A multilayer cortical model to study seizure propagation across microdomains (Basu et al. 2015)

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Accession:206238
A realistic neural network was used to simulate a region of neocortex to obtain extracellular LFPs from ‘virtual micro-electrodes’ and produce test data for comparison with multisite microelectrode recordings. A model was implemented in the GENESIS neurosimulator. A simulated region of cortex was represented by layers 2/3, 5/6 (interneurons and pyramidal cells) and layer 4 stelate cells, spaced at 25 µm in each horizontal direction. Pyramidal cells received AMPA and NMDA inputs from neighboring cells at the basal and apical dendrites. The LFP data was generated by simulating 16-site electrode array with the help of ‘efield’ objects arranged at the predetermined positions with respect to the surface of the simulated network. The LFP for the model is derived from a weighted average of the current sources summed over all cellular compartments. Cell models were taken from from Traub et al. (2005) J Neurophysiol 93(4):2194-232.
References:
1 . Basu I, Kudela P, Korzeniewska A, Franaszczuk PJ, Anderson WS (2015) A study of the dynamics of seizure propagation across micro domains in the vicinity of the seizure onset zone. J Neural Eng 12:046016 [PubMed]
2 . Basu I, Kudela P, Anderson WS (2014) Determination of seizure propagation across microdomains using spectral measures of causality. Conf Proc IEEE Eng Med Biol Soc 2014:6349-52 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex U1 L5B pyramidal pyramidal tract GLU cell; Thalamus reticular nucleus GABA cell; Neocortex spiking low threshold (LTS) neuron; Neocortex spiking regular (RS) neuron; Neocortex layer 2-3 interneuron; Neocortex layer 5 interneuron;
Channel(s): I Na,p; I Na,t; I K; I A; I M; I h; I K,Ca; I A, slow; I L high threshold; I T low threshold; I Calcium;
Gap Junctions: Gap junctions;
Receptor(s): AMPA; GabaA; NMDA;
Gene(s):
Transmitter(s): Glutamate; Gaba; Amino Acids;
Simulation Environment: GENESIS;
Model Concept(s): Activity Patterns; Epilepsy;
Implementer(s): Anderson, WS ; Kudela, Pawel ;
Search NeuronDB for information about:  Thalamus reticular nucleus GABA cell; Neocortex U1 L5B pyramidal pyramidal tract GLU cell; Neocortex U1 L2/6 pyramidal intratelencephalic GLU cell; GabaA; AMPA; NMDA; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I K; I M; I h; I K,Ca; I Calcium; I A, slow; Amino Acids; Gaba; Glutamate;
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BasuEtAl2015
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ModelDescription.pdf
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P6RSd_P6RSc.g
P6RSd_P6RSd.g
P6RSd_P6RSd_Gap.g
P6RSd_raninput.g
P6RSd_ST4RS.g
P6RSd_synapsedefs.g
P6RSd_TCR.g
P6RSdcell3Dpk.p
P6RSdchanpk.g
P6RSdprotodefs.g
P6RSsyncond.g
pgenesis_command
protodefs.g
protospikeB23FS.g
protospikeB5FS.g
protospikeC23FS.g
protospikeC5FS.g
protospikeI23LTS.g
protospikeI5LTS.g
protospikenRT.g
protospikeP23FRBa.g
protospikeP23RSa.g
protospikeP23RSb.g
protospikeP23RSc.g
protospikeP23RSd.g
protospikeP5IBa.g
protospikeP5IBb.g
protospikeP5IBc.g
protospikeP5IBd.g
protospikeP5RSa.g
protospikeP6RSa.g
protospikeP6RSb.g
protospikeP6RSc.g
protospikeP6RSd.g
protospikeST4RS.g
protospikeTCR.g
randominputdefs.g
spikedefs.g
ST4RS.g
ST4RS_B23FS.g
ST4RS_B5FS.g
ST4RS_C23FS.g
ST4RS_C5FS.g
ST4RS_I23LTS.g
ST4RS_I5LTS.g
ST4RS_P23FRBa.g
ST4RS_P23RSa.g
ST4RS_P23RSb.g
ST4RS_P23RSc.g
ST4RS_P23RSd.g
ST4RS_P5IBa.g
ST4RS_P5IBb.g
ST4RS_P5IBc.g
ST4RS_P5IBd.g
ST4RS_P5RSa.g
ST4RS_P6RSa.g
ST4RS_P6RSb.g
ST4RS_P6RSc.g
ST4RS_P6RSd.g
ST4RS_raninput.g
ST4RS_ST4RS.g
ST4RS_ST4RS_Gap.g
ST4RS_synapsedefs.g
ST4RScell3Dpk.p
ST4RSchanpk.g
ST4RSprotodefs.g
ST4RSsyncond.g
synapticdelays.g *
synapticprobsTraub.g
synchansB23FS.g *
synchansB5FS.g *
synchansC23FS.g *
synchansC5FS.g *
synchansI23LTS.g *
synchansI5LTS.g *
synchansnRT.g *
synchansP23FRBa.g *
synchansP23RSa.g *
synchansP23RSb.g *
synchansP23RSc.g *
synchansP23RSd.g *
synchansP5IBa.g *
synchansP5IBb.g *
synchansP5IBc.g *
synchansP5IBd.g *
synchansP5RSa.g *
synchansP6RSa.g *
synchansP6RSb.g *
synchansP6RSc.g *
synchansP6RSd.g *
synchansSPIKEs.g *
synchansSPIKEs_base.g
synchansST4RS.g
synchansTCR.g *
syncond.g
syncond2.g
TCR.g
TCR_B23FS.g
TCR_B5FS.g
TCR_C23FS.g
TCR_C5FS.g
TCR_nRT.g
TCR_P23FRBa.g
TCR_P23RSa.g
TCR_P23RSb.g
TCR_P23RSc.g
TCR_P23RSd.g
TCR_P5IBa.g
TCR_P5IBb.g
TCR_P5IBc.g
TCR_P5IBd.g
TCR_P5RSa.g
TCR_P6RSa.g
TCR_P6RSb.g
TCR_P6RSc.g
TCR_P6RSd.g
TCR_raninput.g
TCR_ST4RS.g
TCR_synapsedefs.g
TCRcellpk.p
TCRchanpk.g
TCRprotodefs.g
TCRsyncond.g
                            
// genesis

//Overall simulation parameters

float tmax = 10 
float dt = 5.0e-5		// sec
floatformat %g
float refresh_factor = 10.0

// Seeding the random number generator used later in the input pattern.
// Seeding with a defined number (integer) allows one to reproduce
// 'random' patterns from one simulation to the next.   Seeding
// without a defined seed gives different results each time you run
// the simulation.

randseed 34521

// setting the simulation clocks

setclock	0 {dt}		        // sec
setclock	1 {dt * refresh_factor} // sec
setclock        2 1.0
// Distributed Processing Setup

int display = 1		// display neurons and graphs
int output = 1		// don't dump neural output to a file

// Thalamocortical and Gaps flags
int thalamocortical
int gaps

gaps = 1   // Gap junction flag
thalamocortical = 1 // TC flag

int batch = (display == 0)	// we are running interactively

echo Genesis started at {getdate}
echo "display = " {display}
echo "output = " {output}
echo "batch = " {batch}
echo ""

// variables
int i_am_Q1, i_am_Q2, i_am_Q3, i_am_Q4	// booleans indicating what cells are assigned
int i_am_Q5, i_am_Q6, i_am_Q7, i_am_Q8  //   to this node
int i_am_Q9, i_am_Q10,i_am_Q11, i_am_Q12
int i_am_Q13, i_am_Q14, i_am_Q15, i_am_Q16

int Nnodes = 81
int sqrtNnodes = 9

// START UP
paron -parallel -nodes {Nnodes} -output /home/pol/NeoDiff/o.out \
   	-executable nxpgenesis
//setfield /post msg_hang_time 100000	// set a very long timeout in case
					// we need to do debugging

// Label nodes (whereami)

i_am_Q1 = {mynode} == 0
i_am_Q2 = {mynode} == 1
i_am_Q3 = {mynode} == 2
i_am_Q4 = {mynode} == 3
i_am_Q5 = {mynode} == 4
i_am_Q6 = {mynode} == 5
i_am_Q7 = {mynode} == 6
i_am_Q8 = {mynode} == 7
i_am_Q9 = {mynode} == 8
i_am_Q10 = {mynode} == 9
i_am_Q11 = {mynode} == 10
i_am_Q12 = {mynode} == 11
i_am_Q13 = {mynode} == 12
i_am_Q14 = {mynode} == 13
i_am_Q15 = {mynode} == 14
i_am_Q16 = {mynode} == 15

echo I am node {mynode}
echo Completed startup at {getdate}

// Neocortex - Setup / Global Variables

int probedex
int probedex2
int gridsize
float sqrtgrdsz
float neuronfrac // Percentage of neurons receiving background

// Flags for minicolumnar architecture

int columntype       // 0 = standard column, all cell types represented in each Minicolumn
                     // 1 = Traub like columns

columntype = 1

// Load Network cell spacings and cell numbers

include netparams.g

// Central Q1 P23RSa current injection

float Q1_P23RSa_centralinj = 0.0

gridsize = P23RSa_NX*P23RSa_NY
probedex = gridsize/2
probedex2 = probedex/2

//===============================
//      Function Definitions
//===============================

function step_tmax
    step {tmax} -time
end

//===============================
//          Data Out
//===============================

function do_asc_file(diskpath, srcpath, field, fd)

        create asc_file /{fd}
        setfield /{fd} filename {diskpath} flush 1 leave_open 1 append 0 float_format %0.9g

        addmsg {srcpath} /{fd}  SAVE {field}

end

//===============================
//    Spike Class  Data Out ASCII
//===============================

function do_classasc_file(diskpath, srcpath, classdex, Nelements, field, fd)

        int Nelements, classdex
        int i

        create asc_file /{fd}
        setfield /{fd} filename {diskpath} flush 1 leave_open 1 append 0 float_format %0.9g

        for (i=1;i<={Nelements};i=i+1)

            addmsg {srcpath}[{i-1}]/soma/spk{classdex} /{fd}  SAVE {field}
        
        end

end

//================================
//    Spike Class  Data Out Binary
//================================

function do_classasc_filebin(diskpath, srcpath, classdex, Nelements, field, fd)

        int Nelements, classdex
        int i

        create disk_out /{fd}
        setfield /{fd} filename {diskpath} flush 1 leave_open 1 append 0

        for (i=1;i<={Nelements};i=i+1)

            addmsg {srcpath}[{i-1}]/soma/spk{classdex} /{fd}  SAVE {field}
        
        end

end


//===============================
//      Set up Network
//===============================

//Table of synaptic conductances

include syncond.g

// Load cell definitions

include celldefs.g

// Define soma SPIKE outputs

include spikedefs

// CREATE NETWORK CONNECTIONS

// Network definitions

barrierall

P23RSa
P23RSb
P23RSc
P23RSd
B23FS
P5IBa
P5IBb
P5IBc
P5IBd
B5FS
P6RSa
P6RSb

if ({columntype == 0})

     P6RSc
     P6RSd

end

C23FS
C5FS
ST4RS
I23LTS
I5LTS

if ({thalamocortical == 1})
     TCR
     nRT
end

P23FRBa
P5RSa

barrierall

// Synaptic weight decay parameters and delays

barrierall
include synapticprobsTraub.g
barrierall

barrierall
include synapticdelays.g
barrierall

barrierall
include axonaldelays.g
barrierall

//Establish Wiring

include netdefs.g

barrierall

echo Made it past netdefs.g! {mynode}

// Create Gap Junctions

if ({gaps == 1})

     barrierall
     include Gapdefs.g
     barrierall

end

// Create Random Background Inputs

include synchansSPIKEs.g

neuronfrac=0.005

include randominputdefs.g

// Output and diagnostics

// Local field potential calculation

include LFP16s.g

//Setup messages for Data File writing

if ( {output == 1} )

    // LFP data write

    include LFP8sASCIIwrite.g

    // Spike Class Output Binary

    include BinarySpikeClasswrite.g

end


check
reset // This initialises and gets everything ready to go.

barrier
step_tmax // Run the sim to time tmax

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