Table 1, Virtual 10-10 Electrode Clusters. Each Cluster of GSN Electrodes Surrounds One

Table 1, Virtual 10-10 electrode clusters. Each cluster of GSN electrodes surrounds one virtual 10-10 electrode.

10-10 Virtual Electrode Cluster / GSN Electrode Numbers
OccipitalZ (Oz) / 72, 76, 77
Occipital1 (O1) / 66, 71, 72
Occipital2 (O2) / 77, 84, 85
InionZ (Iz) / 75, 76, 82, 83
Inion1 (I1) / 70, 71, 74, 75
Inion2 (I2) / 83, 84, 89, 90

`

Table 2. Anatomical labels from stereotaxic atlases for Regions of Interest (ROIs) for CDR source analysis.

Central occipital / Posterior middle-superior temporal lobe
Lobar / Lobar
Occipital pole / Middle and superior temporal, posterior part
Hammers / Hammers
66 / Cuneus left / 30 / Posterior temporal lobe left, middle and superior
67 / Cuneus right / 31 / Posterior temporal lobe right, middle and superior
LPBA / LPBA
67 / L Cuneus / 81 / L superior temporal gyrus
68 / R Cuneus / 82 / R superior temporal gyrus
Middle occipital / 83 / L middle temporal gyrus
84 / R middle temporal gyrus
LPBA / Posterior inferior temporal lobe
63 / L middle occipital gyrus
64 / R middle occipital gyrus / Lobar
Superior Occipital / Inferior temporal, posterior part
Hammers
LPBA / 30 / Posterior temporal lobe left, inferior
61 / L superior occipital gyrus / 31 / Posterior temporal lobe right, inferior
62 / R superior occipital gyrus / LPBA
Lateral inferior occipital lobe / 85 / L inferior temporal gyrus
86 / R inferior temporal gyrus
LPBA
65 / L inferior occipital gyrus, lateral part / Parahippocampalgyrus
66 / R inferior occipital gyrus, lateral part / Hammers
Medial inferior occipital lobe / 9 / Parahippocampal and ambient gyri right
10 / Parahippocampal and ambient gyri left
LPBA / LPBA
65 / L inferior occipital gyrus, medial part / 87 / L parahippocampalgyrus
66 / R inferior occipital gyrus, medial part / 88 / R parahippocampalgyrus
Lingual gyrus / Anterior fusiform gyrus
Hammers / Lobar
64 / Lingual gyrus left / Fusiform gyrus, anterior part
65 / Lingual gyrus right / Hammers:
LPBA / 15 / Lateral occipitotemporalgyrus right, anterior part
89 / L lingual gyrus / 16 / Lateral occipitotemporalgyrus left, anterior part
90 / R lingual gyrus / LPBA:
Parietal lobe / 91 / L fusiform gyrus, anterior part
92 / R fusiform gyrus, anterior part
Lobar / Middle fusiform gyrus
Parietal lobe
Hammers / Lobar
32 / Remainder of parietal lobe left / Fusiform gyrus, middle part
33 / Remainder of parietal lobe right / Hammers:
60 / Postcentralgyrus left / 15 / Lateral occipitotemporalgyrus right, middle part
61 / Postcentralgyrus right / 16 / Lateral occipitotemporalgyrus left, middle part
62 / Superior parietal gyrus left / LPBA
63 / Superior parietal gyrus right / 91 / L fusiform gyrus, middle part
LPBA / 92 / R fusiform gyrus, middle part
41 / L_postcentral_gyrus / Temporal pole
42 / R_postcentral_gyrus
43 / L_superior_parietal_gyrus / Hammers
44 / R_superior_parietal_gyrus / 5 / Anterior temporal lobe, medial part right
45 / L_supramarginal_gyrus / 6 / Anterior temporal lobe, medial part left
46 / R_supramarginal_gyrus / 7 / Anterior temporal lobe, lateral part right
47 / L_angular_gyrus / 8 / Anterior temporal lobe, lateral part left
48 / R_angular_gyrus / LPBA
49 / L_precuneus / 82 / Superior temporal gyrus, anterior part left
50 / R_precuneus / 83 / Superior temporal gyrus, anterior part right

Table3. Fieldtrip Macros

MRI Information structure

mrinumber: 257
ac: [79 92 98]
pc: [78 81 98]
vertex: [80 96 167]
nas: [80 161 77]
lpa: [20 95 54]
rpa: [132 84 53]
dim: [147 170 176]
hdr: [1x1 struct]
fiducials: [14x3 double]
Electrode structure

elecpos: [126x3 double]
chanpos: [126x3 double]
unit: 'mm'
label: {126x1 cell}

Creating the GM+Eyes source MRI

%%
%sourcespace
% Configuration options for cortex segmentation, i.e. for placing dipoles in grey matter
% cfg.mri = can be filename, MRI structure or segmented MRI structure
% cfg.sourceunits = 'auto' (in which case the sourceunits default to the unit in the
% sensor description, if provided). otherwise it defaults to 'cm'
% cfg.threshold = 0.1, relative to the maximum value in the segmentation
% cfg.smooth = 5, smoothing in voxels
mri=[];
mri.dim=mriinfo.dim;
mri.unit='mm';
mri.transform=diag([1 1 1 1]);
mri.coordsys='spm';
%gray matter PVE, mrigm.img and eyes, mrieyes.img
mri.gray=double(mrigmi.mg) + double(mrieyes.img);

Realign MRI volumes to AC-defined space

cfg=[];

cfg.method='fiducial';

cfg.landmark.ac=mriinfo.ac;

cfg.landmark.pc=mriinfo.pc;

cfg.landmark.xzpoint=mriinfo.vertex;

cfg.landmark.right=mriinfo.rpa;

cfg.coordsys='spm';

cfg.parameter='gray';

mri=ft_volumerealign(cfg,mri);

Create the source model grid

cfg=[];

cfg.mri=mri;

cfg.threshold=.25;

cfg.smooth='no';

cfg.elec=elec;

cfg.grid.resolution=mmresol; %1 mm or 3 mm

cfg.grid.unit='mm';

grid = ft_prepare_sourcemodel(cfg,mri);

grid=ft_determine_coordsys(grid,'interactive','no')

Create the 4-compartment BEM MRI
mribem.img is a 4-compartment BEM model, either for participant or average MRI template

%%
mri=[];
mri.dim=[mribem.xdimmribem.ydimmribem.zdim];
mri.unit='mm';
mri.transform=diag([1 1 1 1]);
mri.coordsys='mni';
mri.tissuelabel={'gray','csf','skull','scalp'};
tempimg=double(mribem.img)*0;
for i=1:length(mri.tissuelabel);
tempmri = mribem.threshold(i,i);
tempimg = tempimg+double(tempmri.img);
tempimg (tempimg ~= 0 ) = 1;
mri.(mri.tissuelabel{i}) = double(tempimg);
end

Create a compartment volume mesh

cfg = [];

cfg.method = 'projectmesh';

cfg.tissue = 'gray';

cfg.numvertices = 2000;

mesh = ft_prepare_mesh(cfg, mri);

Prepare the BEM-DIPOLI head model

cfg = [];

cfg.feedback = false;

cfg.method = 'dipoli';

cfg.isolatedsource ='no';

cfg.conductivity =[conductivity(6) conductivity(5) conductivity(3) conductivity(2)]

cfg.tissue ={'scalp','skull','csf','gray'};

vol = ft_prepare_headmodel(cfg,mesh);

Create the FEM MRI

mri=[];
mri.dim=mriinfo.dim;
mri.unit='mm';
mri.transform=diag([1 1 1 1]);
mri.coordsys='spm';
if strfind(mridata.filename,'nma')
mri.tissuelabel={'wm','gm','t2wcsf','dura','skull','scalp','head','eyes','nasalcavity','nma_pve'};
else
mri.tissuelabel={'wm','gm','t2wcsf','dura','skull','scalp','head','eyes','nasalcavity'};
end
%{
#echo const DWORD WHITEMATTER=1
#echo const DWORD GRAYMATTER=2
#echo const DWORD CSF=3
#echo const DWORD DURA=4
#echo const DWORD SKULL=5
#echo const DWORD BONE=SKULL
#echo const DWORD SKIN=6
#echo const DWORD SCALP=SKIN
#echo const DWORD MUSCLE=7
#echo const DWORD EYES=8
#echo const DWORD NASALCAVITY=9
#echo const DWORD NONMYELINATEDAXON=10
%}
for i=1:length(mri.tissuelabel);
tempmri = mridata.threshold(i,i);
tempimg = double(tempmri.img);
tempimg (tempimg ~= 0 ) = 1;
mri.(mri.tissuelabel{i}) = double(tempimg);
end

Prepare the FEM mesh

cfg = [];

cfg.shift = 0.3;

cfg.method = 'hexahedral';

mesh = ft_prepare_mesh(cfg,mri);

Prepate the BEM-SIMBIO head model

cfg = [];

cfg.method ='simbio';

cfg.conductivity=conductivity;

vol = ft_prepare_headmodel(cfg, mesh);

Prepare the lead-field matrix

cfg=[]

cfg.vol=vol;

cfg.elec=elec;

cfg.grid=grid;

cfg.normalize='no';

lf=ft_prepare_leadfield(cfg);

MNE Source reconstruction

cfg=[];

cfg.method='mne';

cfg.vol=vol;

cfg.elec=elec;

cfg.grid=lf;

cfg.projectnoise='yes';

cfg.keepcsd='yes';

cfg.mne.projectnoise='yes';

cfg.mne.keepcsd='yes';

cfg.mne.lambda=.000001;;

cfg.mne.prewhiten='no';

sourcemne=ft_sourceanalysis(cfg,ERPdata);

eLORETA source reconstruction

cfg=[];

cfg.method='eloreta';

cfg.vol=vol;

cfg.elec=elec;

cfg.grid=lf;

cfg.projectnoise='yes';

cfg.keepcsd='yes';

cfg.eloreta.projectnoise='yes';

cfg.eloreta.keepcsd='yes';

cfg.eloreta.keepmom='yes';

cfg.eloreta.lambda=1e-5;

cfg.lambda=cfg.eloreta.lambda;

sourceft=ft_sourceanalysis(cfg,centermean);