RT Journal Article
SR Electronic
T1 Inter-Subject Alignment of MEG Datasets in a Common Representational Space
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 096040
DO 10.1101/096040
A1 Qiong Zhang
A1 Jelmer P. Borst
A1 Robert E. Kass
A1 John R. Anderson
YR 2017
UL http://biorxiv.org/content/early/2017/06/11/096040.abstract
AB Pooling neural imaging data across subjects requires aligning recordings from different subjects. In magnetoencephalography (MEG) recordings, sensors across subjects are poorly correlated both because of differences in the exact location of the sensors, and structural and functional differences in the brains. It is possible to achieve alignment by assuming that the same regions of different brains correspond across subjects. However, this relies on both the assumption that brain anatomy and function are well correlated, and the strong assumptions that go into solving the underdetermined inverse problem given the high dimensional source space. In this paper, we investigated an alternative method that bypasses source-localization. Instead, it analyzes the sensor recordings themselves and aligns their temporal signatures across subjects. We used a multivariate approach, multi-set canonical correlation analysis (M-CCA), to transform individual subject data to a low dimensional common representational space. We evaluated the robustness of this approach over a synthetic dataset, by examining the effect of different factors that add to the noise and individual differences in the data. On a MEG dataset, we demonstrated that M-CCA performs better than a method that assumes perfect sensor correspondence and a method that applies source localization. Lastly, we described how the standard M-CCA algorithm could be further improved with a regularization term that incorporates spatial sensor information.