechanisms are largely unknown. We developed an in vitro model to explore this issue. Hippocampal slices were prepared from adult mice and treated with CoCl2 (2 mM for 10 minutes or 0.1 mM for 1 hour). Spontaneous epileptiform discharges were consistently observed in these slices after washing cobalt, manifesting with oscillatory spike waveform lasting several seconds. These epileptiform discharges were originated from the CA3/hilar areas and correlated with high-frequency firings of in individual CA3 pyramidal neurons. The cobalt-induced epileptiform discharges were dependent upon the activities of glutamate AMPA receptors but not NMDA receptors, but their generation could not be mimicked in naïve slices by pharmacological antagonism of GABAA/GABAB receptors or by increasing synaptic and neuronal activities with high external K+ or 4-aminopyridine. The cobalt-induced epileptiform discharges were readily suppressed by phenytoin (50 mM), TTX (50 nM) or high extracellular Ca2+ (4 mM), and their incidence was reduced by pre-treatment of slices with ascorbic acid (0.5 mM). We suggest that free radical-dependent modulation of persistent Na+ currents and resulting excessive neuronal firings play a major role in cobalt-induced epileptiform activities in mouse hippocampal slices. The role of intrinsic CA3 network activities in initiation of cobalt-induced epileptiform discharges was discussed.
THE DYNAMICS OF SEIZURE TRANSITION: THE ROLE OF INTERNEURONS AND
Seizures occur when populations of neurons discharge synchronously. Our current understanding of the neuronal dynamics that produce such hyper-synchronicity is lacking. Utilizing the intact isolated mouse hippocampus exposed to low-magnesium artificial cerebral spinal fluid, recurrent spontaneous seizures were produced with a distinct pre-seizure transition phase. Intra- and extracellular electrophysiological recordings were obtained from fast spiking (FS) and non-fast spiking (nonFS) interneurons in the stratum oriens, and pyramidal cells in the CA1 region of this structure. This study utilized perforated patch, normal whole cell and high-chloride whole cell recording techniques to monitor spontaneous synaptic and other epileptiform activity during the transition and seizure phases. In pyramidal cells, and FS and nonFS interneurons, the reversal potentials during the transition period (-60mV) were significantly more hyperpolarized than the reversal potentials observed during the seizure (+30mV in interneurons, -30mV in pyramidal cells). Increasing the chloride concentration in the patch pipette (30mM, ECl = -40mV) depolarized the reversal potential during seizure transition to -38mV in all neuronal subtypes, as well as depolarizing the seizure reversal potential in pyramidal cells (-7mV), but not in interneurons. The GABAA receptor antagonist, BMI, completely abolished the seizure transition phase in all three neuronal subtypes, and produced fragmented, shorter seizure-like activity. The reversal potentials of these seizures did not change in FS and nonFS interneurons as compared to low-magnesium induced seizures, but were significantly more positive in pyramidal cells exposed to BMI (23mV). These data suggest that stratum oriens interneurons and CA1 pyramidal cells are exclusively GABAergically driven during the seizure transition and that during a seizure, interneurons are driven by glutamatergic pyramidal cells vis-à-vis a feedback loop, while pyramidal cells are functionally controlled by a mixture of GABAergic and glutamatergic inputs during seizures.
EPILEPTIC TRANSITIONS - INSIGHT FROM A HIPPOCAMPAL MODEL
It has been shown that an enhancement of relative Phase Clustering Index (rPCI) measured from evoked EEG signals anticipates the spontaneous transition to an epileptic seizure (Kalitzin et al., 2005). Using a computational model of a hippocampal CA1 region we investigated a possible explanation for the dependence between rPCI measure and the probability of ictal transition. We identified a number of network parameters that bring the network closer to the seizure threshold and increase rPCI at the same time. The increase of rPCI 'en route' to a seizure always came through the decrease of phase coherency at the lowest (i.e., stimulation) frequency. Model predictions are compared with the experimental data and prospective development of seizure prediction method is suggested.
ARCHITECTURES OF AUTONOMY:
Candidate principles for achieving autonomous neurodynamics include: (i) the capacity to maintain persistent activity (ii) an ability to avoid falling prey to pathological synchrony and limit cycles (iii) a substantial degree of independence from the environment such that transition between activity states or phases can occur in the absence of external input (iv) that patterns and transitions can also be maintained despite a sustained external drive (v) all this while simultaneously remaining responsive to the environment and interacting in the world. This presentation continues an ongoing investigation into the characteristics of network architectures that might support such dynamics. Specifically, I will examine spatiotemporal pattern formation in laminar networks with varying levels of connectivity, ranging from networks with homogeneous unit placement to highly diffuse networks. In these locally connected computational models, the degree of heterogeneity in connectivity is shown to correlate with a propensity to enter sustained activity as a response to both localized and spatially-distributed random input. For a broad range of diffuse networks, the activity triggered by external input displayed sustained patterns of propagating waves. The response, however, was not monotonic and networks showed varying forms of activity ranging from spiral waves to increasingly localized limit-cycle patterns. Changes to threshold and complex pattern formation in these models did not require modification to excitatory-inhibitory balance nor did they require alterations to intrinsic cell properties. These findings highlight the importance of structure to activity pattern formation as well as threshold shifts. Relating network structure to neural dynamics may thus elucidate the architecture principles by which networks support the forms of spatiotemporal patterns most essential for autonomous activity.
UNIDIRECTIONAL ASSOCIATIONS BETWEEN NEURONAL ACTIVITIES
Neuronal subsystems have the remarkable properties of showing coherent, functionally coordinated behavior, while at the same time retaining their independent degrees of freedom. Such duality of simultaneous cooperation and autonomy can be explained by state-dependant, asymmetric relations between the various subsystems. We speculate that asymmetric or ultimately unidirectional interactions play essential role in allowing the exchange of information between the neuronal subsystems and at the same time in preserving the relative autonomy of the various components. The purpose of this contribution is to introduce a suitable measure that can detect asymmetric state relations between activities measured from electrophysiological signals. We use the established non-linear association index, known as h^2, and its extension to the unidirectional phase clustering index (PCI). The last provides a unidirectional measure of the phase synchronization between two signals. Non-stationary frequency representation based on a sequence of Gabor filters can be used for a spectral decomposition of the signals that generates a phase synchronization spectrum. We have studied the behavior of the unidirectional state associations in cases of partial motor seizures and have quantified the associations between ECoG and EMG activities. Using our techniques we can distinguish between causal and deterministic cortical involvement in the seizure generation process. Such analysis can be relevant for a successful epilepsy surgery. Cortical areas that are for example necessary to trigger motor seizures might be better candidates for resection that areas that are just sufficient.
SELF REFERENCE IN BRAIN DYNAMICS
Self Reference in group behavior can be described by the riddle: "If eight birds sit on a branch and you scare one of them, how many will stay on the branch?" The answer is "none" since a bird in the group looks at its comrades in addition to its own input to tune its behavior. The same type of self reference, we notice, is an important principle in many biological systems: a component of a system does not behave by mere considering external input, but only after taking into account the behavior of other components of the system it belongs to. On the system
level, self referencing will be that the system acts on input after comparing and referencing it to its own internal condition and representation. We propose that self-reference is crucial in brain dynamics and will demonstrate it in various levels from synapse of behavior.
NEURODYNAMICS STUDIES BY DIRECTED TRANSFER FUNCTION
The study of neurodynamic phenomena involves determination of the direction of the information transfer between brain structures or pools of neurons. These quantities can be estimated by means of Directed Transfer Function (DTF). DTF is based on multivariate autoregressive model (MVAR) and can be regarded as an extension of the Granger causality measure to the arbitrary number of channels. In case of brain studies simultaneous fitting of all interconnected channels to the model is crucial, since in case of mutually dependent signals, bivariate measures lead to erroneous results. In case when multiple repetitions of the experiment are available short-time DTF (SDTF) allows for the determination of propagation not only in function of frequency, but also in time. In this way dynamical pattern of the information transfer may be obtained.
PHARMACOLOGICALLY INDUCED NEUROMODULATION
The diagnostic process in epilepsy can be quite tedious. Documenting the epileptiform abnormalities on scalp EEG that are needed to support a diagnosis of epilepsy can be quite time-consuming. Considerable effort has been invested in finding ways to perturb the brain into revealing some of its secrets. Although perturbation might intuitively compromise the idea of autonomy of a system, it could also be considered as any type of input that elicits reaction from this innate interactive system, that also reveals part of its mechanisms. Examples of perturbation as applied in a clinical setting of EEG recordings are sleep deprivation, and pharmacological sleep induction.
AXONS: THE MISSING LINK IN NETWORKS
Even though the myelinated and unmyelinated axons are standard elements of neuronal circuits it is generally assumed that the interesting dynamics and plasticity of circuits resides in the synaptic events. Substantial number of studies of peripheral and central myelinated axons identified multitude of ion channel with specific distribution in different regions: high concentration of Na channels in the nodal region while a variety of K channels are distributed internodally (fast K channels) and nodally (slow K) with a variety of structural proteins securing their precise distribution.
COUPLED OSCILLATORS AS ADAPTIVE HIGH PASS FILTERS FOR
Learning and memory rely on the regulation of communication between neurons in the hippocampus. The mossy fiber (MF) pathway connects the dentate gyrus (DG) to the auto-associative CA3 network, and the information it carries is controlled by a feed forward circuit having (i) excitatory synapses between granule cells and both pyramidal cells and interneurons, and (ii) inhibitory synapses between interneurons and pyramidal cells.
SPATIOTEMPORAL FREQUENCY PROFILES OF HIPPOCAMPAL ELECTRICAL ACTIVITY:
Several signal processing tools analyze the level of synchronous neuronal activity or correlation within and between different regions of the brain, as synchronous oscillations are believed to play a large role in neuronal function. Coherence tools have provided effective analysis options, but are constrained by stationarity conditions, time-frequency resolution and the ability to identify non-concurrent commonalities arising from shifts or delays in signal conduction. METHODS: We are proposing a homology function to study concurrent and non-concurrent commonalities in non-stationary neuronal electrical activities. We utilize a modified Multichannel Blind System Identification (MBSI) algorithm with a feature tracking strategy. Here we have implemented the homology function to characterize the commonalities of four extracellular field recordings in the CA1 region of the intact mouse hippocampus under low-Mg2+ conditions. The modified MBSI algorithm identified a generic signal from the four observed signals, which contained their common time-frequency information. The tracking strategy matched these commonalities, via energy peak tracking, to the time-frequency domains of the observed signals to identify concurrent and non-concurrent events. DISCUSSION: Our tracking strategy identified and matched concurrent and non-concurrent features (i.e. frequency bands) amongst the recordings, mapping out their spatiotemporal spread. These common features were observed to propagate in different ways. Future work will involve the implementation of additional tracking strategies.
STATIC IN THE BRAIN: IMPLICATIONS OF STOCHASTIC NEURAL ACTIVITY FOR
A perplexing phenomenon is the often stochastic nature of neural spiking, often considered "noise" that obfuscates neural signal transmission. This stochasticity results from probabilistic synaptic release, background synaptic activity, and intrinsic properties of the neuron, such as ion channel noise and sub-threshold oscillations among various other factors. Using NEURON models to simulate the mammalian thalamocortical visual pathway, our study focuses on three aspects. First, we explore many of the potential sources of noise, and devise methods of modeling them. Second, we try to understand how neurons are able to retain their precise and reliable transmission of information despite noise. And finally, we look at possible useful functions of stochasticity, ultimately trying to answer: Is it a bug or a feature? We also discuss implications that this phenomenon has for the nature of neural coding, connectivity, autonomous neural dynamics, and overall brain function.
Please send comments to: ohayon@chass.utoronto.ca
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