seizure-like events in ca1 of the rat hippocampus
TRANSCRIPT
The Pennsylvania State University
The Graduate School
Huck Institute of Life Sciences
INTERACTIONS BETWEEN ORIENS, BASKET AND PYRAMIDAL CELLS DURING IN VITRO
SEIZURE-LIKE EVENTS IN CA1 OF THE RAT HIPPOCAMPUS
A Dissertation in
Neuroscience
by
Ruchi Parekh
© 2010 Ruchi Parekh
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Doctor of Philosophy
December 2010
ii
The dissertation of Ruchi Parekh was reviewed and approved* by the following:
Steven J Schiff
Brush Chair Professor of Engineering
Professor of Engineering Science and Mechanics, Neurosurgery and Physics
Dissertation Advisor
Chair of Committee
Bernhard Luscher
Professor of Biology
Bruce Gluckman
Associate Professor of Engineering Science and Mechanics, Neurosurgery
Byron Jones
Professor of Biobehavioral Health/Pharmacology
John R. Cressman
Assistant Professor of Physics & Astronomy/Krasnow Inst. for Advanced Study
George Mason University, Fairfax, VA
Special Member
Jokubas Ziburkus
Assistant Professor of Biology and Biochemistry
University of Houston, Houston, TX
Special Member
Ping Li
Professor of Psychology, Linguistics, and Asian Studies
Co-Chair, Neuroscience Graduate Program
*Signatures are on file in the Graduate School.
iii
ABSTRACT
Network activity in the brain is shaped by interactions between cells. There is limited
knowledge about such interactions in pathological conditions like epilepsy. Previous
results from our laboratory showed unique interplay between pyramidal cells and oriens
lacunosum-moleculare interneurons in rat hippocampal CA1 during in vitro seizure-like
events (SLEs). To further investigate the interactions between different sub-types of
interneurons and pyramidal cells, we performed simultaneous dual and triple whole cell
patch and extracellular recordings in pyramidal, basket and oriens cells in the CA1
region of the rat hippocampus. Triple immunofluorescence confirmed the identity of
patched cells. We measured spike frequency, spike and subthreshold correlation
between these cells before, during and after single seizure-like events (SLEs) and during
the inter-seizure intervals (ISIs). We confirmed our previous findings and found a
distinctly different firing pattern between the perisomatically-innervating basket cells
and the dendritically-innervating oriens cells during 4-aminopyridine induced SLEs in the
presence of low magnesium. At the pre-ictal stage of the SLE the subthreshold
correlation between pyramidal and basket cells is strong; oriens cells show high firing
frequency, whereas the basket cells show a gradual increase. At the start of the SLE, the
previously strong subthreshold correlations between pyramidal and basket cells
decrease; all 3 cells show increased firing rates with basket and oriens cells reaching
their maximum firing rates. Following this, subthreshold correlations for all 3 cells
decrease; oriens cells go into depolarization block that is characterized by total spike
failure, whereas pyramidal and basket cells continue firing. SLE termination is defined by
conditions returning to pre-ictal dynamics. During the ISI all three cell types increase
their firing activity at different times before onset of the subsequent seizure.
Our findings further emphasize the importance of cell-type specific differences in
interactions between excitatory and inhibitory cells during SLEs. Other similar cellular
dynamics may be representative of the hippocampal circuitry under conditions that
cause general hyperexcitability. We also show a significant gradual increase in spike
rates in both the inhibitory and excitatory cells, starting at least a minute before seizure
onset. These findings expand on the current knowledge of seizure formation patterns
and can be used in studies of seizure prediction models.
iv
Contents
List of figures ………………………………………………………………………………………………………………..v
Introduction ………………………………………………………………………………………………………………….1
1. What is epilepsy?…………………………………………………………………………………...1
2. Electrophysiological seizures….……………………………………………………………….3
3. Region of interest ………………………………………………………………………………….5
i. The hippocampus in the normal brain ……………………………………….5
ii. The hippocampus and epilepsy ………………………………………………….6
4. Interneuron diversity …………………………………………………………………………….8
5. Seizure models …………………………………………………………………………………….11
i. High K+ ……………………………………………………………………………………..11
ii. Low Mg2+
………………………………………………………………………………….12
iii. Kainic acid ………………………………………………………………………………..13
iv. 4-Aminopyridine ………………………………………………………………………14
Research Design and Methods ……………………………………………………………………………………17
Animals ……………………………………………………..……………………………………………………17
Hippocampal slice preparation ……………………………………………………………………….17
Electrophysiology ……………………………………………………………………………………………17
Immunohistochemistry and confocal microscopy ……………………………………………21
Correlation Analysis ………………………………………………………………………………………..24
Statistical Analysis …………………………………………………………………………………………..28
Results ………………………………………………………………………………………………………………………..30
1. Spike Rate …………………………………………………………………….……………………………34
2. Correlation Analysis………………………………………………………………….…………………39
i. Pyramidal cell - Pyramidal cell …………………………………………………..……39
ii. Basket cell - Basket cell …………………………………………………………………..41
iii. Basket cell - Pyramidal cell ………………………………..……………………………43
iv. Oriens interneuron - Basket cell …………………..…………………………………45
v. Oriens interneuron - Pyramidal cell ………………………..………………………47
Summarized Results …………………..………………………………………………………………………………..50
Pyramidal cells ………………………………………………………………………………………………..50
Basket cells ……………………………………………………………………………………………………..54
Oriens interneurons ………………………………………………………………………………………..55
Discussion ..………………………………………………………………………………………………………………….57
Conclusion …………………………………………………………………………………………………………………..60
References …………………………………………………………………………………………………………………..65
v
List of Figures
Figure 1: Diagram of location of electrodes ………………...................................................18
Figure 2: Electrical membrane properties of patched cells ………………………………………….20
Figure 3: Confocal images ………………………………………………………………………………………..…22
Figure 4: Single seizure and resample analysis ……………………………………………………………25
Figure 5: Interleaving pattern of interplay between OLM and pyramidal cells ……………31
Figure 6: Schematic diagram of cell-types investigated in our experiments ………………..32
Figure 7: Single seizure spike rate ……………………………………………………………………………….36
Figure 8: Inter-seizure interval spike rate ……………………………………………………………………37
Figure 9: Linear regression analysis of inter-seizure interval ……………………………………….38
Figure 10: Correlation analysis of pyramidal-pyramidal cell pairs ……………………………….40
Figure 11: Correlation analysis of basket-basket cell pairs ………………………………………….42
Figure 12: Correlation analysis of basket-pyramidal cell pairs …………………………………….44
Figure 13: Correlation analysis of oriens interneuron-basket cell pairs ……………………….46
Figure 14: Correlation analysis of oriens interneuron-pyramidal cell pairs ………………….49
Figure 15: Summarized resample analysis ………………………………………………………………….53
List of Tables
Table 1: Number of cell pairs patched and analyzed for SLEs ……………………………………23
1
Introduction
1. What is Epilepsy?
Epilepsy is a chronic condition characterized by repeated, unprovoked seizures. It is a
disorder that results from many different underlying conditions. There are many causes
of seizures, like trauma, infection, drugs etc., however not all people that have seizures
are epileptic. More than 5% of all people suffer from at least one epileptic seizure
during their lives and at any one time the prevalence of epilepsy is 0.5-1.0% of the
general population (Somjen 2004, Sanders and Shorvon, 1996).
Depending on the area of the brain involved seizures can be classified as partial or
generalized. Partial seizures can be either simple or complex depending on whether the
patient retains consciousness, in the former, or partially or completely loses it, in the
latter. Temporal lobe epilepsy is so called because complex partial seizures originate
within the structures of the temporal lobe. Simple partial seizures that involve the
motor cortex are called “Jacksonian seizures” that manifest as a twitch in one part of an
extremity and slowly spread over the body. Partial seizures that start in a small area of
the brain and then spread to the opposite side of the brain are called secondary
generalized seizures. Generalized seizures can be either petit mal, where the patient
loses consciousness for a brief time or grand mal, where the patient has convulsive
seizures. Grand mal is also knows as generalized tonic-clonic convulsions. The condition
in which seizures that last for 30 minutes or longer is called status epilepticus. Epilepsy
can be a result of a known brain lesion or disease or it can be due to unknown causes
like genetic abnormalities.
Despite the different initial causes and differences in manifestations of the seizures,
certain underlying pathophysiological features are common. Excessive excitation,
reduced inhibition or an increase in both excitation and inhibition can result in seizures.
In 1929 Hans Berger first published an EEG recording from the intact scalp of a
schizophrenic patient. He described various waveforms seen in different states of
2
wakefulness and sleep giving rise to the current conventions of brain wave frequencies.
In an EEG recording the ictal event of the seizure is seen as a sudden increase in
frequency and amplitude of the waves. The increase in frequency can vary for different
types of seizures. In humans a petit mal seizure is observed as a 3-5 Hz rhythm and a
generalized tonic seizure will have a frequency of 15-20 Hz. Interictal discharges
between seizure events are classified as EEG spikes, sharp waves, poly-spikes, isolated
large amplitude slow waves and isolated spike-wave complexes (Somjen 2004).
Matsumoto and Ajmone-Marsan (1964) first described paroxysmal depolarization shifts
(PDS) in the “epileptic neuron” of the cat cerebral cortex, as “the typical, most
characteristic epileptiform expression of the neuron”. Using topical application of
penicillin as an experimental reproduction of epileptogenic foci, they made extracellular
and intracellular recordings from the cerebral cortex of cats. They studied the
spontaneous and repetitive interictal and ictal activity. They described in detail the giant
depolarizing shift in the membrane potential of a neuron which would often increase
the firing level and then inactivate the spike mechanism. In other cases the spikes would
be preserved during the PDS. A steep repolarization of the membrane potential ended
the PDS. Traynelis and Dingledine (1988) reported novel spontaneous electrographic
seizures in the rat CA1 that contained components of discharges similar to in vivo
recordings during tonic-clonic motor seizures using the high extracellular K+
model.
Simultaneous recordings from the CA3 and CA1 regions showed that spontaneous
interictal bursts in from the CA3 region triggered the electrographic seizures in CA1.
Isolating the CA1 region stopped the seizures, and electrical stimulation of the Schaffer
collaterals re-established the CA1 seizures. They concluded that interictal activity in the
CA3 was necessary but not sufficient for CA1 seizures. Certain other conditions like
elevated extracellular K+([K
+] o) in the CA1 region, lower spike threshold, cell swelling
leading to a decrease in extracellular space, decreased IPSPs and depolarization leading
to NMDA receptor activation were also associated with CA1 seizures.
3
Our knowledge of the complex interactions between different regions of the
hippocampus during seizures has since then steadily increased. Besides differences in
activity in different regions, we now recognize the differences in contribution of distinct
cell sub-types to seizure-like events. Studying these complex interactions at the local
network level is crucial to further our understanding of the formation of seizure
patterns.
2. Electrophysiological seizures
Seizures are the result of an imbalance in the excitation-inhibition ratio. It could be an
increase in excitation, a decrease in inhibition or an asymmetric change in both. Seizure
discharges are synchronized network activity of a population of neurons caused by the
pathology of these neurons. Experimental seizures are often identified by a group of
action potentials riding on a slow depolarizing wave called a paroxysmal depolarizing
shift (PDS). This wave starts with an initial fast depolarization, then continues with a
slow depolarization and end with a hyperpolarization (Somjen 2004).
Though the PDS is the experimental cellular correlate of clinical epileptiform discharges,
it is important to note that the underlying mechanism of PDS is different for different
seizure models. PDSs can be evoked synaptically or generated via intrinsic membrane
current or a combination of both. As Wong and Traub (1983) showed in a disinhibited
hippocampal slice, PDSs are generated via intrinsic membrane current in the CA2-CA3
region then synaptically transmitted to the CA1 region. Another explanation suggests
that the PDSs are giant EPSPs. However the source of the giant EPSPs remains unclear.
One possibility may be reduced inhibition resulting in an increase in excitation or an
overall increase in neurotransmitter release, like that seen in the 4-aminopyridine
seizure model. Then the obvious question to follow is which of these models correctly
represents the clinical epileptiform discharges observed in human patients? There may
not be a single model that correctly represents clinical epileptiform discharges, which
are not just one type. But what is agreed upon is that a PDS is generated by an increase
4
in excitatory synaptic potentials followed by Ca2+
current-driven intrinsic membrane
currents (Somjen 2004).
Specifically in the hippocampus, pyramidal cells in the CA3 region have the intrinsic
ability to generate bursts (hence they are known as the pacemakers). CA1 cells are
unable to do so. The burst discharge is propagated from CA3 to CA1 via synaptic
connections (Wong and Traub 1983). Along with synaptic connections the seizure
discharges propagate via electrical interactions: ephaptic interactions and gap junctions.
Due to the close apposition of the pyramidal cell somata electrical current can flow from
one cell to the next and can influence its timing of excitation. This mechanism is called
ephaptic interactions and it contributes to the initiation of electrographic seizures
(Traynelis and Dingledine 1988). The tightly packed pyramidal neurons in the
hippocampus are connected to each other via gap junctions in their axons. Traub et al
(2001) suggest that “very fast oscillations” that precede the onset of seizures occur due
to gap junctions. They demonstrated that “spontaneous very fast oscillations” were
readily expressed in the presence of a gap junction-opening compound
tetramethylamine (TMA) or when chemical synaptic transmission was suppressed. Using
experimental and simulation data they suggest that gap junction networks may become
pathologically active as a result of impaired synaptic transmission and may be
responsible for initiating seizures. Along the same lines gap junction blockers prevent
the transition from the interictal to the ictal stage in low Mg2+
-induced seizures in rat
hippocampal slices (Koehling et al 2001).
Seizures can also propagate chemically via volume transmission; i.e. the extra glutamate
and K+ in the extrasynaptic space can excite neighboring neurons. Astrocytic networks
may also contribute to the spread. K+ and Ca
2+ can spread through the gap junctions and
the latter can induce the release of glutamate from astrocytes causing further excitation
(Somjen 2004).
5
3. Region of interest – The hippocampus
i. The hippocampus in the normal brain
Technological advances over the decades have expanded our knowledge about the
functional role of the hippocampus in the human brain. Though the human
hippocampus cannot be studied as in detail in vivo, animal in vitro studies have provided
a wealth of information about the neuroanatomy, neurophysiology and functional
characteristics of individual cells, pathways, local circuits and network activities. The role
of the hippocampus in memory is now unquestionable. Studies have established that
the hippocampus is involved in implementing some types of memory and in memory
processing. Its role in declarative memory, spatial memory, and LTP/LTD phenomena,
though not completely clear, is acknowledged.
The functional significance of the hippocampus in declarative memory is attributed to a
patient called H.M. He underwent a bilateral temporal lobe resection to control
intractable epilepsy. The resulting impairment in his memory was intensively studied by
Scoville and Milner (1957) and it revealed a pattern of anterograde and retrograde
amnesia in the patient. He was unable to retain some long-term memory for new facts
or events and he had lost some memory that was acquired before the operation. His
short-term memory remained intact and there was no loss of general intellect. The
information learned from H.M.’s case along with several other patients with similar
resections helped in the understanding of the functional role of the hippocampus.
The hippocampus is assumed to be involved in the initial encoding and storage of spatial
memory. The discovery of “place cells” in the hippocampus by O’Keefe and Dostrovsky
in 1971 led to the study of the hippocampus in spatial memory. On recording electrical
activity from individual cells in the hippocampus of freely moving rats, they found that
the firing patterns of certain cells increased when the rat moved through a particular
region in space. Different “place cells” reacted to different regions. They suggested the
firing of different groups of “place cells” might be the neural representation of spatial
maps of an environment. Many theories have attempted to delve into the implications
6
of a spatial memory; however, how the space map may be represented or used by the
hippocampal circuitry is not yet fully understood (Morris 2007).
Long-term potentiation (LTP), a form of activity-dependent synaptic plasticity, is brought
about by a brief period of coordinated neuronal activity which leads to a long-lasting
increase in synaptic strength. Even though LTP has been studied extensively in the
hippocampus it also occurs in the cortex, thalamus, amygdala, ventral tegmental area,
nucleus accumbens, neostriatum and in the cerebellum. It was Hebb (1949) who
proposed the idea that simultaneous activity between two neurons would lead to
strengthening of their synapses, hence the phrase “neurons that fire together, wire
together”. LTP was first described in 1973 by Timothy Bliss and Terje Lomo and
colleagues at excitatory synapses between the perforant pathway fibers and the granule
cells of the dentate gyrus in the hippocampus and defined it as a persistent
enhancement of EPSP following brief tetanic stimulation of afferent pathway. LTP and
LTD result from experimental protocols that cause synaptic modifications. Thus the
question to what extent the physiological properties of synaptic plasticity are correlated
to learning at the behavioral level remains unanswered. Martin, Grimwood and Morris
(2000) suggest the synaptic plasticity and memory (SPM) hypothesis which states:
“Activity-dependent synaptic plasticity is induced at appropriate synapses during
memory formation and is both necessary and sufficient for the information storage
underlying the type of memory mediated by the brain area in which the plasticity is
observed”.
ii. The hippocampus and epilepsy
The hippocampus is secondarily implicated in diseases like Alzheimer’s and
schizophrenia, is vulnerable to damage as a consequence of trauma, hypoglycemia and
ischemia/hypoxia, but is directly involved in temporal lobe epilepsy (TLE). TLE represents
60% of patients affected with partial epilepsies (Walker, Chan, Thom 2007). Neuronal
cell loss and gliosis in the hippocampus, specifically in the CA1 region is a hallmark of
TLE; however if the sclerosis is the cause or the result of TLE remains unclear. The
7
neuronal cell loss that does follow the onset of epilepsy is due to excitotoxicity. Besides
sclerosis other characteristic pathology of the epileptic hippocampus includes structural
and organizational changes, aberrant neuronal connections, changes in function and
changes in intrinsic membrane properties of the cells. These changes can further
contribute to the probability of epileptogenesis.
Epileptic patients demonstrate a high frequency of temporal lobe foci. Often when all
pharmacologic treatment methods fail, surgical resection of parts of the temporal lobes
controls the intensity and frequency of seizures in some patients. However this is hardly
the kind of treatment one should settle for.
This is why in vitro and in vivo experiments in the hippocampus have been crucial in
helping us understand the underlying dynamics of a seizure. By studying the cellular
correlates of epileptiform discharges, we now know about some of the mechanisms of
network activity during seizures. Experiments allow us to draw inferences about
network activity and its underlying mechanisms, which can be used to isolate
therapeutic targets and develop treatments to control seizures. Experimental data is
also used to create computational models that can simulate conditions that are not
possible to create in an experimental setup.
Besides being the structure most prone to seizures, the importance of the hippocampus
in experimental studies is also due its cytoarchitecture. The layout of the hippocampus
is a very ordered one, where the cell bodies are tightly packed together in one layer and
the dendrites and axons occupy other distinct layers with glial cells and interneurons
scattered throughout. This simple layout allows for easy access to the cell bodies for
intracellular recordings when compared to the neocortex for example, where the cell
bodies, axons and dendrites are found across all five layers (Somjen 2004).
8
4. Interneuron diversity
Network activity in the brain is shaped by interactions between cells. Inhibition has an
important role in balancing excitation and in controlling spike timing (Mann et al 2007).
In the hippocampus, feedforward and feedback inhibition form the two fundamental
modes of inhibition (Elfant et al 2007). The type of inhibition is cell-type specific,
however the underlying principle stays the same, which is, inhibitory innervations on
pyramidal cells results in hyperpolarization independent of the location along the entire
somato-dendritic axis of the pyramidal cell (Glickfield et al 2009). There are at least 21
different types of inhibitory neurons in the rat hippocampal CA1 region (Klausberger et
al 2008) which have been identified based on their electrophysiological characteristics,
intrinsic cellular markers, location of the soma within the hippocampal layers, dendritic
morphology, axonal arborizations and axonal targets (Sik et al 1995; Buhl et al 1996;
Halasy et al 1996; Han 1996; Klausberger et al 2008). These distinct characteristics for
the cell sub-types define their cellular function and contribution to the physiology of the
CA1 network.
Different types of interneurons innervate different domains of the pyramidal cells and
coordinate the activity of pyramidal cell populations in a manner that is temporally
distinct and brain-state-dependent (Klausberger et al 2003). In the current study, we
focused on the oriens lacunosum-moleculare (OLM) and parvalbumin positive basket
cells. In the CA1 the soma of OLM cells are located in stratum oriens with horizontal
somatodendritic orientation. The dendrites span stratum oriens and the axons project
to stratum lacunosum moleculare innervating the distal pyramidal dendrites and are
involved in feedback inhibition. In contrast, basket cell soma are located in the stratum
pyramidale with vertically oriented dendrites that span stratum radiatum and stratum
oriens, and their axons innervate the proximal dendrites and somata of pyramidal cells.
Basket cells receive extrinsic excitatory inputs along with excitatory afferents from the
pyramidal cells (Buhl et al 1996); these extrinsic excitatory inputs and the axons
terminating on and near the soma of the pyramidal cells make it possible for basket cells
9
to be involved in feedforward inhibition. Since the time window for neuronal integration
at the pyramidal cell is narrower for inputs closer to the soma and is broader for distal
inputs (Pouille et al 2001), inhibition is stronger at the soma than at the dendrites.
Dendritically innervating OLM cells control the efficacy and plasticity of glutamatergic
inputs, whereas perisomatically innervating basket cells control the synchrony of action
potential outputs of pyramidal cell populations (Miles et al 1996). Hence local
alterations in patterns of inhibition may result in pathological forms of population
synchrony.
Inhibitory neurons are involved in temporal synchronization of principal cells in the
hippocampus and play a crucial role in rhythm generation (Whittington et al 2003).
During local field gamma oscillations (Gillies et al 2002), in an in vitro model of gamma-
and theta-frequency (30-80 Hz and 5-12 Hz, respectively) oscillations in the
hippocampus, the soma of pyramidal cells receives gamma-frequency IPSPs while the
distal dendrites receive theta-frequency IPSPs. During local field theta-frequency
oscillations, the soma and distal dendrites of the pyramidal cells receive theta-frequency
IPSPs. In simultaneous recordings of pre-synaptic basket and axo-axonic cells and post-
synaptic pyramidal cells (Cobb et al 1995), stimulating the pre-synaptic interneuron at 1-
8 Hz phase-locked the post-synaptic pyramidal cells at theta frequencies; thus the
entrained pyramidal cell’s firing could be increased or decreased by the pre-synaptic
interneuron. Rhythmic activation of the pre-synaptic interneurons entrained both the
sub-threshold and supra-threshold activity of the pyramidal cells. Hence different
interneurons impose different rhythms on the pyramidal cells in a compartment specific
manner.
The differences between OLM and basket cells like location of soma, dendritic
morphology, axonal arborizations and targets, and intrinsic membrane properties also
influence the type of inhibition they impose. In kainate induced local field gamma
oscillations, Pike et al (2000) showed that these two types of interneurons receive
similar rhythmic EPSCs, however, because of the differences in their intrinsic membrane
10
properties the output frequencies are completely different from each other. Given their
role in network synchronization (Gillies et al 2002; Pike et al 2000),in controlling the
global activity within the hippocampal formation, and their importance and vulnerability
in epilepsy (Freund et al 2007) understanding the role of both OLM and basket cells in
epilepsy is crucially important.
Impairment in inhibition function has been implicated in temporal lobe epilepsy. In the
pilocarpine model of chronic temporal lobe epilepsy, Stief et al (2006) showed that
interneurons located at the border between stratum radiatum and stratum lacunosum
moleculare, called SRL interneurons, undergo a reduction in synaptic inhibition and are
thus disinhibited, leading to an increase in the excitation-inhibition ratio in SRL
interneurons. This results in an increase in the feedforward inhibition onto pyramidal
cells. At the same time OLM cells that are involved in feedback inhibition die, thus
reducing the feedback inhibition onto pyramidal cells. This imbalance enhances
feedforward inhibition and contributes to the characteristic synchronized events of
seizure dynamics.
Feedforward inhibition is also a prime determinant controlling the speed of epileptiform
activity propagation. In a zero magnesium (0 Mg2+
) model of epilepsy, the number of
preictal feedforward inhibitory barrages inversely determines the propagation velocity
of epileptiform activity. A loss in pre-ictal feedforward inhibitory barrages is coincident
with an increase in the velocity of epileptiform activity in coronal slices of mouse
occipital cortex (Trevelyan et al 2007).
The current study explores how cell-type specific differences in interactions between
excitatory and inhibitory cells shape SLE dynamics.
11
5. Seizure Models
In vitro and in vivo experiments to study seizure activity aim to create conditions that
are physiologically probable in order to study the mechanisms of seizure dynamics in
the hippocampal network. Hence models using high extracellular K+, low extracellular
Mg2+
, kainic acid injections or 4-aminopyridine perfusions replicate processes that are
present biologically.
i. High K+
The resting membrane potential of a neuron is between -60mV to -80mV and is a result
of net movement of various ions including K+, Na
+ and Cl
- across the membrane. At rest
typically the concentrations of K+, Na
+ and Cl
- ions inside the neuron is (in mM) 140, 15
and 8, and that outside the neuron is (in mM) 3, 150 and 130 (Shepherd 2004). Thus K+
is in higher concentration inside than outside and Na+ and Cl
- are higher outside. Under
normal conditions during the repolarization phase of the action potential the K+
channels open and K+ flows out to repolarize the membrane and return it toward the
negative resting potential.
Under abnormal conditions like paroxysmal discharges provoked by stimulation of the
angular bundle in the rat hippocampus an increase in extracellular K+ ([K
+]o) in the fascia
dentata was observed only in the cell body layer of the granule cells and not in the
dendritic layer (Somjen et al 1985). The sudden and very localized increase in K+ in the
cell body layer led the authors to conclude that the paroxysmal discharges were
associated with an outflow of K+ ions from the soma and not from the dendrites.
Fertzinger and Ranck (1970) loaded radioactive K42
on brain surfaces of rats and cats and
used the efflux of the isotope from the brain as a measure of change in [K+]o during
seizures induced by tetanic stimuli or intravenously administered pentylenetetrazol
(Metrazol). They found a significant increase in interstitial K+ during seizures and
proposed a model of positive feedback loop to show that the increase in [K+]o reinforced
excitation of the cells and resulted in self-sustaining seizures. Termination of seizures
occurred when depolarization induced by K+ inactivated the Na
+ currents and the cells
12
go into “cathodal block”. In the hippocampus, the positive feedback mechanism results
in an accumulation of [K+]o in the CA1 region. This prevents the efflux of K
+ from the cell
making the pyramidal cells excitable and vulnerable to the subsequent interictal burst
which results in a seizure (Traynelis and Dingledine 1988). These results established that
sustained seizure activity leads to the accumulation of [K+]o to 8-12mM and these levels
of [K+]o were sufficient to induce seizures in isolated brain slice preparations. In in vitro
experiments of hippocampal slices increasing bath concentrations of K+ from 3.5mM
(normal) to 7-8mM resulted in spontaneous bursting activity in pyramidal cells (Rutecki
1985, Korn et al 1987, Traynelis and Dingledne 1988). Raising the [K+]o from 7-10mM led
to increase in burst frequency and intensity, and at 12mM [K+]o the bursts become
irregular and less intense (Korn et al 1987). Increasing [K+]o also depolarizes presynaptic
terminals opening voltage-gated Ca2+
channels causing erroneous action potentials to
fire and the unnecessary release of neurotransmitters (Somjen 2004). This forces the
uptake of Cl- into the cell which shifts the reversal potential of inhibitory post-synaptic
potential (EIPSP) positively thus converting the inhibitory synaptic potentials to excitatory
(Chamberlin and Dingledine 1988, Korn et al 1987).
ii. Low Mg2+
Though the role of Mg2+
during seizures is not yet clear, lowering [Mg2+
]o has been
shown to induce interictal discharges and ictal events. Spontaneous interictal bursting in
the CA3 region of the rat hippocampus evolves after 6-21min in Mg2+
-free ACSF.
Following this initial phase ictal-like events formed which began 7-24min after Mg2+
-free
ACSF application (Anderson et al 1986). Epileptiform discharges were observed in
human epileptic neocortex 1.5-2hrs after perfusion of Mg2+
-free ACSF (Avoli et al 1991),
and lowering [Mg2+
]o resulted in epileptiform activity that initiated in the CA3 region and
spread to CA1 in the rat hippocampus (Mody et al 1987). The mechanism underlying the
epileptiform activity under these conditions include removal of Mg2+
block of the N-
methyl-D-aspartate (NMDA) receptors predisposing the cell to prolonged excitation
(Dingledine et al 1999) and also causing increased neurotransmitter release at pre-
13
synaptic boutons (Somjen 2004). Low Mg2+
also eliminates surface charge screening
thus increasing the likelihood of voltage-gated channel activation and excitation on the
cell (Mody et al 1987, Somjen 2004).
iii. Kainic Acid
Kainic acid, isolated from a Japanese seaweed Digenea simplex in 1953, is now used as a
selective agonist at kainate receptors and is a potent neural excitant. Intracerebral
injections of kainic acid have generated epileptiform seizures in the CA3 region of the
rat hippocampus. Seizures propagated to other limbic structures and tissue
histopathology showed neuronal cell loss in the hippocampus as well as other limbic
structures. These are patterns consistent with those seen in human patients with
temporal lobe epilepsy (Ben-Ari 1985).
The highest level of binding sites of kainate is in the stratum lucidum in the CA3 region
of the hippocampus where mossy fibers from the granule cells of the dentate gyrus
synapse onto the somata of CA3 pyramidal cells. Here kainate binds to receptors on CA3
pyramidal cells that have the GluR6 subunit. Since CA3 pyramidal cells are connected to
each other via an extensive network of excitatory collateral synapses, this CA3 region
can serve as a pacemaker for generation of synchronized activity. Injecting kainic acid
intracerebrally (Ben-Ari 2000) at concentrations small enough to not diffuse to the
hippocampus and in structures that are distal from the hippocampus, the CA3 pyramidal
cells are damaged, and through their extensive collateral connections the pyramidal
cells generate synchronized excitatory currents that propagate to the CA1 region and to
other limbic structures.
In the CA1 region inhibitory interneurons expressing kainate receptors with the GluR5
subunit are specifically activated by kainate. Low concentrations of kainate produces
long lasting depolarization and sustained action potentials in the inhibitory interneurons
in the CA1 region. As a result of this increased interneuron activity spontaneous
inhibition is increased in the CA1 pyramidal cells thus reducing seizure propagation and
generation of synchronized activity.
14
Hence, within the hippocampus kainate acts as a “double agent” by enhancing the
activity of both the inhibitory interneurons and the excitatory pyramidal cells via
different receptor subtypes (Ben-Ari and Cossart 2000).
iv. 4-Aminopyridine
4-Aminopyridine (4-AP) was found to increase the release of neurotransmitter in
cholinergic synapses at the neuromuscular junction and also at noradrenergic synapses
in various tissues including cat spleen, vas deferens of rabbit and the ventral horn of rat
spinal cord (Thesleff 1980). At these synapses they found the effect of 4-AP to be pre-
synaptic. 4-AP increased neurotransmitter release during evoked action potentials by
blocking K+ channels pre-synaptically hence increasing Ca
2+ influx. Blocking the K
+
channel prolonged the action potential duration and left the voltage sensitive Ca2+
channels open longer. In 1982 Galvann et al described the convulsant actions of 4-AP in
slice preparations of guinea pig olfactory cortex. Bath application of 4-AP led to an
increase in spontaneous neurotransmitter release, intracellular paroxysmal discharges
and loss of inhibitory post-synaptic potentials (IPSPs) just prior to the onset of these
paroxysmal discharges. The IPSPs then reappeared during the “interictal” event. Rutecki
et al (1987) studied the effect of 4-AP on CA3 pyramidal neurons in rats and showed
that 4-AP induces epileptiform activity by enhancing both excitation and inhibition. They
stated that “epileptiform activity can occur without impairment of synaptic inhibition”.
Perrault and Avoli first studied the effects of 4-AP on pyramidal cells in the CA1 region of
the rat hippocampus (1989). They found low levels of 4-AP resulted in an increase in
stratum radiatum-induced excitatory post-synaptic potentials (EPSPs) and both early
and late IPSPs evoked by orthodromic stimuli increased in amplitude and duration. 4-AP
blocks the transient, rapidly inactivating A-type K+ currents by intracellularly binding to
the voltage-gated K+ channel (Kv channel). In the CA1 hippocampal neurons the A-type
currents are important in dendritic signal processing, synaptic integration and in LTP.
Thus this type of current plays an important role in modulating neuronal excitability.
15
4-AP specifically interacts only with channels that express the Kv1.4, Kv4.1, Kv4.2 and
Kv4.3 α-subunits that form the pore of the channel (Kim et al 2005). Kv4.2 subunits are
highly expressed in the somatodendritic membrane of the CA1 cells and correspond to
the A-type current recorded in these cells, whereas Kv4.3 are more prevalent in the
interneurons in the hippocampus. Kim et al (2005) expressed an EGFP-tagged Kv4.2 in
the CA1 pyramidal cells in an organotypic slice culture and on comparing it with the
dominant negative mutant of Kv4.2 they found that overexpression of Kv4.2 led to an
increase in the A-type current with narrower action potentials and less frequency-
dependent broadening of the action potentials, thus reducing the excitability of the cell.
The expression of the dominant negative mutant displayed broader action potentials
with an increase in frequency-dependent broadening of the action potential and
enhanced action potential back-propagation. Thus confirming previous molecular
localization studies, they showed that Kv4.2 channels specifically contribute to the A-
type current in the CA1 pyramidal neurons.
By using 4-AP to block the Kv channel in the hippocampus the efflux of K+ ions is
reduced resulting in the prolongation of action potential like that seen in the dominant
negative mutant of Kv4.2 mentioned above. 4-AP also leads to an increase in
neurotransmitter release by enhanced Ca2+
influx into the pre-synaptic terminal via
voltage-sensitive Ca2+
channels or due to the reduced K+
repolarizing conductance
(Perrault and Avoli 1989).
The above mentioned epilepsy models are all reliable in their results and give us
patterns of seizure activity that are observed in human patients. The overall significance
of these models is that they allow us to make inferences about seizure dynamics which
can then be applied to study intact brains. The 4-AP model of epilepsy is a suitable
model because it has reliably shown to generate epileptiform activity in in vitro and in
vivo experiments (Perrault and Avoli 1991) and also as a convulsant in clinical
experiments (Thesleff 1980). Some drugs induce epileptiform discharges by decreasing
inhibition, whereas in the 4-AP model both inhibition and excitation are enhanced.
16
Our experiments specifically studied two inhibitory interneuron sub-types in the CA1
region during seizure-like events, and hence it was critical for our purposes to have an
intact inhibitory network to reflect physiological processes in the hippocampus.
17
Research design and methods
Animals
Experiments were performed using male Sprague-Dawley rats (P21-P30).
Hippocampal slice preparation
Animals were anesthetized with diethyl-ether, decapitated and brains extracted into
cold dissection buffer aerated with 95% O2-5%CO2 (in mM: 2.6 KCl, 1.23 NaH2PO4,
26mM NaHCO3, 10 MgCl2, 0.5 CaCl2, 213 sucrose, 20 glucose). The hippocampi were
isolated and transverse slices of 450µm thickness were made using a LEICA vibratome.
Sections were incubated for one hour in artificial cerebrospinal fluid (ACSF) (pH=7.4,
30°C, in mM: 130 NaCl, 1.25 NaH2PO4, 26 NaHCO3, 3.5 KCl, 10 glucose, 1.2 MgSO4, 2
CaCl2) aerated with 95% O2-5%CO2. For recordings slices were transferred to a
submersion recording chamber and perfused with recording ACSF containing (in mM):
130 NaCl, 1.25 NaH2PO4, 26 NaHCO3, 3.5 KCl, 10 glucose, 0.6 MgSO4, 2 CaCl2 and aerated
with 95% O2-5%CO2.
Electrophysiology
Slices were viewed with a fixed-staged Olympus (BX51WI) microscope with a water
immersion objective using differential infrared contrast imaging. Cells to be patched
were identified based on morphological characteristics and location of the cell body
within the hippocampal layers. Putative OLM cells were identified based on the location
of the cell body in stratum oriens and their horizontal somatodendritic orientation.
Putative basket cells were identified based on their location of soma in stratum
pyramidale and the vertical bipolar somatodendritic orientation. Pyramidal cells were
identified based on the location of the cell body in stratum pyramidale, triangular-
shaped soma, and apical and basilar dendritic branching pattern.
18
Whole cell recordings were made using borosilicate glass capillaries (World Precision
Instruments) pulled to obtain resistance of 4-8MΩ, containing (in mM): 116 K gluconate,
6 KCl, 20 HEPES, 0.5 EGTA, 2 NaCl, 10 sodium creatine, 4 MgATP, 0.3 NaGTP and 0.3%
Neurobiotin (pH 7.26, 290 mOsm). Extracellular recordings were made with the same
borosilicate glass capillaries but with 1-3MΩ resistance and containing 0.9% NaCl. Using
micromanipulators (Burleigh PCS-6000) simultaneous dual and triple whole cell and
extracellular recordings were made in the CA1 region of the rat hippocampus (Figure 1).
Figure 1. Diagram of location of electrodes. Extracelullar (black) and whole cell recordings (red) of oriens (left),
basket (middle) and pyramidal (right) cells made in the CA1 region of the rat hippocampus (figure adapted from
Andersen et al. 2007).
After a whole cell patch and holding the cell at -75 to -80mV, negative and positive
square wave current pulses were injected in increments of 50-100pA for 500msec to
determine the membrane properties of the cells and further confirm the identity of the
cell-type (Figure 2) (Ali et al 1998; Buhl et al 1996; Elfant et al 2008; Fujiwara-Tsukamoto
et al 2004). Connectivity between cells in dual and triple patch conditions was
determined by injecting a pulse train current (rate = 25 Hz, pulse width = 4 ms,
amplitude = 800 pA-1200pA) into one of two patched cells and the response was
recorded from the other. However, none of the cell pairs that exhibited SLEs were found
19
to be connected. SLEs were induced using 4-AP (200µm) that has shown to induce
epileptiform discharges in the hippocampus (Perreault and Avoli 1991; Perreault and
Avoli 1992). Data were acquired using Multiclamp 700B amplifiers and pClamp10
acquisition software (Molecular Devices), high pass filtered at 4 kHz (whole cell) and 1
kHz (extracellular) and digitized at 10 kHz (Digidata 1440A, Molecular Devices).
20
Figure 2. Electrical membrane properties of patched cells. Examples of membrane properties of pyramidal cells (a),
basket cells (b) and oriens interneuron cells (c) after 500ms negative and positive current injections.
a.
c.
b.
21
Immunohistochemistry and confocal microscopy
Post-hoc immunohistochemistry analysis further validated the identity of the patched
cells. On completion of recordings, slices were fixed and stored in 4% paraformaldehyde
in 0.1M phosphate buffer for 48 hours. Slices were then transferred to 0.1M phosphate
buffer solution (PBS, pH 7.4).
Slices were incubated in 10% normal goat serum in 0.1M PBS with 0.3% TritonX-100 on
a shaker for 90 minutes at room temperature then rinsed in PBS, following which they
were incubated in primary antibodies for 72 hours on shaker in 4°C. Primary antibodies
were used in the following concentrations: mouse monoclonal anti-parvalbumin
1:1000(Sigma P-3088), and rabbit anti-cholecystokinin 1:1000 (Sigma C-2581).
Slices were rinsed in PBS and incubated in secondary antibodies overnight on a shaker in
4°C. Biocytin filled cells were visualized using TRITC conjugated with avidin (Sigma A-
7169). Primary antibodies were visualized using Alexa Fluor 488 (1:500, Invitrogen A-
21121), and Alexa Fluor 647 (1:500, Invitrogen A-31573) (adapted from Losonczy et al
2002). Slices were rinsed in PBS, mounted on glass slides and coverslipped. Stained cells
were identified for dual or triple immunofluorescence using an Olympus FluoView
FV1000 laser scanning confocal microscope (Figure 3).
22
a. b.
c.
Figure 3. Confocal images. Triple immunofluorescence validated the identity of the cells patched during recordings.
Cells were filled with neurobiotin (red) during the recordings and were stained for parvalbumin (PV, green) and
choleycystokinin (CCK, blue). All the cells that were impaled stain positive for neurobiotin. Pyramidal cells and oriens
cells stain negative for PV and CCK. Basket cells containing endogenous parvalbumin stain green, and blue if they
contain choleycystokinin.
23
Triple immunofluorescence further validated the identity of the cells that were patched
based on morphological characteristics (Table 1). SLEs from only those cell pairs whose
identities were confirmed both morphologically and by using triple immunofluorescence
were included in the correlation analysis. Hence all cell pairs in Table 1 were first
identified based on their morphology (column 1) as seen under IR-DIC before patching
attempts were made. From this pool of cell pairs only some pairs exhibited SLEs (column
2). From the original group of cells (in column 1), we further validated the identity of the
patched cells using triple immunofluorescence (column 3). In doing so, some cell pairs
were found to be wrongly identified. Hence, the number of cell pairs identified as P-P,
which had been initially identified as B-P, increased (column 3). Column 4 represents the
cell pairs that were analyzed for SLEs; this includes cell pairs which exhibited SLEs and
whose identities were confirmed both morphologically and using triple
immunofluorescence. Columns 5 and 6 show the total number of SLEs included in the
single seizure and resample analysis from the cell pairs in column 4. 9 SLEs were
mistakenly omitted for the single seizure analysis but were included in the resample
analysis, hence there are 8 more ISIs in the resample analysis.
1. 2. 3. 4. 5. 6.
# of pairs
morphologically
identified
# of pairs
exhibiting
SLEs
# of pairs
after identity
confirmed
# of pairs
analyzed for
SLEs
# of SLEs
Single
event
analysis
# of SLEs
Resample
analysis
B-P 40 13 7 5 70 46
P-O 8 6 5 5 99 107
B-O 7 7 5 1 5 3
P-P 4 4 10 10 76 55
B-B 2 1 2 2 18 15
Total 61 31 29 23 268 226
Table 1. Number of cell pairs patched and analyzed for SLEs. B, Basket cell; P, Pyramidal cell; O, Oriens cell; SLE,
seizure-like events; ISI, inter-seizure interval.
24
Correlation Analysis
Using subthreshold membrane potentials and spike times of pairs of neurons over
discrete time windows, we tracked the progression of synchrony between the cell types
during seizure-like events (SLEs). The presence of synchronization was determined using
crosscorrelation analysis (Netoff and Schiff 2002).
A seizure-like event was defined as a period of sudden and extreme increase in the firing
activity of a cell with a simultaneous fast positive extracellular shift (FPES; Ziburkus et al,
2006) seen in the extracellular recording, reflecting a network-wide event. We first
analyzed the SLE as single seizures and then resampled the data to study the interval
between two seizures (inter-seizure interval, ISI).
Single SLE analysis - The start of each SLE was determined using the FPES (Figure 4a, red
arrow). Correlation analysis was performed over 1s overlapping windows extending
from 30 sec before to 70 sec after the FPES. This created 100s long files that included
the SLEs.
Resample analysis - To analyze the interval between two consecutive SLEs (inter-seizure
interval) correlation analysis was measured on 1s overlapping windows beginning from
the start of the first SLE (determined using FPES, Figure 4b red arrows) to the start of
the consecutive SLE. The two start times were then fixed and the interval in-between
was resampled in MATLAB (MATLAB R2007a Student version). Inter-seizure intervals
shorter than 50 sec and longer than 300 sec were excluded.
25
a.
b.
Figure 4. Single seizure and resample analysis. Single seizure analysis (a) of oriens interneuron (top trace) and
pyramidal cell (middle trace). Resample analysis (b) of two pyramidal cells (top and middle trace). Start times for both
analysis were determined using the fast positive extracellular shift (FPES) seen in the extracellular recording (bottom
traces in (a) and (b)).
20mV
20mV
0.4mV
10sec
20 mV
0.2 mV
20 sec
20 mV
26
In addition to performing a standard crosscorrelation analysis, we also prepared the
voltage traces in two ways in an attempt to investigate the input and output signals
separately. Output correlations were investigated by measuring the correlations
between the spike rates of the two neurons. Input correlations were investigated by
measuring the correlation between the membrane potentials after removing all action
potentials.
To identify spikes, a threshold was used to determine spike times. The spike time data
was transformed into spike rate data by binning over 100 msec windows. This course
grained spike rate data was then used in our spike correlations. Subthreshold analysis
uses data were the entire action potential is removed from the voltage trace. To
determine spike initiation and spike termination the slope of the membrane potential
was measured over 0.1ms. The slope threshold of spike initiation was set between 20
and 25mV/ms to best detect spikes and the slope of spike termination was set to zero,
to confirm the end of this spike before the start of the subsequent spike.
Subthreshold and total crosscorrelation analysis was measured for each neuron i and j
over overlapping 1s windows using the equation:
/2
/2, 1/2 1/2
/2 /22 2
/2 /2
( ) ( )
( )
( ( )) ( ( ))
T
i j
t T
i jt T t T
i j
t T t T
x t x t
c
x t x t
ττ =−
= =
=− =−
+
=
∑
∑ ∑
Here the voltage ( )x t for neuron ior neuron j has had its mean removed and τ is the
time lag. The Bartlett estimator of cross-correlation standard error ,i jσ (Bartlett, 1946):
27
/2
, ,2 /2,
( ) ( )
( )( 1 )
T
i i j j
Ti j
c c
T
τ
τ τσ τ
τ=−=
+ −
∑
where , ( )i ic τ and
, ( )j jc τ are the crosscorrelation and autocorrelation functions of each
signal and . indicates absolute value. The sum, S , of the cross-correlation values
greater than two times the standard error was calculated to determine the correlation
value for each window:
/ 2
, , ,
/ 2
( ) ( ( ) 2 ( ))T
i j i j i j
T
S c cτ
τ θ τ σ τ=−
= −∑
where θ ,the Heaviside function, takes on the following values: (1 if , ( ) 2 0i jc τ σ− > ,
and 0 otherwise).
To measure spike correlation for each pair of neurons iand j we binned time series of
spikes ( )s t by discretizing time into N intervals of 100ms. To account for spike
correlations we use the following point process correlation
/ 2
,
/ 2
1( ) ( ) ( )
T
i j i j
t T
nmp s t s t
nm Nτ τ
=−
= + −
∑
(Brody, 1991). Here n and m are the average spike rates for neurons i and j across
the window. In order to avoid erroneous higher correlation values in the
crosscorrelation signal, we again used the Bartlett estimator to determine the
significance of , ( )i jp τ using:
28
/ 2
, ,2 / 2,
( ) ( )
( )( 1 )
T
i i j j
Ti j
p p
T
τ
τ τσ τ
τ=−=
+ −
∑
where , ( )i ip τ and
, ( )j jp τ are the correlation and autocorrelation functions of each
signal and . indicates absolute value.
The point process estimator was calculated in the same manner as the subthreshold
and total correlation, using the autocorrelation function
2/ 2
, 2/ 2
1( ) ( ) ( )
T
i i i i
t T
np s t s t
n Nτ τ
=−
= + −
∑
where ,i ip is the autocorrelation function of neuron i.
For resample analysis the average length of the inter-seizure interval was determined.
Using the resample command in MATLAB (MATLAB R2007a Student version), all the
inter-seizure intervals were either stretched or contracted to fit the average.
Statistical Analysis
Averages of spike rates, spike, subthreshold and total cross-correlations were measured
and reported as the standard error of the mean. ANOVA was calculated to determine
significant differences between different times within a SLE and followed by Tukey
multicomparison tests (confidence limit 0.001p < ). Linear regression analysis was
performed (using GraphPad Prism version 5.04 (Trial) for Windows, GraphPad Software,
San Diego, California, USA, www.graphpad.com) for the spike rates during the ISI. For
linear regression analysis the start ISI was determined as the time when the spike rate of
the cell was at its minimum following seizure termination. The end of the ISI was
29
determined to be the time just before the sudden increase in firing activity at seizure
onset.
30
RESULTS
Previous findings (Ziburkus et al 2006) about the unique interleaving pattern of activity
between OLM and pyramidal cells are confirmed in the current experiments. OLM cell
firing reaches its peak just before the start of the SLE. At the start of the SLE the OLM
cells enter into depolarization block which is characterized by a significant drop in firing
activity. Severe depolarization inactivates the voltage-gated Na+ channels thus raising
the firing threshold and the OLM cells are rendered unexcitable. During this drop in
firing of the OLM cells, pyramidal cells display an increase in the firing activity at the
start of the SLE and the excitation comes to a stop when the OLM cells recover and
increase their firing again (Figure 5a).
Prior experiments also showed that spike correlation between pyramidal and OLM cells
showed a significant increase at the beginning and the end of SLEs (Figure 5b).
Subthreshold correlation between these cell types showed a similar pattern (Figure 5c).
31
a.
b.
c.
Figure 5. Interleaving pattern of interplay between OLM and pyramidal cells. Inhibitory (blue) and excitatory
(orange) spike rates during the evolution of SLEs. Inset: average spike rates at expanded time scale (a). Cross
correlation averages for spike (b) and subthreshold (c) activity of pyramidal and OLM cells. Subthreshold correlation
between pyramidal and OLM cells increased at the start of the SLE (marked by inverted black arrow) and then again
towards the middle of the SLE (b). Spike correlation showed an increase at the start of the SLE (blue asterisk) and
after the DB in interneurons (green asterisk) (c). [Reproduced from Ziburkus et al. 2006]
32
Current experiments extended the previous study of the OLM-pyramidal cell
interactions to include the parvalbumin positive basket cells (Figure 6).
Figure 6. Schematic diagram of cell-types in the CA1 region of the rat hippocampus investigated in our experiments.
The soma of these basket cells are located in the pyramidal layer, their dendrites span
the length of the stratum radiatum and also spread into the stratum oriens. Their axons
innervate the pyramidal cells perisomatically. Hence the microcircuit studied in the
current experiments involves two types of inhibitory networks that are complimentary
to each other in regard to their anatomical distribution and the axonal innervations onto
the pyramidal cells.
Schaffer collateral
inputs
Stratum lacunosum
moleculare
Stratum
radiatum
Stratum
pyramidale
Stratum
oriens
Long range
projections
33
Given the differences between the two interneuron sub-types described earlier, we
hypothesized distinct patterns of interaction between pyramidal cells and each of these
interneurons.
We measured spike rates of all three cell types and also measured the total, spike and
subthreshold correlation, as described in detail in the Research Design and Methods
section. Spike rates were measured for individual cells and the cross-correlation was
measured between the different cell pairs to reveal the correlation in activity of the
pyramidal, basket and oriens cells in the different phases of the SLE.
Total correlation is a measure of the correlated activity between the cells. In order to
distinguish between input and output correlations, the subthreshold and spike
correlations are measured separately.
Subthreshold activity reflects the synaptic inputs into the cells and also the resulting
voltage-dependent subthreshold activation of the membrane potential. This analysis
measures the correlation in synaptic network inputs to the cells before, during and after
the SLE. The analysis allows us to observe any distinct patterns of correlated synaptic
input in the different stages of the SLE. Spike correlation analysis reflects the correlation
in neuronal output. We measured the spike cross correlation between the three cell-
types to observe any distinct patterns of correlated firing activity of the cells during the
different stages of the SLE.
As stated in the Research Design and Methods section, the start of each seizure was
determined using the fast positive extracellular shift (FPES). Correlation analysis for
single seizures was performed 30 seconds before to 70 seconds after the FPES.
Resampling analysis was performed over the inter-seizure interval beginning from the
start of one seizure to the start of the subsequent seizure. Also, inter-seizure intervals
shorter than 50 seconds and longer than 300 seconds were excluded. Hence in some
cases the number of SLEs is less in the resample analysis than the single seizure analysis.
34
Since resample analysis was conducted on the interval between pairs of seizures, in
most cases for all analysis the second seizure in a given interval will be the first seizure
of the subsequent interval.
In the associated figures (Figures 7 - 14) we will denote statistical differences with
asterisks/circle/square, or when there are many points we will use a solid bar. The bar
indicates that there are a number of points that are statistically separated from the
point marked with an asterisk/circle/square. However it does not necessarily mean that
all points within the region denoted by the bar are statistically different from the point
marked with an asterisk/circle/square.
1. Spike Rate
Single seizure analysis of spike rates (Figure 7) for pyramidal (green trace, n=245),
basket (red trace, n=93) and oriens interneurons (black trace, n=104) shows a
statistically significant increase in firing rate at seizure onset (30s) for pyramidal (●) and
basket (○) cells and just before seizure iniZaZon for oriens interneurons (*) compared
to pre- and post-seizure (0-30s, 50-100s) intervals.
Resample analysis of the spike rates (Figure 8) for pyramidal (green trace, n=208),
basket (red trace, n=64) and oriens interneurons (black trace, n=110) shows the same
statistically significant increase (*-oriens, ● - pyramidal, ○- basket cells) in firing activity
that is seen in the single seizure analysis. (The start of the first seizure in the resampled
analysis is at 100 seconds which corresponds to the start of the seizure in the single
seizure analysis at 30 seconds, and the start of the second seizure is at 287 seconds).
Linear regression analysis of the spike rates during the ISI shows a statistically significant
increase in firing activity for all three cell types (Figure 9; basket cells (red trace, n= 64)
slope= 0.02175 ± 0.001280, r2= 0.9507, p < 0.0001; pyramidal cells (green trace, n= 208)
35
slope= 0.004037 ± 0.0007630, r2= 0.6363, p < 0.0001; oriens interneurons (black trace,
n= 110) slope= 0.02379 ± 0.002449, r2= 0.8252, p < 0.0001).
36
Figure 7. Single seizure spike rate. Single seizure analysis of the spike rates of pyramidal (green trace, n=245), basket
(red trace, n=93 ) and oriens interneurons (black trace, n=104 ). Oriens interneurons (black trace) show a statistically
significant increase (* in inset) in spike rates just before seizure onset (30s) compared to pre- and post-seizure
periods. Pyramidal (green trace) and basket cells (red trace) show a statistically significant increase (● for pyramidal
cells, ○ for basket cells in insets) in spike rates at seizure onset (30s) compared to pre- and post-seizure periods. Errors
bars=standard error of the mean. [ANOVA, Tukey multi-comparison test, p < 0.001).
0 10 20 30 40 50 60 70 80 90
5
10
15
20
25
30
35
40
45
50
Time (s)
Fre
qu
en
cy
(H
z)
0 10 20 30 40 50 6 0 70 80 90 10 0
10
20
30
40
50
T im e(s )
Fre
qu
en
cy(H
z)
* * *
0 10 20 30 40 50 60 70 80 90 100
2
4
6
8
10
12
14
Time(s)
Fre
qu
en
cy
(Hz)
● ● ●
0 10 20 30 40 50 60 70 80 90 100
5
10
15
20
25
Tim e(s )
Fre
qu
en
cy
(Hz)
○ ○ ○
37
Figure 8. Inter-seizure interval spike rate. Resampled analysis of the spike rates of pyramidal (green trace, n=208),
basket (red trace, n= 64) and oriens interneurons (black trace, n=110). Oriens interneurons (black trace) show a
statistically significant increase (* in inset) in spike rates just before seizure onset (100s) compared to pre- and post-
seizure periods. Pyramidal (green trace) and basket cells (red trace) show a statistically significant increase (● for
pyramidal cells, ○ for basket cells in insets) in spike rates at seizure onset (100s) compared to pre- and post-seizure
periods. Errors bars=standard error of the mean. [ANOVA, Tukey multi-comparison test, p < 0.001).
0 50 100 150 200 250 300 350
5
10
15
20
25
30
35
40
Time (s)
Fre
qu
en
cy (
Hz)
0 50 100 150 200 250 300 350 400
5
10
15
20
25
30
35
40
Time(s)
Fre
qu
en
cy
(Hz)
* * *
0 50 100 150 200 250 300 350 400
2
4
6
8
10
12
Time(s)
Fre
qu
en
cy
(Hz)
○ ○
○
0 50 100 150 200 250 300 350 400
2
4
6
8
10
Time(s)
Fre
qu
en
cy
(Hz)
● ●
●
38
Basket cells
Time (s)
Fre
qu
en
cy (
Hz)
0 50 100 150 2000
1
2
3
4
Oriens interneurons
Time (s)
Fre
qu
en
cy (
Hz)
0 50 100 150 2006
7
8
9
10
11
Pyramidal cells
Time (s)
Fre
qu
en
cy (
Hz)
0 50 100 150 2001.4
1.6
1.8
2.0
2.2
Figure 9. Linear regression analysis of inter-seizure interval. The spike rates during the ISI of basket (n=64), oriens
(n=110) and pyramidal (n=208) cells show a statistically significant increase during the ISI. The graphs in the left
column correspond to the traces in the right column. The ISI intervals used for linear regression analysis of each cell
were as follows (right column): basket cells= 70-150s, oriens interneueons= 45-150s, pyramidal cells= 65-150s. Basket
cells (top row, red trace) slope= 0.02175 ± 0.001280, r2= 0.9507, p < 0.0001; Oriens interneurons (middle row, black
trace) slope= 0.02379 ± 0.002449, r2= 0.8252, p < 0.0001; Pyramidal cells (bottom row, green trace) slope= 0.004037 ±
0.0007630, r2= 0.6363, p < 0.0001. (Start times of seizures in the graphs and traces above are at 0 and 187 seconds,
corresponding to seizure start times of 100 and 287 seconds, respectively, in Figure 8).
0 20 40 60 80 100 120 140 160 180 200
2
4
6
8
10
12
Time(s)
Fre
qu
en
cy
(Hz)
0 20 40 60 80 100 120 140 160 180 200
10
20
30
40
Time(s)
Fre
qu
en
cy
(Hz)
0 20 40 60 80 100 120 140 160 180 200
2
4
6
8
10
Time(s)
Fre
qu
en
cy
(Hz)
39
2. Correlation Analysis
Pyramidal cell-Pyramidal cell (PP)
Total correlation
In both single seizure (n=76) (Figure 10a) and resample analysis (n=55) (Figure 10b) the
start of the seizure shows a significant decrease in total correlation (37s, asterisk)
compared to the pre-, post- (0-30s, 50-95s; bars with asterisks) and inter-seizure interval
(125-225s; bar with asterisk).
Subthreshold correlation
Similar to the total correlation, in both single seizure (n=76) (Figure 10c) and resampled
(n=55) (Figure 10d) analysis, the start of the seizure is characterized by a statistically
significant decrease in correlation (37s, asterisk) compared to the pre-, post- (0-30s, 50-
95s; bars with asterisks) and inter-seizure interval (125-225s; bar with asterisk).
Spike correlation
In the single seizure analysis (n=76) (Figure 10e) the start of the seizure shows a
statistically significant increase (30s asterisk) compared to the pre- (0s, asterisk) and
post- (60-100s) seizure levels. The resampled analysis (n=55) (Figure 10f) reflects this
same significant increase in spike correlation.
40
a.
b.
c.
d.
e. f.
Figure 10. Correlation analysis of pyramidal-pyramidal cell pairs. Total (a, b), Subthreshold (c, d) and Spike
correlation (e, f) measured for single seizure events (left column, n=76) and resampled inter-seizure intervals (right
column, n=55). Error bars=standard error of the mean. [ANOVA, Tukey multi-comparison test, p < 0.001].
0 10 20 30 40 50 60 70 80 90 100
0.007
0.009
0.011
0.013
0.015
Time (s)
A.U
.
* * *
0 10 20 30 40 50 60 70 80 90 100
0.01
0.014
0.018
0.022
0.026
Time (s)
A.U
.
* * *
0 50 100 150 200 250 300 350 400
0.01
0.015
0.02
0.025
0.03
Time (s)
A.U
.
* *
0 10 20 30 40 50 60 70 80 90 100
0.002
0.003
0.004
0.005
Time (s)
A.U
. *
* *
0 50 100 150 200 250 300 350 400
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
Time (s)
A.U
.
* *
0 50 100 150 200 250 300 350 400
.001
.0015
.002
.0025
.003
.0035
Time (s)
A.U
.
* *
*
41
Basket cell-Basket cell (BB)
Total correlation
Using our present conservative approach to significance testing both single seizure
analysis (n=18) (Figure 11a) and resample analysis (n=15) (Figure 11b) show no
statistically significant separations between the different phases of the SLE. However,
certain trends do emerge; the total correlation between basket-basket cell pairs
decreases at the start of the seizure event (37s) and remains low during the body of the
seizure. As the seizure terminates the correlation returns to pre-ictal level. The
correlation remains at this level during the subsequent inter-seizure interval (70-160s).
Subthreshold correlation
As in the total correlation, the subthreshold correlation is devoid of any statistically
significant separations between the phases of the SLE in both the single seizure analysis
(n=18) (Figure 11c) and resample analysis (n=15) (Figure 11d). The overall trend is
similar to that seen in the total correlation.
Spike correlation
Single seizure analysis (n=18) (Figure 11e) shows an increase in correlation that begins
at the start of the seizure, reaching a statistically significant value during the body of the
seizure (40s, asterisk) compared to pre- (0-30s; bar with asterisk) and post-seizure (60-
100s) levels. The resampled data (n=15) (Figure 11f) reflects this same statistically
significant increase in correlation during the body of the seizure (115s, asterisk)
compared to the inter-seizure interval (150-250s; bar with asterisk).
42
a.
b.
c.
d.
e. f.
Figure 11. Correlation analysis of basket-basket cell pairs. Total (a, b), Subthreshold (c, d) and Spike correlation (e, f)
measured for single seizure events (left column, n=18) and resampled inter-seizure intervals (right column, n=15).
Error bars=standard error of the mean. [ANOVA, Tukey multi-comparison test, p < 0.001].
0 10 20 30 40 50 60 70 80 90 100
0.005
0.007
0.009
0.011
0.013
Time (s)
A.U
.
0 50 100 150 200 250 300 350 400
0.005
0.008
0.011
0.014
Time (s)
A.U
.
0 10 20 30 40 50 60 70 80 90 100
0.008
0.012
0.016
0.02
Time (s)
A.U
.
0 50 100 150 200 250 300 350 400
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
Time (s)
A.U
.
0 10 20 30 40 50 60 70 80 90 100
0.001
0.002
0.003
0.004
Time (s)
A.U
.
* * *
0 50 100 150 200 250 300 350 400
.001
.002
.003
.004
Time (s)
A.U
.
* *
43
Basket cell-Pyramidal cell (BP)
Total correlation
In the single seizure analysis (n=70) (Figure 12a) the start of the seizure ( 37s, asterisk) is
characterized by a statistically significant decrease in correlation compared to both pre-
(0-30s; bar with asterisk) and post-seizure levels (50-60s, 80-90s; bars with asterisks)
The same statistically significant decrease in correlation (100s, asterisk) compared to
inter-seizure interval (150s-275s; bar with asterisk) is seen in the resampled data (n=46)
(Figure 12b).
Subthreshold correlation
Both single seizure (n=70) (Figure 12c) and resample (n=46) (Figure 12d) analysis of the
subthreshold correlation show a decrease in correlation at the start of the seizure (37s,
100s; asterisk) which is significantly different from the pre-, post- (Figure 12c, 0-30s, 80-
95s respectively; bar with asterisk) and inter-seizure (Figure 12d, 150-275s) intervals.
Spike correlation
Both single seizure (n=70) (Figure 12e) and resample analysis (n=46) (Figure 12f) show a
statistically significant increase in correlation at the start of the seizure (37s, asterisk)
followed by a statistically significant decrease during the body of the seizure (45s,
asterisk).
44
a.
b.
c.
d.
e.
f. Figure 12. Correlation analysis of basket-pyramidal cell pairs. Total (a, b), Subthreshold (c, d) and Spike correlation
(e, f) measured for single seizure events (left column, n=70) and resampled inter-seizure intervals (right column,
n=46). Error bars=standard error of the mean. [ANOVA, Tukey multi-comparison test, p < 0.001].
0 10 20 30 40 50 60 70 80 90 100
0.008
0.012
0.016
0.02
Time (s)
A.U
.
* * * *
0 50 100 150 200 250 300 350 400
0.01
0.02
0.03
Time (s)
A.U
.
* *
0 10 20 30 40 50 60 70 80 90 100
0.008
0.012
0.016
0.02
Time (s)
A.U
.
* * *
0 50 100 150 200 250 300 350 400
0.01
0.015
0.02
0.025
0.03
Time (s)
A.U
.
* *
0 10 20 30 40 50 60 70 80 90 100
0.002
0.003
0.004
Time (s)
A.U
.
*
*
0 50 100 150 200 250 300 350 400
.001
.0015
.002
.0025
.003
.0035
Time (s)
A.U
.
*
*
45
Oriens interneuron-Basket cell (OB)
Total correlation
Single seizure analysis (n=5) (Figure 13a) shows no significant separations between the
different phases of the SLE. However, the resample analysis (n=3) (Figure 13b) shows a
statistically significant increase in correlation during the body of the seizure (Figure 13b,
110s, asterisk) compared to the inter-seizure interval (Figure 13b, 150-275s, bar with
asterisk).
Subthreshold correlation
Single seizure analysis (n=5) (Figure 13c) does not show significant separations between
the phases of the SLE, however resampled analysis (n=3) (Figure 13d) shows a
statistically significant increase in correlation during the body of the seizure (120s,
asterisk) compared to the inter-seizure interval (200-275s, bar with asterisk).
Spike correlation
Both single seizure (n=5) (Figure 13e) and resample (n=3) (Figure 13f) analysis show no
statistically significant separations between the phases of the SLE. However a trend
does emerge; correlation begins to decrease at the start of the seizure and is at its
lowest value during the body of the seizure when the oriens interneurons do not fire for
a while. The correlation recovers to pre-ictal values then decreases again as the seizure
terminates.
46
a.
b.
c.
d.
e.
f.
Figure 13. Correlation analysis of oriens interneuron-basket cell pairs. Total (a, b), Subthreshold (c, d)
and Spike correlation (e, f) measured for single seizure events (left column, n=5) and resampled inter-
seizure intervals (right column, n=3). Error bars=standard error of the mean. [ANOVA, Tukey multi-comparison
test, p < 0.001].
0 10 20 30 40 50 60 70 80 90 1000
0.01
0.02
0.03
Time (s)
A.U
.
0 10 20 30 40 50 60 70 80 90 100
0.01
0.02
0.03
Time (s)
A.U
.
0 10 20 30 40 50 60 70 80 90 100
0.001
0.003
0.005
Time (s)
A.U
.
0 50 100 150 200 250 300 350 400
.001
.003
.005
.007
Time (s)
A.U
.
0 50 100 150 200 250 300 350 400
0.01
0.02
0.03
0.04
Time (s)
A.U
.
* *
0 50 100 150 200 250 300 350 400
0.01
0.02
0.03
0.04
Time (s)
A.U
.
* *
47
Oriens interneuron-Pyramidal cell (OP)
Total correlation
For the single seizure analysis (n=99) (Figure 14a) the pre-ictal period shows a
statistically significant decrease in correlation (25s, asterisk) compared to the start (30s,
asterisk) and the body (50s, asterisk) of the seizure. The start of the seizure shows a
significant increase in correlation (30s, square) compared to the pre-ictal levels (25s,
square). The body of the seizure shows yet another increase (50s, circle) which is
significantly different from the pre- (0-30s, bar with circle) and post-seizure (65s, circle)
levels.
The resampled data (n=107) (Figure 14b) shows a significant increase in correlation at
seizure onset (100s, square) compared to the body (110s, square) and the inter-seizure
interval (175-250s, bar with square). This was not seen in the single seizure analysis due
to the coarseness of the binning. Also the correlation during the body of the seizure at
140s (circle) is significantly increased compared to the inter-seizure interval (175-250s,
bar with circle).
Subthreshold correlation
Single seizure analysis (n=99) (Figure 14c) shows statistically significant increase in
correlation just before seizure onset (30s, asterisk) compared to pre- (0-30s, asterisk)
and post-seizure (60s, asterisk) values. A second significant increase is seen during the
body of the seizure (50s, circle) compared to pre-seizure interval (0-30s, circle).
The resampled analysis (n=107) (Figure 14d) reveals a statistically significant increase in
correlation just before seizure onset (100s, asterisk) compared to the body of the
seizure and the inter-seizure interval (110-260s, bar with asterisk). As in the total
correlation, this peak was not observed in the single seizure analysis due to the
coarseness of the binning. A second increase during the body of the seizure at 145s
(circle) is significantly different from the inter-seizure interval (160-260s, bar with circle).
48
Spike correlation
The start of the seizure in the single seizure analysis (n=99) (Figure 14e) shows a
significant decrease in spike correlation (37s, asterisk) compared to the pre- (0-30s, bar
with asterisk) and post-seizure (60-110s, bar with asterisk) intervals. The resampled
analysis (n=107) (Figure 14f) shows a statistically significant increase in correlation just
before seizure onset (90s, asterisk) compared to the start of the seizure (100s, asterisk)
and the inter-seizure interval (150-200s, bar with asterisk). A second increase in
correlation during the body of the seizure (125s, circle) is also statistically significant
compared to the decrease at the start of the seizure (100s, circle) and the inter-seizure
interval (150-200s, bar with circle).
49
a.
b.
c.
d.
e.
f. Figure 14. Correlation analysis of oriens-pyramidal cell pairs. Total (a, b), Subthreshold (c, d) and Spike correlation (e,
f) measured for single seizure events (left column, n=99) and resampled inter-seizure intervals (right column, n=107).
Error bars=standard error of the mean. (p < 0.001 for Total and Subthreshold correlation, p = 0 for Spike correlation
since the oriens interneurons stop firing during the body of the seizure). (Each symbol represents significant
differences between points in the SLEs with the same symbol). [ANOVA, Tukey multi-comparison test].
0 10 20 30 40 50 60 70 80 90 100
0.003
0.004
0.005
0.006
0.007
Time (s)
A.U
.
*
*
*▪
▪ ○
○
○
0 50 100 150 200 250 300 350 400
0.004
0.006
0.008
0.01
Time (s)
A.U
.
▪
▪
▪○
○
0 10 20 30 40 50 60 70 80 90 100
0.004
0.006
0.008
0.01
Time (s)
A.U
.
*
* *
○
○
0 50 100 150 200 250 300 350 400
0.003
0.005
0.007
0.009
Time (s)
A.U
.
* *
○
○
0 10 20 30 40 50 60 70 80 90 100
0.001
0.002
0.003
Time (s)
A
.U.
*
* *
0 50 100 150 200 250 300 350 4000
.001
.002
.003
Time (s)
A.U
.
* *
○
○
*○
50
SUMMARIZED RESULTS
The description of the results of the total, subthreshold and spike correlation analysis
between the different cell pairs presented in the above Results section is now discussed
in detail. Each of the three cells (pyramidal, basket and oriens cells) is discussed
separately here.
Pyramidal cells
To understand the role of each cell type in seizure dynamics we will focus on the inputs
and outputs of each one separately. Pyramidal cells receive inputs from Schaffer
collaterals, basket cells, and oriens interneurons and are also innervated by other CA1
pyramidal cells.
The spike correlation of pyramidal cells (Figure 10f and Figure 15c, purple trace) steadily
increases until seizure onset though the firing frequency shows a moderate increase
(Figures 8 and 9; green trace). Pyramidal cell firing rates increase sharply at seizure
onset, at which time their correlation begin a sharp decline. Therefore the strength of
the excitatory output will be increased by higher firing rates, but will be tempered by
the loss of correlated signaling between these cells. The increase in spike correlation
and firing activity seen here before and leading up to the SLEs is similar to the biphasic
network dynamics observed by Cymerblit-Saba and Schiller (2010). They showed an
initial decrease in neuronal synchrony followed by increase in firing activity and
synchronization between neurons leading up to the seizure.
Basket cells provide perisomatic inhibition. Our measurements show that spike
correlations between basket cells (Figure 11e,f and Figure 15c, red trace) as well as their
spike rates (Figures 7 and 8; red trace) reach a maximum value during the body of the
seizure and help control the excitatory output from the pyramidal cells.
51
The oriens interneurons fire at a high frequency and their output correlation value
remains high for most of the SLE (Ziburkus et al 2006). They reach their maximum firing
frequency just before seizure onset before abruptly entering into depolarization block.
During depolarization block the oriens interneurons are unable to generate action
potentials, causing their output correlation to drop to zero. This drop in inhibition may
be responsible for the sudden increase in firing seen in pyramidal cells. As the SLE
terminates the oriens interneurons emerge from the depolarization block with a steady
increase in the firing frequency and the output correlation returns to pre-seizure levels.
Simultaneously the firing frequency of the pyramidal cells decreases. The return of
oriens interneuron firing activity after the depolarization block along with the basket
cells appear to be responsible for reigning in the runaway excitation of the pyramidal
cells.
Finally, the input or subthreshold correlations in the pyramidal cell network remain
relatively constant, and high, leading up to the body of the seizure falling dramatically at
the seizure onset and recovering through the body of the seizure (Figure 15b, purple
trace). This result demonstrates that the cells of the pyramidal network are receiving
common inputs, possibly from the Schaffer collaterals from CA3, leading up to the
seizures. The loss of correlation at the beginning of the seizure (Figure 15b, purple trace
at 100s) may reflect the input from CA3 being cut off by the sudden increase in oriens
cell activity. It could also be a result of intrinsic membrane properties as the pyramidal
cells are engaging in higher firing and are therefore more depolarized during the seizure.
Subthreshold correlation of the pyramidal cells returns to pre-seizure levels as the
seizure terminates possibly reflecting the simultaneous increase in the output
correlation in the basket cell network (Figure 15c, black trace) and oriens firing activity
returning to pre-ictal levels (Figures 7 and 8, black trace).
For the pyramidal cells, correlated excitatory inputs of the Schaffer collaterals are only
partially balanced by a smaller increase in the correlated inhibitory inputs from, first, the
52
oriens cells (before seizure onset) and then later by the basket cell network (after
seizure onset). Inhibitory firing rates in the basket (Figures 7 and 8, red trace,
significance denoted by ○) and oriens (Figures 7 and 8; black trace, significance denoted
by *) networks increase leading up to the onset of the seizure, but oriens inhibition fails
as these cells enter depolarization block (Figures 7 and 8; black trace). Decreasing
excitatory spike correlation occurs at the same time as the basket cell correlation
increases perhaps leading to the end of the seizure.
53
a.
b.
C.
Figure 15. Summarized resample analysis. Total (a), subthreshold (b) and spike (c) correlation of basket-basket (B-B),
basket-pyramidal (B-P), oriens-pyramidal (O-P) and pyramidal-pyramidal (P-P) cell pairs. Traces are the same as the
resampled graphs in figures 10-14, shown here without error bars.
0 50 100 150 200 250 300 350 400
0.005
0.01
0.015
0.02
0.025
Time (s)
A.U
.
B-B
B-P
O-P
P-P
0 50 100 150 200 250 300 350 400
0.005
0.01
0.015
0.02
0.025
Time (s)
A.U
.
B-B
B-P
O-P
P-P
0 50 100 150 200 250 300 350 400
.001
.002
.003
.004
.005
.006Spike Correlation
Time (s)
A.U
.
B-B
B-P
O-P
P-P
54
Basket cells
Basket cells receive input from the perforant pathway, Schaffer collaterals, other
unidentified interneurons and form autapses (Somogyi 2005), although they receive
inputs mostly from the principal cells (Freund and Katona 2007) and these excitatory
inputs are almost twice as numerous as inhibitory inputs (Gulyas 1999). Also, basket cell
dendrites in the stratum radiatum are connected with an extensive network of
dendrodendritic gap junctions (Fukuda and Kosaka 2000). Basket cell axons innervate
the pyramidal cells perisomatically.
PV+ basket cells receive very little inhibitory input; however the largest proportion of
this inhibitory input is at distal dendrites located in the stratum lacunosum moleculare
(Gulyas 1999). If the basket cells do receive any inhibition from the oriens interneurons,
it would not be effective since the majority of the input to the basket cell dendrites is
from the perforant pathway. However basket cells do innervate each other thus possibly
governing their own output firing rate. This is reflected in the spike rate (Figures 7 and
8; red trace) and spike correlation (Figure 15c, black trace) of basket cells, which
gradually begins increasing at seizure onset. The spike correlation reaches its maximum
value shortly after seizure onset although the firing frequency has now returned to pre-
ictal values.
The output correlation of the pyramidal cells increases before seizure onset and this
increase along with increased firing rates in the pyramidal network may contribute to
the gradually increasing firing rates (Figures 7 and 8, red trace) and spike correlation
(Figure 11e, f) in the basket network.
The subthreshold correlation of the basket-basket cell pairs (Figure 15c; black trace and
Figure 11c, d) does not show any significant changes though a trend exists. During the
pre-ictal period the input correlation of these cells increases, then drops at the start of
the seizure, and the input correlation remains low during the body of the seizure. The
55
correlation returns to pre-ictal levels as the seizure terminates and stays here during the
subsequent inter-seizure interval.
Oriens interneurons
The oriens interneurons receive inputs from the CA1 pyramidal cells as well as inputs
from the CA3 via the Schaffer collaterals. However these CA3 fibers are distinct from
those entering the radiatum, hence stratum oriens and stratum radiatum receive
distinct synaptic inputs from different CA3 cells. The dendritic trees of oriens
interneurons lie in the stratum oriens layer and are electrotonically coupled via gap
junctions (Zhang et al 2004). The axons of oriens interneurons in particular the OLM
interneurons, innervate the distal part of the pyramidal cell dendritic tree in stratum
lacunosum moleculare.
Leading up to seizure onset both the output correlation (Figure 10e, f) and the firing
frequency of the pyramidal cells (Figures 8 and 9; green trace) increases. It is possible
that the increasingly synchronized firing output of the pyramidal cells (Figure 15c, purple
trace) before the seizure excites the oriens interneurons and results in their massive
burst of firing activity at the start of the seizure (Figures 7 and 8; black trace).
The subthreshold correlation in the oriens interneuron network remains weak during
the pre-ictal stage compared to during the seizure (not shown here, ref. Ziburkus et al
2006). It increases suddenly and drastically for a few seconds at seizure onset and then
decreases, but to a level higher than the pre-ictal correlation value. The sudden increase
in subthreshold correlation of oriens interneurons may be a result of the pyramidal cell
spike correlation and the spike rate reaching its peak at the same time.
Before summarizing the above dynamics there are several remarkable patterns of
interaction that may further elucidate the network dynamics involved. One such
relationship to be noted is that between pyramidal and basket cells. The input
56
correlation between the pyramidal-basket cell pairs follows that of the pyramidal-
pyramidal cell pairs very closely and is distinctly different from the input correlation
between basket-basket cell pairs. It is known that the apical and basal dendrites of
basket cells spanning stratum radiatum and stratum oriens, respectively, receive inputs
from different CA3 pyramidal cells (Somogyi 2005). We propose that, pyramidal cells
receive the same input and the basket cells receive a subset of that information,
however the different basket cells receive different subsets of that information.
Yet another relationship to note is that between excitation and inhibition during the
seizure. The increased spiking activity of the oriens interneurons before seizure onset
can lead to pyramidal cell depolarization due to GABA-A receptor-mediated influx and
subsequent accumulation of Cl- and an efflux of HCO3
- (Fujiwara-Tsukamoto et al 2003).
The positive shift in chloride reversal decreases the efficacy of inhibition or even making
it excitatory, thus contributing to the “runaway excitation” seen in pyramidal cells
during seizures.
57
DISCUSSION
Dual and triple whole cell recordings of two sub-types of interneurons and pyramidal
cells were performed to explore the interactions between these three cell types before,
during and after an SLE. Basket and oriens interneurons are two functionally and
morphologically distinct sub-classes of interneurons in the CA1 region of the
hippocampus and inhibit pyramidal cells via disparate mechanisms. These three cell
types by no means make up the entire neuronal network but are important since these
two types of GABAergic cells have such a fine degree of specificity for their target
membrane compartments; basket cells innervate the pyramidal cells perisomatically
whereas the oriens interneurons innervate the distal dendrites located in the stratum
lacunosum-moleculare.
Current information tells us that each of the 21 interneuron sub-types identified in the
CA1 region of the hippocampus are morphologically and functionally diverse
(Klausberger and Somogyi 2008). Specific dynamic interactions between interneurons
and pyramidal cells cause distinct brain states. Hence, exploring the functional diversity
of these interneurons during seizures is imperative to further our knowledge about
seizure dynamics in the hippocampal network. Given the differences between oriens
interneurons and basket cells (Klausberger and Somogyi 2008), we hypothesized distinct
and unique roles for both cell types in seizure dynamics.
Since the perisomatic region of pyramidal cells integrates inputs that lead to action
potential generation, basket cells with axons innervating the perisomatic region are
strategically placed to synchronize pyramidal cells (Somogyi 2005). Both basket and
pyramidal cells receive inputs from Schaffer collaterals which would lead to generation
of repetitive action potentials in the pyramidal cells but the strong feedforward
inhibition acts to control this repetitive generation of action potentials. The dendrites of
the basket cells located in stratum lacunosum-moleculare receive extrinsic excitatory
58
afferents and also receive excitatory inputs from the pyramidal cells that they innervate.
Buhl et al (1996) state that given this specifically-targeted excitatory input to the basket
cells and their connections to other interneurons, basket cells play “a key role in the
feedforward control of principal cells” and are also involved in shaping the inhibitory
network activity.
Given the above, it is possible that under normal physiological conditions, basket cells
are involved in both feedforward inhibition (due to the extrinsic excitatory afferents)
and local feedback inhibition (due to the local excitatory inputs from the pyramidal
cells).
In fact, a single perisomatic IPSP can suppress repetitive discharge (Cobb et al 1995). A
single perisomatically-innervating interneuron synapses onto 500-1650 pyramidal cells
whereas a single dendritically-innervating interneuron synapses onto 400-2000 neurons
(Miles et al 1996). Even though the number of synapses is comparable between these
two interneuron cell types, Miles showed that in the CA3, and under normal
physiological conditions, basket cells were far more effective at silencing the activity in
the principal cells of the CA3. This higher degree of efficacy is attributed to more
synchronized IPSPs generated by the basket as compared to the dendritically
innervating cells.
A previous study demonstrated the unique interleaving pattern of activity between OLM
and pyramidal cells (Ziburkus et al 2006). OLM cell activity was shown to book-end the
SLE with increased firing before and after the pyramidal cells show “runaway excitation”
during the body of the seizure. OLM cells receive at least 70% of their glutamatergic
inputs from the CA1 pyramidal cells (Somogyi 2005) and provide feedback inhibition to
distal dendrites of the pyramidal cells, located in the stratum lacunosum-moleculare.
Results from the current study show the spike correlation of basket cells peak during the
59
body of the seizure (Figure 15c, black trace; Figure 11e) at which time the pyramidal
cells’ spike correlation starts to decrease (Figure 15c, purple trace; Figure 10e). From
this observation along with other conclusions stated above and in the Interneuron
Diversity chapter earlier, we can further conclude that, under normal physiological
conditions, perisomatic inhibition more closely regulates efferent signaling of pyramidal
cells than dendritic inhibition. Hence perisomatically-innervating basket cell activity
most likely plays an important role in seizure dynamics.
60
CONCLUSION
In reviewing all of the above information we propose the following summary. During
the ISI the firing activity of the oriens interneuron network is characterized by an
increasing firing rate that is statistically significant (Figure 9, middle row). Concurrently
the firing activity of the pyramidal cells and basket cells also show a statistically
significant increasing firing activity during the ISI (Figure 9, top and bottom rows
respectively).
The output correlation of the pyramidal cell network and the basket cell network also
shows an increasing trend (Figure 15c, purple and red trace respectively). The increase
in the excitatory correlation and activity appears to drive the system into a pre-seizure
state. Seizures are ignited when the effectiveness of the excitatory network overcomes
inhibition as the oriens cells enter a hyper-excited depolarization block.
Inhibition however, is not entirely lost as the basket cell activity and spike correlation
increases past the beginning of the seizure; outlasting the excitatory ramp up. This
inhibition may be responsible for the termination of the seizure, which occurs at the
same time oriens inhibition returns and runaway excitation is reigned in.
In our study, we emphasize cell-specific interactions leading up to, during and following
an SLE. Conceivably each interneuron sub-type in the CA1 region of the hippocampus
plays a specific and unique role in seizure dynamics. Discovering the cellular
mechanisms underlying these seizure interactions is necessary in creating rational
pharmaceutical targets.
Currently, certain antiepileptic drugs like carbamazepine target the sodium channel by
inactivating them thus rendering the cells inexcitable. Others like valproate prevent
entry of calcium at axon terminals preventing neurotransmitter release and also activate
61
potassium conductance thus decreasing excitatory activity. Although effective, these
drugs treat epilepsy by suppressing network-wide activity and can have serious side
effects on normal cognitive function. By delineating cell-specific activity we are creating
the opportunity to target and alter specific cell types without using intrusive treatments
that tamp down the entire network.
In order to minimize seizures one would like to reduce the synchronization and
excitation seen in pyramidal cells. This is a common target of epileptic drugs to reduce
neuronal activity (gabapentin, felbamate). However, reduction of excitatory activity will
inevitably have effects on normal cognitive activity.
In addition, the intense firing of oriens interneurons may lead to chloride-loading of
pyramidal cells. This causes a positive shift in the chloride reversal decreasing the
efficacy of inhibition from oriens interneurons and possibly even resulting in net
excitation (Rivera et al 2005). Diuretics like furosemide are known to be anti-convulsant
by inhibiting chloride transport however its exact mechanism of action remains unclear
(Staley 2002). It is possible that furosemide prevents chloride-loading of pyramidal cells
thus preventing the chloride reversal and contains the excessive excitatory activity of
pyramidal cells. Another mechanism of action of diuretics is to draw water out of the
cells thus increasing the surrounding extracellular matrix volume (Staley 2002). The
swelling of the cell soma results in enhanced ephaptic interactions that contribute to
the spread of paroxysmal discharges and enhanced synchronization in pyramidal cell
population, and diuretics are able to reduce the excitability of pyramidal cells by
shrinking them. This effect may contribute to the enhanced synchronization seen in our
results where the pyramidal cells display increase spike correlation before seizure onset.
Second we look at the oriens interneurons that go into depolarization block. These
interneurons may go into depolarization block due to the inactivation of action
potential-generating mechanisms brought on by a persistent-sodium current which
62
provides the driving force for potassium efflux and maintains the open potassium
channels, resulting in [K+]o buildup (Bikson 2003) or due to the lack of oxygen locally.
The failure of oriens interneurons during the depolarization block releases inhibition on
pyramidal cells which display excessive excitation during seizures. One possible
therapeutic target would be to prevent depolarization block by reducing oriens
interneuron spiking activity. With reduced firing activity the action potential-generating
mechanisms will not be inactivated and hence prevent depolarization block.
Third, our results showed that basket cells are last to reach their maximum firing
frequency during the SLE (Figures 7 and 8, red trace). Basket cells are “strategically
placed” for perisomatic inhibition which is more effective than dendritic inhibition in
modulating the output of the pyramidal cells. Hence, one possible therapeutic option
would be to target the excitability of basket cells to increase their firing activity earlier,
thus cutting short the seizure or preventing it all together. This could be done by a cell-
targeted increase in efficacy of glutamate receptors on the basket cells. If basket cells
are more responsive to the pyramidal input they are more likely to fire earlier and thus
can terminate or prevent the seizure all together.
Fourth, any typical changes in activity of cells, if any, during the inter-seizure intervals
can be monitored to predict future seizures. Recent findings (Cymerblit-Sabba and
Shiller 2010) show a “typical network dynamics signature” that preceded seizures
induced in vivo. They made multi-electrode single-unit recordings in the rat
hippocampus and induced seizures using picrotoxin, kainate or pilocarpine. The typical
biphasic network dynamics they observed before seizure onset was a decrease in
interneuronal synchronization followed by an increase in the firing activity and
synchronization of the network leading up to the start of the seizure. Current data in the
literature about preictal states is confined to surface and intracranial EEG recordings but
Cymerblit-Sabba and Shiller (2010) extend that knowledge by using multielectrode
63
single-unit recording to study these network dynamics at the single-neuron and multi-
neuron level.
We performed simultaneous extracellular and dual/triple whole-cell recordings from
three different cell types in the CA1 of the rat hippocampus and monitored the activity
before, during and after SLEs. Our findings confirm Cymerblit-Sabba and Shiller’s
excitatory cell rate and correlations findings, and furthermore we provide the first
contrasting patterns and pre-seizure interactions of two different inhibitory
interneurons. We demonstrate changes in firing activity and correlations of all three cell
types during the inter-seizure interval leading up to seizure onset. We observed a
significant increase in spike rates for all three cells approximately 60-105 seconds before
seizure onset (Figure 9). A similar increase in firing activity (Bower and Buckmaster
2008) was also observed in the granule cells of the dentate gyrus 4 minutes before the
onset of local seizures. Using tetrodes to record from single granule cells they measured
the firing rates of the granule cells before and during spontaneous seizures in the rat
pilocarpine model of temporal lobe epilepsy. They found significant changes in action
potential rates of granule cells before the onset of spontaneous seizures. The firing
rates of 19% of granule cells recorded did not change before or during seizures, many
showed a significant decrease in firing frequency before seizure onset, some showed an
increase followed by a decrease in firing frequency (“bimodal firing pattern”) before
seizure , however, 34% of granule cells showed an increase in firing frequency before
the seizure. This increase in firing began 4 minutes before seizure onset. Findings from
our experiments show a significant increase in firing activity of all three cell types that
begins 1 to almost 2 minutes before seizure onset in CA1.
In conclusion, the findings of the above two studies and the results from our
experiments underline the fact that transitions from interictal to ictal stages in an
epileptic brain can be monitored by the changes in firing activity of the cells before
seizure onset. However, a more sensitive approach to monitor changes in activity would
be the correlation measures, as we have described in our work. Using multi-contact strip
64
electrodes to record multiple single cell activity, we can monitor and measure the
correlation of spike activity between multiple cells and thus predict seizures. Seizure
prediction algorithms can be used in novel treatments like closed-loop
neurostimulators, where the neurostimulators can detect typical network activity
preceding a seizure and can produce a specific stimulation protocol that prevents the
oncoming seizure (Cymerblit-Sabba and Shiller 2010).
In addition, though we are unable to fully explain the mechanisms underlying the
interactions we have observed in our experiments, distinct patterns of interactions
between the various cell sub-types do clearly emerge. Hence, seizures are a culmination
of a ramping up of excitatory activity and correlation which is initially kept in check by
interneuron activity. This balance however cannot be maintained as inhibition fails
leading to a release of the pyramidal cell activity which is only brought under control by
the increased activity in correlation in the basket cell network. Understanding the role
of feedback inhibition to suppress seizure onset and perisomatic inhibition to terminate
seizures may lead to novel and more effective treatments of epileptic disorders.
65
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70
VITA
Ruchi Parekh
The author was born and raised in Bombay, India. She attended Ohio Wesleyan
University in Delaware, Ohio where she earned her B.A. in Psychology in 2002. At
Pennsylvania State University she conducted her doctoral research in epilepsy in the
laboratory of Dr. Steven J. Schiff. While finishing this work, she accepted a postdoctoral
position with Dr. Giorgio Ascoli at George Mason University.