Biophysically realistic neural modeling of the MEG mu rhythm (Jones et al. 2009)

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"Variations in cortical oscillations in the alpha (7–14 Hz) and beta (15–29 Hz) range have been correlated with attention, working memory, and stimulus detection. The mu rhythm recorded with magnetoencephalography (MEG) is a prominent oscillation generated by Rolandic cortex containing alpha and beta bands. Despite its prominence, the neural mechanisms regulating mu are unknown. We characterized the ongoing MEG mu rhythm from a localized source in the finger representation of primary somatosensory (SI) cortex. Subjects showed variation in the relative expression of mu-alpha or mu-beta, which were nonoverlapping for roughly 50% of their respective durations on single trials. To delineate the origins of this rhythm, a biophysically principled computational neural model of SI was developed, with distinct laminae, inhibitory and excitatory neurons, and feedforward (FF, representative of lemniscal thalamic drive) and feedback (FB, representative of higher-order cortical drive or input from nonlemniscal thalamic nuclei) inputs defined by the laminar location of their postsynaptic effects. ..."
1 . Jones SR, Pritchett DL, Sikora MA, Stufflebeam SM, Hämäläinen M, Moore CI (2009) Quantitative analysis and biophysically realistic neural modeling of the MEG mu rhythm: rhythmogenesis and modulation of sensory-evoked responses. J Neurophysiol 102:3554-72 [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 V1 L6 pyramidal corticothalamic GLU cell; Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell;
Channel(s): I Na,t; I T low threshold; I K; I h;
Gap Junctions:
Receptor(s): GabaA; GabaB; AMPA; NMDA;
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Touch;
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic GLU cell; Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell; GabaA; GabaB; AMPA; NMDA; I Na,t; I T low threshold; I K; I h;
N_ALPHA = 150 // not changeable unless change the explicit for loops below
objref AlphaFF[N_ALPHA]
objref AlphaFB[N_ALPHA]

// Alpha gids are contiguous after real cells
// declare the gids and provide
gidAlphabegin = gid_end
gidAlphaend = gidAlphabegin + 2*N_ALPHA
FF_type = 0
FB_type = 1
objref gidvecAlpha
gidvecAlpha = new Vector()
for (; i < gidAlphaend; i += pc.nhost) {
//gid = alphagid(type, index)
func alphagid() {
	return gidAlphabegin + $1*N_ALPHA + $2

objref GaussRand
GaussRand=new Random()

proc aff() {local r
	r = GaussRand.normal($2, $3)
	if (pc.gid_exists(alphagid(FF_type, $1))) {
		AlphaFF[$1].pp.start = r - stim_delay_offset

proc afb() {local r
	r = GaussRand.normal($2, $3)
	if (pc.gid_exists(alphagid(FB_type, $1))) {
		AlphaFB[$1].pp.start = r - stim_delay_offset

proc make_MuBursts(){local i, gid  localobj nil
// Usage: make_MuBursts(start-time-variance)// Creates 150 feed-forward and  feed-back sources
// 10 feed-forward and 10 feed-back at every 100ms
// Feed-Forward
for i=0,149{ 
	gid = alphagid(FF_type, i)
	if (pc.gid_exists(gid)) {
AlphaFF[i] = new FeedX()
AlphaFF[i].pp.number=2 //CHANGED THIS!!
AlphaFF[i].pp.interval = 10
AlphaFF[i].pp.start = 50
		pc.cell(gid, new NetCon(AlphaFF[i].pp, nil))

for i=0,9 if (pc.gid_exists(alphagid(FF_type, i))) {AlphaFF[i].pp.number = 0} // Not Used// Start at 150ms.

for i=10,19 aff(i,150,$1) //{AlphaFF[i].pp.start=GaussRand.normal(150,$1)}
for i=20,29 aff(i,250,$1) //{AlphaFF[i].pp.start= GaussRand.normal(250,$1)}
for i=30,39 aff(i,350,$1) //{AlphaFF[i].pp.start= GaussRand.normal(350,$1)}
for i=40,49 aff(i,450,$1) //{AlphaFF[i].pp.start= GaussRand.normal(450,$1)}
for i=50,59 aff(i,550,$1) //{AlphaFF[i].pp.start= GaussRand.normal(550,$1)} 
for i=60,69 aff(i,650,$1) //{AlphaFF[i].pp.start= GaussRand.normal(650,$1)}
for i=70,79 aff(i,750,$1) //{AlphaFF[i].pp.start= GaussRand.normal(750,$1)}
for i=80,89 aff(i,850,$1) //{AlphaFF[i].pp.start= GaussRand.normal(850,$1)}
for i=90,99 aff(i,950,$1) //{AlphaFF[i].pp.start= GaussRand.normal(950,$1)}
for i=100,109 aff(i,1050,$1) //{AlphaFF[i].pp.start= GaussRand.normal(1050,$1)}
for i=110,119 aff(i,1150,$1) //{AlphaFF[i].pp.start= GaussRand.normal(1150,$1)}
for i=120,129 aff(i,1250,$1) //{AlphaFF[i].pp.start= GaussRand.normal(1250,$1)}
for i=130,139 aff(i,1350,$1) //{AlphaFF[i].pp.start= GaussRand.normal(1350,$1)}
for i=140,149 aff(i,1450,$1) //{AlphaFF[i].pp.start= GaussRand.normal(1450,$1)}
//// Feedback
for i=0,149{ 
	gid = alphagid(FB_type, i)
	if (pc.gid_exists(gid)) {
AlphaFB[i] = new FeedX()
AlphaFB[i].pp.number=2 //THIS IS STILL 2 - CHANGE IT?
AlphaFB[i].pp.interval = 10
AlphaFB[i].pp.start = 50
		pc.cell(gid, new NetCon(AlphaFB[i].pp, nil))
for i=0,9 if (pc.gid_exists(alphagid(FB_type, i))) {AlphaFB[i].pp.number = 0} // Not Used// Start at 150ms.

for i=10,19 afb(i,150,$1) //{AlphaFB[i].pp.start=GaussRand.normal(150,$1)}
for i=20,29 afb(i,250,$1) //{AlphaFB[i].pp.start= GaussRand.normal(250,$1)}
for i=30,39 afb(i,350,$1) //{AlphaFB[i].pp.start= GaussRand.normal(350,$1)}
for i=40,49 afb(i,450,$1) //{AlphaFB[i].pp.start= GaussRand.normal(450,$1)}
for i=50,59 afb(i,550,$1) //{AlphaFB[i].pp.start= GaussRand.normal(550,$1)} 
for i=60,69 afb(i,650,$1) //{AlphaFB[i].pp.start= GaussRand.normal(650,$1)}
for i=70,79 afb(i,750,$1) //{AlphaFB[i].pp.start= GaussRand.normal(750,$1)}
for i=80,89 afb(i,850,$1) //{AlphaFB[i].pp.start= GaussRand.normal(850,$1)}
for i=90,99 afb(i,950,$1) //{AlphaFB[i].pp.start= GaussRand.normal(950,$1)}
for i=100,109 afb(i,1050,$1) //{AlphaFB[i].pp.start= GaussRand.normal(1050,$1)}
for i=110,119 afb(i,1150,$1) //{AlphaFB[i].pp.start= GaussRand.normal(1150,$1)}
for i=120,129 afb(i,1250,$1) //{AlphaFB[i].pp.start= GaussRand.normal(1250,$1)}
for i=130,139 afb(i,1350,$1) //{AlphaFB[i].pp.start= GaussRand.normal(1350,$1)}
for i=140,149 afb(i,1450,$1) //{AlphaFB[i].pp.start= GaussRand.normal(1450,$1)}
// For implementing stochastic interval// for //i=5,9{AlphaFF[i].pp.start=GaussRand.interval=GuassRand.normal($2,$3)}

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