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
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 *
                            
objref compmap, allow, x

obfunc synaptic_compmap_construct () { local nrow, ncol  localobj f, s, tmpmap
/*
***************************************************************
Parameter, Description:
$1 thisno, maybe this double will be replaced in NEURON?
$2 num_postsynaptic_cells,  another double
// returned compmap(i,j), Matrix object=compartment #on postsyn cell j for ith presyn input
$3 num_presyninputs_perpostsyn_cell, a double 
$4 num_allowcomp, another double
$o5 allow, a Vector object of allowed postsyn compartments
$6 display, another double

c Construct a map of compartments at connections of one presynaptic
c cell to type to a postsynaptic cell type.
c compmap (i,j) = compartment number on postsynaptic cell j of its
c  i'th presynaptic input.
c display is an integer flag.  If display = 1, print compmap

        INTEGER thisno,
     &   num_postsynaptic_cells,
     &   num_presyninputs_perpostsyn_cell,
     &   compmap (num_presyninputs_perpostsyn_cell, 
     &                  num_postsynaptic_cells),
     &   num_allowcomp, allow(num_allowcomp)
c num_allowcomp = number of different allowed compartments
c allow = list of allowed compartments
        INTEGER i,j,k,l,m,n,o,p
        INTEGER display

        double precision seed, x(1)
***************************************************************
*/
//	print "arrived"
//	objref seed
	seed = new Vector()
        seed.append(377.e0)

        num_postsynaptic_cells = $2
	ncol=$2
        num_presyninputs_perpostsyn_cell = $3
	nrow=$3
	num_allowcomp = $4
	objref allow
	allow = $o5
	display = $6

	objref compmap
	compmap = new Matrix(num_presyninputs_perpostsyn_cell+1, num_postsynaptic_cells+1)
  if (!use_p2c_net_connections) {
//            map = 0
            k = 1
// print "num_postsynaptic_cells, num_presyninputs_perpostsyn_cell = ",num_postsynaptic_cells, num_presyninputs_perpostsyn_cell
// print "matrix size = ",compmap.nrow(),compmap.ncol()

        for ii = 1, num_postsynaptic_cells {
        for jj = 1, num_presyninputs_perpostsyn_cell {
            x = durand (seed, k, x)
// c This defines a compartment     
           LL = int ( x.x[0] * (num_allowcomp) ) + 1
//	 print "jj,ii: ",jj,ii, " LL=",LL
        if (LL > num_allowcomp) {
		print " unnexpected boundary issue in synaptic_compmap_construct()"
		LL = num_allowcomp
	}
// print allow.x(L)
           compmap.x[jj][ii] = allow.x[LL]

        }
        }

	thisno = $1
// c Possibly print out map when done.
       if ((display == 1) && (thisno == 0)) {
	print "SYNAPTIC COMPARTMENT MAP "
        for i = 1, num_postsynaptic_cells {
         printf("%6d %6d %6d\n", compmap.x(1,i), compmap.x(2,i), \
         compmap.x(num_presyninputs_perpostsyn_cell,i))               

        }
       }
  }else{
	// read from file created by port2colossus
	s = new String()
	sprint(s.s, "../../p2c/compmap/%s.dat", $s7)
//printf("%s %d %d\n", s.s, nrow, ncol)
	f = new File()
	f.ropen(s.s)
	tmpmap = new Matrix(ncol, nrow) // need to transpose
	tmpmap.scanf(f, ncol, nrow)
	tmpmap = tmpmap.transpose
	tmpmap.bcopy(0,0,nrow, ncol, 1, 1, compmap)
  }
       return compmap
}

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