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
data_stndbs_4.5hz
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
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 *
                            
setuptime = startsw()
{xopen("hoc/defvar.hoc")}

// load balancing implies several runs.
// 0) load balancing just plain off (but will read mcomplex.dat if exists)
// 1) create a mcomplex.dat file (only one cpu needed)
// 2) create single split balance.dat file (any number of cpus can be used)
//  manually: run the balcomp program to create balance.ncpu files
// 3) run with single split load balancing on (ncpu corresponding to files from balcomp)
// add more for whole cell and multisplit load balancing
// 4) create whole cell load balance file
// 5) run with whole cell load balance file
// 6) create multisplit load balance information file
// 7) run using multisplit load balance

default_var("load_balance_phase", 0)
default_var("one_tenth_ncell", 1)
default_var("use_gap", 1)
default_var("use_ectopic", 1)
default_var("use_inject", 1)
default_var("awake", 0)
default_var("default_delay", 0.5)
default_var("ranseedbase", 1)
default_var("use_traubexact", 0) // will be reset to 0 if load balance
default_var("wholecell_prefix", "cxwhole")
default_var("multisplit_prefix", "cx")
default_var("multisplit_nhost", 256)
default_var("msoptfactor", 0.8)
default_var("nthread", 1)
default_var("savestatetest", 0)


default_var("spike_compress", 5)
default_var("cacheeffic", 1)  // for multisplit it is always 1
default_var("multisend", 0)
default_var("selfevents", 0)

default_var("fakerank", -1)
default_var("fakenhost", -1)

default_var("jordan_trace",0)

default_var("jEFP1x",0)
default_var("jEFP1y",500)
default_var("jEFP1z",0)
default_var("sigmae",0.3*10^-6)



strdef varfn
sprint(varfn, "stndbs.dat")

objref tfn[14]
strdef trace
objref Jtrace[14]
 
 
objref IN_MATRIX
objref input_file2
IN_MATRIX = new Matrix() 		// N = size of IN_VECTOR
input_file2 = new File()
input_file2.ropen("Iintra.dat") 		// Same file.dat as exported from MATLAB
IN_MATRIX.scanf(input_file2,10,800001)
input_file2.close()


if (jordan_trace == 1){
{sprint(trace, "data_stndbs_4.5hz/traces/trace_suppyrRS_%s",varfn)}
tfn[0] = new String(trace)
{sprint(trace, "data_stndbs_4.5hz/traces/trace_suppyrFRB_%s",varfn)}
tfn[1] = new String(trace)
{sprint(trace, "data_stndbs_4.5hz/traces/trace_supaxax_%s",varfn)}
tfn[2] = new String(trace)
{sprint(trace, "data_stndbs_4.5hz/traces/trace_spinstell_%s",varfn)}
tfn[3] = new String(trace)
{sprint(trace, "data_stndbs_4.5hz/traces/trace_tuftIB_%s",varfn)}
tfn[4] = new String(trace)
{sprint(trace, "data_stndbs_4.5hz/traces/trace_tuftRS_%s",varfn)}
tfn[5] = new String(trace)
{sprint(trace, "data_stndbs_4.5hz/traces/trace_nontuftRS_%s",varfn)}
tfn[6] = new String(trace)
{sprint(trace, "data_stndbs_4.5hz/traces/trace_deepaxax_%s",varfn)}
tfn[7] = new String(trace)
{sprint(trace, "data_stndbs_4.5hz/traces/trace_deepbask_%s",varfn)}
tfn[8] = new String(trace)
{sprint(trace, "data_stndbs_4.5hz/traces/trace_supbask_%s",varfn)}
tfn[9] = new String(trace)
{sprint(trace, "data_stndbs_4.5hz/traces/trace_supLTS_%s",varfn)}
tfn[10] = new String(trace)
{sprint(trace, "data_stndbs_4.5hz/traces/trace_deepLTS_%s",varfn)}
tfn[11] = new String(trace)
{sprint(trace, "data_stndbs_4.5hz/traces/trace_TCR_%s",varfn)}
tfn[12] = new String(trace)
{sprint(trace, "data_stndbs_4.5hz/traces/trace_nRT_%s",varfn)}
tfn[13] = new String(trace)
for i = 0, 13 {
	Jtrace[i] = new Vector()
}
}

objref myout
objref tvec
tvec = new Vector()

objref EF[1]
EF[0] = new Vector()

objref EFtvec
EFtvec = new Vector()
double efmm[12]
double efm[1]


gfac_AMPA = 1
gfac_NMDA = 1
gfac_GABAA = 1

use_p2c_net_connections = 0 // not 0, requires p2c emitted  map and compmap files

{localloadfile("manage_setup_stndbs_4.5hz.hoc")}

steps_per_ms = 40
dt = .025
secondorder = 0
default_var("mytstop", 20000)
tstop = mytstop

pc.timeout(0)


//{load_file("balanal.hoc")}
//if (pc.nhost >= 128) {thread_per_piece()}

if (0) { pc.runworker()  pc.done() quit() }

//{finitialize(-65) cvode_local(1) cvode.atol(1e-4)} // the finitialze avoids /0 in BREAKPOINT

prun()

//if (pc.nhost >= 128) {balanalfile("balanal.dat")}

endtime = startsw()

if (pc.id == 0) { print "tstop = ", tstop }
if (pc.id == 0) { print "RunTime: ", runtime }

//{localloadfile("prcellstate.hoc")}
proc pcs() {local spgid
	spgid = thishost_gid($1)
	if (spgid >= 0) prcellstate(load_balance_phase, spgid)
	pc.runworker()  pc.done() quit()
}
//pcs(103)

spike2file()

if (pc.nhost > 5) {cvode_active(1)} // to count equations
{pc.runworker()}

print "Maximum integration interval: ", mindelay()
getstat()
prhist()
print_spike_stat_info()

{pc.done()}

perf2file()
endtime = startsw() - endtime
print "endtime ", endtime

// if (!serial) { quit() }

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