of 30
1
Multi
-
modal characterization and simulation of human epileptic circuitry
Anatoly Buchin
1
*
,
Rebecca de Frates
1,x
,
Anirban Nandi
1,x
,
Rusty Mann
1
,
Peter Chong
1
,
Lindsay
Ng
1
, Jeremy Miller
1
, Rebecca Hodge
1
,
Brian Kalmbach
1,2
,
Soumita
Bose
1,
3
,
Ueli Rutishauser
4
,
5
,
Stephen McConoughey
1
,
Ed Lein
1
,7
,
Jim Berg
1
,
Staci Sorensen
1
,
Ryder Gwinn
6
,
Christof
Koch
1
,
Jonathan Ting
1
,7
, Costas A. Anastassiou
1,
8
*
Affiliations
:
1
Allen Institute for Brain Science, Seattle, WA, USA*
2
University of Washington, Seattle, USA
3
CiperHealth
,
San Francisco
, USA
4
Cedars
-
Sinai Medical Center, Los Angeles CA, USA
5
California Institute of Technology, Pasadena, CA, USA
6
Swedish Medical C
enter, Seattle, WA, USA
7
University of Washington, Seattle, WA, USA
8
University of British Columbia, Vancouver, BC, CA
x
Equal author contribution
Current address: Institute for Advanced Clinical Trials for Children, WA, USA
*
Corresponding authors:
CAA (
costasa@alleninstitute.org
,
costas.anastassiou@gmail.com
)
and
AB
(
anatolyb@
alleninstitute.org
)
.
CC-BY 4.0 International license
was not certified by peer review) is the author/funder. It is made available under a
The copyright holder for this preprint (which
this version posted April 25, 2020.
.
https://doi.org/10.1101/2020.04.24.060178
doi:
bioRxiv preprint
2
Abstract
Temporal lobe epilepsy is the fourth most common neurological disorder with about 40% of
patients not responding to pharmacological treatment. Increased cellular loss in the hippocampus
is linked to disease severity and patho
logical phenotypes such as heightened seizure propensit
y.
While the hippocampus is the target of therapeutic interventions such as temporal lobe resection,
the impact of the disease at the cellular level remains unclear in humans.
Here
we show that
propert
ies of hippocampal granule cells change with disease progression as measured in living,
resected hippocampal tissue excised from epilepsy patients. We show that granule cells increase
excitability and shorten response latency while also enlarging in cellul
ar volume, surface area
and spine density. Single
-
cell RNA sequencing combined with simulations ascribe the observed
electrophysiological changes to gradual modification in three key ion channel conductances:
BK,
Cav2.2 and Kir2.1. In a bio
-
realistic compu
tational network model, we show that the changes
related to disease progression bring the circuit into a more excitable state. In turn, we observe
that by reversing these changes in the three key conductances produces a less excitable, “early
disease
-
like”
state.
These results provide mechanistic understanding of epilepsy in humans and
will inform future therapies such as viral gene delivery to reverse the course of the disorder.
Main Text
Epilepsy is one of the most common neurologic ailments
and
temporal lobe epilepsy (TLE)
is its
most commonly diagnosed form affecting
approximately 65 million people worldwide
.
Despite
considerable advances in the diagnosis and treatment of such seizure di
sorders, the cellular and
molecular mechanisms
leading to
TLE
-
related seizures remain
unclear
. Notably,
approximately
40% of TLE patients exhibit pharmaco
-
resistance, i.e. lack of response to conventional
.
CC-BY 4.0 International license
was not certified by peer review) is the author/funder. It is made available under a
The copyright holder for this preprint (which
this version posted April 25, 2020.
.
https://doi.org/10.1101/2020.04.24.060178
doi:
bioRxiv preprint
3
anticonvulsive treatments
(
1
)
.
H
ippocampal sclerosis (HS)
,
a neuropathological condition
associated with
cell loss
and gliosis, has been
linked
to increased
occurrence of TLE
with
elevated degree of HS constituting a hallmark of disease progression. The s
clerotic hippocampus
is
thought to be
the most likely origin of chronic seizures in TLE patients and is the target of
temporal lobe resection intervention.
The efficacy
of these
surgical
interventions
(
i.e., 65
-
80% of
TLE patients become seizure
-
free
)
further implicates the sclerotic hippocampus as a prominent
member of the
pathophysiological network
(
2
,
3
)
. Patients with increased HS typically suffer
from more frequent seizure activity
(
4
8
)
, an observation also replicated in rodents
(
9
)
.
Furthermore
, HS
ha
s
been associated with a spectrum of adverse effects such as longer epilepsy
duration
(
10
13
)
,
an earlier st
age of onset
(
8
,
14
,
15
)
as well as the presence of early aberrant
neurological insults such as febrile convulsions
(
8
,
14
,
15
)
.
To mechanistically understand the link between disease
progression and increased seizure
propensity in TLE patients, we
studied
live
human brain tissue excised during temporal
lobectomy
(Fig. 1 and Table S1).
The
excised
brain tissue
was
quantified
neuropathologically
via
the Wyler grade or WG (WG range:
1
-
4)
(
16
)
based on
light microscopic
examination of
the
mesial temporal damage in temporal lobectomy specimens
(Fig. 1A)
:
degree 1 (WG1)
corresponds to none/mild
HS
, while degree
3 or
4 (WG4) corresponds to severe
HS
. We use a
data generation and analysis platform
(
17
)
to characterize disease
-
related features in
gene
expression (RNA
-
seq),
electrophysiology, morphology, spine density and gene expression at
single
-
cell resolution.
Specifically, we concentrated
on g
ranule cells (GCs)
of
the
dentate gyrus
(DG), a hippocamp
al region. Studies in animal models and human neurosurgical specimens have
implicated DG GCs
in the generation and support of seizure activity by a plethora of
.
CC-BY 4.0 International license
was not certified by peer review) is the author/funder. It is made available under a
The copyright holder for this preprint (which
this version posted April 25, 2020.
.
https://doi.org/10.1101/2020.04.24.060178
doi:
bioRxiv preprint
4
mechanisms spanning from altered excitabili
ty
, changes in morphology
, protein expression,
neuron
al loss, synaptic re
-
organization and altered connectivity patterns
(
18
20
)
.
We pursued the
multi
-
modal characterization of human GCs across patients and report how their
properties
change with disease progression, i.e. increasing WG (
Fig. 1
B
and
1
C
,
S
1
and
S
2
).
Single
-
cell gene expression
changes
in
human
granule cells with
TLE progression
To
characterize
the expression profile
in
the
human
epileptic hippocampus, we analy
zed the gene
expression
data
of 230 ion channel
-
coding
and 1600 projection
-
coding
specific for
GC
s (
Fig.
1
B
)
.
Analysis of the sc
RNA
-
seq data set exhibits that a large number of genes are differentially
expressed with disease progression in GCs clearly separating WG1 and WG4 (Fig. 1B). In
addition, we found that the proportion of GCs was larger in WG1 compared to WG4 patients
(
57%
vs.
30% of all cells
, respectively
). These findings
indicate
that
a number of GC
properties
change with disease progression as well as the
progressive cells loss in human hippocampus with
degree of hippocampal sclerosis.
To study the cellular changes impli
cated by the sc RNA
-
seq
data, we performed a sc electrophysiology and morphology survey of GCs in the same
human
brain slices and examined how
these
properties
change with disease progression (Fig. 1C).
Excitability changes in granule cells with
TLE prog
ression
W
e
employed
whole
-
cell patch
-
clamp
ing
in
human
DG GCs imposing a battery of intracellular
stimuli
t
o
assess electrophysiological
changes associated with HS. The standard protocol (1 s
-
long dc current injections of different amplitude
s
) was applied
to
112 patch
-
clamped
GCs
(Fig
.
2A). A set of 3
0
electrophysiological features
was
extracted for every experiment (Table S
2
)
and
evaluated over the range of injected current amplitudes.
Based on these e
lectrophysiology
.
CC-BY 4.0 International license
was not certified by peer review) is the author/funder. It is made available under a
The copyright holder for this preprint (which
this version posted April 25, 2020.
.
https://doi.org/10.1101/2020.04.24.060178
doi:
bioRxiv preprint
5
features
, we observed robust
separation
between WG1 and WG4
cases
(Fig
.
2
B
-
C; tSNE:
t
-
distributed stochastic neighbor embedding
(
21
)
). To
identify
the
electrophysiology
features
separating
WG1 vs. WG4
,
we used two methods
: i)
pairwise feature comparison in combination
with
Man
n
-
Whitney
U
-
testing (
p
<0.05/
n
,
Bonferroni
-
corrected for multiple comparisons
n
)
,
which
resulted in
9
out of 3
0
features exhibiting statistically significant difference (Fig
.
2
D
)
; ii)
random forest classification
reaching
c
lassification accuracy
of 81%
(cha
nce
level
: 50%
;
classifier
trained
on all electrophysiological features
;
out
-
of
-
bag
error;
Fig
.
2
E
). When
compar
ing
the classifying features
resulting from
the two methods, we found that
5
out of
9
statistically significant features
were
shared (Fig
.
2
D and
2
E
).
The most prominent
electrophysiological features affected by disease progression are the spike frequency
-
current (“f
-
I”) gain (WG1 GCs have a higher gain than WG4) and the time
-
to
-
spike (WG4
GCs have a
shorter time
-
to
-
spike duration than WG1), two prominent excitability parameters of a neuron
(Fig. 2F). To cross
-
validate against patient
-
specific effects, patient
-
out
-
validation (classifier
trained on data from 6 patients predicting WG
-
score of
7
th
patient) classification performance
reached 71% (chance level: 50%; Table S3).
Altered granule cell morphology and
spine density
Next,
we ask to what extent the morphology of human GCs is affected by
disease progression
.
To do so, we reconstructed th
e dendritic morphology of
102
single GCs (Fig
.
3A)
and used a set
of
49
morphology features to assess WG
-
dependent alterations (Table S
4
). We found that
morphologies
differ between neurons from different levels of disease progression
(Fig
.
3B
-
C
).
Following
the same methodology as for electrophysiology features, we used two tests to detect
the features leading to the morphology separation (Fig. 3D
-
E). R
andom forest classification
(
out
-
.
CC-BY 4.0 International license
was not certified by peer review) is the author/funder. It is made available under a
The copyright holder for this preprint (which
this version posted April 25, 2020.
.
https://doi.org/10.1101/2020.04.24.060178
doi:
bioRxiv preprint
6
of
-
bag
error)
resulted in morphology
-
based classification performance of 7
4
%, i.e. comparable to
that of electrophysiology features.
Notably,
most features point to disease progression correlating
with thicker (i.e. increased surface area and volume) GC morphologies. W
hen
training on
both
electrophysiological (Fig
.
2) and morphology features (Fig
.
3)
for the subset of GCs (
n
=77)
where both data modalities are present
, classifi
er
performance reached
81
%
(
Fig. S
3
)
.
To what extent is synaptic input along GCs affected by d
isease progression?
To
determine
the
amount of
excitatory
synaptic input along GCs we
estimate
the
spine
density
in a subset of the
morphological biocytin reconstructions
(
Fig
.
3
F)
.
A
verage spine density
increases approximately
two
-
fold
from
WG1
to
WG4
.
This suggests an increase in synaptic drive of GCs with disease
progression
.
Notably, differences in spine density
occurred
both
along the apical and basal
dendrites
.
Overall
,
our results
from spine density measurements
indicate that DG GCs
in severe
stage
s
of
HS
receive increased excitatory synaptic drive
compared to
mild HS.
Ion conductance changes
To investigate the functional consequences o
f TLE and HS on electrophysiological,
morphological and connectivity
properties of human GCs,
we generate
d
a serie
s of biophysically
detailed,
anatomically realistic,
conductance
-
based single
-
neuron
models
(
22
)
. To produce
faithful sin
gle
-
neuron models, a key question is which ionic conductances to account for. Our
starting point for the ionic conductance recipe relied on a detailed GC model based on the rodent
literature that
involv
es
7 voltage
-
dependent K
-
channels, 4 voltage
-
dependent
Ca
-
channels, 2 Ca
-
dependent K
-
channels as well as two voltage
-
dependent Na
-
and Na/K (HCN) channels
distributed along different parts of the morphology
(
Fig
.
4A
,
(
23
)
)
. To assess to what extent these
.
CC-BY 4.0 International license
was not certified by peer review) is the author/funder. It is made available under a
The copyright holder for this preprint (which
this version posted April 25, 2020.
.
https://doi.org/10.1101/2020.04.24.060178
doi:
bioRxiv preprint
7
conductances are truly present in human GCs, in a second step, we analyzed
fluorescence
activated cell sorted single
-
cell RNA
-
seq data from four human brain specimens
(
24
)
to test for
the expression of genes associated to the ionic conductances used in our models. We found that
the vast majority of the genes associated with the conductances are indeed expressed in human
DG neurons (Fig. 4B).
To generate human GC
single
-
neu
ron models
we used
a
3
-
stage
approach leveraging a
genetic
optimization
framework which
rel
ies
on comparisons between experimental and model
electrophysiology features for a particular morphology reconstruction
(
Fig.
S
4
,
(
25
27
)
).
Using
this optimization framework, we generated GC models accounting for active ionic conductances
along their entire soma, axonal and dendritic arbor. In total, we developed
12
single
-
cell
models
per
W
G (data originating
from multiple patients)
in an unbi
ased manner that captured a
multitude of experimental observables such as differences between WG1 vs. WG4 in terms of
spike frequency response to increasing injection currents, time
-
to
-
spike, etc. (
Fig. 4C and
Fig.
S
5) As a result, when classifying electro
physiology features from these models, WG1 vs. WG4
models are clearly separated and, importantly, the features leading to this separation are in
agreement with the ones leading to separation of experimental electrophysiology features (Fig.
4D and Fig.
S
5).
Thus, our human GC models generated by the workflow faithfully reproduce
within
-
cell type similarities as well as degree
-
of
-
pathology differences measured in our
experiments.
The development of realistic conductance
-
based models and the inclusion of 15
distinct
ion
ic
c
onductances
shown to express in human GC neurons
offers a computational
framework
t
o
study
.
CC-BY 4.0 International license
was not certified by peer review) is the author/funder. It is made available under a
The copyright holder for this preprint (which
this version posted April 25, 2020.
.
https://doi.org/10.1101/2020.04.24.060178
doi:
bioRxiv preprint
8
disease progression changes
at
a mechanistic
level.
Pairwise
compar
ison
between WG1 vs. WG4
models
pointed to
three
conductances exhibiting
the most
prominent
, WG
-
related
difference
: Ca
-
dependent K
-
channel (BK), Cav2.2 and
K
ir2.1. Specifically, we found that reducing BK
-
and
increasing Cav2.2
-
and Kir2.1
-
conductance from their respective WG1 value
(percentual change
of me
dian
conductance from WG1 to WG4: BK, 56% increase; Cav2.2, 28% decrease; Kir2.1:
52
% decrease)
resulted in electrophysiology properties closely resembling WG4 cases (
Fig
.
4
E,
left; Fig.
S
6).
To test these model predictions, we also performed single
-
cell RNA
-
s
equencing in
the resected hippocampal tissue of a subset of our patient cohort (4 out of 7 patients; 2 WG1, 2
WG4) and quantified the expression of
genes associated with the three conductances, namely
KCNMA1 (BK), CACNA1B (Cav2.2) and KCNJ2 (
K
ir2.1). We fo
und that the trends (size
effect) predicted by the models for the two ionic conductances are supported by the RNA
-
seq
data (their absolute numbers expressed in
unique molecular identifiers or UMI
), i.e. we observed
upregulation of BK and downregulation of
Cav2.2 with disease progression (
Fig
.
4
E). The
direction of changes associated with Kir2.1 was inconclusive due to low number of gene reads.
How do the three identified ion conductances affect changes observed in electrophysiology
properties
, i.e. the
f
-
I slope and time
-
to
-
first
-
spike?
W
e
used the aforementioned s
ingle
-
neuron
models and
performed
a sensititivity analysis by perturbing
parameter
s
such as
somatic Cav2.2
and Ki
r
2.1 conductances
and measuring their impact
on the f
-
I slope and time
-
to
-
first
-
spike
(
Fig.
S7
)
.
Importantly,
the
conductance perturbations
in this analysis were
similar to the ones brought
about by the disease progression.
Out of
the three conductances,
we found that
only
the
decrease
in
somatic
Cav2.2
from WG1 to
WG4
affected the
f
-
I gain
in the same manner
as observed
experiment
ally
(
Fig S7
)
.
Moreover,
a
decrease
in
dendritic
Ki
r
2.1
conductance
was the only
.
CC-BY 4.0 International license
was not certified by peer review) is the author/funder. It is made available under a
The copyright holder for this preprint (which
this version posted April 25, 2020.
.
https://doi.org/10.1101/2020.04.24.060178
doi:
bioRxiv preprint
9
perturbation
that resulted
in a reduction
of
spike latency
(
Fig. S7
)
.
It follows that changes
in
somatic
Cav2.2
and
dendritic
Kir2.
1
conductances
brought about by the disease
best
explain the
changes observed in the most salient electrophysiology properties, respectively.
Circuit excitability
How are
the
differences
observed at the single
-
cell level
manifested in a
network
setting? W
e
used the single
-
neuron models to generate a network mimicking key features of DG circuit
ry,
consisting of biophysically realistic and connected excitatory (GC) and inhibitory (human basket
cells
, BCs
) neurons (
network:
5
00 GC models
,
6 human
BCs
(
18
)
) (
Fig
.
5A
).
We tested the role
of WG
-
dependent alterations on network dynami
cs via two independent
manipulations: (i) by
altering the network GC
-
composition between WG1 and WG4
GC
models while maintaining all
other aspects of the network identical (i.e.
BC composition,
connectivity, synaptic weight,
external input, etc.), and (ii)
by
doubling
the synaptic density between GCs from WG1 to WG4
(
Fig
.
5B
).
While Poisson
-
like, external input resulted in asynchronous GC and BC output for an
unconnected network (“WG1 no syn”), instantiating recurrent connectivity between WG1 GCs
and BCs (“
WG1”) resulted in recurring, burst activity. We use burst activity as a proxy for
hippocampal circuit excitability. Substituting WG1 GC models with WG4 ones while preserving
all other aspects of the circuit (“WG4”) resulted in increased frequency of burst
occurrence (
Fig
.
5B
-
C
). Markedly, increased excitability for the “WG4” network occurred despite WG4 GCs
exhibiting decreased spike
-
frequency gain (Fig. 2F and 4C) compared to WG1. This can be
attributed to their shorter reaction time (i.e. broadly related
to the ‘time
-
to
-
spike’ feature) which
effectively translates into reduced rheobase. Following our observation of the approximate
doubling of spine density from WG1 to WG4 (Fig. 3F), we instantiated a 2
-
fold increase in
.
CC-BY 4.0 International license
was not certified by peer review) is the author/funder. It is made available under a
The copyright holder for this preprint (which
this version posted April 25, 2020.
.
https://doi.org/10.1101/2020.04.24.060178
doi:
bioRxiv preprint
10
recurrent connectivity between WG4 GC
s in the network (“WG4 x2”) that resulted in even
stronger, sharper and more frequent burst events (Fig. 5B
-
C). Importantly, network excitability is
substantially reversed when the three ionic conductances found to be differentially expressed
between WG1 a
nd WG4 GCs (BK, Cav2.2, Kir2.1) are substituted in “WG4” GC
-
models with
their “WG1”
-
like values while all other aspects of the network remained unperturbed (“WG4
alt”). We conclude that GC
-
specific alterations observed during TLE
-
progression in the human
h
ippocampus can readily lead to increased, recurring circuit excitability congruent with clinical
observations of p
atients with increased HS
.
Moreover, combined, disease
-
related alterations in
BK, Cav2.2 and Kir2.1 conductances predicted via data
-
driven, bi
ophysical modeling and
supported by independent, single
-
cell RNA
-
seq analysis, critically influence circuit dynamics
dictating the transition from suppressed (WG1) to elevated (WG4) circuit excitability.
Discussion
How cells in the human brain ch
a
nge with disease progression and become hyperexcitable
contributing to temporal lobe epilepsy seizures remains unanswered. This is particularly true for
neurons in the human hippocampus, a brain region tightly linked to seizure initiation and
support.
We f
ound that
human
granule cells, a
prominent excitatory
cell type
in the hippocampus
that survives
disease progression, alter
a number of
their properties
that
are significantly affected
by TLE and HS progression
as witnessed in the
ir
genomic signature
.
How
are these changes
manifested?
Electrophysiologically, the most prominent difference in GCs with disease
progression is the decreas
ed
f
-
I gain.
Yet
, shorter
spike
latency of WG4 GCs vs. their WG1
counterparts
leads to
elevated, recurring circuit excitabilit
y
(despite reduced f
-
I gain)
, an
observation
in
-
line with
clinical observations
in
patients with
increased HS
typically suffer
ing
.
CC-BY 4.0 International license
was not certified by peer review) is the author/funder. It is made available under a
The copyright holder for this preprint (which
this version posted April 25, 2020.
.
https://doi.org/10.1101/2020.04.24.060178
doi:
bioRxiv preprint
11
from
increased number of seizures
(
4
8
)
and
an earlier stage of onset
(
8
,
14
,
15
)
.
Our works
suggests that a
lteration of GC and network excitability with disease progression
is
linked via
single
-
cell
RNA
-
seq
and modeling
to three i
onic conductances,
BK, Cav2.2, Kir2.1.
In the context of human epilepsies, BK channels have gathered considerable attention, e.g.
(
28
)
.
Yet, gain
-
of
-
function of BK exhibited in our patient cohort as well as des
cribed in patients of
genetic epilepsies has been difficult to explain mechanistically
(
29
31
)
. Here, we offer a
mechanistic framework relating gain
-
of
-
function in BK channels of human granule cells, key
regulators of hippocampal excitability
(
20
)
, with increased network excitability. Our findings in
human GCs also broadly agree with observations in rodents, in which a seizure
-
induced switch
in BK
-
channels results in an excitability increase of cortical and DG neurons
(
32
,
33
)
.
Concurrently, our
single
-
cell
perturbation analysis attributed changes in f
-
I slope and time
-
to
-
first
-
spike
in the
reduction
of
the
Cav2.2
and Kir2.1
conductance
, respectively,
during
TLE
progression.
Also referred to as N
-
type Ca c
urrent
,
it
ha
s
been implicated
in
TLE
with a number
of anti
-
convulsant medications impacting it
(
34
37
)
. Notably, our data suggest that, to revert
disease progression, a gain
-
of
-
function in
Cav2.2
is required.
Alteration in the expression level
of Kir2
-
channels has also been linked to excitability changes of granule cells in TLE
(
38
,
39
)
.
While we detected WG
-
dependent Kir2
-
conductance differences in our GC models, RNA
-
seq
revealed that overall expression of thes
e channels is comparatively low (Fig. 4B
and 4
E; in
agreement with
(
40
)
).
.
CC-BY 4.0 International license
was not certified by peer review) is the author/funder. It is made available under a
The copyright holder for this preprint (which
this version posted April 25, 2020.
.
https://doi.org/10.1101/2020.04.24.060178
doi:
bioRxiv preprint
12
W
e observed significant differences in the morphology and spine
density of GCs with disease
progression with GCs from severely sclerotic cases exhibiting thicker dendrites and double the
spine density
(
41
)
.
Despite clear indication that recurrent excitation between GCs impacts
network excitability and dynamics, our simulations, in agreement with experiments in rodents
(
42
)
, de
monstrate that three ionic conductances, if reverted to their WG1
-
level (i.e. reducing the
BK and increasing the Cav2.2 and Kir2.1 conductance by approx. 30
-
50%),
can substantially
reduce network excitability.
This emerging mechanistic
understanding of differences brought
about with disease progression underlying TLE, points toward improved therapies such as viral
delivery in a cell type
-
specific manner of genes for specific electrical conductances that can
reverse the deleterious pathop
hysiology effects of epilepsy.
.
CC-BY 4.0 International license
was not certified by peer review) is the author/funder. It is made available under a
The copyright holder for this preprint (which
this version posted April 25, 2020.
.
https://doi.org/10.1101/2020.04.24.060178
doi:
bioRxiv preprint
13
Citations
1.
P. Kwan, J. W. Sander, The natural history of epilepsy: an epidemiological view.
J. Neurol.
Neurosurg. Psychiatry
.
75
, 1376
1381 (2004).
2.
S. Wiebe, W. T. Blume, J. P. Girvin
, M. Eliasziw, A Randomized, Controlled Trial of
Surgery for Temporal
-
Lobe Epilepsy.
N. Engl. J. Med.
345
, 311
318 (2001).
3.
S. Spencer, L. Huh, Outcomes of epilepsy surgery in adults and children.
Lancet Neurol.
7
,
525
537 (2008).
4.
D. G. Vossler, D.
L. A. Kraemer, R. C. Knowlton, B. O. Kjos, S. W. Rostad, A. R. Wyler,
A. M. Haltiner, H. Hasegawa, R. J. Wilkus, Temporal ictal electroencephalographic
frequency correlates with hippocampal atrophy and sclerosis.
Ann. Neurol.
43
, 756
762
(1998).
5.
R. Käl
viäinen, T. Salmenperä, K. Partanen, P. Vainio, P. Riekkinen, A. Pitkänen, Recurrent
seizures may cause hippocambal damage in temporal lobe epilepsy.
Neurology
.
50
, 1377
1382 (1998).
6.
T. Salmenperä, R. Kälviäinen, K. Partanen, A. Pitkänen, Hippocampal a
nd amygdaloid
damage in partial epilepsy: A cross
-
sectional MRI study of 241 patients.
Epilepsy Res.
46
,
69
82 (2001).
7.
D. G. Vossler, D. L. Kraemer, A. M. Haltiner, S. W. Rostad, B. O. Kjos, B. J. Davis, J. D.
Morgan, L. M. Caylor, Intracranial EEG in
Temporal Lobe Epilepsy: Location of Seizure
Onset Relates to Degree of Hippocampal Pathology.
Epilepsia
.
45
, 497
503 (2004).
8.
R. Kälviäinen, T. Salmenperä, in
Progress in Brain Research
(Elsevier, 2002;
http://www.sciencedirect.com/science/article/pii/S
007961230235026X), vol. 135 of
Do
seizures damage the brain
, pp. 279
295.
9.
M. Dunleavy, C. K. Schindler, S. Shinoda, S. Crilly, D. C. Henshall, Neurogenic function
in rats with unilateral hippocampal sclerosis that experienced early
-
life status epilepti
cus.
Int. J. Physiol. Pathophysiol. Pharmacol.
6
, 199
208 (2014).
10.
S. S. Spencer, G. McCarthy, D. D. Spencer, Diagnosis of medial temporal lobe seizure
onset: relative specificity and sensitivity of quantitative MRI.
Neurology
.
43
, 2117
2117
(1993).
11
.
H. Jokeit, A. Ebner, S. Arnold, M. Schüller, C. Antke, Y. Huang, H. Steinmetz, R. J. Seitz,
O. W. Witte, Bilateral reductions of hippocampal volume, glucose metabolism, and Wada
hemispheric memory performance are related to the duration of mesial t
emporal lobe
epilepsy.
J. Neurol.
246
, 926
933 (1999).
12.
M. Seeck, F. Lazeyras, K. Murphy, A. Naimi, G. P. Pizzolatto, N. de Tribolet, J. Delavelle,
J.
-
G. Villemure, T. Landis, Psychosocial functioning in chronic epilepsy: relation to
hippocampal volume
and histopathological findings.
Epileptic. Disord.
1
, 179
86 (2000).
.
CC-BY 4.0 International license
was not certified by peer review) is the author/funder. It is made available under a
The copyright holder for this preprint (which
this version posted April 25, 2020.
.
https://doi.org/10.1101/2020.04.24.060178
doi:
bioRxiv preprint
14
13.
D. Fuerst, J. Shah, W. J. Kupsky, R. Johnson, A. Shah, B. Hayman
Abello, T. Ergh, Q.
Poore, A. Canady, C. Watson, Volumetric MRI, pathological, and neuropsychological
progression in
hippocampal sclerosis.
Neurology
.
57
, 184
188 (2001).
14.
M. R. Trenerry, C. R. Jack, F. W. Sharbrough, G. D. Cascino, K. A. Hirschorn, W. R.
Marsh, P. J. Kelly, F. B. Meyer, Quantitative MRI hippocampal volumes: association with
onset and duration of ep
ilepsy, and febrile convulsions in temporal lobectomy patients.
Epilepsy Res.
15
, 247
252 (1993).
15.
S. S. Keller, U. C. Wieshmann, C. E. Mackay, C. E. Denby, J. Webb, N. Roberts, Voxel
based morphometry of grey matter abnormalities in patients with medi
cally intractable
temporal lobe epilepsy: effects of side of seizure onset and epilepsy duration.
J. Neurol.
Neurosurg. Psychiatry
.
73
, 648
655 (2002).
16.
A. R. Wyler, F. Curtis Dohan, J. B. Schweitzer, A. D. Berry, A grading system for mesial
temporal p
athology (hippocampal sclerosis) from anterior temporal lobectomy.
J. Epilepsy
.
5
, 220
225 (1992).
17.
B. E. Kalmbach, A. Buchin, B. Long, J. Close, A. Nandi, J. A. Miller, T. E. Bakken, R. D.
Hodge, P. Chong, R. de Frates, K. Dai, Z. Maltzer, P. R. Nicovich, C. D. Keene, D. L.
Silbergeld, R. P. Gwinn, C. Cobbs, A. L. Ko, J. G. Ojemann, C. Koch, C. A. Anast
assiou,
E. S. Lein, J. T. Ting, h
-
Channels Contribute to Divergent Intrinsic Membrane Properties of
Supragranular Pyramidal Neurons in Human versus Mouse Cerebral Cortex.
Neuron
.
100
,
1194
-
1208.e5 (2018).
18.
V. Santhakumar, I. Aradi, I. Soltesz, Role of
Mossy Fiber Sprouting and Mossy Cell Loss
in Hyperexcitability: A Network Model of the Dentate Gyrus Incorporating Cell Types and
Axonal Topography.
J. Neurophysiol.
93
, 437
453 (2005).
19.
R. J. Morgan, I. Soltesz, Nonrandom connectivity of the epileptic
dentate gyrus predicts a
major role for neuronal hubs in seizures.
Proc. Natl. Acad. Sci.
105
, 6179
6184 (2008).
20.
E. M. Goldberg, D. A. Coulter, Mechanisms of epileptogenesis: a convergence on neural
circuit dysfunction.
Nat. Rev. Neurosci.
14
,
337
349 (2013).
21.
L. van der Maaten, G. Hinton, Visualizing Data using t
-
SNE.
J. Mach. Learn. Res.
9
, 2579
2605 (2008).
22.
C. Koch,
Biophysics of Computation: Information Processing in Single Neurons
(Oxford
University Press, 2004).
23.
M. Beining, L
. A. Mongiat, S. W. Schwarzacher, H. Cuntz, P. Jedlicka, T2N as a new tool
for robust electrophysiological modeling demonstrated for mature and adult
-
born dentate
granule cells.
eLife
.
6
, e26517 (2017).
24.
M. J. Hawrylycz, E. S. Lein, A. L. Guillozet
-
Bon
gaarts, E. H. Shen, L. Ng, J. A. Miller, L.
N. van de Lagemaat, K. A. Smith, A. Ebbert, Z. L. Riley, C. Abajian, C. F. Beckmann, A.
Bernard, D. Bertagnolli, A. F. Boe, P. M. Cartagena, M. M. Chakravarty, M. Chapin, J.
.
CC-BY 4.0 International license
was not certified by peer review) is the author/funder. It is made available under a
The copyright holder for this preprint (which
this version posted April 25, 2020.
.
https://doi.org/10.1101/2020.04.24.060178
doi:
bioRxiv preprint
15
Chong, R. A. Dalley, B. David Daly, C.
Dang, S. Datta, N. Dee, T. A. Dolbeare, V. Faber,
D. Feng, D. R. Fowler, J. Goldy, B. W. Gregor, Z. Haradon, D. R. Haynor, J. G. Hohmann,
S. Horvath, R. E. Howard, A. Jeromin, J. M. Jochim, M. Kinnunen, C. Lau, E. T. Lazarz, C.
Lee, T. A. Lemon, L. Li, Y.
Li, J. A. Morris, C. C. Overly, P. D. Parker, S. E. Parry, M.
Reding, J. J. Royall, J. Schulkin, P. A. Sequeira, C. R. Slaughterbeck, S. C. Smith, A. J.
Sodt, S. M. Sunkin, B. E. Swanson, M. P. Vawter, D. Williams, P. Wohnoutka, H. R.
Zielke, D. H. Geschw
ind, P. R. Hof, S. M. Smith, C. Koch, S. G. N. Grant, A. R. Jones, An
anatomically comprehensive atlas of the adult human brain transcriptome.
Nature
.
489
,
391
399 (2012).
25.
A. Nandi, T. Chartrand, W. V. Geit, A. Buchin, Z. Yao, S. Y. Lee, Y. Wei, B. Ka
lmbach, B.
Lee, E. Lein, J. Berg, U. Sümbül, C. Koch, B. Tasic, C. A. Anastassiou,
bioRxiv
, in press,
doi:10.1101/2020.04.09.030239.
26.
C. P. Mosher, Y. Wei, J. Kamiński, A. Nandi, A. N. Mamelak, C. A. Anastassiou, U.
Rutishauser, Cellular Classes in the
Human Brain Revealed In
Vivo by Heartbeat
-
Related
Modulation of the Extracellular Action Potential Waveform.
Cell Rep.
30
, 3536
-
3551.e6
(2020).
27.
C. M. Schneider
-
Mizell, A. L. Bodor, F. Collman, D. Brittain, A. A. Bleckert, S.
Dorkenwald, N. L. Turner,
T. Macrina, K. Lee, R. Lu, J. Wu, J. Zhuang, A. Nandi, B. Hu,
J. Buchanan, M. M. Takeno, R. Torres, G. Mahalingam, D. J. Bumbarger, Y. Li, T.
Chartrand, N. Kemnitz, W. M. Silversmith, D. Ih, J. Zung, A. Zlateski, I. Tartavull, S.
Popovych, W. Wong, M. Cas
tro, C. S. Jordan, E. Froudarakis, L. Becker, S. Suckow, J.
Reimer, A. S. Tolias, C. Anastassiou, H. S. Seung, R. C. Reid, N. M. da Costa,
bioRxiv
, in
press, doi:10.1101/2020.03.31.018952.
28.
W. Du, J. F. Bautista, H. Yang, A. Diez
-
Sampedro, S.
-
A. You, L
. Wang, P. Kotagal, H. O.
Lüders, J. Shi, J. Cui, G. B. Richerson, Q. K. Wang, Calcium
-
sensitive potassium
channelopathy in human epilepsy and paroxysmal movement disorder.
Nat. Genet.
37
, 733
738 (2005).
29.
P. N’Gouemo, Targeting BK (big potassium) chan
nels in epilepsy.
Expert Opin. Ther.
Targets
.
15
, 1283
1295 (2011).
30.
A. Leo, R. Citraro, A. Constanti, G. D. Sarro, E. Russo, Are big potassium
-
type Ca2+
-
activated potassium channels a viable target for the treatment of epilepsy?
Expert Opin.
Ther. Tar
gets
.
19
, 911
926 (2015).
31.
R. Köhling, J. Wolfart, Potassium Channels in Epilepsy.
Cold Spring Harb. Perspect. Med.
6
, a022871 (2016).
32.
S. Shruti, R. L. Clem, A. L. Barth, A seizure
-
induced gain
-
of
-
function in BK channels is
associated with elevate
d firing activity in neocortical pyramidal neurons.
Neurobiol. Dis.
30
, 323
330 (2008).
33.
L. E. Whitmire, L. Ling, V. Bugay, C. M. Carver, S. Timilsina, H.
-
H. Chuang, D. B. Jaffe,
M. S. Shapiro, J. E. Cavazos, R. Brenner, Downregulation of KCNMB4 expres
sion and
.
CC-BY 4.0 International license
was not certified by peer review) is the author/funder. It is made available under a
The copyright holder for this preprint (which
this version posted April 25, 2020.
.
https://doi.org/10.1101/2020.04.24.060178
doi:
bioRxiv preprint
16
changes in BK channel subtype in hippocampal granule neurons following seizure activity.
PLOS ONE
.
12
, e0188064 (2017).
34.
A. Stefani, F. Spadoni, G. Bernardi, Differential Inhibition by Riluzole, Lamotrigine, and
Phenytoin of Sodium and Calcium
Currents in Cortical Neurons: Implications for
Neuroprotective Strategies.
Exp. Neurol.
147
, 115
122 (1997).
35.
A. Stefani, F. Spadoni, G. Bernardi, Gabapentin inhibits calcium currents in isolated rat
brain neurons.
Neuropharmacology
.
37
, 83
91 (1998).
36.
T. B. Schumacher, H. Beck, C. Steinhauser, J. Schramm, C. E. Elger, Effects of Phenytoin,
Carbamazepine, and Gabapentin on Calcium Channels in Hippocampal Granule Cells from
Patients with Temporal Lobe Epilepsy.
Epilepsia
(1998), , doi:10.1111/j.1528
-
1157.1998.tb01387.x.
37.
S. Remy, H. Beck, Molecular and cellular mechanisms of pharmacoresistance in epilepsy.
Brain
.
129
, 18
35 (2006).
38.
M. Stegen, C. C. Young, C. A. Haas, J. Zentner, J. Wolfart, Increased leak conductance in
dentate gyrus granule cells of temporal lobe epilepsy patients with Ammon’s horn sclerosis.
Epilepsia
.
50
, 646
653 (2009).
39.
M. Stegen, F. Kirchheim, A. Hanuschkin
, O. Staszewski, R. W. Veh, J. Wolfart, Adaptive
Intrinsic Plasticity in Human Dentate Gyrus Granule Cells during Temporal Lobe Epilepsy.
Cereb. Cortex
.
22
, 2087
2101 (2012).
40.
H. Beck, H. Clusmann, T. Kral, J. Schramm, U. Heinemann, C. E. Elger, Potass
ium
currents in acutely isolated human hippocampal dentate granule cells.
J. Physiol.
498
, 73
85 (1997).
41.
M. Isokawa, Remodeling Dendritic Spines of Dentate Granule Cells in Temporal Lobe
Epilepsy Patients and the Rat Pilocarpine Model.
Epilepsia
.
41
,
S14
S17 (2000).
42.
K. Heng, M. M. Haney, P. S. Buckmaster, High
-
dose rapamycin blocks mossy fiber
sprouting but not seizures in a mouse model of temporal lobe epilepsy.
Epilepsia
.
54
, 1535
1541 (2013).
43.
Documentation
-
Allen Cell Types Database, (ava
ilable at http://help.brain
-
map.org/display/celltypes/Documentation?preview=/8323525/10813530/CellTypes_Morph
_Overview.pdf).
44.
RNA
-
Seq Data :: Allen Brain Atlas: Cell Types, (available at http://celltypes.brain
-
map.org/rnaseq).
45.
R. D. Hodge, T. E. B
akken, J. A. Miller, K. A. Smith, E. R. Barkan, L. T. Graybuck, J. L.
Close, B. Long, N. Johansen, O. Penn, Z. Yao, J. Eggermont, T. Höllt, B. P. Levi, S. I.
Shehata, B. Aevermann, A. Beller, D. Bertagnolli, K. Brouner, T. Casper, C. Cobbs, R.
Dalley, N. D
ee, S.
-
L. Ding, R. G. Ellenbogen, O. Fong, E. Garren, J. Goldy, R. P. Gwinn,
.
CC-BY 4.0 International license
was not certified by peer review) is the author/funder. It is made available under a
The copyright holder for this preprint (which
this version posted April 25, 2020.
.
https://doi.org/10.1101/2020.04.24.060178
doi:
bioRxiv preprint
17
D. Hirschstein, C. D. Keene, M. Keshk, A. L. Ko, K. Lathia, A. Mahfouz, Z. Maltzer, M.
McGraw, T. N. Nguyen, J. Nyhus, J. G. Ojemann, A. Oldre, S. Parry, S. Reynolds, C.
Rimorin,
N. V. Shapovalova, S. Somasundaram, A. Szafer, E. R. Thomsen, M. Tieu, G.
Quon, R. H. Scheuermann, R. Yuste, S. M. Sunkin, B. Lelieveldt, D. Feng, L. Ng, A.
Bernard, M. Hawrylycz, J. W. Phillips, B. Tasic, H. Zeng, A. R. Jones, C. Koch, E. S. Lein,
Conserv
ed cell types with divergent features in human versus mouse cortex.
Nature
, 1
8
(2019).
46.
Cell Features :: Allen Brain Atlas: Cell Types, (available at http://celltypes.brain
-
map.org/data).
47.
allensdk.ephys package
Allen SDK dev
documentation, (available at
http://alleninstitute.github.io/AllenSDK/allensdk.ephys.html).
48.
W. Van Geit, M. Gevaert, G. Chindemi, C. Rössert, J.
-
D. Courcol, E. B. Muller, F.
Schürmann, I. Segev, H. Markram, BluePyOpt: Leveraging Open Source Software a
nd
Cloud Infrastructure to Optimise Model Parameters in Neuroscience.
Front.
Neuroinformatics
.
10
(2016), doi:10.3389/fninf.2016.00017.
49.
H. Markram, E. Muller, S. Ramaswamy, M. W. Reimann, M. Abdellah, C. A. Sanchez, A.
Ailamaki, L. Alonso
-
Nanclares, N
. Antille, S. Arsever, G. A. A. Kahou, T. K. Berger, A.
Bilgili, N. Buncic, A. Chalimourda, G. Chindemi, J.
-
D. Courcol, F. Delalondre, V. Delattre,
S. Druckmann, R. Dumusc, J. Dynes, S. Eilemann, E. Gal, M. E. Gevaert, J.
-
P. Ghobril, A.
Gidon, J. W. Graham
, A. Gupta, V. Haenel, E. Hay, T. Heinis, J. B. Hernando, M. Hines, L.
Kanari, D. Keller, J. Kenyon, G. Khazen, Y. Kim, J. G. King, Z. Kisvarday, P. Kumbhar, S.
Lasserre, J.
-
V. Le Bé, B. R. C. Magalhães, A. Merchán
-
Pérez, J. Meystre, B. R. Morrice, J.
Mull
er, A. Muñoz
-
Céspedes, S. Muralidhar, K. Muthurasa, D. Nachbaur, T. H. Newton, M.
Nolte, A. Ovcharenko, J. Palacios, L. Pastor, R. Perin, R. Ranjan, I. Riachi, J.
-
R.
Rodríguez, J. L. Riquelme, C. Rössert, K. Sfyrakis, Y. Shi, J. C. Shillcock, G. Silberberg
,
R. Silva, F. Tauheed, M. Telefont, M. Toledo
-
Rodriguez, T. Tränkler, W. Van Geit, J. V.
Díaz, R. Walker, Y. Wang, S. M. Zaninetta, J. DeFelipe, S. L. Hill, I. Segev, F. Schürmann,
Reconstruction and Simulation of Neocortical Microcircuitry.
Cell
.
163
, 45
6
492 (2015).
50.
M. L. Hines, N. T. Carnevale, The NEURON simulation environment.
Neural Comput.
9
,
1179
1209 (1997).
51.
S. L. Gratiy, Y. N. Billeh, K. Dai, C. Mitelut, D. Feng, N. W. Gouwens, N. Cain, C. Koch,
C. A. Anastassiou, A. Arkhipov, BioNet: A
Python interface to NEURON for modeling
large
-
scale networks.
PLOS ONE
.
13
, e0201630 (2018).
52.
D. B. Jaffe, B. Wang, R. Brenner, SHAPING OF ACTION POTENTIALS BY TYPE I
AND TYPE II BK CHANNELS.
Neuroscience
.
192
, 205
218 (2011).
.
CC-BY 4.0 International license
was not certified by peer review) is the author/funder. It is made available under a
The copyright holder for this preprint (which
this version posted April 25, 2020.
.
https://doi.org/10.1101/2020.04.24.060178
doi:
bioRxiv preprint