Biomedical Engineering Reference
In-Depth Information
We then computed the Hilbert envelopes of the reconstructed voxel time courses,
and computed the envelope-to-envelope coherence. The results of the envelope-to-
envelope coherence are shown in Fig.
7.5
b in which the top, middle, and bottom
panels show the magnitude, imaginary, and corrected imaginary coherence images,
respectively. The magnitude coherence image detects only the seed source. In the
imaginary coherence image, the interacting sources are not very clearly detected,
but in the corrected imaginary coherence image, the first and the third sources are
detectedwith reasonable clarity. In these results, the difference between the imaginary
coherence and corrected imaginary coherence images is significantly large because
the magnitude envelope coherence between the source time courses is as large as 0.7.
We computed the image of the envelope-to-envelope correlation, and the results
are shown in Fig.
7.6
. In this figure, the image of the envelope correlation is shown
in (a) and the image of the residual envelope correlation is in (b). In both images,
the first and the third sources that were interacting with the second (seed) source can
be observed. While the original envelope correlation image contains seed blur, the
residual envelope correlation image is free from such spurious activity.
We finally show the results of statistical thresholding method described in
Sect.
7.8
. The method was applied to the alpha-band coherence imaging results in
Fig.
7.3
. The thresholded images are shown in Fig.
7.7
. The spurious baseline activity
existing in the unthresholded images in Fig.
7.3
are removed, and it is much easier
to interpret the results in the thresholded images in Fig.
7.7
.
References
1. J.-M. Schoffelen, J. Gross, Source connectivity analysis with MEG and EEG. Hum. Brain
Mapp.
30
, 1857-1865 (2009)
2. J. Gross, J. Kujara, M. Hämäläinen, L. Timmermann, A. Schnitzler, R. Salmelin, Dynamic
imaging of coherent sources: studying neural interactions in the human brain. Proc. Natl.
Acad. Sci. U.S.A.
98
, 694-699 (2001)
3. A.G. Guggisberg, S.M. Honma, A.M. Findlay, S.S. Dalal, H.E. Kirsch, M.S. Berger, S.S.
Nagarajan, Mapping functional connectivity in patients with brain lesions. Ann. Neurol.
63
,
193-203 (2007)
4. P. Belardinelli, L. Ciancetta, M. Staudt, V. Pizzella, A. Londei, N.B.G.L. Romani, C. Braun,
Cerebro-muscular and cerebro-cerebral coherence in patients with pre- and perinatally acquired
unilateral brain lesions. NeuroImage
37
, 1301-1314 (2007)
5. W.H.R. Miltner, C. Braun, M. Arnold, H. Witte, E. Taub, Coherence of gamma-band EEG
activity as a basis for associative learning. Nature
397
, 434-436 (1999)
6. K. Sekihara, S.S. Nagarajan,
Adaptive Spatial Filters for Electromagnetic Brain Imaging
(Springer, Berlin, 2008)
7. K. Sekihara, J.P. Owen, S. Trisno, S.S. Nagarajan, Removal of spurious coherence in MEG
source-space coherence analysis. IEEE Trans. Biomed. Eng.
58
, 3121-3129 (2011)
8. G. Nolte, O.B.L. Wheaton, Z. Mari, S. Vorbach, M. Hallett, Identifying true brain interaction
from EEG data using the imaginary part of coherency. Clin. Neurophysiol.
115
, 2292-2307
(2004)
9. R.D. Pascual-Marqui, Instantaneous and lagged measurements of linear and nonlinear depen-
dence between groups of multivariate time series: frequency decomposition (2007). arXiv
preprint
arXiv:0711.1455
