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

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"... 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."
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]
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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;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Deep brain stimulation; Evoked LFP;
Implementer(s): Kumaravelu, Karthik [kk192 at];
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;
durand.hoc *
groucho_gapbld.hoc *
groucho_gapbld_mix.hoc *
network_specification_interface.hoc *
serial_or_par_wrapper.hoc *
synaptic_compmap_construct.hoc *
synaptic_map_construct.hoc *
// network_specification_interface.hoc
// from NetGUI[0], the GUI Network Builder

objref cells, nclist, netcon
{cells = new List() nclist = new List()}

func cell_append() {
	return cells.count - 1

func nc_append() { // srcindex, tarcelindex, synindex
	if ($3 >= 0) {
		netcon.weight = $4  netcon.delay = $5
	} else {
		netcon.weight = $4  netcon.delay = $5
return nclist.count-1

/* instantiate network with commands like
cell_append(new deepaxax_Cell(), -80, 0, 0)
cell_append(new deepaxax_Cell(),  80, 0, 0)

nc_append(1, 0, 0, 4, 8)
nc_append(1, 0, 1, 3, 7)
nc_append(0, 1, 0, 2, 6)
nc_append(0, 1, 0, 1, 5)
first two arguments:
where 0 is the first deepaxax cell, 1 is the second.
next argument:
the synapse number 0 is an AlphaSynKinT0, 1 is an NMDA1 rcptr.
where 1,2,3,4 are weights, 5,6,7,8 are delays

other examples from NetGUI's export of a NetStim hoc file (*'s removed before /'s)
//Network instantiation

  /* NetStim_slow0 /  cell_append(new NetStim_slow_NetStim(),	-131,	 19, 0)
  /* NetStim1 /  cell_append(new NetStim_NetStim(),	-123,	 -30, 0)
  /* Cell2 /  cell_append(new Cell_Cell(),	45,	 -4, 0)
  /* NetStim_slow0 -> Cell2.PulseSyn0 /  nc_append(0,   2, 0,  0,1)
  /* NetStim1 -> Cell2.PulseSyn0 /  nc_append(1,   2, 0,  0,1)


/* note: the automatically created from FORTRAN cell templates
contain the cell templates in the style that would be written
from network builder. */

// here is a template from network builder for the stimulation of cells

begintemplate S_NetStim
public pp, connect2target, x, y, z, position, is_art
objref pp
proc init() {
    pp = new NetStim(.5)
// the interval is expected to be reset in each object instance
    pp.interval = 1e+08
// this arbitrarily large number allows the poisson process
// to deliver as many spikes as would occur without restraint
// in the simulation
    pp.number = 1e8
    pp.start = 0
    pp.noise = 1 // setting for a poisson process
func is_art() { return 1 }
proc connect2target() { $o2 = new NetCon(pp, $o1) }
proc position(){x=$1  y=$2  z=$3}
endtemplate S_NetStim

// here is a function which sets up constant current injections into
// the somas of some cell types

objref iclamp_const_list, tmpobj
iclamp_const_list = new List()  // don't know if we will need these

proc set_const_curr_inj() { 
// arguments past are
// double cellname_base, double num_cellname, Vector curr_cellname
//        $1                    $2                   $o3

	for i=1,$2 { // loop over all the cells
	// create the IClamp_const

	// insert IClamp_const

	    cells.object($1 + i).comp[1] tmpobj = new IClamp_const(0.5)
//	    cells.object($1 + i).synlist.append(tmpobj)  // should this be on this list?

	    tmpobj.amp = $o3.x[i]

if (pmesg) print "made it to end of network_specification_interface.hoc"