Functional consequences of cortical circuit abnormalities on gamma in schizophrenia (Spencer 2009)

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Accession:144477
"Schizophrenia is characterized by cortical circuit abnormalities, which might be reflected in gamma-frequency (30–100 Hz) oscillations in the electroencephalogram. Here we used a computational model of cortical circuitry to examine the effects that neural circuit abnormalities might have on gamma generation and network excitability. The model network consisted of 1000 leaky integrateand- fi re neurons with realistic connectivity patterns and proportions of neuron types [pyramidal cells (PCs), regular-spiking inhibitory interneurons, and fast-spiking interneurons (FSIs)]. ... The results of this study suggest that a multimodal approach, combining non-invasive neurophysiological and structural measures, might be able to distinguish between different neural circuit abnormalities in schizophrenia patients. ..."
Reference:
1 . Spencer KM (2009) The functional consequences of cortical circuit abnormalities on gamma oscillations in schizophrenia: insights from computational modeling. Front Hum Neurosci 3:33 [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):
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: IDL;
Model Concept(s): Oscillations; Simplified Models; Schizophrenia; Brain Rhythms;
Implementer(s): Spencer, Kevin M. [kevin_spencer at hms.harvard.edu];
Search NeuronDB for information about:  GabaA; AMPA; NMDA; 
function wers_thresh, data, thresh, freq_range, neg=neg

common path, home_path, single_path, avg_path, ps_path, latadj_path, dv_path, $
             pca_path, bhv_path, image_path, ers_path, ica_path
common params, n_ids, n_points, n_chans, period, epoch_range, filt_width, base_range
common wers_params, n_wers_pts, wers_range, wers_base_range, n_scales, scales

data2 = data
data2[n_ids:*,*,*] = 0.

chan_ix = [indgen(61),64]  ; monopolar channels, excluding Nas (not used) (note A1/A2 are mastoids)
n_chan_ix = n_elements(chan_ix)

;scales = dindgen(n_scales) * 0.125
;scales = 2d0^(scales)*(2*period)
scale_range = 1000./freq_range
scale_ix = where((scales GE scale_range[1]) AND (scales LE scale_range[0]), n_scale_ix)

pts_ix = n_ids + indgen(n_wers_pts) + ms_to_pts(wers_range[0],/wers)

for i=0,n_chan_ix-1 do $
  for j=0,n_scale_ix-1 do begin
    cycle = round(scales[scale_ix[j]]/period)
    ;cycle = round(0.5 * scales[scale_ix[j]]/period)
    ;cycle=1

    if (n_elements(thresh) EQ 1) then $
      if (keyword_set(neg)) then $
        for k=0,n_wers_pts-cycle do begin
          sel = data[pts_ix[k]:pts_ix[k]+cycle-1,scale_ix[j],chan_ix[i]] LE -(thresh)
          if (total(sel) EQ cycle) then $
            data2[pts_ix[k]:pts_ix[k]+cycle-1,scale_ix[j],chan_ix[i]] = data[pts_ix[k]:pts_ix[k]+cycle-1,scale_ix[j],chan_ix[i]]
        endfor $
      else $
        for k=0,n_wers_pts-cycle do begin
          sel = data[pts_ix[k]:pts_ix[k]+cycle-1,scale_ix[j],chan_ix[i]] GE thresh
          if (total(sel) EQ cycle) then $
            data2[pts_ix[k]:pts_ix[k]+cycle-1,scale_ix[j],chan_ix[i]] = data[pts_ix[k]:pts_ix[k]+cycle-1,scale_ix[j],chan_ix[i]]
        endfor $
    else $
      for k=0,n_wers_pts-cycle do begin
        sel_lo = data[pts_ix[k]:pts_ix[k]+cycle-1,scale_ix[j],chan_ix[i]] LE thresh[0]
        sel_hi = data[pts_ix[k]:pts_ix[k]+cycle-1,scale_ix[j],chan_ix[i]] GE thresh[1]
        if ((total(sel_lo) EQ cycle) OR (total(sel_hi) EQ cycle)) then $
          data2[pts_ix[k]:pts_ix[k]+cycle-1,scale_ix[j],chan_ix[i]] = data[pts_ix[k]:pts_ix[k]+cycle-1,scale_ix[j],chan_ix[i]]
      endfor
  endfor ;j

print, 'wers_thresh: done'
return, data2
end

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