SCZ-associated variant effects on L5 pyr cell NN activity and delta osc. (Maki-Marttunen et al 2018)

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Accession:237469
" … Here, using computational modeling, we show that a common biomarker of schizophrenia, namely, an increase in delta-oscillation power, may be a direct consequence of altered expression or kinetics of voltage-gated ion channels or calcium transporters. Our model of a circuit of layer V pyramidal cells highlights multiple types of schizophrenia-related variants that contribute to altered dynamics in the delta frequency band. Moreover, our model predicts that the same membrane mechanisms that increase the layer V pyramidal cell network gain and response to delta-frequency oscillations may also cause a decit in a single-cell correlate of the prepulse inhibition, which is a behavioral biomarker highly associated with schizophrenia."
Reference:
1 . Mäki-Marttunen T, Krull F, Bettella F, Hagen E, Næss S, Ness TV, Moberget T, Elvsåshagen T, Metzner C, Devor A, Edwards AG, Fyhn M, Djurovic S, Dale AM, Andreassen OA, Einevoll GT (2019) Alterations in Schizophrenia-Associated Genes Can Lead to Increased Power in Delta Oscillations. Cereb Cortex 29:875-891 [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: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): Ca pump; I A, slow; I h; I K; I K,Ca; I K,leak; I L high threshold; I M; I Na,p; I Na,t; I T low threshold;
Gap Junctions: Gap junctions;
Receptor(s): AMPA; NMDA; Gaba;
Gene(s): Cav1.2 CACNA1C; Cav1.3 CACNA1D; Cav3.3 CACNA1I; HCN1; Kv2.1 KCNB1; Nav1.1 SCN1A; PMCA ATP2B2;
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON; Python; LFPy;
Model Concept(s): Schizophrenia; Oscillations;
Implementer(s): Maki-Marttunen, Tuomo [tuomo.maki-marttunen at tut.fi];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; AMPA; NMDA; Gaba; I Na,p; I Na,t; I L high threshold; I T low threshold; I K; I K,leak; I M; I h; I K,Ca; I A, slow; Ca pump; Gaba; Glutamate;
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scznet
approxhaynet
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simseedburst_func_withLFP.pyc
                            
TITLE AMPA and NMDA receptor with presynaptic short-term plasticity 


COMMENT
AMPA and NMDA receptor conductance using a dual-exponential profile
presynaptic short-term plasticity based on Fuhrmann et al. 2002
Implemented by Srikanth Ramaswamy, Blue Brain Project, July 2009
Etay: changed weight to be equal for NMDA and AMPA, gmax accessible in Neuron

ENDCOMMENT


NEURON {

        POINT_PROCESS ProbUDFsyn2  
        RANGE tau_r, tau_d
        RANGE Use, u, Dep, Fac, u0
        RANGE i, g, e, gmax
        NONSPECIFIC_CURRENT i
	POINTER rng
}

PARAMETER {

        tau_r = 0.2   (ms)  : dual-exponential conductance profile
        tau_d = 1.7    (ms)  : IMPORTANT: tau_r < tau_d
        Use = 1.0   (1)   : Utilization of synaptic efficacy (just initial values! Use, Dep and Fac are overwritten by BlueBuilder assigned values) 
        Dep = 100   (ms)  : relaxation time constant from depression
        Fac = 10   (ms)  :  relaxation time constant from facilitation
        e = 0     (mV)  : AMPA and NMDA reversal potential
    	gmax = .001 (uS) : weight conversion factor (from nS to uS)
    	u0 = 0 :initial value of u, which is the running value of Use
}

COMMENT
The Verbatim block is needed to generate random nos. from a uniform distribution between 0 and 1 
for comparison with Pr to decide whether to activate the synapse or not
ENDCOMMENT
   
VERBATIM

#include<stdlib.h>
#include<stdio.h>
#include<math.h>

double nrn_random_pick(void* r);
void* nrn_random_arg(int argpos);

ENDVERBATIM
  

ASSIGNED {

        v (mV)
        i (nA)
	g (uS)
        factor
	rng
	weight_NMDA
}

STATE {
        A       : state variable to construct the dual-exponential profile - decays with conductance tau_r_AMPA
        B       : state variable to construct the dual-exponential profile - decays with conductance tau_d_AMPA
}

INITIAL{

  LOCAL tp
        
	A = 0
  B = 0
	
        
	tp = (tau_r*tau_d)/(tau_d-tau_r)*log(tau_d/tau_r) :time to peak of the conductance
	      
	factor = -exp(-tp/tau_r)+exp(-tp/tau_d) : Normalization factor - so that when t = tp, gsyn = gpeak
        factor = 1/factor
 
}

BREAKPOINT {

        SOLVE state METHOD cnexp
        g = gmax*(B-A) :compute time varying conductance as the difference of state variables B and A
        i = g*(v-e) :compute the driving force based on the time varying conductance, membrane potential, and reversal
}

DERIVATIVE state{

        A' = -A/tau_r
        B' = -B/tau_d
}


NET_RECEIVE (weight, Pv, Pv_tmp, Pr, u, tsyn (ms)){
	
        INITIAL{
                Pv=1
                u=u0
                tsyn=t
            }

        : calc u at event-
        if (Fac > 0) {
                u = u*exp(-(t - tsyn)/Fac) :update facilitation variable if Fac>0 Eq. 2 in Fuhrmann et al.
           } else {
                  u = Use  
           } 
           if(Fac > 0){
                  u = u + Use*(1-u) :update facilitation variable if Fac>0 Eq. 2 in Fuhrmann et al.
           }    

        
            Pv_tmp  = 1 - (1-Pv) * exp(-(t-tsyn)/Dep) :Probability Pv for a vesicle to be available for release, analogous to the pool of synaptic
                                                      :resources available for release in the deterministic model. Eq. 3 in Fuhrmann et al.
            Pr  = u * Pv_tmp                          :Pr is calculated as Pv * u (running value of Use)
            Pv_tmp  = Pv_tmp - u * Pv_tmp             :update Pv as per Eq. 3 in Fuhrmann et al.
            :printf("Pv = %g\n", Pv)
            :printf("Pr = %g\n", Pr)
                
		   if (erand() < Pr){
                    tsyn = t
	            Pv = Pv_tmp
                    A = A + weight*factor
                    B = B + weight*factor
                }
}

PROCEDURE setRNG() {
VERBATIM
    {
        /**
         * This function takes a NEURON Random object declared in hoc and makes it usable by this mod file.
         * Note that this method is taken from Brett paper as used by netstim.hoc and netstim.mod
         * which points out that the Random must be in negexp(1) mode
         */
        void** pv = (void**)(&_p_rng);
        if( ifarg(1)) {
            *pv = nrn_random_arg(1);
        } else {
            *pv = (void*)0;
        }
    }
ENDVERBATIM
}

FUNCTION erand() {
VERBATIM
	    //FILE *fi;
        double value;
        if (_p_rng) {
                /*
                :Supports separate independent but reproducible streams for
                : each instance. However, the corresponding hoc Random
                : distribution MUST be set to Random.negexp(1)
                */
                value = nrn_random_pick(_p_rng);
		        //fi = fopen("RandomStreamMCellRan4.txt", "w");
                //fprintf(fi,"random stream for this simulation = %lf\n",value);
                //printf("random stream for this simulation = %lf\n",value);
                return value;
        }else{
ENDVERBATIM
                : the old standby. Cannot use if reproducible parallel sim
                : independent of nhost or which host this instance is on
                : is desired, since each instance on this cpu draws from
                : the same stream
                erand = exprand(1)
VERBATIM
        }
ENDVERBATIM
        :erand = value :This line must have been a mistake in Hay et al.'s code, it would basically set the return value to a non-initialized double value.
                       :The reason it sometimes works could be that the memory allocated for the non-initialized happened to contain the random value
                       :previously generated (or if _p_rng is always a null pointer). However, here we commented this line out.
}

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