theta, beta But not alpha-band EEG connectivity has implications for dual task performance in Parkinson's disease.

People with Parkinson's disease (PD) have difficulty performing dual tasks or simultaneous movements, even if the same movements can be easily performed individually. This has particular significance clinically, as for example falling injuries may occur if care is not taken to perform tasks one at a time. We investigated whether this difficultyx results from impaired dopamine-modulated connectivity. We recorded the EEG in PD subjects off and on l-dopa medication performing simultaneous and unimanual tracking tasks. To deal with the inherent non-stationarity of the EEG during motor tasks, we segmented the data into task-related sections based on transient synchronisation between independent components of the data, before assessing the mutual information (MI) between each EEG channel pair. In both tasks, PD subjects off-medication demonstrated enhanced fronto-central and decreased occipital synchronisation within theta and alpha bands, and widespread increased beta-band synchronisation, compared to controls. Synchronisation changes in theta and beta bands were partially normalised by l-dopa, but l-dopa had relatively little effect on alpha band synchronisation. When comparing simultaneous movements to unimanual tracking, PD subjects off-medication demonstrated synchronisation changes within theta and beta bands, however alpha connectivity was largely unchanged. These results suggest that downstream influences of impaired basal ganglia function on cortico-cortical connectivity may result in difficulties with dual task performance in PD.

A novel segmentation, mutual information network framework for EEG analysis of motor tasks.

BACKGROUND: Monitoring the functional connectivity between brain regions is becoming increasingly important in elucidating brain functionality in normal and disease states. Current methods of detecting networks in the recorded electroencephalogram (EEG) such as correlation and coherence are limited by the fact that they assume stationarity of the relationship between channels, and rely on linear dependencies. In contrast to diseases of the brain cortex (e.g. Alzheimer's disease), with motor disorders such as Parkinson's disease (PD) the EEG abnormalities are most apparent during performance of dynamic motor tasks, but this makes the stationarity assumption untenable. METHODS: We therefore propose a novel EEG segmentation method based on the temporal dynamics of the cross-spectrogram of the computed Independent Components (ICs). We then utilize mutual information (MI) as the metric for determining also nonlinear statistical dependencies between EEG channels. Graphical theoretical analysis is then applied to the derived MI networks. The method was applied to EEG data recorded from six normal subjects and seven PD subjects off medication. One-way analysis of variance (ANOVA) tests demonstrated statistically significant difference in the connectivity patterns between groups. RESULTS: The results suggested that PD subjects are unable to independently recruit different areas of the brain while performing simultaneous tasks compared to individual tasks, but instead they attempt to recruit disparate clusters of synchronous activity to maintain behavioral performance. CONCLUSION: The proposed segmentation/MI network method appears to be a promising approach for analyzing the EEG recorded during dynamic behaviors.