[FieldTrip] Beamforming on resting state data
Tobias Navarro Schroeder
tobiasnavarroschroeder at googlemail.com
Mon May 30 17:58:32 CEST 2011
I performed DICS beamforming on resting-state data (i.e. no task, no
baseline, eyes closed) of a clinical population and controls.
According to the sensor data the patients have more gamma-band (60-90
Hz) activity over temporal areas (somewhat spread-out).
Curiously, after beamforming and group comparisons with 'indepsamplesT'
permutation statistics the difference map shows more gamma-band (60-90
Hz) activity for controls in the left, middle-temporal gyrus.
Hence, the source-level results are opposite to the sensor-level results
(although more spatially confined). This is *not* a problem of the design
matrix, or confusing the groups.
Question 1: Does anybody have experience with opposite results on source and
The individual raw beamforming results (interpolated on the subjects' MRIs)
look suspicios as well. Due to the 'unit gain constraint' there should be an
overestimation of power in the center of the head, but only a minority of
subjects show that. Often I see temporal or parietal activity.
Would a lack of a blob in the center of the head in individual,
raw, beamforming data indicate an erroneous source analysis?
Thanks a lot for help and suggestions.
Best, Tobias Navarro Schröder
ps: The configurations for calculating the covariance and the beamforming is
1) % Compute the covariance matrices
cfg.foi = 75;
cfg.foilim = [75 75];
cfg.tapsmofrq = 15; % 75 +- 15 = 60 - 90 Hz
'maxperlen'; % default
'wltconvol'; % wavelet analysis
cfg.width = 5;
% Width of the wavelet. Determines the temporal and spectral resolution of
the analysis constant.
2) % Perform the actual beamforming
% load grids of MRI volume
fullnameGrid = [ CurSubject.mripath CurSubject.MRIModel];
cfg = load(fullnameGrid);
cfg.vol = cfg.hdm;
cfg.frequency = 75;
'yes'; % keep the beamformer filters (virtual electrodes) for later use
'yes'; % useful for connectivity calculations. Dipole moments..
cfg.latency = 1;
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