Computational analysis of NN activity and spatial reach of sharp wave-ripples (Canakci et al 2017)

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Network oscillations of different frequencies, durations and amplitudes are hypothesized to coordinate information processing and transfer across brain areas. Among these oscillations, hippocampal sharp wave-ripple complexes (SPW-Rs) are one of the most prominent. SPW-Rs occurring in the hippocampus are suggested to play essential roles in memory consolidation as well as information transfer to the neocortex. To-date, most of the knowledge about SPW-Rs comes from experimental studies averaging responses from neuronal populations monitored by conventional microelectrodes. In this work, we investigate spatiotemporal characteristics of SPW-Rs and how microelectrode size and distance influence SPW-R recordings using a biophysical model of hippocampus. We also explore contributions from neuronal spikes and synaptic potentials to SPW-Rs based on two different types of network activity. Our study suggests that neuronal spikes from pyramidal cells contribute significantly to ripples while high amplitude sharp waves mainly arise from synaptic activity. Our simulations on spatial reach of SPW-Rs show that the amplitudes of sharp waves and ripples exhibit a steep decrease with distance from the network and this effect is more prominent for smaller area electrodes. Furthermore, the amplitude of the signal decreases strongly with increasing electrode surface area as a result of averaging. The relative decrease is more pronounced when the recording electrode is closer to the source of the activity. Through simulations of field potentials across a high-density microelectrode array, we demonstrate the importance of finding the ideal spatial resolution for capturing SPW-Rs with great sensitivity. Our work provides insights on contributions from spikes and synaptic potentials to SPW-Rs and describes the effect of measurement configuration on LFPs to guide experimental studies towards improved SPW-R recordings.
1 . Canakci S, Toy MF, Inci AF, Liu X, Kuzum D (2017) Computational analysis of network activity and spatial reach of sharp wave-ripples. PLoS One 12:e0184542 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell; Hippocampus CA1 basket cell;
Channel(s): I Na,t; I A; I K; I h;
Gap Junctions: Gap junctions;
Receptor(s): NMDA; GabaA; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Oscillations; Spatio-temporal Activity Patterns;
Implementer(s): Canakci, Sadullah [scanakci at]; Inci, Ahmet F [afinci at sabanciuniv,edu]; Toy, Faruk [faruk.toy at]; Liu, Xin [xil432 at]; Kuzum, Duygu [dkuzum at];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; GabaA; NMDA; Glutamate; I Na,t; I A; I K; I h;
// $Id: interpxyz.hoc,v 1.2 2005/09/10 23:02:15 ted Exp $
/* Computes xyz coords of nodes in a model cell 
   whose topology & geometry are defined by pt3d data.
   Expects sections to already exist, and that the xtra mechanism has been inserted

Called in setpointers.hoc

// original data, irregularly spaced
objref xx, yy, zz, length
// interpolated data, spaced at regular intervals
objref xint, yint, zint, range

proc grindaway() { local ii, nn, kk, xr
	forall {
	  if (ismembrane("xtra")) {
		// get the data for the section
		nn = n3d()
		xx = new Vector(nn)
		yy = new Vector(nn)
		zz = new Vector(nn)
		length = new Vector(nn)

		for ii = 0,nn-1 {
			xx.x[ii] = x3d(ii)
			yy.x[ii] = y3d(ii)
			zz.x[ii] = z3d(ii)
			length.x[ii] = arc3d(ii)

		// to use Vector class's .interpolate() 
		// must first scale the independent variable
		// i.e. normalize length along centroid

		// initialize the destination "independent" vector
		range = new Vector(nseg+2)

		// length contains the normalized distances of the pt3d points 
		// along the centroid of the section.  These are spaced at 
		// irregular intervals.
		// range contains the normalized distances of the nodes along the 
		// centroid of the section.  These are spaced at regular intervals.
		// Ready to interpolate.

		xint = new Vector(nseg+2)
		yint = new Vector(nseg+2)
		zint = new Vector(nseg+2)
		xint.interpolate(range, length, xx)
		yint.interpolate(range, length, yy)
		zint.interpolate(range, length, zz)

		// for each node, assign the xyz values to x_xtra, y_xtra, z_xtra
//		for ii = 0, nseg+1 {
// don't bother computing coords of the 0 and 1 ends
// also avoid writing coords of the 1 end into the last internal node's coords
		for ii = 1, nseg {
			xr = range.x[ii]
			x_xtra(xr) = xint.x[ii]
			y_xtra(xr) = yint.x[ii]
			z_xtra(xr) = zint.x[ii]

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