However, these parcellations are agnostic to variations of the effects of interest within the ROI. In many situations the ROIs are selected using existing anatomically and functionally defined parcellations. This approach relies on a robust estimate of activity in the ROIs and may not be applicable when weak cognitive effects are investigated. In conventional MEG or EEG studies, these ROIs also need to be anatomically small in order to reduce temporal signal cancellations. In functional connectivity analysis, such ROIs are either selected with help of source localization or manually delineated for each individual based on specific criteria, such as task dependent modulation in activity e.g. In addition, searching within the whole brain may, on the one hand, increase the risk of false positives and, on the other hand, reduce the sensitivity for weaker but functionally relevant connections.Īn alternative approach is to focus on particular regions in the brain based on prior hypothesis. In both of the aforementioned approaches, one has to correct for a massive number of comparisons in order to control the family-wise error rates (FWER). In all-to-all parcel comparisons, a simplified strategy is often taken by averaging the functional connectivity estimates within certain time and frequency bands such as alpha (8–12 Hz) or beta (13–30 Hz), but this results in the loss of temporal and spectral specificity. However, this approach provides sufficient statistical power for detecting differences across the experimental manipulations of interest only when the effect sizes are very large and the clusters are spatially continuous across the cerebral cortex. In the former, cluster-based statistics is used to find differences between experimental conditions in time, frequency, and space 9, 14, 15. Depending on the hypothesis and the experiment, the functional connectivity is assessed either from a seed region to the rest of the brain 7, 8, 9 or between all atlas-based (cortical) parcels 10, 11, 12, 13. This challenge is particularly pronounced in functional connectivity analysis. However, within this multifaceted data, finding the modulation associated with a particular effect or contrast of interest in the brain can be challenging. Typically, neuroscience studies relate particular variation in the brain such as source activation, inter-regional functional connectivity, or oscillatory power to certain experimental paradigm or behavioral measures. The open-source Matlab code implementing PeSCAR are provided online.ĮEG and MEG are ideal techniques to non-invasively measure brain activity with high temporal-spectral and reasonable spatial resolution 1, 2, 3, 4, 5, 6. We demonstrate the processing steps with simulated and real human data. We call this new approach the Permutation Statistics for Connectivity Analysis between ROI (PeSCAR). This results in the ability to identify statistically significant connectivity patterns with spectral, temporal, and spatial specificity while correcting for multiple comparisons using nonparametric permutation methods. Here, we propose a novel approach, which enhances the statistical power for weak and spatially discontinuous effects. However, if an effect is distributed unevenly over a large ROI and variable across subjects, it may not be detectable using conventional methods. Many MEG/EEG studies address this complexity by using a hypothesis-driven approach, which focuses on particular regions of interest (ROI). This multi-dimensional aspect of the MEG/EEG based connectivity increases the challenges of the analysis and interpretation of the data. Connectivity estimates based on electroencephalography (EEG) and magnetoencephalography (MEG) are unique in their ability to provide neurophysiologically meaningful spectral and temporal information non-invasively.
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