Epilepsy: Error of Scales?Ann Arbor, MI 2007

Theoden Netoff

University of Minnesota, BME

Homeostasis and Epilepsy

• Neurons are in constant state of flux

• There is no single solution of ion channel densities to achieve a particular behavior

• There are many changes in response to an event like a seizure:– Changes in ion channel densities– Changes in neuronal dynamics– Changes in network coupling

Ih modulation following a Seizure:two models, two different results

• Shah and Johnston– Kanic acid injection.– EC Layer III Pyramidal

Neurons– Decreased Ih density

in dendrites– Hypothesis:

Decreasing Ih increases synaptic efficacy and increases excitability of the cells.

• Chen and Soltesz– Febrile seizures– CA1 Pyramidal Cells– Increase in Ih current– Hypothesis: Increasing

Ih causes rebound excitation following inhibition.

Ih: Hyperpolarizing activated cationic current.

Chen and Soltesz

The “Sag” current

Opposing effects of Ih

Santoro and BaramThe multiple personalities of h-channels. TINS 26(10)550:554

Dynamic clamp

• Computer controlled delivery of current to a cell

• Complex protocols• Simulation of ion

channels• Simulation of synapses• Simulation of neurons to

make “hybrid” networks

Vm

Iapp

Iapp

Vm

Phase Response Curve

500 1000 1500 2000 2500 3000 3500 4000 4500 5000-50

-40

-30

-20

-10

0

10

mV

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50.6

0.65

0.7

0.75

0.8

0.85

sec

nA

T

Phase Response Curve

500 1000 1500 2000 2500 3000 3500 4000 4500 5000-50

-40

-30

-20

-10

0

10

mV

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50.6

0.65

0.7

0.75

0.8

0.85

sec

nA

Phase Response Curve

500 1000 1500 2000 2500 3000 3500 4000 4500 5000-50

-40

-30

-20

-10

0

10

mV

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50.6

0.65

0.7

0.75

0.8

0.85

sec

nA

Phase Response Curve

0 50 100 150-5

0

5

10

15

20

25

in

mse

c

in msec0 10 20 30 40 50 60 70 80

-2

0

2

4

6

8

10

in

mse

c

in msec

Type 1 Type 2Excitatory Input

Predicted excitatory interaction

0 50 100 150-5

0

5

10

15

20

25

0 50 100 150-5

0

5

10

15

20

25

i

n m

sec

in msec

- =

0 50 100 150-30

-20

-10

0

10

20

30

Tn2 - T

n1

Tn+

12

- T

n+1

1

Fixedpoints of Spike time difference map (STDM)

0 50 100 150-30

-20

-10

0

10

20

30

Tn2 - T

n1

Tn+

12

- T

n+1

1

Measuring from Neurons

STRCs measured and network behaviors.

Effects of Ih on PRC and network synchrony

0 20 40 60 80 100 120-40

-20

0

20

40

Spi

ke t

ime

adva

nce

(mse

c)

Time since last spike

Correlation coefficient=0.7485

50

100

150

ISI

pert

urbe

d0 50 100 150 200 250

80

100

120

ISI

un-p

ertu

rbed

Spike num

0 20 40 60 80 100 120-40

-20

0

20

40

Spi

ke t

ime

adva

nce

(mse

c)

Time since last spike

Correlation coefficient=0.61583

50

100

150

ISI

pert

urbe

d

0 50 100 150 200 25050

100

150

ISI

un-p

ertu

rbed

Spike num0 50 100

-10

0

10

20

0 50 100-20

0

20

0 50 100-10

0

10

20

0 50 100-20

0

20

-200 -100 0 100 2000

5

10

15

-200 -100 0 100 2000

10

20

W/o added Ih W/ added Ih

No Ih, Added Ih

PRC

STDM

STDH

Effects of Ih on two cell networks

-150 -100 -50 0 50 100 1500

2

4

6

Cell 2 Spike Triggered Cross correlation, Points left of 0 indicate neuron fired before computer

80 100 120 1400

0.050.1

Cell 1 Spike Intervals, mean ISI = 101.5005

80 100 120 1400

0.050.1

Cell 2 Spike Interval, mean ISI = 99.8156

0 50 100 150 200 250 300 350 400 450 500-100

0

100

Spike Number

ST

D

-150 -100 -50 0 50 100 1500

2

4

6

8

Cell 2 Spike Triggered Cross correlation, Points left of 0 indicate neuron fired before computer

90 100 110 1200

0.050.1

Cell 1 Spike Intervals, mean ISI = 103.9169

90 100 110 1200

0.050.1

Cell 2 Spike Interval, mean ISI = 103.6663

0 100 200 300 400 500 600 700-100

0

100

Spike Number

ST

D

-150 -100 -50 0 50 100 1500

1

2

3

4

5

6

7

w/o Ih

w/ Ih

W/o Ih W/ Ih

Spike time differences w/o Ih

Spike time differences w/ Ih

Network Hypothesis

• Raising Ih or lowering Ih may depend on whether activity is caused by feedforward or feedback network activity

↑ Activity

↓ Ih

↑ Activity

↑ Ih

Homeostatic effects of changing Ih

• Increasing Ih ↓ synaptic efficacy

• ↓ in efficacy early in spiking phase• Phase dependent ↓ makes network ↑ synchrony• In Hippocampus:

– ↑ Ih ↓ activity because it is a feedforward network (CA3→CA1) and dampens network input.

• In Entorhinal cortex:– ↑ Ih ↑ activity because it is a feedback network by

synchronizing the excitatory cells

Question:

Homeostatic mechanisms work at the level of the individual neuron.

Is epilepsy be caused by discrepancies between homeostatic mechanisms at the cellular and their actions at a network scale?

Acknowledgements

John WhiteNancy KopellJonathan BettencourtAlan DorvalBrian Burton

Grants:Postdoctoral NRSA:

5F32MH066555-02

Fellowships:Center for BioDynamics (Boston University)