Mechanisms of magnetic stimulation of central nervous system neurons (Pashut et al. 2011)

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Accession:138321
Transcranial magnetic stimulation (TMS) is a widely applied tool for probing cognitive function in humans and is one of the best tools for clinical treatments and interfering with cognitive tasks. Surprisingly, while TMS has been commercially available for decades, the cellular mechanisms underlying magnetic stimulation remain unclear. Here we investigate these mechanisms using compartmental modeling. We generated a numerical scheme allowing simulation of the physiological response to magnetic stimulation of neurons with arbitrary morphologies and active properties. Computational experiments using this scheme suggested that TMS affects neurons in the central nervous system (CNS) primarily by somatic stimulation.
Reference:
1 . Pashut T, Wolfus S, Friedman A, Lavidor M, Bar-Gad I, Yeshurun Y, Korngreen A (2011) Mechanisms of magnetic stimulation of central nervous system neurons. PLoS Comput Biol 7:e1002022 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neocortex V1 L6 pyramidal corticothalamic GLU cell; Squid axon;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Action Potential Initiation; Magnetic stimulation;
Implementer(s): Korngreen, Alon [alon.korngreen at gmail.com]; Pashut, Tamar [tamar.pashut at gmail.com];
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic GLU cell;
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pashut2011
TwoDimensions
Neuron
cells
cad2.mod *
child.mod *
childa.mod *
epsp.mod *
it2.mod *
kaprox.mod *
kca.mod *
km.mod *
kv.mod *
na.mod *
SlowCa.mod *
xtra.mod *
alon.ses
BACModel.hoc
BACModel_mag.hoc
Display.ses *
magstim.hoc
                            
COMMENT

kv.mod

Potassium channel, Hodgkin-Huxley style kinetics
Kinetic rates based roughly on Sah et al. and Hamill et al. (1991)

Author: Zach Mainen, Salk Institute, 1995, zach@salk.edu
	
ENDCOMMENT

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
	SUFFIX kv
	USEION k READ ek WRITE ik
	RANGE n, gk, gbar
	RANGE ninf, ntau
	GLOBAL Ra, Rb
	GLOBAL q10, temp, tadj, vmin, vmax
}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(pS) = (picosiemens)
	(um) = (micron)
} 

PARAMETER {
	gbar = 5   	(pS/um2)	: 0.03 mho/cm2
	v 		(mV)
								
	tha  = 25	(mV)		: v 1/2 for inf
	qa   = 9	(mV)		: inf slope		
	
	Ra   = 0.02	(/ms)		: max act rate
	Rb   = 0.002	(/ms)		: max deact rate	

	dt		(ms)
	celsius		(degC)
	temp = 23	(degC)		: original temp 	
	q10  = 2.3			: temperature sensitivity

	vmin = -120	(mV)
	vmax = 100	(mV)
} 


ASSIGNED {
	a		(/ms)
	b		(/ms)
	ik 		(mA/cm2)
	gk		(pS/um2)
	ek		(mV)
	ninf
	ntau (ms)	
	tadj
}
 

STATE { n }

INITIAL { 
	trates(v)
	n = ninf
}

BREAKPOINT {
        SOLVE states
	gk = tadj*gbar*n
	ik = (1e-4) * gk * (v - ek)
} 

LOCAL nexp

PROCEDURE states() {   :Computes state variable n 
        trates(v)      :             at the current v and dt.
        n = n + nexp*(ninf-n)
        VERBATIM
        return 0;
        ENDVERBATIM
}

PROCEDURE trates(v) {  :Computes rate and other constants at current v.
                      :Call once from HOC to initialize inf at resting v.
        LOCAL tinc
        TABLE ninf, nexp
	DEPEND dt, celsius, temp, Ra, Rb, tha, qa
	
	FROM vmin TO vmax WITH 199

	rates(v): not consistently executed from here if usetable_hh == 1

        tadj = q10^((celsius - temp)/10)

        tinc = -dt * tadj
        nexp = 1 - exp(tinc/ntau)
}


PROCEDURE rates(v) {  :Computes rate and other constants at current v.
                      :Call once from HOC to initialize inf at resting v.

        a = Ra * (v - tha) / (1 - exp(-(v - tha)/qa))
        b = -Rb * (v - tha) / (1 - exp((v - tha)/qa))
        ntau = 1/(a+b)
	ninf = a*ntau
}