Inter‐subject alignment of MEG datasets in a common representational space

• Published in: Human brain mapping Vol. 38; no. 9; pp. 4287 - 4301
• Main Authors: Zhang, Qiong, Borst, Jelmer P., Kass, Robert E., Anderson, John R.
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.09.2017; John Wiley and Sons Inc
• Subjects: Algorithms; Alignment; Anatomy; Brain; Brain - physiology; Brain Mapping - methods; Bypasses; canonical correlation analysis; common representational space; Computer Simulation; Correlation analysis; Humans; Inverse problems; Localization; Magnetoencephalography; Magnetoencephalography - methods; Multivariate Analysis; Neuroimaging; Position (location); Principal Component Analysis; Regularization; Sensors; Structure-function relationships; subject alignment; common representational space; canonical correlation analysis; magnetoencephalography; subject alignment
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.23689

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 under‐determined inverse problem given the high‐dimensional source space. In this article, 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, multiset 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 an MEG dataset, we demonstrated that M‐CCA performs better than a method that assumes perfect sensor correspondence and a method that applies source localization. Last, we described how the standard M‐CCA algorithm could be further improved with a regularization term that incorporates spatial sensor information. Hum Brain Mapp 38:4287–4301, 2017. © 2017 Wiley Periodicals, Inc.