Thalamic network model of deep brain stimulation in essential tremor (Birdno et al. 2012)

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"... Thus the decreased effectiveness of temporally irregular DBS trains is due to long pauses in the stimulus trains, not the degree of temporal irregularity alone. We also conducted computer simulations of neuronal responses to the experimental stimulus trains using a biophysical model of the thalamic network. Trains that suppressed tremor in volunteers also suppressed fluctuations in thalamic transmembrane potential at the frequency associated with cerebellar burst-driver inputs. Clinical and computational findings indicate that DBS suppresses tremor by masking burst-driver inputs to the thalamus and that pauses in stimulation prevent such masking. Although stimulation of other anatomic targets may provide tremor suppression, we propose that the most relevant neuronal targets for effective tremor suppression are the afferent cerebellar fibers that terminate in the thalamus."
1 . Birdno MJ, Kuncel AM, Dorval AD, Turner DA, Gross RE, Grill WM (2012) Stimulus features underlying reduced tremor suppression with temporally patterned deep brain stimulation. J Neurophysiol 107:364-83 [PubMed]
2 . Yi G, Grill WM (2018) Frequency-dependent antidromic activation in thalamocortical relay neurons: effects of synaptic inputs. J Neural Eng 15:056001 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Axon;
Brain Region(s)/Organism:
Cell Type(s): Thalamus geniculate nucleus/lateral principal GLU cell;
Gap Junctions:
Receptor(s): GabaA; GabaB; AMPA; NMDA; Glutamate; Gaba;
Transmitter(s): Gaba; Ions;
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Action Potential Initiation; Temporal Pattern Generation; Axonal Action Potentials; Therapeutics; Deep brain stimulation;
Search NeuronDB for information about:  Thalamus geniculate nucleus/lateral principal GLU cell; GabaA; GabaB; AMPA; NMDA; Glutamate; Gaba; Gaba; Ions;
ampa.mod *
ampacer.mod *
ampactx.mod *
asymtrain.mod *
AXNODE75mb.mod *
FakeExcSyn.mod *
gabaa.mod *
gababKG.mod *
ihshift.mod *
ik2.mod *
isikdr.mod *
isina.mod *
it.mod *
kdyn.mod *
leakdepol.mod *
mdltrdyn.mod *
nmda.mod *
nmdacer.mod *
nmdactx.mod *
PARAK75.mod *
: simple first-order model of potassium dynamics
: Durstewitz D, Seamans JK, Sejnowski TJ (2000) Dopamine-mediated
: stabilization of delay-period activity in a network model of
: prefrontal cortex. J Neurophysiol 83:1733-50

	SUFFIX kdyn
	USEION k READ ik WRITE ko, ki 
	RANGE ko, ki, tk, tk0, dep, tkfac

	(mM) = (milli/liter)
	(mA) = (milliamp)
	F    = (faraday) (coul)

	tk0    = 1342.4  (ms)           : decay time constant
	tkfac = 0.025  : factor for multiplying tauk equation
	koinf = 3 	(mM)      : equilibrium k+ concentration
	dep   = 290e-3 (micron)     : depth of shell for k+ diffusion
	KAF   = 1 ()		  : K accumulation factor

	ik     (mA/cm2)
	tk	   (ms)
	ki     (mM)	  :

STATE { ko (mM) }

	SOLVE states METHOD cnexp

        ko'= (1e4)*KAF*ik/(F*dep) + (koinf-ko)/tk

	tk = tk0
	ko = koinf
	ki = 106

PROCEDURE evaluate_tau() { LOCAL tauk 
	tauk = (tkfac*((ko-koinf)^2)) + (tk0/1000) 	: Removed (-) piece to prevent (-) Taus. Based on regression of Cordingley & Sonjen, 1978 Fig. 3b
												: Changed from 0.025*... to 0.015*...
	tk = tauk*1000 : convert to ms from sec
	ki = 106 

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