Rhesus Monkey Layer 3 Pyramidal Neurons: Young vs aged PFC (Coskren et al. 2015)

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Accession:168858
Layer 3 (L3) pyramidal neurons in the lateral prefrontal cortex (LPFC) of rhesus monkeys exhibit dendritic regression, spine loss and increased action potential (AP) firing rates during normal aging. The relationship between these structural and functional alterations, if any, is unknown. Computational models using the digital reconstructions with Hodgkin-Huxley and AMPA channels allowed us to assess relationships between demonstrated age-related changes and to predict physiological changes that have not yet been tested empirically. Tuning passive parameters for each model predicted significantly higher membrane resistance (Rm) in aged versus young neurons. This Rm increase alone did not account for the empirically observed fI-curves, but coupling these Rm values with subtle differences in morphology and membrane capacitance Cm did. The predicted differences in passive parameters (or other parameters with similar effects) are mathematically plausible, but must be tested empirically.
Reference:
1 . Coskren PJ, Luebke JI, Kabaso D, Wearne SL, Yadav A, Rumbell T, Hof PR, Weaver CM (2015) Functional consequences of age-related morphologic changes to pyramidal neurons of the rhesus monkey prefrontal cortex. J Comput Neurosci 38:263-83 [PubMed]
Citations  Citation Browser
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 L2/3 pyramidal GLU cell;
Channel(s): I Na,t; I A; I K; I M; I h; I K,Ca; I Calcium; I_AHP;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Influence of Dendritic Geometry; Detailed Neuronal Models; Action Potentials; Aging/Alzheimer`s;
Implementer(s): Weaver, Christina [christina.weaver at fandm.edu];
Search NeuronDB for information about:  Neocortex L2/3 pyramidal GLU cell; I Na,t; I A; I K; I M; I h; I K,Ca; I Calcium; I_AHP;
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CoskrenEtAl2015
HHmodel
Scripts
NeuronMechanisms
x86_64
.libs
kvz_nature.mod *
max.mod *
naz_nature.mod *
origlen.mod *
peak.mod *
vsource.mod *
kvz_nature.c *
kvz_nature.lo *
libnrnmech.la *
max.c *
max.lo *
mod_func.c *
mod_func.lo *
naz_nature.c *
naz_nature.lo *
origlen.c *
origlen.lo *
peak.c *
peak.lo *
special *
vsource.c *
vsource.lo *
                            
COMMENT

na.mod

Sodium channel, Hodgkin-Huxley style kinetics.  

Kinetics were fit to data from Huguenard et al. (1988) and Hamill et
al. (1991)

qi is not well constrained by the data, since there are no points
between -80 and -55.  So this was fixed at 5 while the thi1,thi2,Rg,Rd
were optimized using a simplex least square proc

voltage dependencies are shifted approximately from the best
fit to give higher threshold

Author: Zach Mainen, Salk Institute, 1994, zach@salk.edu

added myexp P.V. 24.7.98

ENDCOMMENT

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

NEURON {
	SUFFIX na
	USEION na READ ena WRITE ina
	RANGE m, h, gna, gbar
	GLOBAL tha, thi1, thi2, qa, qi, qinf, thinf
	RANGE minf, hinf, mtau, htau
	GLOBAL Ra, Rb, Rd, Rg
	GLOBAL q10, temp, tadj, vmin, vmax, vshift
}

PARAMETER {
	gbar = 1000   	(pS/um2)	: 0.12 mho/cm2
	vshift = -10	(mV)		: voltage shift (affects all)
								
	tha  = -35	(mV)		: v 1/2 for act		(-42)
	qa   = 9	(mV)		: act slope		
	Ra   = 0.182	(/ms)		: open (v)		
	Rb   = 0.124	(/ms)		: close (v)		

	thi1  = -50	(mV)		: v 1/2 for inact 	
	thi2  = -75	(mV)		: v 1/2 for inact 	
	qi   = 5	(mV)	        : inact tau slope
	thinf  = -65	(mV)		: inact inf slope	
	qinf  = 6.2	(mV)		: inact inf slope
	Rg   = 0.0091	(/ms)		: inact (v)	
	Rd   = 0.024	(/ms)		: inact recov (v) 

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

	v 		(mV)
	dt		(ms)
	celsius		(degC)
	vmin = -120	(mV)
	vmax = 100	(mV)
}


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

ASSIGNED {
	ina 		(mA/cm2)
	gna		(pS/um2)
	ena		(mV)
	minf 		hinf
	mtau (ms)	htau (ms)
	tadj
}
 

STATE { m h }

INITIAL { 
	trates(v+vshift)
	m = minf
	h = hinf
}

BREAKPOINT {
        SOLVE states
        gna = tadj*gbar*m*m*m*h
	ina = (1e-4) * gna * (v - ena)
} 

LOCAL mexp, hexp 

PROCEDURE states() {   :Computes state variables m, h, and n 
        trates(v+vshift)      :             at the current v and dt.
        m = m + mexp*(minf-m)
        h = h + hexp*(hinf-h)
        VERBATIM
        return 0;
        ENDVERBATIM
}

PROCEDURE trates(v) {  
                      
        LOCAL tinc
        TABLE minf, mexp, hinf, hexp
	DEPEND dt, celsius, temp, Ra, Rb, Rd, Rg, tha, thi1, thi2, qa, qi, qinf
	
	FROM vmin TO vmax WITH 199

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

        tadj = q10^((celsius - temp)/10)
        tinc = -dt * tadj

        mexp = 1 - myexp(tinc/mtau)
        hexp = 1 - myexp(tinc/htau)
}


PROCEDURE rates(vm) {  
        LOCAL  a, b

	a = trap0(vm,tha,Ra,qa)
	b = trap0(-vm,-tha,Rb,qa)
	mtau = 1/(a+b)
	minf = a*mtau

		:"h" inactivation 

	a = trap0(vm,thi1,Rd,qi)
	b = trap0(-vm,-thi2,Rg,qi)
	htau = 1/(a+b)
	hinf = 1/(1+myexp((vm-thinf)/qinf))
}



FUNCTION myexp(x) {
	if (x < -100) {
	myexp = 0
	}else{
	myexp = exp(x)
	}
}






FUNCTION trap0(v,th,a,q) {
	if (fabs(v/th) > 1e-6) {
	        trap0 = a * (v - th) / (1 - myexp(-(v - th)/q))
	} else {
	        trap0 = a * q
 	}
}