Impedance spectrum in cortical tissue: implications for LFP signal propagation (Miceli et al. 2017)

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Accession:224923
" ... Here, we performed a detailed investigation of the frequency dependence of the conductivity within cortical tissue at microscopic distances using small current amplitudes within the typical (neuro)physiological micrometer and sub-nanoampere range. We investigated the propagation of LFPs, induced by extracellular electrical current injections via patch-pipettes, in acute rat brain slice preparations containing the somatosensory cortex in vitro using multielectrode arrays. Based on our data, we determined the cortical tissue conductivity over a 100-fold increase in signal frequency (5-500 Hz). Our results imply at most very weak frequency-dependent effects within the frequency range of physiological LFPs. Using biophysical modeling, we estimated the impact of different putative impedance spectra. Our results indicate that frequency dependencies of the order measured here and in most other studies have negligible impact on the typical analysis and modeling of LFP signals from extracellular brain recordings."
Reference:
1 . Miceli S, Ness TV, Einevoll GT, Schubert D (2017) Impedance Spectrum in Cortical Tissue: Implications for Propagation of LFP Signals on the Microscopic Level Eneuro 4:1-15
Model Information (Click on a link to find other models with that property)
Model Type: Extracellular; Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neocortex V1 L6 pyramidal corticothalamic GLU cell;
Channel(s): I Na,p; I Na,t; I L high threshold; I A; I h; I K,Ca; I Calcium; I A, slow;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Python; NEURON;
Model Concept(s): Extracellular Fields; Methods; Simplified Models; Detailed Neuronal Models;
Implementer(s):
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic GLU cell; I Na,p; I Na,t; I L high threshold; I A; I h; I K,Ca; I Calcium; I A, slow;
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tissue_impedance_impact
hay
sim_results
ampa.mod
Ca_HVA.mod *
Ca_LVAst.mod *
CaDynamics_E2.mod *
epsp.mod *
glutamate.mod
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod
NaTa_t.mod *
NaTs2_t.mod
siniclamp.mod
SK_E2.mod *
SKv3_1.mod *
stim.mod
cell1.hoc
cell1.rot
custom_codes.hoc
hay_active_declarations.py
                            
:Comment : mtau deduced from text (said to be 6 times faster than for NaTa)
:Comment : so I used the equations from NaT and multiplied by 6
:Reference : Modeled according to kinetics derived from Magistretti & Alonso 1999
:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21

NEURON	{
	SUFFIX Nap_Et2
	USEION na READ ena WRITE ina
	RANGE gNap_Et2bar, gNap_Et2, ina
}

UNITS	{
	(S) = (siemens)
	(mV) = (millivolt)
	(mA) = (milliamp)
}

PARAMETER	{
	gNap_Et2bar = 0.00001 (S/cm2)
}

ASSIGNED	{
	v	(mV)
	ena	(mV)
	ina	(mA/cm2)
	gNap_Et2	(S/cm2)
	mInf
	mTau
	mAlpha
	mBeta
	hInf
	hTau
	hAlpha
	hBeta
}

STATE	{
	m
	h
}

BREAKPOINT	{
	SOLVE states METHOD cnexp
	gNap_Et2 = gNap_Et2bar*m*m*m*h
	ina = gNap_Et2*(v-ena) 
}

DERIVATIVE states	{
	rates()
	m' = (mInf-m)/mTau
	h' = (hInf-h)/hTau
}

INITIAL{
	rates()
	m = mInf
	h = hInf
}

PROCEDURE rates(){
  LOCAL qt
  qt = 2.3^((34-21)/10)

	UNITSOFF
		mInf = 1.0/(1+exp((v- -52.6)/-4.6))
    if(v == -38){
    	v = v+0.0001
    }
		mAlpha = (0.182 * (v- -38))/(1-(exp(-(v- -38)/6)))
		mBeta  = (0.124 * (-v -38))/(1-(exp(-(-v -38)/6)))
		mTau = 6*(1/(mAlpha + mBeta))/qt

  	if(v == -17){
   		v = v + 0.0001
  	}
    if(v == -64.4){
      v = v+0.0001
    }

		hInf = 1.0/(1+exp((v- -48.8)/10))
    hAlpha = -2.88e-6 * (v + 17) / (1 - exp((v + 17)/4.63))
    hBeta = 6.94e-6 * (v + 64.4) / (1 - exp(-(v + 64.4)/2.63))
		hTau = (1/(hAlpha + hBeta))/qt
	UNITSON
}

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