Cortical Basal Ganglia Network Model during Closed-loop DBS (Fleming et al 2020)

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Accession:262046
We developed a computational model of the cortical basal ganglia network to investigate closed-loop control of deep brain stimulation (DBS) for Parkinson’s disease (PD). The cortical basal ganglia network model incorporates the (i) the extracellular DBS electric field, (ii) antidromic and orthodromic activation of STN afferent fibers, (iii) the LFP detected at non-stimulating contacts on the DBS electrode and (iv) temporal variation of network beta-band activity within the thalamo-cortico-basal ganglia loop. The model facilitates investigation of clinically-viable closed-loop DBS control approaches, modulating either DBS amplitude or frequency, using an LFP derived measure of network beta-activity.
Reference:
1 . Fleming JE, Dunn E, Lowery MM (2020) Simulation of Closed-Loop Deep Brain Stimulation Control Schemes for Suppression of Pathological Beta Oscillations in Parkinson’s Disease Frontiers in Neuroscience 14:166
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Extracellular; Axon;
Brain Region(s)/Organism: Basal ganglia; Neocortex;
Cell Type(s): Hodgkin-Huxley neuron;
Channel(s): I K; I Sodium; I Calcium; I_AHP; I L high threshold; I T low threshold;
Gap Junctions:
Receptor(s): GabaA; AMPA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python; PyNN;
Model Concept(s): Deep brain stimulation; Parkinson's; Beta oscillations; Activity Patterns; Extracellular Fields;
Implementer(s): John E. Fleming, John E [john.fleming at ucdconnect.ie];
Search NeuronDB for information about:  GabaA; AMPA; I L high threshold; I T low threshold; I K; I Sodium; I Calcium; I_AHP; Gaba; Glutamate;
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Cortex_BasalGanglia_DBS_model
Updated_PyNN_Files
readme.html
Cortical_Axon_I_Kd.mod
Cortical_Axon_I_Kv.mod
Cortical_Axon_I_Leak.mod
Cortical_Axon_I_Na.mod
Cortical_Soma_I_K.mod
Cortical_Soma_I_Leak.mod
Cortical_Soma_I_M.mod
Cortical_Soma_I_Na.mod
Destexhe_Static_AMPA_Synapse.mod
Destexhe_Static_GABAA_Synapse.mod
Interneuron_I_K.mod
Interneuron_I_Leak.mod
Interneuron_I_Na.mod
myions.mod *
pGPeA.mod
pSTN.mod *
SynNoise.mod
Thalamic_I_leak.mod
Thalamic_I_Na_K.mod
Thalamic_I_T.mod
xtra.mod
burst_level_1.txt *
burst_level_10.txt
burst_level_2.txt *
burst_level_3.txt *
burst_level_4.txt
burst_level_5.txt
burst_level_6.txt *
burst_level_7.txt *
burst_level_8.txt *
burst_level_9.txt
burst_times_1.txt
burst_times_10.txt
burst_times_2.txt
burst_times_3.txt
burst_times_4.txt
burst_times_5.txt
burst_times_6.txt
burst_times_7.txt
burst_times_8.txt
burst_times_9.txt
Controllers.py
Cortical_Basal_Ganglia_Cell_Classes.py
cortical_collateral_electrode_distances.txt
cortical_xy_pos.txt
CorticalAxonInterneuron_Connections.txt
CorticalSomaThalamic_Connections.txt
CorticalSTN_Connections.txt
Electrode_Distances.py
Global_Variables.py
GPe_Stimulation_Order.txt
GPeGPe_Connections.txt
GPeGPi_Connections.txt *
GPeSTN_Connections.txt
GPiThalamic_Connections.txt
InterneuronCortical_Connections.txt
run_CBG_Model_Amplitude_Modulation_Controller.py
run_CBG_Model_Frequency_Modulation_Controller.py
run_CBG_Model_to_SS.py
STN_xy_pos.txt
STNGPe_Connections.txt
STNGPi_Connections.txt *
Striatal_Spike_Times.npy
StriatalGPe_Connections.txt
ThalamicCorticalSoma_Connections.txt
                            
TITLE simple GABA-A receptors, based on AMPA model (discrete connections)

COMMENT
-----------------------------------------------------------------------------

	Simple model for glutamate AMPA receptors
	=========================================

  - FIRST-ORDER KINETICS, FIT TO WHOLE-CELL RECORDINGS

    Whole-cell recorded postsynaptic currents mediated by AMPA/Kainate
    receptors (Xiang et al., J. Neurophysiol. 71: 2552-2556, 1994) were used
    to estimate the parameters of the present model; the fit was performed
    using a simplex algorithm (see Destexhe et al., J. Computational Neurosci.
    1: 195-230, 1994).

  - SHORT PULSES OF TRANSMITTER (0.3 ms, 0.5 mM)

    The simplified model was obtained from a detailed synaptic model that 
    included the release of transmitter in adjacent terminals, its lateral 
    diffusion and uptake, and its binding on postsynaptic receptors (Destexhe
    and Sejnowski, 1995).  Short pulses of transmitter with first-order
    kinetics were found to be the best fast alternative to represent the more
    detailed models.

  - ANALYTIC EXPRESSION

    The first-order model can be solved analytically, leading to a very fast
    mechanism for simulating synapses, since no differential equation must be
    solved (see references below).



References

   Destexhe, A., Mainen, Z.F. and Sejnowski, T.J.  An efficient method for
   computing synaptic conductances based on a kinetic model of receptor binding
   Neural Computation 6: 10-14, 1994.  

   Destexhe, A., Mainen, Z.F. and Sejnowski, T.J. Synthesis of models for
   excitable membranes, synaptic transmission and neuromodulation using a 
   common kinetic formalism, Journal of Computational Neuroscience 1: 
   195-230, 1994.

See also:

   http://www.cnl.salk.edu/~alain
   http://cns.fmed.ulaval.ca

-----------------------------------------------------------------------------
ENDCOMMENT



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

NEURON {
	POINT_PROCESS GABAa_S
	RANGE C, R, R0, R1, g, Cmax
	NONSPECIFIC_CURRENT i
	:GLOBAL Cdur, Alpha, Beta, Erev, blockTime, Rinf, Rtau
}

UNITS {
	(nA) = (nanoamp)
	(mV) = (millivolt)
	(umho) = (micromho)
	(mM) = (milli/liter)
}

PARAMETER {

	Cmax	= 0.5	(mM)		: max transmitter concentration	(set = 1 to match ~/netcon/ampa.hoc)
	Cdur	= 0.3	(ms)		: transmitter duration (rising phase)
	Alpha	= 10.5	(/ms mM)	: forward (binding) rate
	Beta	= 0.166	(/ms)		: backward (unbinding) rate
	Erev	= -80	(mV)		: reversal potential
	blockTime = 2	(ms)		: time window following dbs event during which non-dbs events are blocked
}


ASSIGNED {
	v		(mV)		: postsynaptic voltage
	i 		(nA)		: current = g*(v - Erev)
	g 		(umho)		: conductance
	C		(mM)		: transmitter concentration
	R					: fraction of open channels
	R0					: open channels at start of time period
	Rinf				: steady state channels open
	Rtau		(ms)	: time constant of channel binding
	on					: rising phase of PSC
	gmax				: max conductance
	tLast
	nspike
	collisionBlock
}

INITIAL {
	R = 0
	C = 0
	Rinf = Cmax*Alpha / (Cmax*Alpha + Beta)
	Rtau = 1 / ((Alpha * Cmax) + Beta)
	on = 0
	R0 = 0
	nspike = 0
	collisionBlock = 0
}

BREAKPOINT {
	SOLVE release
	i = R*(v - Erev)
}

PROCEDURE release() {


	if (on) {				: transmitter being released?

	   R = gmax*Rinf + (R0 - gmax*Rinf) * exptable (- (t - tLast) / Rtau)
				
	} else {				: no release occuring

  	   R = R0 * exptable (- Beta * (t - tLast))
	}

}


: following supports both saturation from single input and
: summation from multiple inputs
: if spike occurs during CDur then new off time is t + CDur
: ie. transmitter concatenates but does not summate
: Note: automatic initialization of all reference args to 0 except first

NET_RECEIVE(weight, ncType, ncPrb) {LOCAL ok, tmp

	:ncType 0=presyn cell, 1=dbs activated axon
	:MOVED TO dbsStim.mod 4/11/07		ncPrb probability that incoming event causes PSP

	INITIAL {
	}

	: flag is an implicit argument of NET_RECEIVE and  normally 0
      if (flag == 0) { : a spike, so turn on if not already in a Cdur pulse
		ok = 0

		if (ncType == 1) {
			collisionBlock = collisionBlock + 1
			net_send(blockTime, -1)

:			tmp = scop_random()
:			if (tmp <= ncPrb) {
:				ok = 1
:			}
			ok = 1

		}
		else 
		if (collisionBlock == 0) {
			ok = 1
		}

		if (ok) {
			if (!on) {
				on = 1
				tLast = t
				R0 = R
				gmax = weight	:weight not additive from separate sources as in original ampa.mod
			}

			nspike = nspike + 1
			: come again in Cdur with flag = current value of nspike
			net_send(Cdur, nspike)			
		}
      }
	else
	if (flag == nspike) { : if this associated with last spike then turn off
		if (on) {
			on = 0
			tLast = t
			R0 = R
			gmax = 0
		}
	} 
	else
	if (flag == -1) {
		collisionBlock = collisionBlock - 1
	}


}


FUNCTION exptable(x) { 
	TABLE  FROM -10 TO 10 WITH 2000

	if ((x > -10) && (x < 10)) {
		exptable = exp(x)
	} else {
		exptable = 0.
	}
}

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