Behavior and electrophysiological effects on striatum-nigra circuit after high frequency stimulation. Relevance to Parkinson and epilepsy.

The phenomenon of plasticity in the striatum, and its relation with the striatum-nigra neuronal circuit has clinical and neurophysiological relevance to Parkinson and epilepsy. High frequency stimulation (HFS) can induce neural plasticity. Furthermore, it is possible to induce plasticity in the dorsal striatum and this can be modulated by substantia nigra activity. But it has not been shown yet what would be the effects in the striatum-nigra circuit after plasticity induction in striatum with HSF. Literature also misses a detailed description of the way back loop of the circuit: the striatal firing rate after substantia nigrás inhibition. We here conducted: First Experiment, application of HFS in dorsomedial striatum and measure of spontaneous and longlasting behavior expression in the open field three days later; Second, application of single pulses on dorsomedial striatum and measure of the evoked potentials in substantia nigra before and after HFS; Third Experiment: inhibition of substantia nigra and recording of the firing rate of dorsomedial striatum. HFS in dorsomedial striatum caused increased locomotion behaviors, but not classical stereotypy. However, rats had either an increase or decrease in substantia nigrás evoked potentials. Also, substantia nigrás inhibition caused an increase in dorsomedial striatum firing rate. Present data are suggestive of a potential application of HFS in striatum, as an attempt to modulate behavior rigidity and hypokinesia of diseases involving the basal ganglia, especially Parkinson´s Disease.

Classification of Parkinson's disease motor phenotype: a machine learning approach.

To assess the cortical activity in people with Parkinson's disease (PwP) with different motor phenotype (tremor-dominant-TD and postural instability and gait difficulty-PIGD) and to compare with controls. Twenty-four PwP (during OFF and ON medication) and twelve age-/sex-/handedness-matched healthy controls underwent electrophysiological assessment of spectral ratio analysis through electroencephalography (EEG) at resting state and during the hand movement. We performed a machine learning method with 35 attributes extracted from EEG. To verify the efficiency of the proposed phenotype-based EEG classification the random forest and random tree were tested (performed 30 times, using a tenfolds cross validation in Weka environment). The analyses based on phenotypes indicated a slowing down of cortical activity during OFF medication state in PwP. PD with TD phenotype presented this characteristic at resting and the individuals with PIGD presented during the hand movement. During the ON state, there is no difference between phenotypes at resting nor during the hand movement. PD phenotypes may influence spectral activity measured by EEG. Random forest machine learning provides a slightly more accurate, sensible and specific approach to distinguish different PD phenotypes. The phenotype of PD might be a clinical characteristic that could influence cortical activity.