from epilepsy to migraine to stroke: a unifying framework
DESCRIPTION
Invited talk at the DPG Spring MeetingTRANSCRIPT
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From epilepsy to migraine to stroke:A unifying framework
(Or: Act neurons like steam engines?)
Markus A. Dahlem (HU Berlin) &Niklas Hubel (TU Berlin)
Joint Focus Session DY/BP: Dynamical Patterns in Neural Systems: From
Brain Function to Dysfunction, April 1, 2014
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Outline
Introduction
Unifying ion dynamics in the brain
Application: From genotype to phenotype
Summary
![Page 3: From epilepsy to migraine to stroke: A unifying framework](https://reader033.vdocuments.mx/reader033/viewer/2022060116/558377cbd8b42ac8128b4ae5/html5/thumbnails/3.jpg)
Outline
Introduction
Unifying ion dynamics in the brain
Application: From genotype to phenotype
Summary
![Page 4: From epilepsy to migraine to stroke: A unifying framework](https://reader033.vdocuments.mx/reader033/viewer/2022060116/558377cbd8b42ac8128b4ae5/html5/thumbnails/4.jpg)
Top three with respect to costs & burden
In Europe
27 Migraines
22 Strokes
15.5 Epilepsy
billion Euro each year.
Balak and Elmaci (2005) European
Journal of Neurology 12
“What is particularly interesting tonote is that the most recent reportsstate that migraine alone is responsibleof almost 3% of disability attributableto a specific disease worldwide, also inconsideration of its comorbidity. Thisplaces migraine as the 8th mostburdensome diseases, the 7th amongnon-communicable diseases and the 1st
among the neurological disordersranked in the GBD report.”
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Models fill in the ‘gaps’ in clinical obervability
insid
e ce
ll
outsid
e ce
ll
a e
sensory aura (15min)
visual aura (0min)
behavior, perceptionsensory processing
(a) genetics defects, e.g. FHM, CADASIL, multifactorial (GWAS)
(b) Hodgkin-Huxley type, single cell electrophysiology models.
(c) Neural mass/fields population models, with subpopulations havingspecific synaptic receptor distribution.
(d) Local circuits, including the migraine generator network in the brainstem
(e) Pain, loss of function, seizing/convulsions, mental dysfunctions, imparedsensory and cognitive processing
• MAD, Migraine generator network and spreading depression dynamics as neuromodulation targets in episodicmigraine. Chaos, 23, 046101 (2013)• MAD, Migraines and Cortical Spreading Depression, Encyclopedia of Computational Neuroscience, (in press)
CADASIL: Cerebral Autosomal-Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy;
FHM: Familial hemiplegic migraine; GWAS: genome-wide association study
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Models fill in the ‘gaps’ in clinical obervabilityI
II
III
IV
V
VI0 5 10 15 20 25 30 35
time (s)
100
50
0
50
volt
age
(mV
)
V
EK
ENa
Iapp
seizure-likeafterdischarges
depolarization block
dominance pump current
m-gatedeactivation
begin I -drivenrepolarizationNa
+
tran
smem
bra
ne &
cellu
lar
level
mole
cula
r le
vel &
g
eneti
cs
b c
insid
e ce
ll
outsid
e ce
ll
a
off on
HY,TH
SPG
SSN
TCC
PAG
LC
RVMTG
cortex
cranial circulation & innervation
bone
dSD
cortico-thalamicaction
release noxious agentse
sensory aura (15min)
visual aura (0min)
behavior, perceptionsensory processing
balanced excitation and inhibition in ion-based models
org
an level
(a) genetics defects, e.g. FHM, CADASIL, multifactorial (GWAS)
(b) Hodgkin-Huxley type, single cell electrophysiology models.
(c) Neural mass/fields population models, with subpopulations havingspecific synaptic receptor distribution.
(d) Local circuits, including the migraine generator network in the brainstem
(e) Pain, loss of function, seizing/convulsions, mental dysfunctions, imparedsensory and cognitive processing
• MAD, Migraine generator network and spreading depression dynamics as neuromodulation targets in episodicmigraine. Chaos, 23, 046101 (2013)• MAD, Migraines and Cortical Spreading Depression, Encyclopedia of Computational Neuroscience, (in press)
CADASIL: Cerebral Autosomal-Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy;
FHM: Familial hemiplegic migraine; GWAS: genome-wide association study
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Outline
Introduction
Unifying ion dynamics in the brain
Application: From genotype to phenotype
Summary
![Page 8: From epilepsy to migraine to stroke: A unifying framework](https://reader033.vdocuments.mx/reader033/viewer/2022060116/558377cbd8b42ac8128b4ae5/html5/thumbnails/8.jpg)
From
HH-type conductance-based
toconductance- & ion-based models (2nd generation model)
C∂V
∂t= −INa − IK − Ileak
−Ipump
+Iapp (1)
INa = gNam3h(V − ENa)
IK = gKn4(V − EK )
Ileak = gleak(V − Vrest)
∂n
∂t= αn(1 − n) − βn,
∂h
∂t· · · (2) − (4)
∂[ion]e∂t
= − A
FVoloIion
∂[ion]i∂t
=A
FVoliIion (5) − · · ·
HH: Hodgkin-Huxley
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From HH-type conductance-based toconductance- & ion-based models (2nd generation model)
3Na+
2K+
K+
Na+
K+
K+
Na+
Extracellular Space
Bath/Vasculature
Neuron
Cl
Diff
usi
on
Cl-
-
C∂V
∂t= −INa − IK − Ileak−Ipump+Iapp (1)
INa = gNam3h(V − ENa)
IK = gKn4(V − EK )
Ileak = gleak(V − Vrest)
∂n
∂t= αn(1 − n) − βn,
∂h
∂t· · · (2) − (4)
∂[ion]e∂t
= − A
FVoloIion
∂[ion]i∂t
=A
FVoliIion (5) − · · ·
HH: Hodgkin-Huxley
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From HH-type conductance-based toconductance- & ion-based models (2nd generation model)
ion reservoirs
isolated boundary
syst
em
surroundingsenergysource
extracellular
intracellular
C∂V
∂t= −INa − IK − Ileak−Ipump+Iapp (1)
INa = gNam3h(V − ENa)
IK = gKn4(V − EK )
Ileak = gleak(V − Vrest)
∂n
∂t= αn(1 − n) − βn,
∂h
∂t· · · (2) − (4)
∂[ion]e∂t
= − A
FVoloIion
∂[ion]i∂t
=A
FVoliIion (5) − · · ·
HH: Hodgkin-Huxley
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Unifying ion dynamics in epilepsy, migraine, and stroke
Some terminology is due:heterogenous open system
ion reservoirs
isolated boundary
syst
em
surroundingsenergysource
extracellular
intracellular
• P. Dreier, ... MAD ... Is spreading depolarization characterized by an abrupt, massive release of Gibbs free energyfrom the human brain cortex? The Neuroscientist 19,25-42 (2012)
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Unifying ion dynamics in epilepsy, migraine, and stroke
Some terminology is due:heterogenous “closed” system
ion reservoirs
isolated boundary
syst
em
surroundingsenergysource
extracellular
intracellular
• P. Dreier, ... MAD ... Is spreading depolarization characterized by an abrupt, massive release of Gibbs free energyfrom the human brain cortex? The Neuroscientist 19,25-42 (2012)
![Page 13: From epilepsy to migraine to stroke: A unifying framework](https://reader033.vdocuments.mx/reader033/viewer/2022060116/558377cbd8b42ac8128b4ae5/html5/thumbnails/13.jpg)
Unifying ion dynamics in epilepsy, migraine, and stroke
Some terminology is due:heterogenous isolated “plus” system
ion reservoirs
isolated boundary
syst
em
surroundingsenergysource
extracellular
intracellular
• P. Dreier, ... MAD ... Is spreading depolarization characterized by an abrupt, massive release of Gibbs free energyfrom the human brain cortex? The Neuroscientist 19,25-42 (2012)
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Many, many, parameters, but most fixed by experiments
Table: Parameters for ion–based model – Part 2
Name Value & unit Description
Cm 1 µF/cm2 membrane capacitanceφ 3/msec gating time scale parameterg lNa 0.0175 mS/cm2 sodium leak conductance
g gNa 100 mS/cm2 max. gated sodium conductance
g lK 0.05 mS/cm2 potassium leak conductance
g gK 40 mS/cm2 max. gated potassium conductance
g lCl 0.02 mS/cm2 chloride leak conductance
Na0i 25.23 mM/l intracell. sodium conc.
Na0e 125.31 mM/l extracell. sodium conc.
K 0i 129.26 mM/l intracell. potassium conc.
K 0e 4 mM/l extracell. potassium conc.
Cl0i 9.9 mM/l intracell. chloride conc.
Cl0e 123.27 mM/l extracell. chloride conc.
E 0Na 39.74 mV sodium Nernst potential
E 0K -92.94 mV potassium Nernst potential
E 0Cl -68 mV chloride Nernst potential
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And still more parameters, but most fixed by experiments
Table: Parameters for ion–based model – Part 2
Name Value & unit Description
ωi 2.16 µm3 intracell. volumeωe 0.72 µm3 extracell. volumeF 96485 C/Mol Faraday’s constantAm 0.922 µm2 membrane surface
γ 9.556e–6 µm2MolC
conversion factorρ 6.8 µA/cm2 max. pump currentk1 5e–5/sec/(mM/l) buffering ratek1 5e–5/sec backward buffering rateλ 3e–2/sec diffusive coupling strengthKbath 4 mM/l potassium conc. of extracell. bathB0 500 mM/l buffer conc.
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Fixed points in 2nd generation HH
I “closed” system & leaky membrane
I “closed” system & voltage–gated membrane
I open system & voltage–gated membrane
(This will help to understand periodic solutions)
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Model without voltage-gating: Pump establishes polarizedstate (beyond a Gibbs-Donnan equilibrium)
ion reservoirs
isolated boundary
syst
em
surroundingsenergysource
extracellular
intracellular
only leak currents
0 5 10 15 20Imax in µA/ cm2
100
80
60
40
20
0
Vmax
inmVolt
polarized physiological state
• N. Hubel et al., Bistable dynamics underlying excitability of ion homeostasis in neuron models (in press PLOSComp. Biology)
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Model with voltage-gating: Bistability!
ion reservoirs
isolated boundary
syst
em
surroundingsenergysource
extracellular
intracellular
gated currents
0 5 10 15 20Imax in µA/ cm2
100
80
60
40
20
0
Vmax
inmVolt HB
HBHB
polarized physiological state
depolarized pathophysiological state
• N. Hubel et al., Bistable dynamics underlying excitability of ion homeostasis in neuron models (in press PLOSComp. Biology)
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Choices: Current and pump equations, ions, ...Two pump types
Iion,pumped ,1([K ]o , [Na]i ) = Imax
(1 +
KmK
[K ]o
)−2(1 +
KmNa
[Na]i
)−3
Iion,pumped ,2([K ]o , [Na]i ) = Imax1
1 + e(25−[Na]i/3)
1
1 + e(5.5−[K ]o)
HH current or GHK currents
Iion = gion(V − Eion)
Iion = V αF Pion[ion]i − [ion]oe
−αV
1 − e−αV
With or without chloride dynamics
d[Cl−]
dt= ... or 0
cf. Krogh-Madsen et al. Am. J. Physiol. Heart Circ. Physiol., 289,398-413 (2005).
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We gave it a fair shake. It’s robust
0
20
40
60
80
100
120
140
160
180
ρinµ
A/cm
2
I p,B
,exc
l.Cl−
,Ner
nst
I p,B
,inc
l.Cl−
,Ner
nst
I p,B
,exc
l.Cl−
,GH
K
I p,B
,inc
l.Cl−
,GH
K
I p,A
,exc
l.Cl−
,Ner
nst
I p,A
,inc
l.Cl−
,Ner
nst
I p,A
,exc
l.Cl−
,GH
K
I p,A
,inc
l.Cl−
,GH
K
1 2 3 4 5 6 7 8
Stability Regimes of Ion-Based Modelsstbl. depol. fixed pointbistablestbl. pol. fixed point
0
5 1 2 3 4 5 6 7 8
0.1 2.0 4.0 6.0 8.0 10.0
χA
0
5
10
15
20
25
30
35
ρinµ
A/cm
2
HB1
LP2 HB2
HB3
2 10 20 30 40 50
f in %
0
5
10
15
20
25
30
35
ρinµ
A/cm
2 HB1
LP2HB2
HB3
stbl. depol. fixed pointbistablestbl. pol. fixed point
0.1
2.0
LP10.1
2.0
LP1
• N. Hubel et al., Bistable dynamics underlying excitability of ion homeostasis in neuron models (in press PLOSComp. Biology)
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HH 2nd -generation “closed” systems are bistable
C∂V
∂t= −
ion∑(Iion,gated + Iion,pumped)
fixed point!= 0
current, pump, and gating equations . . .
∂[K+]e∂t
=A
FVole(IK+,gated + IK+,pumped)
+λ([K+]bath − [K+]e)︸ ︷︷ ︸buffer to bath
fixed point!= 0
f.p.!= 0
ion reservoirs
isolated boundary
syst
em
surroundingsenergysource
extracellular
intracellular
0 5 10 15 20Imax in µA/ cm2
100
80
60
40
20
0
Vmax
inmVolt HB
HBHB
polarized physiological state
depolarized pathophysiological state
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Open system: Diffusion (buffering) of potassium
C∂V
∂t= −
ion∑(Iion,gated + Iion,pumped)
fixed point!= 0
current, pump, and gating equations . . .
∂[K+]e∂t
=A
FVole(IK+,gated + IK+,pumped)+λ([K+]bath − [K+]e)︸ ︷︷ ︸
buffer to bathfixed point!
= 0
f.p.!= 0
ion reservoirs
isolated boundary
syst
em
surroundingsenergysource
extracellular
intracellular
0 5 10 15 20Imax in µA/ cm2
100
80
60
40
20
0
Vmax
inmVolt HB
HBHB
polarized physiological state
depolarized pathophysiological state
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Periodic solutions in 2nd generation HH
I open system & voltage–gated membrane
I full bifurcation analysis
I slow–fast analysis
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Time scales in ion dynamics
1st generation Hodgkin–Huxley model
0.01ms RC membrane time constant
1ms ion gating
2nd generation Hodgkin–Huxley model has in addition
1s volume–to–surface–area ratio / permeability
100s potassium regulation
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Unified minimal (4D) model ofspiking, seizures and spreading depression
5 10 15 20
Kbath / (mM/l)
−150
−100
−50
0
50
mV
HB1
LP1
LP2 HB2 HB3 HB4
LP1lc
LP2lc
TR1
TR2TR3TR4PD
membrane potential
5 10 15 20
Kbath / (mM/l)
0
10
20
30
40
50
60
70
80
90
mM
/l
HB1
LP2 LP1HB2
HB3HB4
extrac. potassiumstable FPunstable FPstable LCunstable LCstable torus
0 100 200 300 400 500
t / sec.
−100
−80
−60
−40
−20
0
20
40
60
mV
potentialEK
ENa
ECl
V
0.0 0.5 1.0 1.5 2.0
t / sec.
−80
−60
−40
−20
0
20
40
60
mV
0 500 1000 1500 2000
t / sec.
−100
−80
−60
−40
−20
0
20
40
60
mV
120130140
ion conc.
0 100 200 300 400 500
t / sec.
10
20
120
130
0.0 0.5 1.0 1.5 2.0
t / sec.
15
20
0 500 1000 1500 2000
t / sec.
0
20
40
60
80
100
120
140
mM
/l
Ki
Nai
Cli
Ke
Nae
Cle
a)
b)
c)
mM
/lm
M/l
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Unified minimal (4D) model ofspiking, seizures and spreading depression
5 10 15 20
Kbath / (mM/l)
−150
−100
−50
0
50
mV
HB1
LP1
LP2 HB2 HB3 HB4
LP1lc
LP2lc
TR1
TR2TR3TR4PD
membrane potential
5 10 15 20
Kbath / (mM/l)
0
10
20
30
40
50
60
70
80
90
mM
/l
HB1
LP2 LP1HB2
HB3HB4
extrac. potassiumstable FPunstable FPstable LCunstable LCstable torus
5 10 15 20
Kbath / (mM/l)
20
40
60
80
100
120
140
160
mM
/l
LP1
LP2
HB1
HB2HB3
HB4
LP1lc
LP2lc
TR1
TR2TR3
TR4
extrac. sodium
5 10 15 20
Kbath / (mM/l)
−80
−60
−40
−20
0
20
40
mM
/l
LP1
LP2
HB1
HB2 HB3HB4
LP1lc
LP2lc
TR1
TR2
TR3
TR4
potassium gain/loss Ke
stable FPunstable FPstable LCunstable LCstable torus
6.7 6.9 7.1
HB1 TR4
PD
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Unified minimal (4D) model ofspiking, seizures and spreading depression
−60 −40 −20 0 20 40
Ke / (mM/l)
−80
−60
−40
−20
0
20
40
mV
HB1
LP1HB2
LP2
HB3HB4
LP1lc
LP2lc
LP3lcLP4lc
membrane potential for simple model
−60 −40 −20 0 20 40
Ke / (mM/l)
0
10
20
30
40
50
60
mM
/l
HB1
LP1HB2
LP2
HB3HB4
LP1lc
LP2lc
LP3lc
LP4lc
extrac. potassium for simple modelstable FPunstable FPstable LCunstable LC
−49 −47 −45 −43
HB3
LP1lc
27 29 31
HB1
0.00 0.03
+2.87×101
HB1
PD LP5lc
LP6lc
−49 −47 −45 −43
HB3LP1lc
27 29 31
LP1
LP3lc
5 10 15 20
Kbath / (mM/l)
20
40
60
80
100
120
140
160
mM
/l
LP1
LP2
HB1
HB2HB3
HB4
LP1lc
LP2lc
TR1
TR2TR3
TR4
extrac. sodium
5 10 15 20
Kbath / (mM/l)
−80
−60
−40
−20
0
20
40
mM
/l
LP1
LP2
HB1
HB2 HB3HB4
LP1lc
LP2lc
TR1
TR2
TR3
TR4
potassium gain/loss Ke
stable FPunstable FPstable LCunstable LCstable torus
6.7 6.9 7.1
HB1 TR4
PD
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Slow–fast analysis using K+ gain–and–loss
−80
−60
−40
−20
0
HB1
LP1HB2
LP2
HB3HB4
LP1lc
LP2lc
LP3lcLP4lcp
ote
nti
al
(tra
nsm
em
.)
−60 −40 −20 0 20 400
10
20
30
40
50
60
HB1
LP1HB2
LP2
HB3HB4
LP1lc
LP2lc
LP3lc
LP4lc
particle exchange (potassium ions)
pota
ssiu
m
(extr
ace
ll.)
mM
mM
mV
Open systems yield someKe dynamics:
dKedt
= ...
Ke can be subsituted by Ke
(alternative formulation)
dKedt
= λ(Kbath − Ke)
with
Ke = K 0e + ωi
ωe(K 0
i − Ki ) + Ke
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The “migraine–aura–ischemic–stroke” cycle
−80
−60
−40
−20
0
HB1
LP1HB2
LP2
HB3HB4
LP1lc
LP2lc
LP3lcLP4lcp
ote
nti
al
(tra
nsm
em
.)
−60 −40 −20 0 20 400
10
20
30
40
50
60
HB1
LP1HB2
LP2
HB3HB4
LP1lc
LP2lc
LP3lc
LP4lc
particle exchange (potassium ions)
pota
ssiu
m
(extr
ace
ll.)
mM
mM
mV
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The “migraine–aura–ischemic–stroke” cycle
−80
−60
−40
−20
0
HB1
LP1HB2
LP2
HB3HB4
LP1lc
LP2lc
LP3lcLP4lcp
ote
nti
al
(tra
nsm
em
.)
−60 −40 −20 0 20 400
10
20
30
40
50
60
HB1
LP1HB2
LP2
HB3HB4
LP1lc
LP2lc
LP3lc
LP4lc
particle exchange (potassium ions)
pota
ssiu
m
(extr
ace
ll.)
mM
mM
mV
trans-membraneevents
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The “migraine–aura–ischemic–stroke” cycle
−80
−60
−40
−20
0
HB1
LP1HB2
LP2
HB3HB4
LP1lc
LP2lc
LP3lcLP4lcp
ote
nti
al
(tra
nsm
em
.)
−60 −40 −20 0 20 400
10
20
30
40
50
60
HB1
LP1HB2
LP2
HB3HB4
LP1lc
LP2lc
LP3lc
LP4lc
particle exchange (potassium ions)
pota
ssiu
m
(extr
ace
ll.)
mM
mM
mV
+
+
iso-intracellularconcentration
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The “migraine–aura–ischemic–stroke” cycle
−80
−60
−40
−20
0
HB1
LP1HB2
LP2
HB3HB4
LP1lc
LP2lc
LP3lcLP4lcp
ote
nti
al
(tra
nsm
em
.)
−60 −40 −20 0 20 400
10
20
30
40
50
60
HB1
LP1HB2
LP2
HB3HB4
LP1lc
LP2lc
LP3lc
LP4lc
particle exchange (potassium ions)
pota
ssiu
m
(extr
ace
ll.)
mM
mM
mV
+
+
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The “migraine–aura–ischemic–stroke” cycle
−80
−60
−40
−20
0
HB1
LP1HB2
LP2
HB3HB4
LP1lc
LP2lc
LP3lcLP4lcp
ote
nti
al
(tra
nsm
em
.)
−60 −40 −20 0 20 400
10
20
30
40
50
60
HB1
LP1HB2
LP2
HB3HB4
LP1lc
LP2lc
LP3lc
LP4lc
particle exchange (potassium ions)
pota
ssiu
m
(extr
ace
ll.)
mM
mM
mV
+
+
release ofGibbs freeenergy
Open question:Can we treat this cycle inanalogy to a steam engine?
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The “ceiling level” of [K+]e in seizure activity
−80
−60
−40
−20
0
HB1
LP1HB2
LP2
HB3HB4
LP1lc
lc
lcLP4lcp
ote
nti
al
(tra
nsm
em
.)
−60 −40 −20 0 20 400
10
20
30
40
50
60
HB1
LP1HB2
LP2
HB3HB4
LP1lc
LP4lc
particle exchange (potassium ions)
pota
ssiu
m
(extr
ace
ll.)
mM
mM
mV
LP2
LP3
LP2lc
LP3lc
seizure-likeactivity
"ceiling level"
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Outline
Introduction
Unifying ion dynamics in the brain
Application: From genotype to phenotype
Summary
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From genotype to cellular phenotype (just the recipe)
Tail currents to HH parameters:
120 80 40 0 40V / mV
5
10
15
20
τ/ms
wild-type
mutant
0
1−1e
1
h
deinactivation
0 10t / ms120
10V / mV
0
1e
1
h
inactivation
0 10t / ms120
10V / mV
τ ∗hτh τ ∗
hτh
Reduced firing rate!
0 20 40 60 80 100 120 140 160 180Iapp / µA cm−2
0
50
100
150
200
wild-type
mutant
lower fire rate =hypoexcitablein rate-basedpopulation models
F(
) /
Hz
I app
More susceptible to migraine
0 20 40 60 80 100t / s
140
100
60
20
20
60
V /
mV
mutantV
EK
ENa
20%
100%
0 20 40 60 80 100t / s
140
100
60
20
20
60
V /
mV
wild-typeV
EK
ENa
20%
100%
13.6s
7.2s
• M.A. Dahlem, J. Schumacher, N Hubel, Linking a genetic defect in migraine to spreading depression in acomputational model (submitted arXiv 1403.6801)
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Outline
Introduction
Unifying ion dynamics in the brain
Application: From genotype to phenotype
Summary
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Conclusions
I Including ion dynamics into a Hodghin-Huxley frameworkyields slow quasiperodic dynamics:
I important bifurcation parameter is gain–and–loss of ions,I explain the “ceiling level” of [K+]e in seizure activity,I explain the “migraine–aura–ischemic–stroke” contiuum.
I No synaptic currents needed for slow dynamics, in particular,no metabotropic receptor that acts through a secondarymessenger, like GABAB .
I Remark : Ultra–slow (or near–DC (direct current)) activitythat cannot be observed by electroencephalography (EEG),because it is susceptible to uncontrollable artifacts such aschanges in the resistance of the dura.However : subdural electrode recordings provided recentlydirect and unequivocal evidence that such dynamics occurs inabundance in people with structural brain damage.
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Cooperation & Funding
Niklas Hubel, Julia Schumacher,Thomas Isele
Steven Schiff(Penn State Center for Neural Engineering)
Jens Dreier(Department of Neurology, Charite; University Medicine, Berlin)
berlin
Migraine Aura Foundation