Deconstruction of cortical evoked potentials generated by subthalamic DBS (Kumaravelu et al 2018)

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Accession:244262
"... High frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN) suppresses parkinsonian motor symptoms and modulates cortical activity. ... Cortical evoked potentials (cEP) generated by STN DBS reflect the response of cortex to subcortical stimulation, and the goal was to determine the neural origin of cEP using a two-step approach. First, we recorded cEP over ipsilateral primary motor cortex during different frequencies of STN DBS in awake healthy and unilateral 6-OHDA lesioned parkinsonian rats. Second, we used a biophysically-based model of the thalamocortical network to deconstruct the neural origin of the cEP. The in vivo cEP included short (R1), intermediate (R2) and long-latency (R3) responses. Model-based cortical responses to simulated STN DBS matched remarkably well the in vivo responses. R1 was generated by antidromic activation of layer 5 pyramidal neurons, while recurrent activation of layer 5 pyramidal neurons via excitatory axon collaterals reproduced R2. R3 was generated by polysynaptic activation of layer 2/3 pyramidal neurons via the cortico-thalamic-cortical pathway. Antidromic activation of the hyperdirect pathway and subsequent intracortical and cortico-thalamo-cortical synaptic interactions were sufficient to generate cEP by STN DBS, and orthodromic activation through basal ganglia-thalamus-cortex pathways was not required. These results demonstrate the utility of cEP to determine the neural elements activated by STN DBS that might modulate cortical activity and contribute to the suppression of parkinsonian symptoms."
Reference:
1 . Kumaravelu K, Oza CS, Behrend CE, Grill WM (2018) Model-based deconstruction of cortical evoked potentials generated by subthalamic nucleus deep brain stimulation. J Neurophysiol 120:662-680 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex; Thalamus;
Cell Type(s): Neocortex M1 L6 pyramidal corticothalamic GLU cell; Neocortex M1 L5B pyramidal pyramidal tract GLU cell; Neocortex M1 L4 stellate GLU cell; Hodgkin-Huxley neuron; Neocortex layer 4 neuron; Neocortex fast spiking (FS) interneuron; Neocortex primary motor area pyramidal layer 5 corticospinal cell;
Channel(s): I Na,p; I K; I Sodium; I_KD; I Calcium; I T low threshold; I L high threshold; I_AHP;
Gap Junctions: Gap junctions;
Receptor(s): AMPA; Gaba; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Deep brain stimulation; Evoked LFP;
Implementer(s): Kumaravelu, Karthik [kk192 at duke.edu];
Search NeuronDB for information about:  Neocortex M1 L6 pyramidal corticothalamic GLU cell; Neocortex M1 L5B pyramidal pyramidal tract GLU cell; Neocortex M1 L4 stellate GLU cell; AMPA; NMDA; Gaba; I Na,p; I L high threshold; I T low threshold; I K; I Sodium; I Calcium; I_AHP; I_KD; Gaba; Glutamate;
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cEP_stndbs_4.5hz
cells
dat
hoc
net
readme.txt
alphasyndiffeq.mod *
alphasynkin.mod *
alphasynkint.mod *
ampa.mod *
ar.mod *
cad.mod *
cal.mod *
cat.mod *
cat_a.mod *
gabaa.mod *
iclamp_const.mod *
k2.mod *
ka.mod *
ka_ib.mod *
kahp.mod *
kahp_deeppyr.mod *
kahp_slower.mod *
kc.mod *
kc_fast.mod *
kdr.mod *
kdr_fs.mod *
km.mod *
naf.mod *
naf_tcr.mod *
naf2.mod *
nap.mod *
napf.mod *
napf_spinstell.mod *
napf_tcr.mod *
par_ggap.mod
pulsesyn.mod *
rampsyn.mod *
rand.mod *
ri.mod *
traub_nmda.mod *
balanal.hoc *
balcomp.hoc *
cell_templates.hoc *
clear.hoc *
finit.hoc *
fortmap.hoc *
gidcell.hoc *
gidcell.ses *
Iintra.dat.zip
init_stndbs_4.5hz.hoc
manage_setup_stndbs_4.5hz.hoc
onecell.hoc *
onecell.ses *
perf.dat
prcellstate.hoc *
printcon.hoc *
run_stndbs_4.5hz.q
spkplt.hoc *
vclampg.hoc *
vcompclamp.hoc *
vcompsim.hoc *
                            
load_file("nrngui.hoc")
load_file("hoc/defvar.hoc")
load_file("hoc/traubcon.hoc")
load_file("fortmap.hoc")
load_file("cell_templates.hoc")

// til the shift bug in the mod files are fixed (table depends on range variable)
usetable_naf2 = 0
usetable_naf = 0
usetable_napf_spinstell = 0
usetable_napf_tcr = 0

objref runinfo_, choice_, cell, nil, stim1, stim2, mchoice_
runinfo_ = new List()
strdef tstr1, tstr2
for i=1,14 {getstr(tstr1) runinfo_.append(new String(tstr1))}
deepaxax	0 0.4
deepbask 	0 0.3
deepLTS		0 0.4
nontuftRS	0 0.8
nRT		0 0.3
spinstell	0 -0.25 10 0.25
supaxax		0 0.4
supbask		0 0.4
supLTS		0 0.4
suppyrFRB	0 0.0 10 0.4
suppyrRS	0 0.0 5 0.75
TCR		0 -0.9 100 0.5
tuftIB		0 0 10 1.5
tuftRS		0 -0.4 50 0.8

//for i=0, runinfo_.count-1 {print runinfo_.object(i).s}

proc mkpanel() {local i
	exact_ = 0  normal_ = 1
	choice_ = new Vector(runinfo_.count)
	xpanel("Cell Run")
	for i=0, runinfo_.count-1 {
		sscanf(runinfo_.object(i).s, "%s", tstr1)
		sprint(tstr2, "runcell(%d) run()", i)
		xcheckbox(tstr1, &choice_.x[i], tstr2)
	}
	xlabel("")
	xcheckbox("NEURON style ri", &normal_, "exact(0)")
	xcheckbox("Exact traub style ri", &exact_, "exact(1)")
	if (name_declared("showtstspk")) {
		xlabel("")
		xbutton("Raster for test sim", "showtstspk()")
	}
	xpanel(20, 80, 0)
}
mkpanel()

proc mkpanel2() {local i
	exact_ = 0  normal_ = 1
	mchoice_ = new Vector(runinfo_.count)
	xpanel("Cell Make")
	for i=0, runinfo_.count-1 {
		sscanf(runinfo_.object(i).s, "%s", tstr1)
		sprint(tstr2, "mkcell(%d) run()", i)
		xcheckbox(tstr1, &mchoice_.x[i], tstr2)
	}
	xlabel("")
	xpanel(20, 500, 0)
}
//mkpanel2()

load_file("onecell.ses")

proc exact() {
	normal_ = ($1 == 0)
	exact_ = ($1 != 0)
	runcell(cellnum)
}

proc runcell() {local i, t1,a1,t2,a2  localobj vvec, tvec, f, g
	cellnum = $1
	choice_.fill(0)
	choice_.x[$1] = 1
	t2 = 1e9   a2 = 0
	i = sscanf(runinfo_.object($1).s, "%s %lf %lf %lf %lf", tstr1, \
		&t1, &a1, &t2, &a2)
//	print tstr1, t1, a1, t2, a2
	cell = nil
	sprint(tstr2, "cell = new %s()", tstr1)
	execute(tstr2)
	access cell.comp[1]
	define_shape()
	g = Graph[0]
	g.erase_all
	f = new File()
	sprint(tstr2, "dat/%s_v_F.dat", tstr1)
//	sprint(tstr2, "../p2c/data/GROUCHO110.%s", tstr1)
	clipboard_retrieve(tstr2)
	tvec = hoc_obj_[1]
	vvec = hoc_obj_[0]
	vvec.line(g, tvec, 2, 1)
	g.exec_menu("Keep Lines")
	g.exec_menu("Keep Lines")
	stim1 = nil
	stim2 = nil
	stim1 = new IClamp(.5)
	stim2 = new IClamp(.5)
	stim1.del = t1  stim2.del = t2
	stim1.amp = .3  stim2.amp = 0
	stim1.dur = 50  stim2.dur = 1e9
	sprint(tstr2, "cell.comp[%d].v(.5)", cell.presyn_comp)
	g.addvar(tstr2)
	if (exact_) { traubexact(cell, tci) }
}

runcell(0)

objref cell2

proc mkcell() {local i, t1,a1,t2,a2  localobj vvec, tvec, f, g
	cellnum = $1
	mchoice_.fill(0)
	mchoice_.x[$1] = 1
	t2 = 1e9   a2 = 0
	i = sscanf(runinfo_.object($1).s, "%s %lf %lf %lf %lf", tstr1, \
		&t1, &a1, &t2, &a2)
//	print tstr1, t1, a1, t2, a2
	cell2 = nil
	sprint(tstr2, "cell2 = new %s()", tstr1)
	execute(tstr2)
}

objref fih0_, fih1_
fih0_ = new FInitializeHandler(0, "finit0()")
fih1_ = new FInitializeHandler(1, "finit1()")

proc finit0() { local ix
	if (cell != nil) {
		ix = cell.type()
		forsec cell.all { v = type_vinit.x[ix] }
		if (ix == TCRtype) {
			forsec cell.all { v = -85 }
		}
	}
	if (cell2 != nil) {
		ix = cell2.type()
		forsec cell2.all { v = type_vinit.x[ix] }
		if (ix == TCRtype) {
			forsec cell2.all { v = -85 }
		}
	}
}

proc finit1() { local ix
	if (cell != nil) {
		ix = cell.type()
		if (ix == TCRtype) {
			forsec cell.all { v = type_vinit.x[ix] }
		}
	}
	if (cell2 != nil) {
		ix = cell2.type()
		forsec cell2.all { v = type_vinit.x[ix] }
		if (ix == TCRtype) {
			forsec cell2.all { v = type_vinit.x[ix] }
		}
	}
}


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