Random fields-Union intersection tests for detecting functional connectivity in EEG/MEG imaging
| Autor: | Roberto C. Sotero, Felix Carbonell, Nelson J. Trujillo-Barreto, Keith J. Worsley |
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| Rok vydání: | 2009 |
| Předmět: |
Time Factors
Computer science Electroencephalography Statistical parametric mapping Facial recognition system medicine Humans Radiology Nuclear Medicine and imaging Evoked Potentials Research Articles Statistic Statistical hypothesis testing Brain Mapping Random field Radiological and Ultrasound Technology medicine.diagnostic_test business.industry Brain Magnetoencephalography Signal Processing Computer-Assisted Pattern recognition Pattern Recognition Visual Neurology Face Multivariate Analysis Multiple comparisons problem Neurology (clinical) Artificial intelligence Anatomy business Neuroscience Algorithms |
| Zdroj: | Hum Brain Mapp |
| ISSN: | 1097-0193 1065-9471 |
| DOI: | 10.1002/hbm.20685 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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