In Parkinson's disease (PD), the oscillatory activity recorded from the basal ganglia shows dopamine-dependent changes. In the “off” parkinsonian motor state, there is prominent activity in the beta band (12–30 Hz) that is mostly attenuated after dopaminergic therapy (“on” medication state). The on state is also characterized by activity in the gamma (60–80 Hz) and high-frequency (300 Hz) bands that is modulated by movement. We recorded local field potentials from a group of 15 PD patients (three females) treated with bilateral deep brain stimulation of the subthalamic nucleus, using a high sampling rate (2 kHz) and filters suitable to study high-frequency activity (0.3–1000 Hz). We observed high-frequency oscillations (HFOs) in both the off and on motor states. In the off state, the amplitude of the HFOs was coupled to the phase of the abnormal beta activity. The beta-coupled HFOs showed little or even negative movement-related changes in amplitude. Moreover, the degree of movement-related modulation of the HFOs correlated negatively with the rigidity/bradykinesia scores. In the on motor state, the HFOs were liberated from this beta coupling, and they displayed marked movement-related amplitude modulation. Cross-frequency interactions between the phase of slow activities and the amplitude of fast frequencies have been attributed an important role in information processing in cortical structures. Our findings suggest that nonlinear coupling between frequencies may not only be a physiological mechanism (as shown previously) but also that it may participate in the pathophysiology of parkinsonism.

We study a Kuramoto model in which the oscillators are associated with the nodes of a complex network and the interactions include a phase frustration, thus preventing full synchronization. The system organizes into a regime of remote synchronization where pairs of nodes with the same network symmetry are fully synchronized, despite their distance on the graph. We provide analytical arguments to explain this result, and we show how the frustration parameter affects the distribution of phases. An application to brain networks suggests that anatomical symmetry plays a role in neural synchronization by determining correlated functional modules across distant locations.

The pathophysiology of levodopa-induced dyskinesias (LID) in Parkinson's disease is not well understood. We have recorded local field potentials (LFP) from macroelectrodes implanted in the subthalamic nucleus (STN) of 14 patients with Parkinson's disease following surgical treatment with deep brain stimulation. Patients were studied in the ‘Off’ medication state and in the ‘On’ motor state after administration of levodopa–carbidopa (po) or apomorphine (sc) that elicited dyskinesias in 11 patients. The logarithm of the power spectrum of the LFP in selected frequency bands (4–10, 11–30 and 60–80 Hz) was compared between the ‘Off’ and ‘On’ medication states. A peak in the 11–30 Hz band was recorded in the ‘Off’ medication state and reduced by 45.2% (P < 0.001) in the ‘On’ state. The ‘On’ was also associated with an increment of 77. 6% (P < 0.001) in the 4–10 Hz band in all patients who showed dyskinesias and of 17.8% (P < 0.001) in the 60–80 Hz band in the majority of patients. When dyskinesias were only present in one limb (n = 2), the 4–10 Hz peak was only recorded in the contralateral STN. These findings suggest that the 4–10 Hz oscillation is associated with the expression of LID in Parkinson's disease.

Independent component analysis (ICA) is a novel technique that calculates independent components from mixed signals. A hypothetical clinical application is to remove artifacts in EEG. The goal of this study was to apply ICA to standard EEG recordings to eliminate well-known artifacts, thus quantifying its efficacy in an objective way. Eighty samples of recordings with spikes and evident artifacts of electrocardiogram (EKG), eye movements, 50-Hz interference, muscle, or electrode artifact were studied. ICA components were calculated using the Joint Approximate Diagonalization of Eigen-matrices (JADE) algorithm. The signal was reconstructed excluding those components related to the artifacts. A normalized correlation coefficient was used as a measure of the changes caused by the suppression of these components. ICA produced an evident clearing-up of signals in all the samples. The morphology and the topography of the spike were very similar before and after the removal of the artifacts. The correlation coefficient showed that the rest of the signal did not change significantly. Two examiners independently looked at the samples to identify the changes in the morphology and location of the discharge and the artifacts. In conclusion, ICA proved to be a useful tool to clean artifacts in short EEG samples, without having the disadvantages associated with the digital filters. The distortion of the interictal activity measured by correlation analysis was minimal.

We analyze the connectivity structure of weighted brain networks extracted from spontaneous magnetoencephalographic signals of healthy subjects and epileptic patients (suffering from absence seizures) recorded at rest. We find that, for the activities in the 5\char21{}14~Hz range, healthy brains exhibit a sparse connectivity, whereas the brain networks of patients display a rich connectivity with a clear modular structure. Our results suggest that modularity plays a key role in the functional organization of brain areas during normal and pathological neural activities at rest.

Flicker stimuli of variable frequency (2-90 Hz) elicit a steady-state visual-evoked response (SSVER) in the electroencephalogram (EEG) with the same frequency as the stimulus. In humans, the amplitude of this response peaks at ∼15 Hz, decreasing at higher stimulation frequencies. It was not known whether this peak response corresponds to increased synaptic activity in the visual cortex or to other mechanisms [for instance, the temporal coherence (phase summation) of evoked responses]. We studied the SSVER in 16 normal volunteers by means of visual stimulation at 14 different frequencies (from 5 to 60 Hz) while recording the EEG. In nine subjects of the group, we measured regional cerebral blood flow (rCBF) with positron emission tomography (PET)-H₂¹⁵O at rest and during visual stimulation at five different frequencies: 5, 10, 15, 25, and 40 Hz. We confirmed that the amplitude of the SSVER in occipital regions peaks at 15 Hz stimulation. Applying to the PET rCBF data a contrast weighted by the amplitude of the SSVER, we determined that the primary visual cortex rCBF follows an activation pattern similar to the SSVER. This finding suggests that the amplitude of the SSVER corresponds to increased synaptic activity, specifically in Brodmann9s area 17. Additionally, this study showed that visual stimulation at 40 Hz causes selective activation of the macular region of the visual cortex, and that a region in the dorsal aspect of the Crus I lobule of the left cerebellar hemisphere is activated during repetitive visual stimulation.