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]
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 L2/6 pyramidal intratelencephalic 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 V1 L2/6 pyramidal intratelencephalic 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
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README.html
cad.mod *
cal.mod
cat.mod
hcurrent.mod
k2.mod
ka.mod
kahp.mod *
kc.mod
kdr.mod
km.mod
kvz_nature.mod *
mar.mod
max.mod
naf.mod
nap.mod
naz_nature.mod *
origlen.mod *
pass_wRel.mod
peak.mod *
shunt.mod
skahp.mod
Voffset.mod
vsource.mod *
aniruddha_young10axon.hoc
coskren_make_gui.hoc
fixnseg.hoc
init.hoc
linear_conductances_traub.hoc
main_CoskrenEtAl_extTraub.hoc
make_gui.hoc
make_gui2.hoc
mosinit.hoc
readcell_nomechanisms.hoc
scaleRm_aug3f.hoc
screenshot.png
Vkeep.ses
                            
/****** 
    aged neuron Aug3IR2f is the one that Aniruddha spent so much time fitting.  We don't 
    want to fit passive parameters of each neuron anew to this model.  Instead, scale the 
    Rm values of this model relative to the sizes of the optimized values in the HH 
    passive parameter optimizations.
    
    Christina Weaver, Aug 30 2014
******/

func scaleRm_vsAug3f() {
	if( $1 == 6 ) 	{ // dec15IR2e
	    RMfac = 0.397317032
	}
	if( $1 == 7 ) 	{ // jun7d
	    RMfac = 1.072474104
	}
	if( $1 == 8 ) 	{ // may3IR2d
	    RMfac = 0.575547631
	}
	if( $1 == 9 ) 	{ // may3IR2h
	    RMfac = 0.315299711
	}
	if( $1 == 10 ) 	{ // may3IR2i
	    RMfac = 0.44686704
	}
	if( $1 == 11 ) 	{ // may3IR2t
	    RMfac = 0.404313126
	}
	if( $1 == 0 ) 	{ // aug3IR2a
	    RMfac = 0.72718628
	}
	if( $1 == 1 ) 	{ // aug3c
	    RMfac = 0.67030056
	}
	if( $1 == 2 ) 	{ // aug3IR2e
	    RMfac = 0.849040584
	}
	if( $1 == 3 ) 	{ // aug3IR2f
	    RMfac = 1
	}
	if( $1 == 4 ) 	{ // aug3IR2g
	    RMfac = 0.698930209
	}
	if( $1 == 5 ) 	{ // feb27IR2n
	    RMfac = 1.23158431
	}

	return RMfac
}




func scaleCm_vsAug3f() {
	if( $1 == 6 ) 	{ // dec15IR2e
	    CMfac = 1.842065654
	}
	if( $1 == 7 ) 	{ // jun7d
	    CMfac = 2.743216805
	}
	if( $1 == 8 ) 	{ // may3IR2d
	    CMfac = 1.737475659
	}
	if( $1 == 9 ) 	{ // may3IR2h
	    CMfac = 2.133695652
	}
	if( $1 == 10 ) 	{ // may3IR2i
	    CMfac = 3.74588612
	}
	if( $1 == 11 ) 	{ // may3IR2t
	    CMfac = 2.876590946
	}
	if( $1 == 0 ) 	{ // aug3IR2a
	    CMfac = 0.754346912
	}
	if( $1 == 1 ) 	{ // aug3c
	    CMfac = 1.623757851
	}
	if( $1 == 2 ) 	{ // aug3IR2e
	    CMfac = 0.94736087
	}
	if( $1 == 3 ) 	{ // aug3IR2f
	    CMfac = 1
	}
	if( $1 == 4 ) 	{ // aug3IR2g
	    CMfac = 0.96420649
	}
	if( $1 == 5 ) 	{ // feb27IR2n
	    CMfac = 1.282665046
	}

	return CMfac
}



/*********************************************

	scaling Cm as suggested by the customized HH model parameters, then applying to the 
	aug3f Cm = .833333 that Aniruddha determined, leads to some young neurons with high 
	firing.  Plus it seems unlikely that the Cm value would vary by THAT much in 
	young vs. aged neurons without Jennie seeing it in the time constant.  So readjust
	the parameter:  Reduce the customized scale factor for young neurons by 25%.
	Note that aged neurons are scaled by the original 'customized passive' scale factor.
	
*********************************************/	
func scaleCmYg75_vsAug3f() {

	if( $1 == 6 ) 	{ // dec15IR2e
	    CMfac = 0.921032827
	}
	if( $1 == 7 ) 	{ // jun7d
	    CMfac = 1.371608403
	}
	if( $1 == 8 ) 	{ // may3IR2d
	    CMfac = 0.86873783
	}
	if( $1 == 9 ) 	{ // may3IR2h
	    CMfac = 1.066847826
	}
	if( $1 == 10 ) 	{ // may3IR2i
	    CMfac = 1.87294306
	}
	if( $1 == 11 ) 	{ // may3IR2t
	    CMfac = 1.438295473
	}
	
	// aged neurons:  scale by the originally calculated amount
	if( $1 == 0 ) 	{ // aug3IR2a
	    CMfac = 0.754346912
	}
	if( $1 == 1 ) 	{ // aug3c
	    CMfac = 1.623757851
	}
	if( $1 == 2 ) 	{ // aug3IR2e
	    CMfac = 0.94736087
	}
	if( $1 == 3 ) 	{ // aug3IR2f
	    CMfac = 1
	}
	if( $1 == 4 ) 	{ // aug3IR2g
	    CMfac = 0.96420649
	}
	if( $1 == 5 ) 	{ // feb27IR2n
	    CMfac = 1.282665046
	}

	return CMfac
}
























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