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
Citations  Citation Browser
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 Low-threshold calcium current for Thalamic Neuron

COMMENT
  
  Model Reference: 
  
  Rubin, J.E. and Terman, D., 2004. "High frequency stimulation 
  of the subthalamic nucleus eliminates pathological thalamic 
  rhythmicity in a computational model."
  Journal of computational neuroscience, 16(3), pp.211-235.

  
  Implemented by John Fleming - john.fleming@ucdconnect.ie - 06/12/18
  
  Edits: 
  
ENDCOMMENT


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

NEURON {
	SUFFIX thalamic_i_t
	USEION ca WRITE ica				: Using ca ion, treat the reversal potential as a parameter and write to ica so the total ca current can be tracked
	RANGE g_T, i_t					: Calcium current, specific conductance and equilibrium potential
}

PARAMETER {
	eca = 0 (mV)
	i_t = 0.0 (mA/cm2)
	g_T = 0.5 (S/cm2)
}

ASSIGNED {
	v (mV)
	ica (mA/cm2)
	r_inf
	tau_r (ms)
	p_inf
}

STATE {
	r 
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	ica = g_T*p_inf*p_inf*r*(v - eca)
	i_t = ica 							: Record i_t (just this calcium current) to check it is working
}

UNITSOFF

INITIAL {
	settables(v)
	r = r_inf
}

DERIVATIVE states {
	settables(v)
	r' = (r_inf - r)/tau_r
}

PROCEDURE settables(v) {
	TABLE r_inf, p_inf, tau_r FROM -100 TO 100 WITH 400
	
	r_inf = 1/(1+exp((v+84)/4))
	tau_r = (28+exp(-(v+25)/10.5))
	p_inf = 1/(1+exp(-(v+60)/6.2))
}

UNITSON