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

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Accession:230861
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.
Reference:
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]
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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: 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;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Oscillations; Spatio-temporal Activity Patterns;
Implementer(s): Canakci, Sadullah [scanakci at bu.edu]; Inci, Ahmet F [afinci at sabanciuniv,edu]; Toy, Faruk [faruk.toy at metu.edu.tr]; Liu, Xin [xil432 at end.ucsd.edu]; Kuzum, Duygu [dkuzum at eng.ucsd.edu];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; GabaA; NMDA; Glutamate; I Na,t; I A; I K; I h;
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CanakciEtAl2017
mod
ca1ih.mod *
ca1ika.mod *
ca1ikdr.mod *
ca1ina.mod *
caolmw.mod *
capr.mod *
edrive.mod *
exp2synampa.mod *
exp2synampapre.mod *
exp2synnmda.mod *
exp2synnmdaperm.mod *
exp2synnmdapre.mod *
fvpre.mod *
gap.mod *
halfgap.mod *
icaolmw.mod *
icapr.mod *
iholmkop.mod *
iholmw.mod *
ihpyrkop.mod *
kahppr.mod *
kaolmkop.mod *
kapyrkop.mod *
kcaolmw.mod *
kcpr.mod *
kdrbwb.mod *
kdrca1.mod *
kdrolmkop.mod *
kdrpr.mod *
kdrpyrkop.mod *
na3n.mod *
nafbwb.mod *
nafolmkop.mod *
nafpr.mod *
nafpyrkop.mod *
noisesyn.mod
par_ggap.mod *
vecevent.mod *
xtra.mod *
aux_fun.inc *
                            
: $Id: xtra.mod,v 1.3 2009/02/24 00:52:07 ted Exp ted $

COMMENT
This mechanism is intended to be used in conjunction 
with the extracellular mechanism.  Pointers specified 
at the hoc level must be used to connect the 
extracellular mechanism's e_extracellular and i_membrane 
to this mechanism's ex and im, respectively.

xtra does three useful things:

1. Serves as a target for Vector.play() to facilitate 
extracellular stimulation.  Assumes that one has initialized 
a Vector to hold the time sequence of the stimulus current.
This Vector is to be played into the GLOBAL variable is 
(GLOBAL so only one Vector.play() needs to be executed), 
which is multiplied by the RANGE variable rx ("transfer 
resistance between the stimulus electrode and the local 
node").  This product, called ex in this mechanism, is the 
extracellular potential at the local node, i.e. is used to 
drive local e_extracellular.

2. Reports the contribution of local i_membrane to the 
total signal that would be picked up by an extracellular 
recording electrode.  This is computed as the product of rx, 
i_membrane (called im in this mechanism), and the surface area 
of the local segment, and is reported as er.  The total 
extracellularly recorded potential is the sum of all er_xtra 
over all segments in all sections, and is to be computed at 
the hoc level, e.g. with code like

func fieldrec() { local sum
  sum = 0
  forall {
    if (ismembrane("xtra")) {
      for (x,0) sum += er_xtra(x)
    }
  }
  return sum
}

Bipolar recording, i.e. recording the difference in potential 
between two extracellular electrodes, can be achieved with no 
change to either this NMODL code or fieldrec(); the values of 
rx will reflect the difference between the potentials at the 
recording electrodes caused by the local membrane current, so 
some rx will be negative and others positive.  The same rx 
can be used for bipolar stimulation.

Multiple monopolar or bipolar extracellular recording and 
stimulation can be accommodated by changing this mod file to 
include additional rx, er, and is, and changing fieldrec() 
to a proc.

3. Allows local storage of xyz coordinates interpolated from 
the pt3d data.  These coordinates are used by hoc code that 
computes the transfer resistance that couples the membrane 
to extracellular stimulating and recording electrodes.


Prior to NEURON 5.5, the SOLVE statement in the BREAKPOINT block 
used METHOD cvode_t so that the adaptive integrators wouldn't miss 
the stimulus.  Otherwise, the BREAKPOINT block would have been called 
_after_ the integration step, rather than from within cvodes/ida, 
causing this mechanism to fail to deliver a stimulus current 
when the adaptive integrator is used.

With NEURON 5.5 and later, this mechanism abandons the BREAKPOINT 
block and uses the two new blocks BEFORE BREAKPOINT and  
AFTER BREAKPOINT, like this--

BEFORE BREAKPOINT { : before each cy' = f(y,t) setup
  ex = is*rx*(1e6)
}
AFTER SOLVE { : after each solution step
  er = (10)*rx*im*area
}

This ensures that the stimulus potential is computed prior to the 
solution step, and that the recorded potential is computed after.
ENDCOMMENT

NEURON {
	SUFFIX xtra
	RANGE rx, er
	RANGE x, y, z
	GLOBAL is
	POINTER im, ex
}

PARAMETER {
	: default transfer resistance between stim electrodes and axon
	rx = 1 (megohm) : mV/nA
	x = 0 (1) : spatial coords
	y = 0 (1)
	z = 0 (1)
}

ASSIGNED {
	v (millivolts)
	is (milliamp)
	ex (millivolts)
	im (milliamp/cm2)
	er (microvolts)
	area (micron2)
}

INITIAL {
	ex = is*rx*(1e6)
	er = (10)*rx*im*area
: this demonstrates that area is known
: UNITSOFF
: printf("area = %f\n", area)
: UNITSON
}

: Use BREAKPOINT for NEURON 5.4 and earlier
: BREAKPOINT {
:	SOLVE f METHOD cvode_t
: }
:
: PROCEDURE f() {
:	: 1 mA * 1 megohm is 1000 volts
:	: but ex is in mV
:	ex = is*rx*(1e6)
:	er = (10)*rx*im*area
: }

: With NEURON 5.5 and later, abandon the BREAKPOINT block and PROCEDURE f(),
: and instead use BEFORE BREAKPOINT and AFTER BREAKPOINT

BEFORE BREAKPOINT { : before each cy' = f(y,t) setup
  ex = is*rx*(1e6)
}
AFTER SOLVE { : after each solution step
  er = (10)*rx*im*area
}