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
hoc
balcomp.hoc *
defvar.hoc *
lbcreate.hoc *
mscreate.hoc *
parlib.hoc
parlib2.hoc *
traubcon.hoc *
traubcon_net.hoc *
                            
// The change to a connection coefficient gets changed back to
// its value determined by diam,L,Ra,topology after any change of any
// of those properties in any section.
// However the topology change implied by the traub_exact process is persistent.
// Thus one possibility is to do the traub_exact topology change
// along with the connection coefficient setting AFTER a complete setup
// that includes gaps, synapses, and stimuli, and then let NEURON do its
// thing in response to diam_changed,
// and then change all the connection coefficients.
// Another possiblity, which perhaps is not as efficient but is
// certainly simpler, is to
// let traub_exact do its thing on the creation of each cell, which will accomplish
// the persistent topology change, and save the info regarding the
// connection coefficients, and then fill them again after the complete setup.
// We choose the latter.

// for all cells
proc reset_connection_coefficients() {local i, gid, ix  localobj cell
	if (use_traubexact) {
		// do the topology first
		for pcitr(&i, &gid) {
			cell = pc.gid2cell(gid)
			ix = cell.type
			traubexact_topology(cell, traubExactInfo.tci[ix], traubExactInfo.traub_parent[ix])
		}
		doNotify()
		// now the coefficients
		for pcitr(&i, &gid) {
			cell = pc.gid2cell(gid)
			ix = cell.type
			traubexact_coef(cell, traubExactInfo.tci[ix], traubExactInfo.traub_parent[ix])
		}
	}
}