Cerebello-thalamo-cortical loops play a major role in the emergence of pathological tremors and voluntary rhythmic movements. It is unclear whether these loops differ anatomically or functionally in different types of tremor. We compared age- and sex-matched groups of patients with Parkinson's disease or essential tremor and healthy controls (n = 34 per group). High-density 256-channel EEG and multi-channel EMG from extensor and flexor muscles of both wrists were recorded simultaneously while extending the hands against gravity with the forearms supported. Tremor was thereby recorded from patients, and voluntarily mimicked tremor was recorded from healthy controls. Tomographic maps of EEG-EMG coherence were constructed using a beamformer algorithm coherent source analysis. The direction and strength of information flow between different coherent sources were estimated using time-resolved partial-directed coherence analyses. Tremor severity and motor performance measures were correlated with connection strengths between coherent sources. The topography of oscillatory coherent sources in the cerebellum differed significantly among the three groups, but the cortical sources in the primary sensorimotor region and premotor cortex were not significantly different. The cerebellar and cortical source combinations matched well with known cerebello-thalamo-cortical connections derived from functional MRI resting state analyses according to the Buckner-atlas. The cerebellar sources for Parkinson's tremor and essential tremor mapped primarily to primary sensorimotor cortex, but the cerebellar source for mimicked tremor mapped primarily to premotor cortex. Time-resolved partial-directed coherence analyses revealed activity flow mainly from cerebellum to sensorimotor cortex in Parkinson's tremor and essential tremor and mainly from cerebral cortex to cerebellum in mimicked tremor. EMG oscillation flowed mainly to the cerebellum in mimicked tremor, but oscillation flowed mainly from the cerebellum to EMG in Parkinson's and essential tremor. The topography of cerebellar involvement differed among Parkinson's, essential and mimicked tremors, suggesting different cerebellar mechanisms in tremorogenesis. Indistinguishable areas of sensorimotor cortex and premotor cerebral cortex were involved in all three tremors. Information flow analyses suggest that sensory feedback and cortical efferent copy input to cerebellum are needed to produce mimicked tremor, but tremor in Parkinson's disease and essential tremor do not depend on these mechanisms. Despite the subtle differences in cerebellar source topography, we found no evidence that the cerebellum is the source of oscillation in essential tremor or that the cortico-bulbo-cerebello-thalamocortical loop plays different tremorogenic roles in Parkinson's and essential tremor. Additional studies are needed to decipher the seemingly subtle differences in cerebellocortical function in Parkinson's and essential tremors.

Electroencephalography (EEG) and magnetoencephalography (MEG) are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2–4 Hz) and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS) with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC). MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR) level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT) task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG.

We show that the oscillating cerebral networks underlying classical essential tremor and aging-related tremor differ, suggesting a pathophysiological difference.

At the sensor level many aspects, such as spectral power, functional and effective connectivity as well as relative-power-ratio ratio (RPR) and spatial resolution have been comprehensively investigated through both electroencephalography (EEG) and magnetoencephalography (MEG). Despite this, differences between both modalities have not yet been systematically studied by direct comparison. It remains an open question as to whether the integration of EEG and MEG data would improve the information obtained from the above mentioned parameters. Here, EEG (64-channel system) and MEG (275 sensor system) were recorded simultaneously in conditions with eyes open (EO) and eyes closed (EC) in 29 healthy adults. Spectral power, functional and effective connectivity, RPR, and spatial resolution were analyzed at five different frequency bands (delta, theta, alpha, beta and gamma). Networks of functional and effective connectivity were described using a spatial filter approach called the dynamic imaging of coherent sources (DICS) followed by the renormalized partial directed coherence (RPDC). Absolute mean power at the sensor level was significantly higher in EEG than in MEG data in both EO and EC conditions. At the source level, there was a trend towards a better performance of the combined EEG+MEG analysis compared with separate EEG or MEG analyses for the source mean power, functional correlation, effective connectivity for both EO and EC. The network of coherent sources and the spatial resolution were similar for both the EEG and MEG data if they were analyzed separately. Results indicate that the combined approach has several advantages over the separate analyses of both EEG and MEG. Moreover, by a direct comparison of EEG and MEG, EEG was characterized by significantly higher values in all measured parameters in both sensor and source level. All the above conclusions are specific to the resting state task and the specific analysis used in this study to have general conclusion multi-center studies would be helpful.

SUMMARYObjective: The aim of our study was to investigate the neuronal networks underlying background oscillations of epileptic encephalopathy with continuous spikes and waves during slow sleep (CSWS). Methods: Sleep electroencephalography (EEG) studies before and after the treatment were investigated in 15 patients with CSWS. To investigate functional and effective connectivity within the network generating the delta activity in the background sleep EEG, the methods of dynamic imaging of coherent sources (DICS) and renormalized partial directed coherence (RPDC) were applied. Results: Independent of etiology and severity of epilepsy, background EEG pattern in patients with CSWS before treatment is associated with the complex network of coherent sources in medial prefrontal cortex, somatosensory association cortex/posterior cingulate cortex, medial prefrontal cortex, middle temporal gyrus/parahippocampal gyrus/insular cortex, thalamus, and cerebellum. The analysis of information flow within this network revealed that the medial parietal cortex, the precuneus, and the thalamus act as central hubs, driving the information flow to other areas, especially to the temporal and frontal cortex. The described CSWS-specific pattern was no longer observed in patients with normalized sleep EEG. In addition, frequency of spiking showed a strong linear correlations with absolute source power, source coherence strength, and source RPDC strength at both time points: (1) Spike and wave index (SWI) versus absolute source power at EEG1 (r = 0.56; p = 0.008) and at EEG2 (r = 0.45; p = 0.009); (2) SWI versus source coherence strength at EEG1 (r = 0.71; p = 0.005) and at EEG2 (r = 0.52; p = 0.006); and (3) SWI versus source RPDC strength at EEG1 (r = 0.65; p = 0.003) and at EEG2 (r = 0.47; p = 0.009). Significance: The leading role of the precuneus and thalamus in the hierarchical organization of the network underlying the background EEG points toward the significance of fluctuations of vigilance in the generation of CSWS. This hierarchical network organization appears to be specific for CSWS as it is resolved after successful treatment.