Elsevier

Epilepsy & Behavior

Volume 13, Issue 3, October 2008, Pages 511-522
Epilepsy & Behavior

An open hypothesis: Is epilepsy learned, and can it be unlearned?

https://doi.org/10.1016/j.yebeh.2008.05.007Get rights and content

Abstract

Plasticity is central to the ability of a neural system to learn and also to its ability to develop spontaneous seizures. What is the connection between the two? Learning itself is known to be a destabilizing process at the algorithmic level. We have investigated necessary constraints on a spontaneously active Hebbian learning system and find that the ability to learn appears to confer an intrinsic vulnerability to epileptogenesis on that system. We hypothesize that epilepsy arises as an abnormal learned response of such a system to certain repeated provocations. This response is a network-level effect. If epilepsy really is a learned response, then it should be possible to reverse it, that is, to unlearn epilepsy. Unlearning epilepsy may then provide a new approach to its treatment.

Introduction

The primary functions of the brain are to transmit, process, and store information about the body and the environment. Higher-order functions such as problem solving and adaptation also exist in some animals. We refer to all these functions loosely as components of the learning process. Plasticity of neurons and of the connections between neurons is central to these capabilities. Plasticity is also central to epileptogenesis [1]. Is this simply an unhappy coincidence? Or is there a deeper reason why plasticity plays a role in both learning and epileptogenesis?

How pathological plasticity leads to epileptogenesis is a subject of intense interest. There are an enormous number of ways in which plasticity can go wrong at all levels of description [2], [3], [4], [5], [6], [7], [8], [9]. Particularly at the genetic level, the process of epileptogenesis is a bewildering complex with many contributory factors. Indeed, so intricately is normal brain function dependent on the proper mix of receptors, channels, chemical environment, and other factors that it may appear a miracle that so many animals are able to function at all, and that more of us are not subject to epilepsy.

In principle, any abnormality in gene expression or molecular environment associated with clinical or electrophysiological evidence of epileptogenesis may represent one of three possibilities: each abnormality may be a causative factor, a response to epileptogenesis, or a noncontributory, incidental finding. It is often difficult to classify each abnormality in one of these three classes. Prinz et al. have cautioned that a great variety of combinations of receptor and ion channel types can result in the same electrophysiological behavior [10]. Thus, it may be necessary to understand how all the constitutive parts come together to understand the behavior of the whole, at least at the whole neuron level and likely also at the network level.

It would be useful to have organizing principles for the functional behavior of biological neural systems. In this article, we review some of the recent advances in this regard. As a consequence of some of these organizing principles, the ability of a neural system to learn appears to confer an intrinsic vulnerability to epileptogenesis on that system. We hypothesize that epilepsy arises as an abnormal learned response of such a system to certain repeated provocations. This response is a network-level effect. If, as we hypothesize, epilepsy really is a learned response, then it should be possible to reverse it, that is, to unlearn epilepsy. Unlearning epilepsy may then provide a new approach to its treatment.

We also propose that there must be at least three conditions for the development of spontaneous seizures, or epilepsy. These are: (1) neuronal hyperexcitability resulting from an imbalance between excitatory and inhibitory influences, (2) overconnectivity in space leading to abnormally wide spatial spread of neuronal activity, and (3) overconnectivity in time leading to abnormally persistent activity. All three conditions must exist for spontaneous seizures to occur, although all three conditions do not have to be present continuously in an epileptic brain. These three conditions are distinct, and each is a potential target for treatment.

Section snippets

Background: Hebbian learning, neuronal avalanches, and critical connectivity

Here we review the recent literature that bears on learning and the self-organizing properties of biological neural systems. The key points are: (1) learning destabilizes both the activity and connectivity of a neural system; (2) there is a certain level of connectivity, called critical connectivity, that optimizes brain performance; and (3) there are homeostatic mechanisms that maintain stable levels of activity and critical connectivity in the face of the destabilizing effects of learning.

Are there other necessary conditions for epileptogenesis?

This question arose because even though we provoked our computational model by subjecting it to prolonged postictal states, we failed to detect spontaneous seizurelike events in the computational model. The system was too stable. In our original definition of the conditional probability P(i, j;t), we took P(i, j;t) to mean the probability that node i will fire if node j fired in the immediately preceding time step, 4 ms earlier. Inputs from times earlier than one time step back are “forgotten.”

Summary and future directions

We have proposed that there are at least three necessary conditions for epileptogenesis. The first one, neuronal hyperexcitability, is the textbook standard. Treatments aimed at suppressing neuronal hyperexcitability typically involve pharmacological suppression of excitatory drive within a circuit or boosting of inhibitory drive. Such interventions do not “cure” epilepsy in that the seizure circuit remains intact and can still resurface at unpredictable times. Future interventions may involve

Methods

Acute slices were prepared from Sprague–Dawley rats 14–35 days old (Harlan). Rats were deeply anesthetized with Halothane and then decapitated. Brains were removed and immediately placed for 3 minutes in ice-cold artificial cerebrospinal fluid (ACSF) containing (in mM): sucrose 125, KCl 3, NaH2PO4·H2O 1.25, NaHCO3 26, MgSO4·7H2O 2, CaCl2·2H2O 2, D-glucose 10, saturated with 95% O2/5% CO2. After cooling, brains were blocked into ∼5 mm3 sections containing somatosensory cortex, striatum, and

Acknowledgments

D.H. was supported by Grant 1KL2RR025012-01 from the National Institutes of Health. J.M.B. was supported by a grant from the National Science Foundation.

References (83)

  • J. Waters et al.

    Backpropagating action potentials in neurones: measurement, mechanisms and potential functions

    Prog Biophys Mol Biol

    (2005)
  • W.B. Levy et al.

    Interpreting hippocampal function as recoding and forecasting

    Neural Netw

    (2005)
  • J.D. Rolston et al.

    Precisely timed spatiotemporal patterns of neural activity in dissociated cortical cultures

    Neuroscience

    (2007)
  • B.C. Albensi et al.

    Electrical stimulation protocols for hippocampal synaptic plasticity and neuronal hyper-excitability: are they effective or relevant?

    Exp Neurol

    (2007)
  • P.K. Crumrine

    Vagal nerve stimulation in children

    Semin Pediatr Neurol

    (2000)
  • E.B. Montgomery et al.

    Unsupervised clustering algorithm for N -dimensional data

    J Neurosci Methods

    (2005)
  • H.E. Scharfman

    Epilepsy as an example of neural plasticity

    Neuroscientist

    (2002)
  • P.A. Williams et al.

    Development of spontaneous seizures after experimental status epilepticus: implications for understanding epileptogenesis

    Epilepsia

    (2007)
  • R.A. Bender et al.

    Epileptogenesis in the developing brain: what can we learn from animal models?

    Epilepsia

    (2007)
  • J.W. Chen et al.

    Advances in the pathophysiology of status epilepticus

    Acta Neurol Scand Suppl

    (2007)
  • H.E. Scharfman

    The neurobiology of epilepsy

    Curr Neurol Neurosci Rep

    (2007)
  • P.B. Crino

    Gene expression, genetics, and genomics in epilepsy: some answers, more questions

    Epilepsia

    (2007)
  • A. Pitkanen et al.

    Epileptogenesis in experimental models

    Epilepsia

    (2007)
  • R.D. Traub et al.

    Combined experimental/simulation studies of cellular and network mechanisms of epileptogenesis in vitro and in vivo

    J Clin Neurophysiol

    (2005)
  • A.A. Prinz et al.

    Similar network activity from disparate circuit parameters

    Nat Neurosci

    (2004)
  • D. Hebb

    The organization of behavior: a neuropsychological theory

    (1949)
  • E. Marder et al.

    Modeling stability in neuron and network function: the role of activity in homeostasis

    Bioessays

    (2002)
  • L.F. Abbott et al.

    Synaptic plasticity: taming the beast

    Nat Neurosci

    (2000)
  • C. Wirth et al.

    Spatiotemporal evolution of excitation and inhibition in the rat barrel cortex investigated with multielectrode arrays

    J Neurophysiol

    (2004)
  • J.M. Beggs et al.

    Neuronal avalanches in neocortical circuits

    J Neurosci

    (2003)
  • J.M. Beggs et al.

    Neuronal avalanches are diverse and precise activity patterns that are stable for many hours in cortical slice cultures

    J Neurosci

    (2004)
  • A. Tang et al.

    A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro

    J Neurosci

    (2008)
  • D. Hsu et al.

    Simple spontaneously active Hebbian learning model: homeostasis of activity and connectivity, and consequences for learning and epileptogenesis

    Phys Rev E

    (2007)
  • P. Bak et al.

    Self-organized criticality: an explanation of the 1/f noise

    Phys Rev Lett

    (1987)
  • M. Paczuski et al.

    Avalanche dynamics in evolution, growth, and depinning models

    Phys Rev E

    (1996)
  • Priesemann V, Wibral M, Munk MHJ. Detection of neuronal avalanches under incomplete sampling conditions in models of...
  • Hahn G, Havenith MN, Yu S, et al. Neuronal avalanches in vivo and in spiking activity. In: Society for Neuroscience...
  • A. Mazzoni et al.

    On the dynamics of the spontaneous activity in neuronal networks

    PLoS ONE

    (2007)
  • J.M. Beggs

    The criticality hypothesis: how local cortical networks might optimize information processing

    Philos Transact A Math Phys Eng Sci

    (2008)
  • Beggs JM. Neuronal avalanche. In: Scholarpedia 2007. Available at:...
  • C. Haldeman et al.

    Critical branching captures activity in living neural networks and maximizes the number of metastable States

    Phys Rev Lett

    (2005)
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