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We test the hypothesis that interaction between the human basal ganglia and cerebral cortex involves activity in multiple functional circuits characterized by their frequency of oscillation, phase characteristics, dopamine dependency and topography. To this end we took recordings from macroelectrodes (MEs) inserted into the subthalamic nucleus (STN) in eight awake patients following functional neurosurgery for Parkinson's disease. An EEG was also recorded, as were the signals from MEs in the globus pallidus interna (GPi) in two of the cases. Coherence between EEG and ME potentials was apparent in three major frequency bands, 2-10 Hz, 10-30 Hz and 70-85 Hz. These rhythmic activities differed in their cortical topography, although coherence was always strongest over the midline. Coherence between EEG and ME potentials in the 70-85 Hz band was only recorded in patients treated with levodopa. Cortical activity phase led that in the basal ganglia in those oscillatory activities with frequencies <30 Hz. In contrast, STN and GPi phase led cortex in the 70-85 Hz band. The temporal differences in the way in which cortical activity led or lagged behind that in STN/GPi were similar, around 20 ms, regardless of the overall direction of information flow and frequency band. We conclude that the basal ganglia may receive multiple cortical inputs at frequencies <30 Hz and, in the presence of dopaminergic activity, produce a high frequency drive back to the cerebral cortex, in particular the supplementary motor area (SMA).

There is a wealth of data suggesting that behavioural events are reflected in the basal ganglia through phasic changes in the discharge of individual neurones. Here we investigate whether events are also reflected in momentary changes in the degree of synchronization between neuronal elements. We simultaneously recorded local potentials (LPs) from the subthalamic nucleus (STN) and/or ipsilateral globus pallidus interna (GPi) or scalp EEG during voluntary movements of a hand-held joystick in six awake patients following neurosurgery for Parkinson's disease. Without medication the power within the STN and the coherence between the STN and the GPi were dominated by activity with a frequency of <30 Hz. This coupling was attenuated by movement. In the presence of exogenous dopaminergic stimulation, power within the STN and coherence between the STN and the GPi was dominated by activity at 70-85 Hz, which increased with movement. The movement-related changes in coherence between the STN and EEG showed a similar pattern of pharmacological dependence, as seen subcortically. Movement-related frequency-specific changes in synchronization occur in the basal ganglia and extend to involve subcortico-cortical motor loops. The dynamic organization of activities in the frequency domain might provide a means for temporal co-ordination within and across different processing streams in the basal ganglia. This organization is critically dependent on the level of dopaminergic activity.

Background: Recent studies on the medical treatment of Parkinson's disease (PD) led to the introduction of the so called Deep Brain Stimulation (DBS) technique. This particular therapy allows to contrast actively the pathological activity of various Deep Brain structures, responsible for the well known PD symptoms. This technique, frequently joined to dopaminergic drugs administration, replaces the surgical interventions implemented to contrast the activity of specific brain nuclei, called Basal Ganglia (BG). This clinical protocol gave the possibility to analyse and inspect signals measured from the electrodes implanted into the deep brain regions. The analysis of these signals led to the possibility to study the PD as a specific case of dynamical synchronization in biological neural networks, with the advantage to apply the theoretical analysis developed in such scientific field to find efficient treatments to face with this important disease. Experimental results in fact show that the PD neurological diseases are characterized by a pathological signal synchronization in BG. Parkinsonian tremor, for example, is ascribed to be caused by neuron populations of the Thalamic and Striatal structures that undergo an abnormal synchronization. On the contrary, in normal conditions, the activity of the same neuron populations do not appear to be correlated and synchronized. Results: To study in details the effect of the stimulation signal on a pathological neural medium, efficient models of these neural structures were built, which are able to show, without any external input, the intrinsic properties of a pathological neural tissue, mimicking the BG synchronized dynamics. We start considering a model already introduced in the literature to investigate the effects of electrical stimulation on pathologically synchronized clusters of neurons. This model used Morris Lecar type neurons. This neuron model, although having a high level of biological plausibility, requires a large computational effort to simulate large scale networks. For this reason we considered a reduced order model, the Izhikevich one, which is computationally much lighter. The comparison between neural lattices built using both neuron models provided comparable results, both without traditional stimulation and in presence of all the stimulation protocols. This was a first result toward the study and simulation of the large scale neural networks involved in pathological dynamics. Using the reduced order model an inspection on the activity of two neural lattices was also carried out at the aim to analyze how the stimulation in one area could affect the dynamics in another area, like the usual medical treatment protocols require. The study of population dynamics that was carried out allowed us to investigate, through simulations, the positive effects of the stimulation signals in terms of desynchronization of the neural dynamics. Conclusions: The results obtained constitute a significant added value to the analysis of synchronization and desynchronization effects due to neural stimulation. This work gives the opportunity to more efficiently study the effect of stimulation in large scale yet computationally efficient neural networks. Results were compared both with the other mathematical models, using Morris Lecar and Izhikevich neurons, and with simulated Local Field Potentials (LFP). [ABSTRACT FROM AUTHOR]