Random fields—Union intersection tests for detecting functional connectivity in EEG/MEG imaging
Article first published online: 30 JAN 2009
Copyright © 2009 Wiley-Liss, Inc.
Human Brain Mapping
Volume 30, Issue 8, pages 2477–2486, August 2009
How to Cite
Carbonell, F., Worsley, K. J., Trujillo-Barreto, N. J. and Sotero, R. C. (2009), Random fields—Union intersection tests for detecting functional connectivity in EEG/MEG imaging. Hum. Brain Mapp., 30: 2477–2486. doi: 10.1002/hbm.20685
- Issue published online: 9 JUL 2009
- Article first published online: 30 JAN 2009
- Manuscript Accepted: 19 SEP 2008
- Manuscript Revised: 12 AUG 2008
- Manuscript Received: 31 MAR 2008
- random fields;
Electrophysiological (EEG/MEG) imaging challenges statistics by providing two views of the same underlying spatio-temporal brain activity: a topographic view (EEG/MEG) and tomographic view (EEG/MEG source reconstructions). It is a common practice that statistical parametric mapping (SPM) for these two situations is developed separately. In particular, assessing statistical significance of functional connectivity is a major challenge in these types of studies. This work introduces statistical tests for assessing simultaneously the significance of spatio-temporal correlation structure between ERP/ERF components as well as that of their generating sources. We introduce a greatest root statistic as the multivariate test statistic for detecting functional connectivity between two sets of EEG/MEG measurements at a given time instant. We use some new results in random field theory to solve the multiple comparisons problem resulting from the correlated test statistics at each time instant. In general, our approach using the union-intersection (UI) principle provides a framework for hypothesis testing about any linear combination of sensor data, which allows the analysis of the correlation structure of both topographic and tomographic views. The performance of the proposed method is illustrated with real ERP data obtained from a face recognition experiment. Hum Brain Mapp 2009. © 2009 Wiley-Liss, Inc.