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]
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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;
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
net
durand.hoc *
groucho.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 *
                            
// groucho_gapbld.hoc
/*
*****************************this is one big comment ***************************
from            SUBROUTINE GROUCHO_gapbld (thisno, numcells, numgj,
     &       gjtable, allowedcomps, num_allowedcomps, display)
c       Construct a gap-junction network for groucho.f
$1 thisno double
$2 numcells = number of cells in population, e.g. number of tufted IB cells
$3 numgj = total number of gj to be formed in this population
// this matrix is returned: gjtable = table of gj's: each row is a gj.  
     Entries are: cell A,
c    compartment on cell A; cell B, compartment on cell B
$o4 c allowedcomps = a list of compartments where gj allowed to form
$5 num_allowedcomps = number of compartments in a cell on which a gj 
c    might form.
$6 display is an integer flag.  If display = 1, print gjtable

        INTEGER thisno, numcells, numgj, gjtable(numgj,4),
     &    num_allowedcomps, allowedcomps(num_allowedcomps)
        INTEGER i,j,k,l,m,n,o,p, ictr /0/
c ictr keeps track of how many gj have been "built"
        INTEGER display

        double precision seed, x(2), y(2)

Note: this function is for gap junctions that form between a cells that are
members of a population of a single cell type
*****************************this is one big comment ***************************
*/
objref gjtable, x, y, allowedcomps
obfunc groucho_gapbld() {localobj used
// see above note for arguments $1,$2,$3,$o4,$5
// print "arrived"
	thisno = $1
	numcells = $2
	numgj = $3
	allowedcomps = $o4
	num_allowedcomps = $5
	display = $6

	seed = new Vector()
	seed.append(137.e0)

	objref gjtable
	gjtable = new Matrix(numgj+1, 4+1) // fortran notation indicies start at 1

	ictr = 0
	k = 2
	not_unique = 0 // make global so not local in loops
        used = new Matrix(numcells+1, numcells+1, 2) // sparse

// 2
// print "starting loop"
	while (ictr < numgj) {
 //         print "ictr = ",ictr
	  not_unique = 1 // 1 is true, 0 is false
	    while (not_unique) {
		x = durand (seed, k, x)
// This defines a candidate cell pair
		y = durand (seed, k, y)
// This defines a candidate pair of compartments

		i = int ( x.x[0] * numcells ) + 1
		j = int ( x.x[1] * numcells ) + 1
//		print "i,j: ",i,", ",j
// no longer required		if (i.eq.0) i = 1
// no longer required		if (i.gt.numcells) i = numcells
// no longer required		if (j.eq.0) j = 1
// no longer required		if (j.gt.numcells) j = numcells

// Is the unordered cell pair (i,j) in the list so far?
// not necessary to be this efficient  if (ictr.eq.0) goto 1

 		not_unique = 0
 if (0) {
		for eL = 1, ictr {
//		  print "compare i,j with eL = ",eL, " : ",gjtable.x(eL,1),", ",gjtable.x(eL,3)
		  if ((gjtable.x(eL,1) == i) && (gjtable.x(eL,3) == j)) { not_unique = 1 }
 		  if ((gjtable.x(eL,1) == j) && (gjtable.x(eL,3) == i)) { not_unique = 1 }
		}
//		print " at end of loop not_unique = ",not_unique
  }else{
                if (used.getval(i, j) || used.getval(j, i)){
                        not_unique = 1
                }else{
                        used.x[i][j] = 1
                }
		if (one_tenth_ncell) {
			not_unique = 0
		}
  }
	    } // while replaces if (not_unique.eq.1) goto 2

// Proceed with construction
// 1
	  ictr = ictr + 1
          m = int ( y.x[0] * num_allowedcomps ) + 1
          n = int ( y.x[1] * num_allowedcomps ) + 1
//	print "assigning quantities: ", i, ", ", j, ", ", allowedcomps.x[m], ", ",allowedcomps.x[n]

         gjtable.x(ictr,1) = i
         gjtable.x(ictr,3) = j
         gjtable.x(ictr,2) = allowedcomps.x (m)
         gjtable.x(ictr,4) = allowedcomps.x (n)
	}
//            if (ictr.lt.numgj) goto 2

// Possibly print out gjtable when done.
       if ((display == 1) && (thisno == 0)) {
        print " GJTABLE "
        for i = 1, numgj {
        printf("%6d %6d %6d %6d",gjtable.x(i,1), gjtable.x(i,2), \
                gjtable.x(i,3), gjtable.x(i,4))
        }
       }
	return gjtable
}