What can be found in scalp EEG spectrum beyond common frequency bands. EEG-fMRI study

OBJECTIVE

The scalp EEG spectrum is a frequently used marker of neural activity. Commonly, the preprocessing of EEG utilizes constraints, e.g. dealing with a predefined subset of electrodes or a predefined frequency band of interest. Such treatment of the EEG spectrum neglects the fact that particular neural processes may be reflected in several frequency bands and/or several electrodes concurrently, and can overlook the complexity of the structure of the EEG spectrum.

APPROACH

We showed that the EEG spectrum structure can be described by parallel factor analysis (PARAFAC), a method which blindly uncovers the spatial-temporal-spectral patterns of EEG. We used an algorithm based on variational Bayesian statistics to reveal nine patterns from the EEG of 38 healthy subjects, acquired during a semantic decision task. The patterns reflected neural activity synchronized across theta, alpha, beta and gamma bands and spread over many electrodes, as well as various EEG artifacts.

MAIN RESULTS

Specifically, one of the patterns showed significant correlation with the stimuli timing. The correlation was higher when compared to commonly used models of neural activity (power fluctuations in distinct frequency band averaged across a subset of electrodes) and we found significantly correlated hemodynamic fluctuations in simultaneously acquired fMRI data in regions known to be involved in speech processing. Further, we show that the pattern also occurs in EEG data which were acquired outside the MR machine. Two other patterns reflected brain rhythms linked to the attentional and basal ganglia large scale networks. The other patterns were related to various EEG artifacts.

SIGNIFICANCE

These results show that PARAFAC blindly identifies neural activity in the EEG spectrum and that it naturally handles the correlations among frequency bands and electrodes. We conclude that PARAFAC seems to be a powerful tool for analysis of the EEG spectrum and might bring novel insight to the relationships between EEG activity and brain hemodynamics.

Functional neuroanatomy of vocalization in patients with Parkinson's disease

In Parkinson's disease (PD) both speech production and self-monitoring of voiced speech are altered.

METHODS

In our previous study we used functional magnetic resonance imaging (fMRI) to examine which brain areas are involved in overt reading in nine female PD patients (mean age 66.0 ± 11.6 years) compared with eight age-matched healthy female controls (mean age 62.2 years ± 12.3). Here we performed the post-hoc seed-based functional connectivity analysis of our data to assess the functional connectivity between the periaqueductal gray matter (PAG; i.e. the core subcortical structure involved in human vocalization) and other brain regions in the same groups of PD patients and controls.

RESULTS

In PD patients as compared with controls we observed increased connectivity between PAG and basal ganglia, posterior superior temporal gyrus, supramarginal and fusiform gyri and inferior parietal lobule on the right side. In the PD group, the connectivity strength in the right putamen and the right sypramarginal gyrus was correlated with variability of pitch while the connectivity strength in the right posterior superior temporal gyrus and in the right inferior parietal lobule was correlated with speech loudness.

CONCLUSION

We observed functional reorganization in PD patients as compared with controls in both the motor basal ganglia-thalamo-cortical circuitry and cortical areas known to be engaged in-auditory and somatosensory feedback control of voiced speech. These changes were hemisphere-specific and might either reflect effects of dopaminergic treatment or at least partially successful compensatory mechanisms involved in early-stage PD.