EEG correlates of spatial orientation in the human retrosplenial complex.

Studies on spatial navigation reliably demonstrate that the retrosplenial complex (RSC) plays a pivotal role for allocentric spatial information processing by transforming egocentric and allocentric spatial information into the respective other spatial reference frame (SRF). While more and more imaging studies investigate the role of the RSC in spatial tasks, high temporal resolution measures such as electroencephalography (EEG) are missing. To investigate the function of the RSC in spatial navigation with high temporal resolution we used EEG to analyze spectral perturbations during navigation based on allocentric and egocentric SRF. Participants performed a path integration task in a clearly structured virtual environment providing allothetic information. Continuous EEG recordings were decomposed by independent component analysis (ICA) with subsequent source reconstruction of independent time source series using equivalent dipole modeling. Time-frequency transformation was used to investigate reference frame-specific orientation processes during navigation as compared to a control condition with identical visual input but no orientation task. Our results demonstrate that navigation based on an egocentric reference frame recruited a network including the parietal, motor, and occipital cortices with dominant perturbations in the alpha band and theta modulation in frontal cortex. Allocentric navigation was accompanied by performance-related desynchronization of the 8-13 Hz frequency band and synchronization in the 12-14 Hz band in the RSC. The results support the claim that the retrosplenial complex is central to translating egocentric spatial information into allocentric reference frames. Modulations in different frequencies with different time courses in the RSC further provide first evidence of two distinct neural processes reflecting translation of spatial information based on distinct reference frames and the computation of heading changes.

Optimizing EEG ICA decomposition with data cleaning in stationary and mobile experiments.

Electroencephalography (EEG) studies increasingly utilize more mobile experimental protocols, leading to more and stronger artifacts in the recorded data. Independent Component Analysis (ICA) is commonly used to remove these artifacts. It is standard practice to remove artifactual samples before ICA to improve the decomposition, for example using automatic tools such as the sample rejection option of the AMICA algorithm. However, the effects of movement intensity and the strength of automatic sample rejection on ICA decomposition have not been systematically evaluated. We conducted AMICA decompositions on eight open-access datasets with varying degrees of motion intensity using varying sample rejection criteria. We evaluated decomposition quality using mutual information of the components, the proportion of brain, muscle, and 'other' components, residual variance, and an exemplary signal-to-noise ratio. Within individual studies, increased movement significantly decreased decomposition quality, though this effect was not found across different studies. Cleaning strength significantly improved the decomposition, but the effect was smaller than expected. Our results suggest that the AMICA algorithm is robust even with limited data cleaning. Moderate cleaning, such as 5 to 10 iterations of the AMICA sample rejection, is likely to improve the decomposition of most datasets, regardless of motion intensity.

The effect of mutual information on independent component analysis in EEG/MEG analysis: a simulation study.

OBJECTIVE: This study investigated the influence of mutual information (MI) on temporal and dipole reconstruction based on independent components (ICs) derived from independent component analysis (ICA). METHOD: Artificial electroencephalogram (EEG) datasets were created by means of a neural mass model simulating cortical activity of two neural sources within a four-shell spherical head model. Mutual information between neural sources was systematicallyvaried. RESULTS: Increasing spatial error for reconstructed locations of ICs with increasing MI was observed. By contrast, the reconstruction error for the time course of source activity was largely independent of MI but varied systematically with Gaussianity of the sources. CONCLUSION: Independent component analysis is a viable tool for analyzing the temporal activity of EEG/MEG (magnetoencephalography) sources even if the underlying neural sources are mutually dependent. However, if ICA is used as a preprocessing algorithm for source localization, mutual information between sources introduces a bias in the reconstructed locations of the sources. SIGNIFICANCE: Studies using ICA-algorithms based on MI have to be aware of possible errors in the spatial reconstruction of sources if these are coupled with other neural sources.

The neural basis of ego- and allocentric reference frames in spatial navigation: evidence from spatio-temporal coupled current density reconstruction.

Different strategies in spatial navigation during passages through computer-simulated tunnels were investigated by means of EEG source reconstruction. The tunnels consisted of straight and curved segments and provided only visual flow, but no landmark, information. At the end of each tunnel passage, subjects had to indicate their end position relative to the starting point of the tunnel. Even though the visual information was identical for all subjects, two different strategy groups were identified: one group using an egocentric and the other group an allocentric reference frame. The current density reconstruction revealed the use of one or the other reference frame to be associated with distinct cortical activation patterns during critical stages of the task. For both strategy groups, an occipito-temporal network was dominantly active during the initial, straight tunnel segment. With turns in the tunnel, however, the activation patterns started to diverge, reflecting translational and/or rotational changes in the underlying coordinate systems. Computation of an egocentric reference frame was associated with prevailing activity within a posterior parietal-premotor network, with additional activity in frontal areas. In contrast, computation of an allocentric reference frame was associated with dominant activity within an occipito-temporal network, confirming right-temporal structures to play a crucial role for an allocentric representation of space.