A neural mass model for critical assessment of brain connectivity (Ursino et al 2020)

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Accession:263637
We use a neural mass model of interconnected regions of interest to simulate reliable neuroelectrical signals in the cortex. In particular, signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. Then we investigated whether bivariate Transfer Entropy (TE) can be used to detect a statistically significant connection from data (as in binary 0/1 networks), and even if connection strength can be quantified (i.e., the occurrence of a linear relationship between TE and connection strength). Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions. However, nonlinear phenomena dramatically affect the assessment of connectivity, since they may significantly reduce TE estimation. Software included here allows the simulation of neural mass models with a variable number of ROIs and connections, the estimation of TE using the free package Trentool, and the realization of figures to compare true connectivity with estimated values.
Reference:
1 . Ursino M, Ricci G, Magosso E (2020) Transfer Entropy as a Measure of Brain Connectivity: A Critical Analysis With the Help of Neural Mass Models Front Comput Neurosci . [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Neural mass; Connectionist Network; Synapse;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Neocortex layer 5 interneuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: MATLAB (web link to model); MATLAB; Trentool;
Model Concept(s): Brain Rhythms; Connectivity matrix; Delay;
Implementer(s): Ursino, Mauro [mauro.ursino at unibo.it]; Ricci, Giulia [Giulia.Ricci at unibo.it]; Magosso, Elisa [elisa.magosso at unibo.it];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; Gaba; Glutamate;
% 3 ROIs TE Calculation

clear all
close all
clc
 
ft_defaults;
load('sim_data_Fig5_0a')
OutputDataPath='TE_data_Fig5_0a';


% Input data in TRENTOOL
data=[];
ntrials=10;

%time indices for individual trials
data.time=cell(10,1);
for i=1:ntrials
    data.time{i,1}=tt;
end

% labels of channels
data.label=cell(3,1);
data.label{1,1}='X';
data.label{2,1}='Y';
data.label{3,1}='Z';

% data for each trial, where each trial holds samples from all channels
data.trial=cell(1,10);
for i=1:ntrials
chr=int2str(i);
eval(['data.trial{1,i}=eeg',chr,'']);
end

data.fsample=100;


%% define cfg for TEprepare
cfgTEP=[];
cfgTEP.toi=[min(data.time{1,1}),max(data.time{1,1})];
cfgTEP.channel=data.label;
cfgTEP.predicttime_u=15; 
cfgTEP.predicttimemax_u=18; 
cfgTEP.predicttimemin_u=12;  
cfgTEP.predicttimestepsize=1;

cfgTEP.TEcalctype='VW_ds';
cfgTEP.trialselect='no';
cfgTEP.minnrtrials=1;  
cfgTEP.actthrvalue=30; 

cfgTEP.optimizemethod='ragwitz';
cfgTEP.ragdim=4:8;  
cfgTEP.ragtaurange=[0.8 1.8];  
cfgTEP.ragtausteps=10;  
cfgTEP.flagNei='Mass';   
cfgTEP.sizeNei=4;
cfgTEP.repPred=100;

data_prepared=TEprepare(cfgTEP,data);


%% define cfg for TEsurrogatestats or InteractionDelayReconstruction_calculate
cfgTESS=[];
cfgTESS.optdimusage='indivdim';
cfgTESS.tail=1;
cfgTESS.surrogatetype='trialshuffling';
cfgTESS.extracond='Faes_Method';
cfgTESS.shifttest='no';
cfgTESS.MIcalc=1;
cfgTESS.fileidout=strcat(OutputDataPath);
 
TGA_results=InteractionDelayReconstruction_calculate(cfgTEP,cfgTESS,data);

%% Graph correction for multivariate effects
% define cfg for TEgraphanalysis
cfgGA=[];   
cfgGA.threshold=3;
cfgGA.cmc=1;
TGA_results=TEgraphanalysis(cfgGA,TGA_results);
save(OutputDataPath,'TGA_results');