Parkinsonism-related β oscillations in the primate basal ganglia networks – Recent advances and clinical implications

Globus pallidus externus drives increase in network-wide alpha power with propofol-induced loss-of-consciousness in humans

States of consciousness are likely mediated by multiple parallel yet interacting cortico-subcortical recurrent networks. Although the mesocircuit model has implicated the pallidocortical circuit as one such network, this circuit has not been extensively evaluated to identify network-level electrophysiological changes related to loss of consciousness (LOC). We characterize changes in the mesocircuit in awake versus propofol-induced LOC in humans by directly simultaneously recording from sensorimotor cortices (S1/M1) and globus pallidus interna and externa (GPi/GPe) in 12 patients with Parkinson disease undergoing deep brain stimulator implantation. Propofol-induced LOC is associated with increases in local power up to 20 Hz in GPi, 35 Hz in GPe, and 100 Hz in S1/M1. LOC is likewise marked by increased pallidocortical alpha synchrony across all nodes, with increased alpha/low beta Granger causal (GC) flow from GPe to all other nodes. In contrast, LOC is associated with decreased network-wide beta coupling and beta GC from M1 to the rest of the network. Results implicate an important and possibly central role of GPe in mediating LOC-related increases in alpha power, supporting a significant role of the GPe in modulating cortico-subcortical circuits for consciousness. Simultaneous LOC-related suppression of beta synchrony highlights that distinct oscillatory frequencies act independently, conveying unique network activity.

Detecting rhythmic spiking through the power spectra of point process model residuals

Objective

Oscillations figure prominently as neurological disease hallmarks and neuromodulation targets. To detect oscillations in a neuron's spiking, one might attempt to seek peaks in the spike train's power spectral density (PSD) which exceed a flat baseline. Yet for a non-oscillating neuron, the PSD is not flat: The recovery period ("RP", the post-spike drop in spike probability, starting with the refractory period) introduces global spectral distortion. An established "shuffling" procedure corrects for RP distortion by removing the spectral component explained by the inter-spike interval (ISI) distribution. However, this procedure sacrifices oscillation-related information present in the ISIs, and therefore in the PSD. We asked whether point process models (PPMs) might achieve more selective RP distortion removal, thereby enabling improved oscillation detection.

Approach

In a novel "residuals" method, we first estimate the RP duration (nr) from the ISI distribution. We then fit the spike train with a PPM that predicts spike likelihood based on the time elapsed since the most recent of any spikes falling within the preceding nr milliseconds. Finally, we compute the PSD of the model's residuals.

Main results

We compared the residuals and shuffling methods' ability to enable accurate oscillation detection with flat baseline-assuming tests. Over synthetic data, the residuals method generally outperformed the shuffling method in classification of true- versus false-positive oscillatory power, principally due to enhanced sensitivity in sparse spike trains. In single-unit data from the internal globus pallidus (GPi) and ventrolateral anterior thalamus (VLa) of a parkinsonian monkey -- in which alpha-beta oscillations (8-30 Hz) were anticipated -- the residuals method reported the greatest incidence of significant alpha-beta power, with low firing rates predicting residuals-selective oscillation detection.

Significance

These results encourage continued development of the residuals approach, to support more accurate oscillation detection. Improved identification of oscillations could promote improved disease models and therapeutic technologies.

A mouse model of sleep disorders in Parkinson's disease showing distinct effects of dopamine D2-like receptor activation

Excessive daytime sleepiness (EDS) and sleep fragmentation are often observed in Parkinson's disease (PD) patients and are poorly understood despite their considerable impact on quality of life. We examined the ability of a neurotoxin-based mouse model of PD to reproduce these disorders and tested the potential counteracting effects of dopamine replacement therapy. Experiments were conducted in female mice with a unilateral 6-hydroxydopamine lesion of the medial forebrain bundle, leading to the loss of dopamine neurons projecting to the dorsal and ventral striatum. Sham-operated mice were used as control. Electroencephalographic and electromyographic recording was used to identify and quantify awaken, rapid eye movement (REM) and non-REM (NREM) sleep states. PD mice displayed enhanced NREM sleep and reduced wakefulness during the active period of the 24-hour circadian cycle, indicative of EDS. In addition, they also showed fragmentation of NREM sleep and increased slow-wave activity, a marker of sleep pressure. Electroencephalographic analysis of the PD model also revealed decreased density and increased length of burst-like thalamocortical oscillations (spindles). Treatment of PD mice with the dopamine receptor agonist, pramipexole, but not with L-DOPA, counteracted EDS by reducing the number, but not the length, of NREM sleep episodes during the first half of the active period. The present model recapitulates some prominent PD-related anomalies affecting sleep macro- and micro-structure. Based on the pharmacological profile of pramipexole these results also indicate the involvement of impaired dopamine D2/D3 receptor transmission in EDS.

Dopaminergic Dependency of Cholinergic Pallidal Neurons

We employed the whole-cell patch-clamp method and ChAT-Cre mice to study the electrophysiological attributes of cholinergic neurons in the external globus pallidus. Most neurons were inactive, although approximately 20% displayed spontaneous firing, including burst firing. The resting membrane potential, the whole neuron input resistance, the membrane time constant and the total neuron membrane capacitance were also characterized. The current-voltage relationship showed time-independent inward rectification without a "sag". Firing induced by current injections had a brief initial fast adaptation followed by tonic firing with minimal accommodation. Intensity-frequency plots exhibited maximal average firing rates of about 10 Hz. These traits are similar to those of some cholinergic neurons in the basal forebrain. Also, we examined their dopamine sensitivity by acutely blocking dopamine receptors. This action demonstrated that the membrane potential, excitability, and firing pattern of pallidal cholinergic neurons rely on the constitutive activity of dopamine receptors, primarily D2-class receptors. The blockade of these receptors induced a resting membrane potential hyperpolarization, a decrease in firing for the same stimulus, the disappearance of fast adaptation, and the emergence of a depolarization block. This shift in physiological characteristics was evident even when the hyperpolarization was corrected with D.C. current. Neither the currents that generate the action potentials nor those from synaptic inputs were responsible. Instead, our findings suggest, that subthreshold slow ion currents, that require further investigation, are the target of this novel dopaminergic signaling.

Propagation of Oscillations in the Indirect Pathway of the Basal Ganglia

Oscillatory signals propagate in the basal ganglia from prototypic neurons in the external globus pallidus (GPe) to their target neurons in the substantia nigra pars reticulata (SNr), internal pallidal segment, and subthalamic nucleus. Neurons in the GPe fire spontaneously, so oscillatory input signals can be encoded as changes in timing of action potentials within an ongoing spike train. When GPe neurons were driven by an oscillatory current in male and female mice, these spike-timing changes produced spike-oscillation coherence over a range of frequencies extending at least to 100 Hz. Using the known kinetics of the GPe→SNr synapse, we calculated the postsynaptic currents that would be generated in SNr neurons from the recorded GPe spike trains. The ongoing synaptic barrage from spontaneous firing, frequency-dependent short-term depression, and stochastic fluctuations at the synapse embed the input oscillation into a noisy sequence of synaptic currents in the SNr. The oscillatory component of the resulting synaptic current must compete with the noisy spontaneous synaptic barrage for control of postsynaptic SNr neurons, which have their own frequency-dependent sensitivities. Despite this, SNr neurons subjected to synaptic conductance changes generated from recorded GPe neuron firing patterns also became coherent with oscillations over a broad range of frequencies. The presynaptic, synaptic, and postsynaptic frequency sensitivities were all dependent on the firing rates of presynaptic and postsynaptic neurons. Firing rate changes, often assumed to be the propagating signal in these circuits, do not encode most oscillation frequencies, but instead determine which signal frequencies propagate effectively and which are suppressed.SIGNIFICANCE STATEMENT Oscillations are present in all the basal ganglia nuclei, include a range of frequencies, and change over the course of learning and behavior. Exaggerated oscillations are a hallmark of basal ganglia pathologies, and each has a specific frequency range. Because of its position as a hub in the basal ganglia circuitry, the globus pallidus is a candidate origin for oscillations propagating between nuclei. We imposed low-amplitude oscillations on individual globus pallidus neurons at specific frequencies and measured the coherence between the oscillation and firing as a function of frequency. We then used these responses to measure the effectiveness of oscillatory propagation to other basal ganglia nuclei. Propagation was effective for oscillation frequencies as high as 100 Hz.

Translating Pathological Brain Activity Primers in Parkinson's Disease Research

Translational experimental approaches that help us better trace Parkinson's disease (PD) pathophysiological mechanisms leading to new therapeutic targets are urgently needed. In this article, we review recent experimental and clinical studies addressing abnormal neuronal activity and pathological network oscillations, as well as their underlying mechanisms and modulation. Our aim is to enhance our knowledge about the progression of Parkinson's disease pathology and the timing of its symptom's manifestation. Here, we present mechanistic insights relevant for the generation of aberrant oscillatory activity within the cortico-basal ganglia circuits. We summarize recent achievements extrapolated from available PD animal models, discuss their advantages and limitations, debate on their differential applicability, and suggest approaches for transferring knowledge on disease pathology into future research and clinical applications.

Machine Learning Identifies a Rat Model of Parkinson's Disease via Sleep-Wake Electroencephalogram

Alpha-synuclein induced degeneration of the midbrain substantia nigra pars compact (SNc) dopaminergic neurons causes Parkinson's disease (PD). Rodent studies demonstrate that nigrostriatal dopamine stimulates pallidal neurons which, via the topographical pallidocortical pathway, regulate cortical activity and functions. We hypothesize that nigrostriatal dopamine acting at the basal ganglia regulates cortical activity in sleep and wake state, and its depletion systemically alters electroencephalogram (EEG) across frequencies during sleep-wake state. Compared to control rats, 6-hydroxydopamine induced selective SNc lesions increased overall EEG power (positive synchronization) across 0.5-60 Hz during wake, NREM (non-rapid eye movement) sleep, and REM sleep. Application of machine learning (ML) to seven EEG features computed at a single or combined spectral bands during sleep-wake differentiated SNc lesions from controls at high accuracy. ML algorithms construct a model based on empirical data to make predictions on subsequent data. The accuracy of the predictive results indicate that nigrostriatal dopamine depletion increases global EEG spectral synchronization in wake, NREM sleep, and REM sleep. The EEG changes can be exploited by ML to identify SNc lesions at a high accuracy.

Alterations of resting-state networks of Parkinson's disease patients after subthalamic DBS surgery

The implantation of deep brain stimulation (DBS) electrodes in Parkinson's disease (PD) patients can lead to a temporary improvement in motor symptoms, known as the stun effect. However, the network alterations induced by the stun effect are not well characterized. As therapeutic DBS is known to alter resting-state networks (RSN) and subsequent motor symptoms in patients with PD, we aimed to investigate whether the DBS-related stun effect also modulated RSNs. Therefore, we analyzed RSNs of 27 PD patients (8 females, 59.0 +- 8.7 years) using magnetoencephalography and compared them to RSNs of 24 age-matched healthy controls (8 females, 62.8 +- 5.1 years). We recorded 30 min of resting-state activity two days before and one day after implantation of the electrodes with and without dopaminergic medication. RSNs were determined by use of phase-amplitude coupling between a low frequency phase and a high gamma amplitude and examined for differences between conditions (i.e., pre vs post surgery). We identified four RSNs across all conditions: sensory-motor, visual, fronto-occipital, and frontal. Each RSN was altered due to electrode implantation. Importantly, these changes were not restricted to spatially close areas to the electrode trajectory. Interestingly, pre-operative RSNs corresponded better with healthy control RSNs regarding the spatial overlap, although the stun effect is associated with motor improvement. Our findings reveal that the stun effect induced by implantation of electrodes exerts brain wide changes in different functional RSNs.

Sleep Alterations in a Mouse Model of Spinocerebellar Ataxia Type 3

Spinocerebellar ataxia type 3 (SCA3) is a neurodegenerative disorder showing progressive neuronal loss in several brain areas and a broad spectrum of motor and non-motor symptoms, including ataxia and altered sleep. While sleep disturbances are known to play pathophysiologic roles in other neurodegenerative disorders, their impact on SCA3 is unknown. Using spectrographic measurements, we sought to quantitatively characterize sleep electroencephalography (EEG) in SCA3 transgenic mice with confirmed disease phenotype. We first measured motor phenotypes in 18-31-week-old homozygous SCA3 YACMJD84.2 mice and non-transgenic wild-type littermate mice during lights-on and lights-off periods. We next implanted electrodes to obtain 12-h (zeitgeber time 0-12) EEG recordings for three consecutive days when the mice were 26-36 weeks old. EEG-based spectroscopy showed that compared to wild-type littermates, SCA3 homozygous mice display: (i) increased duration of rapid-eye movement sleep (REM) and fragmentation in all sleep and wake states; (ii) higher beta power oscillations during REM and non-REM (NREM); and (iii) additional spectral power band alterations during REM and wake. Our data show that sleep architecture and EEG spectral power are dysregulated in homozygous SCA3 mice, indicating that common sleep-related etiologic factors may underlie mouse and human SCA3 phenotypes.

Cortical network formation based on subthalamic beta bursts in Parkinson's disease

Recent evidence suggests that beta bursts in subthalamic nucleus (STN) play an important role in Parkinsonian pathophysiology. We studied the spatio-temporal relationship between STN beta bursts and cortical activity in 26 Parkinson's disease (PD) patients undergoing deep brain stimulation (DBS) surgery. Postoperatively, we simultaneously recorded STN local field potentials (LFP) from externalized DBS leads and cortical activity using whole-brain magnetoencephalography. Event-related magnetic fields (ERF) were averaged time-locked to STN beta bursts and subjected to source localization. Our results demonstrate that ERF exhibiting activity significantly different from baseline activity were localized within areas functionally related to associative, limbic, and motor systems as well as regions pertinent for visual and language processing. Our data suggest that STN beta bursts are involved in network formation between STN and cortex. This interaction is in line with the idea of parallel processing within the basal ganglia-cortex loop, specifically within the functional subsystems of the STN (i.e., associative, limbic, motor, and the related cortical areas). ERFs within visual and language-related cortical areas indicate involvement of beta bursts in STN-cortex networks beyond the associative, limbic, and motor loops. In sum, our results highlight the involvement of STN beta bursts in the formation of multiple STN - cortex loops in patients with PD.

Synaptic determinants of cholinergic interneurons hyperactivity during parkinsonism

Parkinson’s disease is a neurodegenerative ailment generated by the loss of dopamine in the basal ganglia, mainly in the striatum. The disease courses with increased striatal levels of acetylcholine, disrupting the balance among these modulatory transmitters. These modifications disturb the excitatory and inhibitory balance in the striatal circuitry, as reflected in the activity of projection striatal neurons. In addition, changes in the firing pattern of striatal tonically active interneurons during the disease, including cholinergic interneurons (CINs), are being searched. Dopamine-depleted striatal circuits exhibit pathological hyperactivity as compared to controls. One aim of this study was to show how striatal CINs contribute to this hyperactivity. A second aim was to show the contribution of extrinsic synaptic inputs to striatal CINs hyperactivity. Electrophysiological and calcium imaging recordings in Cre-mice allowed us to evaluate the activity of dozens of identified CINs with single-cell resolution in ex vivo brain slices. CINs show hyperactivity with bursts and silences in the dopamine-depleted striatum. We confirmed that the intrinsic differences between the activity of control and dopamine-depleted CINs are one source of their hyperactivity. We also show that a great part of this hyperactivity and firing pattern change is a product of extrinsic synaptic inputs, targeting CINs. Both glutamatergic and GABAergic inputs are essential to sustain hyperactivity. In addition, cholinergic transmission through nicotinic receptors also participates, suggesting that the joint activity of CINs drives the phenomenon; since striatal CINs express nicotinic receptors, not expressed in striatal projection neurons. Therefore, CINs hyperactivity is the result of changes in intrinsic properties and excitatory and inhibitory inputs, in addition to the modification of local circuitry due to cholinergic nicotinic transmission. We conclude that CINs are the main drivers of the pathological hyperactivity present in the striatum that is depleted of dopamine, and this is, in part, a result of extrinsic synaptic inputs. These results show that CINs may be a main therapeutic target to treat Parkinson’s disease by intervening in their synaptic inputs.

Linking α-synuclein-induced synaptopathy and neural network dysfunction in early Parkinson’s disease

The prodromal phase of Parkinson’s disease is characterized by aggregation of the misfolded pathogenic protein α-synuclein in select neural centres, co-occurring with non-motor symptoms including sensory and cognitive loss, and emotional disturbances. It is unclear whether neuronal loss is significant during the prodrome. Underlying these symptoms are synaptic impairments and aberrant neural network activity. However, the relationships between synaptic defects and network-level perturbations are not established. In experimental models, pathological α-synuclein not only impacts neurotransmission at the synaptic level, but also leads to changes in brain network-level oscillatory dynamics—both of which likely contribute to non-motor deficits observed in Parkinson’s disease. Here we draw upon research from both human subjects and experimental models to propose a ‘synapse to network prodrome cascade’ wherein before overt cell death, pathological α-synuclein induces synaptic loss and contributes to aberrant network activity, which then gives rise to prodromal symptomology. As the disease progresses, abnormal patterns of neural activity ultimately lead to neuronal loss and clinical progression of disease. Finally, we outline goals and research needed to unravel the basis of functional impairments in Parkinson’s disease and other α-synucleinopathies.

A translational perspective on pathophysiological changes of oscillatory activity in dystonia and parkinsonism

Intracerebral recordings from movement disorders patients undergoing deep brain stimulation have allowed the identification of pathophysiological patterns in oscillatory activity that correlate with symptom severity. Changes in oscillatory synchrony occur within and across brain areas, matching the classification of movement disorders as network disorders. However, the underlying mechanisms of oscillatory changes are difficult to assess in patients, as experimental interventions are technically limited and ethically problematic. This is why animal models play an important role in neurophysiological research of movement disorders. In this review, we highlight the contributions of translational research to the mechanistic understanding of pathological changes in oscillatory activity, with a focus on parkinsonism and dystonia, while addressing the limitations of current findings and proposing possible future directions.

Subthalamic nucleus deep brain stimulation driven by primary motor cortex γ2 activity in parkinsonian monkeys

In parkinsonism, subthalamic nucleus (STN) electrical deep brain stimulation (DBS) improves symptoms, but may be associated with side effects. Adaptive DBS (aDBS), which enables modulation of stimulation, may limit side effects, but limited information is available about clinical effectiveness and efficaciousness. We developed a brain-machine interface for aDBS, which enables modulation of stimulation parameters of STN-DBS in response to γ2 band activity (80-200 Hz) of local field potentials (LFPs) recorded from the primary motor cortex (M1), and tested its effectiveness in parkinsonian monkeys. We trained two monkeys to perform an upper limb reaching task and rendered them parkinsonian with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. Bipolar intracortical recording electrodes were implanted in the M1, and a recording chamber was attached to access the STN. In aDBS, the M1 LFPs were recorded, filtered into the γ2 band, and discretized into logic pulses by a window discriminator, and the pulses were used to modulate the interval and amplitude of DBS pulses. In constant DBS (cDBS), constant stimulus intervals and amplitudes were used. Reaction and movement times during the task were measured and compared between aDBS and cDBS. The M1-γ2 activities were increased before and during movements in parkinsonian monkeys and these activities modulated the aDBS pulse interval, amplitude, and dispersion. With aDBS and cDBS, reaction and movement times were significantly decreased in comparison to DBS-OFF. The electric charge delivered was lower with aDBS than cDBS. M1-γ2 aDBS in parkinsonian monkeys resulted in clinical benefits that did not exceed those from cDBS. However, M1-γ2 aDBS achieved this magnitude of benefit for only two thirds of the charge delivered by cDBS. In conclusion, M1-γ2 aDBS is an effective therapeutic approach which requires a lower electrical charge delivery than cDBS for comparable clinical benefits.

A bidirectional Hopf bifurcation analysis of Parkinson's oscillation in a simplified basal ganglia model

In this paper, we study the parkinson oscillation mechanism in a computational model by bifurcation analysis and numerical simulation. Oscillatory activities can be induced by abnormal coupling weights and delays. The bidirectional Hopf bifurcation phenomena are found in simulations, which can uniformly explain the oscillation mechanism in this model. The Hopf1 represents the transition between the low firing rate stable state (SS) and oscillation state (OS), the Hopf2 represents the transition between the high firing rate stable state (HSS) and the OS, the mechanisms of them are different. The Hopf1 and Hopf2 bifurcations both show that when the state transfers from the stable region to the oscillation region, oscillatory activities always originate from the beta frequency band, and then gradually evolve into the alpha frequency band, the theta frequency band and delta frequency band in this model. We find that the changing trends of the DF and oscillation amplitude (OSAM) are contrary, oscillation activities in lower frequency band are more stable than that in higher frequency band. The effect of the delay in inhibitory pathways is greater than that of in excitatory pathways, and appropriate delays improve the discharge activation level (DAL) of the system. In all, we infer that oscillations can be induced by the follow factors: 1. Improvement of the DAL of the globus pallidus externa (GPe); 2. Reduce the DAL of the GPe from the HSS or the discharge saturation state; 3. The GPe can also resonate with the subthalamic nucleus (STN).

Eight-hours conventional versus adaptive deep brain stimulation of the subthalamic nucleus in Parkinson's disease

This study compares the effects on motor symptoms between conventional deep brain stimulation (cDBS) and closed-loop adaptive deep brain stimulation (aDBS) in patients with Parkinson’s Disease. The aDBS stimulation is controlled by the power in the beta band (12–35 Hz) of local field potentials recorded directly by subthalamic nucleus electrodes. Eight subjects were assessed in two 8-h stimulation sessions (first day, cDBS; second day, aDBS) with regular levodopa intake and during normal daily activities. The Unified Parkinson’s Disease Rating Scale (UPDRS) part III scores, the Rush scale for dyskinesias, and the total electrical energy delivered to the tissues per second (TEEDs) were significantly lower in the aDBS session (relative UPDRS mean, cDBS: 0.46 ± 0.05, aDBS: 0.33 ± 0.04, p = 0.015; UPDRS part III rigidity subset mean, cDBS: 2.9143 ± 0.6551 and aDBS: 2.1429 ± 0.5010, p = 0.034; UPDRS part III standard deviation cDBS: 2.95, aDBS: 2.68; p = 0.047; Rush scale, cDBS 2.79 ± 0.39 versus aDBS 1.57 ± 0.23, p = 0.037; cDBS TEEDs mean: 28.75 ± 3.36 µj s⁻¹, aDBS TEEDs mean: 16.47 ± 3.33, p = 0.032 Wilcoxon’s sign rank test). This work further supports the safety and effectiveness of aDBS stimulation compared to cDBS in a daily session, both in terms of motor performance and TEED to the patient.

Adaptive Parameter Modulation of Deep Brain Stimulation Based on Improved Supervisory Algorithm

Clinically deployed deep brain stimulation (DBS) for the treatment of Parkinson's disease operates in an open loop with fixed stimulation parameters, and this may result in high energy consumption and suboptimal therapy. The objective of this manuscript is to establish, through simulation in a computational model, a closed-loop control system that can automatically adjust the stimulation parameters to recover normal activity in model neurons. Exaggerated beta band activity is recognized as a hallmark of Parkinson's disease and beta band activity in model neurons of the globus pallidus internus (GPi) was used as the feedback signal to control DBS of the GPi. Traditional proportional controller and proportional-integral controller were not effective in eliminating the error between the target level of beta power and the beta power under Parkinsonian conditions. To overcome the difficulties in tuning the controller parameters and improve tracking performance in the case of changes in the plant, a supervisory control algorithm was implemented by introducing a Radial Basis Function (RBF) network to build the inverse model of the plant. Simulation results show the successful tracking of target beta power in the presence of changes in Parkinsonian state as well as during dynamic changes in the target level of beta power. Our computational study suggests the feasibility of the RBF network-driven supervisory control algorithm for real-time modulation of DBS parameters for the treatment of Parkinson's disease.

Connectivity of EEG synchronization networks increases for Parkinson's disease patients with freezing of gait

Freezing of gait (FoG), a paroxysmal gait disturbance commonly experienced by patients with Parkinson's disease (PD), is characterized by sudden episodes of inability to generate effective forward stepping. Recent studies have shown an increase in beta frequency of local-field potentials in the basal-ganglia during FoG, however, comprehensive research on the synchronization between different brain locations and frequency bands in PD patients is scarce. Here, by developing tools based on network science and non-linear dynamics, we analyze synchronization networks of electroencephalography (EEG) brain waves of three PD patient groups with different FoG severity. We find higher EEG amplitude synchronization (stronger network links) between different brain locations as PD and FoG severity increase. These results are consistent across frequency bands (theta, alpha, beta, gamma) and independent of the specific motor task (walking, still standing, hand tapping) suggesting that an increase in severity of PD and FoG is associated with stronger EEG networks over a broad range of brain frequencies. This observation of a direct relationship of PD/FoG severity with overall EEG synchronization together with our proposed EEG synchronization network approach may be used for evaluating FoG propensity and help to gain further insight into PD and the pathophysiology leading to FoG.

Modeling Parkinson's Disease: Not Only Rodents?

Parkinson’s disease (PD) is a common chronic progressive multifactorial neurodegenerative disease. In most cases, PD develops as a sporadic idiopathic disease. However, in 10%–15% of all patients, Mendelian inheritance of the disease is observed in an autosomal dominant or autosomal recessive manner. To date, mutations in seven genes have been convincingly confirmed as causative in typical familial forms of PD, i.e., SNCA, LRRK2, VPS35, PRKN, PINK1, GBA, and DJ-1. Family and genome-wide association studies have also identified a number of candidate disease genes and a common genetic variability at 90 loci has been linked to risk for PD. The analysis of the biological function of both proven and candidate genes made it possible to conclude that mitochondrial dysfunction, lysosomal dysfunction, impaired exosomal transport, and immunological processes can play important roles in the development of the pathological process of PD. The mechanisms of initiation of the pathological process and its earliest stages remain unclear. The study of the early stages of the disease (before the first motor symptoms appear) is extremely complicated by the long preclinical period. In addition, at present, the possibility of performing complex biochemical and molecular biological studies familial forms of PD is limited. However, in this case, the analysis of the state of the central nervous system can only be assessed by indirect signs, such as the level of metabolites in the cerebrospinal fluid, peripheral blood, and other biological fluids. One of the potential solutions to this problem is the analysis of disease models, in which it is possible to conduct a detailed in-depth study of all aspects of the pathological process, starting from its earliest stages. Many modeling options are available currently. An analysis of studies published in the 2000s suggests that toxic models in rodents are used in the vast majority of cases. However, interesting and important data for understanding the pathogenesis of PD can be obtained from other in vivo models. Within the framework of this review, we will consider various models of PD that were created using various living organisms, from unicellular yeast (Saccharomyces cerevisiae) and invertebrate (Nematode and Drosophila) forms to various mammalian species.

α and θ oscillations in the subthalamic nucleus are potential biomarkers for Parkinson's disease with depressive symptoms

INTRODUCTION

Abnormal α oscillations in the bed nucleus of stria terminalis and subgenual cingulate of patients with depression correlate with symptom severity. Some Parkinson's disease (PD) patients also have abnormal θ-α oscillations in the subthalamic nucleus (STN). However, the relationship between abnormal θ-α oscillations and depressive symptoms in PD patients has not been determined. This study explored the correlation between α and θ oscillations of the STN and depressive symptoms in PD patients.

METHODS

We conducted a retrospective case-control study on 36 PD patients with (dPD group) or without depressive symptoms (nPD group), analyzing the difference in the average power spectral density (PSD) of α and θ oscillations of the local field potential (LFP) recorded in the STN during deep brain stimulation (DBS), and their correlation with the Hamilton depression rating scale (HAMD) of PD patients during the same period.

RESULTS

The dPD group had a higher PSD of α oscillations and a lower PSD of θ oscillations in the left ventral STN. The PSD of α oscillations of the left ventral STN were positively correlated with the severity of depressive symptoms, whereas the PSD of θ oscillations of this location was negatively correlated with severity of depressive symptoms. The PSD of α and θ oscillations did not correlate with motor symptoms, sleep quality, or quality of life score.

CONCLUSION

Abnormal α and θ oscillations of the left ventral STN could be used as biomarkers of PD with depressive symptoms, which might guide STN-DBS treatment.

Subthalamic Nucleus Activity Influences Sensory and Motor Cortex during Force Transduction

The subthalamic nucleus (STN) is proposed to participate in pausing, or alternately, in dynamic scaling of behavioral responses, roles that have conflicting implications for understanding STN function in the context of deep brain stimulation (DBS) therapy. To examine the nature of event-related STN activity and subthalamic-cortical dynamics, we performed primary motor and somatosensory electrocorticography while subjects (n = 10) performed a grip force task during DBS implantation surgery. Phase-locking analyses demonstrated periods of STN-cortical coherence that bracketed force transduction, in both beta and gamma ranges. Event-related causality measures demonstrated that both STN beta and gamma activity predicted motor cortical beta and gamma activity not only during force generation but also prior to movement onset. These findings are consistent with the idea that the STN participates in motor planning, in addition to the modulation of ongoing movement. We also demonstrated bidirectional information flow between the STN and somatosensory cortex in both beta and gamma range frequencies, suggesting robust STN participation in somatosensory integration. In fact, interactions in beta activity between the STN and somatosensory cortex, and not between STN and motor cortex, predicted PD symptom severity. Thus, the STN contributes to multiple aspects of sensorimotor behavior dynamically across time.

Electrophysiological biomarkers for deep brain stimulation outcomes in movement disorders: state of the art and future challenges

Several neurological diseases are accompanied by rhythmic oscillatory dysfunctions in various frequency ranges and disturbed cross-frequency relationships on regional, interregional, and whole brain levels. Knowledge of these disease-specific oscillopathies is important mainly in the context of deep brain stimulation (DBS) therapy. Electrophysiological biomarkers have been used as input signals for adaptive DBS (aDBS) as well as preoperative outcome predictors. As movement disorders, particularly Parkinson's disease (PD), are among the most frequent DBS indications, the current research of DBS is the most advanced in the movement disorders field. We reviewed the literature published mainly between 2010 and 2020 to identify the most important findings concerning the current evolution of electrophysiological biomarkers in DBS and to address future challenges for prospective research.

Optimized Propofol Anesthesia Increases Power of Subthalamic Neuronal Activity in Patients with Parkinson's Disease Undergoing Deep Brain Stimulation

Introduction

Propofol is a general anesthetic option for deep brain stimulation (DBS) of the subthalamic nucleus (STN) of patients with Parkinson's disease (PD). However, its effects on STN activity and neuropsychological outcomes are controversial. The optimal propofol anesthesia for asleep DBS is unknown. This study investigated the safety and effectiveness of an optimized propofol anesthesia regimen in asleep DBS.

Methods

This retrospective study enrolled 68 PD patients undergoing bilateral STN-DBS surgery. All patients received local scalp anesthesia, with (asleep group, n = 35) or without (awake group, n = 33) propofol-remifentanil general anesthesia by target-controlled infusion under electroencephalogram monitoring. The primary outcome was subthalamic neuronal spiking characterization during microelectrode recording. The secondary outcomes were clinical outcomes including motor, cognition, mind, sleep, and quality of life at 6 months.

Results

Significantly increased delta and theta power were obtained under propofol anesthesia (awake vs. asleep group, mean ± standard deviation; delta: 31.97 ± 9.87 vs. 39.77 ± 10.56, p < 0.01; theta: 21.09 ± 5.55 vs. 24.82 ± 6.63, p = 0.01). After excluding the influence of confounding factors of age and preoperative motor scores, there was a statistically significant influence on the delta, theta, and alpha power of STN neuronal activity under different anesthesia regimens (delta: β = 2.64, p < 0.01; theta: β = 2.11, p < 0.01; alpha: β = 1.42, p = 0.01). There were no differences in modified burst index, firing rate, tract numbers of microelectrode recording, and other clinical outcomes between the two groups.

Conclusion

Optimized propofol anesthesia enhanced the delta, theta, and alpha power in STN compared with the awake technique and likely contributed to target recognition under propofol anesthesia. These results demonstrate that propofol is suitable, but needs to be optimized, for asleep STN-DBS.

Trial Registration

Chinese Clinical Trial Registry Identification number: ChiCTR2100045942. Registered 29 April 2021–Retrospectively registered

Supplementary Information

The online version contains supplementary material available at 10.1007/s40120-021-00259-y.

Machine learning-based personalized subthalamic biomarkers predict ON-OFF levodopa states in Parkinson patients

Objective.Adaptive deep brain stimulation (aDBS) based on subthalamic nucleus (STN) electrophysiology has recently been proposed to improve clinical outcomes of DBS for Parkinson's disease (PD) patients. Many current models for aDBS are based on one or two electrophysiological features of STN activity, such as beta or gamma activity. Although these models have shown interesting results, we hypothesized that an aDBS model that includes many STN activity parameters will yield better clinical results. The objective of this study was to investigate the most appropriate STN neurophysiological biomarkers, detectable over long periods of time, that can predict OFF and ON levodopa states in PD patients.Approach.Long-term local field potentials (LFPs) were recorded from eight STNs (four PD patients) during 92 recording sessions (44 OFF and 48 ON levodopa states), over a period of 3-12 months. Electrophysiological analysis included the power of frequency bands, band power ratio and burst features. A total of 140 engineered features was extracted for 20 040 epochs (each epoch lasting 5 s). Based on these engineered features, machine learning (ML) models classified LFPs as OFF vs ON levodopa states.Main results.Beta and gamma band activity alone poorly predicts OFF vs ON levodopa states, with an accuracy of 0.66 and 0.64, respectively. Group ML analysis slightly improved prediction rates, but personalized ML analysis, based on individualized engineered electrophysiological features, were markedly better, predicting OFF vs ON levodopa states with an accuracy of 0.8 for support vector machine learning models.Significance.We showed that individual patients have unique sets of STN neurophysiological biomarkers that can be detected over long periods of time. ML models revealed that personally classified engineered features most accurately predict OFF vs ON levodopa states. Future development of aDBS for PD patients might include personalized ML algorithms.

Early decreases in cortical mid-gamma peaks coincide with the onset of motor deficits and precede exaggerated beta build-up in rat models for Parkinson's disease

Evidence suggests that exaggerated beta range local field potentials (LFP) in basal ganglia-thalamocortical circuits constitute an important biomarker for feedback for deep brain stimulation in Parkinson's disease patients, although the role of this phenomenon in triggering parkinsonian motor symptoms remains unclear. A useful model for probing the causal role of motor circuit LFP synchronization in motor dysfunction is the unilateral dopamine cell-lesioned rat, which shows dramatic motor deficits walking contralaterally to the lesion but can walk steadily ipsilaterally on a circular treadmill. Within hours after 6-OHDA injection, rats show marked deficits in ipsilateral walking with early loss of significant motor cortex (MCx) LFP peaks in the mid-gamma 41-45 Hz range in the lesioned hemisphere; both effects were reversed by dopamine agonist administration. Increases in MCx and substantia nigra pars reticulata (SNpr) coherence and LFP power in the 29-40 Hz range emerged more gradually over 7 days, although without further progression of walking deficits. Twice-daily chronic dopamine antagonist treatment induced rapid onset of catalepsy and also reduced MCx 41-45 Hz LFP activity at 1 h, with increases in MCx and SNpr 29-40 Hz power/coherence emerging over 7 days, as assessed during periods of walking before the morning treatments. Thus, increases in high beta power in these parkinsonian models emerge gradually and are not linearly correlated with motor deficits. Earlier changes in cortical circuits, reflected in the rapid decreases in MCx LFP mid-gamma LFP activity, may contribute to evolving plasticity supporting increased beta range synchronized activity in basal ganglia-thalamocortical circuits after loss of dopamine receptor stimulation.

High-Frequency Oscillations in the Pallidum: A Pathophysiological Biomarker in Parkinson's Disease?

BACKGROUND

Abnormal oscillatory neural activity in the beta-frequency band (13-35 Hz) is thought to play a role in Parkinson's disease (PD); however, increasing evidence points to alterations in high-frequency ranges (>100 Hz) also having pathophysiological relevance.

OBJECTIVES

Studies have found that power in subthalamic nucleus (STN) high-frequency oscillations is increased with dopaminergic medication and during voluntary movements, implicating these brain rhythms in normal basal ganglia function. The objective of this study was to investigate whether similar signaling occurs in the internal globus pallidus (GPi), a nucleus increasingly used as a target for deep brain stimulation (DBS) for PD.

METHODS

Spontaneous and movement-related GPi field potentials were recorded from DBS leads in 5 externalized PD patients on and off dopaminergic medication, as well as from 3 rhesus monkeys before and after the induction of parkinsonism with the neurotoxin 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine.

RESULTS

In the parkinsonian condition, we identified a prominent oscillatory peak centered at 200-300 Hz that increased during movement. In patients the magnitude of high-frequency oscillation modulation was negatively correlated with bradykinesia. In monkeys, high-frequency oscillations were mostly absent in the naive condition but emerged after the neurotoxin 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine. In patients, spontaneous high-frequency oscillations were significantly attenuated on-medication.

CONCLUSIONS

Our findings provide evidence in support of the hypothesis that exaggerated, movement-modulated high-frequency oscillations in the GPi are pathophysiological features of PD. These findings suggest that the functional role(s) of high-frequency oscillations may differ between the STN and GPi and motivate additional investigations into their relationship to motor control in normal and diseased states.

Eight cylindrical contact lead recordings in the subthalamic region localize beta oscillations source to the dorsal STN

BACKGROUND

In Parkinson's disease (PD) patients, the subthalamic nucleus (STN) has prominent oscillatory activity in the beta band, which may be related to the motor symptoms severity. Local field potential (LFP) studies using standard four-contact deep brain stimulation (DBS) leads indicate that the source of beta activity in the STN region is the dorsolateral segment of the nucleus. However, these leads have few contacts outside of the STN, making the source localization of beta activity around the STN region uncertain.

OBJECTIVE

This study aimed to investigate the electrophysiological characteristics of the STN and the surrounding area in PD to better locate the source of these oscillations and their clinical relevance.

METHODS

Eight PD patients were bilaterally implanted in the STN with the eight ring-contact DBS lead (Boston Scientific Corporation). LFPs were recorded intra-operatively from each DBS contact in the off medication state at rest. Each contact location was normalized relative to the STN borders based on microelectrode recordings. For each recording, power spectral density was computed, averaged over multiple frequency bands and phase reversal analysis was used to localize the source of oscillatory activity. Beta burst, high-frequency activity (HFA), and phase-amplitude coupling (PAC) were also computed. Neurophysiological signatures were correlated with hemibody symptoms severity and clinical outcomes.

RESULTS

Beta band power and phase reversal localized the beta oscillator to the dorsal STN and correlated with pre-operative off medication hemibody bradykinesia and rigidity score. The contact along the electrode with the largest beta oscillatory power co-localized with the independently chosen optimized contact used for long-term chronic DBS. Lastly, beta bursting, HFA, and Beta-HFA PAC co-localized with the beta oscillator at the dorsal STN, and Beta-HFA PAC correlated with DBS effect.

CONCLUSIONS

Our findings support the hypothesis that the primary source of beta oscillations is located in dorsal STN, and argue against the alternative hypothesis that beta activity in the STN region arises from volume conduction from other sources. We demonstrate intrinsic STN beta-HFA PAC as an independent marker of DBS effect.

Cortico-Striatal Oscillations Are Correlated to Motor Activity Levels in Both Physiological and Parkinsonian Conditions

Oscillatory neural activity in the cortico-basal ganglia-thalamocortical (CBGTC) loop is associated with the motor state of a subject, but also with the availability of modulatory neurotransmitters. For example, increased low-frequency oscillations in Parkinson’s disease (PD) are related to decreased levels of dopamine and have been proposed as biomarkers to adapt and optimize therapeutic interventions, such as deep brain stimulation. Using neural oscillations as biomarkers require differentiating between changes in oscillatory patterns associated with parkinsonism vs. those related to a subject’s motor state. To address this point, we studied the correlation between neural oscillatory activity in the motor cortex and striatum and varying degrees of motor activity under normal and parkinsonian conditions. Using rats with bilateral or unilateral 6-hydroxydopamine lesions as PD models, we correlated the motion index (MI)—a measure based on the physical acceleration of the head of rats—to the local field potential (LFP) oscillatory power in the 1–80 Hz range. In motor cortices and striata, we observed a robust correlation between the motion index and the oscillatory power in two main broad frequency ranges: a low-frequency range [5.0–26.5 Hz] was negatively correlated to motor activity, whereas a high-frequency range [35.0–79.9 Hz] was positively correlated. We observed these correlations in both normal and parkinsonian conditions. In addition to these general changes in broad-band power, we observed a more restricted narrow-band oscillation [25–40 Hz] in dopamine-denervated hemispheres. This oscillation, which seems to be selective to the parkinsonian state, showed a linear frequency dependence on the concurrent motor activity level. We conclude that, independently of the parkinsonian condition, changes in broad-band oscillatory activities of cortico-basal ganglia networks (including changes in the relative power of low- and high-frequency bands) are closely correlated to ongoing motions, most likely reflecting he operations of these neural circuits to control motor activity. Hence, biomarkers based on neural oscillations should focus on specific features, such as narrow frequency bands, to allow differentiation between parkinsonian states and physiological movement-dependent circuit modulation.

What is the true discharge rate and pattern of the striatal projection neurons in Parkinson's disease and Dystonia?

Dopamine and striatal dysfunctions play a key role in the pathophysiology of Parkinson's disease (PD) and Dystonia, but our understanding of the changes in the discharge rate and pattern of striatal projection neurons (SPNs) remains limited. Here, we recorded and examined multi-unit signals from the striatum of PD and dystonic patients undergoing deep brain stimulation surgeries. Contrary to earlier human findings, we found no drastic changes in the spontaneous discharge of the well-isolated and stationary SPNs of the PD patients compared to the dystonic patients or to the normal levels of striatal activity reported in healthy animals. Moreover, cluster analysis using SPN discharge properties did not characterize two well-separated SPN subpopulations, indicating no SPN subpopulation-specific (D1 or D2 SPNs) discharge alterations in the pathological state. Our results imply that small to moderate changes in spontaneous SPN discharge related to PD and Dystonia are likely amplified by basal ganglia downstream structures.

Basal ganglia beta oscillations during sleep underlie Parkinsonian insomnia

Sleep disorders are among the most debilitating comorbidities of Parkinson's disease (PD) and affect the majority of patients. Of these, the most common is insomnia, the difficulty to initiate and maintain sleep. The degree of insomnia correlates with PD severity and it responds to treatments that decrease pathological basal ganglia (BG) beta oscillations (10-17 Hz in primates), suggesting that beta activity in the BG may contribute to insomnia. We used multiple electrodes to record BG spiking and field potentials during normal sleep and in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced Parkinsonism in nonhuman primates. MPTP intoxication resulted in severe insomnia with delayed sleep onset, sleep fragmentation, and increased wakefulness. Insomnia was accompanied by the onset of nonrapid eye movement (NREM) sleep beta oscillations that were synchronized across the BG and cerebral cortex. The BG beta oscillatory activity was associated with a decrease in slow oscillations (0.1-2 Hz) throughout the cortex, and spontaneous awakenings were preceded by an increase in BG beta activity and cortico-BG beta coherence. Finally, the increase in beta oscillations in the basal ganglia during sleep paralleled decreased NREM sleep, increased wakefulness, and more frequent awakenings. These results identify NREM sleep beta oscillation in the BG as a neural correlate of PD insomnia and suggest a mechanism by which this disorder could emerge.

Subthalamic nucleus activity dynamics and limb movement prediction in Parkinson's disease

Whilst exaggerated bursts of beta frequency band oscillatory synchronization in the subthalamic nucleus have been associated with motor impairment in Parkinson's disease, a plausible mechanism linking the two phenomena has been lacking. Here we test the hypothesis that increased synchronization denoted by beta bursting might compromise information coding capacity in basal ganglia networks. To this end we recorded local field potential activity in the subthalamic nucleus of 18 patients with Parkinson's disease as they executed cued upper and lower limb movements. We used the accuracy of local field potential-based classification of the limb to be moved on each trial as an index of the information held by the system with respect to intended action. Machine learning using the naïve Bayes conditional probability model was used for classification. Local field potential dynamics allowed accurate prediction of intended movements well ahead of their execution, with an area under the receiver operator characteristic curve of 0.80 ± 0.04 before imperative cues when the demanded action was known ahead of time. The presence of bursts of local field potential activity in the alpha, and even more so, in the beta frequency band significantly compromised the prediction of the limb to be moved. We conclude that low frequency bursts, particularly those in the beta band, restrict the capacity of the basal ganglia system to encode physiologically relevant information about intended actions. The current findings are also important as they suggest that local subthalamic activity may potentially be decoded to enable effector selection, in addition to force control in restorative brain-machine interface applications.

Debugging Adaptive Deep Brain Stimulation for Parkinson's Disease

Deep brain stimulation (DBS) is a successful treatment for patients with Parkinson's disease. In adaptive DBS, stimulation is titrated according to feedback about clinical state and underlying pathophysiology. This contrasts with conventional stimulation, which is fixed and continuous. In acute trials, adaptive stimulation matches the efficacy of conventional stimulation while delivering about half the electrical energy. The latter means potentially fewer side-effects. The next step is to determine the long-term efficacy, efficiency, and side-effect profile of adaptive stimulation, and chronic trials are currently being considered by the medical devices industry. However, there are several different approaches to adaptive DBS, and several possible limitations have been highlighted. Here we review the findings to date to ascertain how and who to stimulate in chronic trials designed to establish the long-term utility of adaptive DBS. © 2020 International Parkinson and Movement Disorder Society.

A Population Model of Deep Brain Stimulation in Movement Disorders From Circuits to Cells

For more than 30 years, deep brain stimulation (DBS) has been used to target the symptoms of a number of neurological disorders and in particular movement disorders such as Parkinson’s disease (PD) and essential tremor (ET). It is known that the loss of dopaminergic neurons in the substantia nigra leads to PD, while the exact impact of this on the brain dynamics is not fully understood, the presence of beta-band oscillatory activity is thought to be pathological. The cause of ET, however, remains uncertain, however pathological oscillations in the thalamocortical-cerebellar network have been linked to tremor. Both of these movement disorders are treated with DBS, which entails the surgical implantation of electrodes into a patient’s brain. While DBS leads to an improvement in symptoms for many patients, the mechanisms underlying this improvement is not clearly understood, and computational modeling has been used extensively to improve this. Many of the models used to study DBS and its effect on the human brain have mainly utilized single neuron and single axon biophysical models. We have previously shown in separate models however, that the use of population models can shed much light on the mechanisms of the underlying pathological neural activity in PD and ET in turn, and on the mechanisms underlying DBS. Together, this work suggested that the dynamics of the cerebellar-basal ganglia thalamocortical network support oscillations at frequency range relevant to movement disorders. Here, we propose a new combined model of this network and present new results that demonstrate that both Parkinsonian oscillations in the beta band and oscillations in the tremor frequency range arise from the dynamics of such a network. We find regions in the parameter space demonstrating the different dynamics and go on to examine the transition from one oscillatory regime to another as well as the impact of DBS on these different types of pathological activity. This work will allow us to better understand the changes in brain activity induced by DBS, and allow us to optimize this clinical therapy, particularly in terms of target selection and parameter setting.

Oscillotherapeutics - Time-targeted interventions in epilepsy and beyond

Oscillatory brain activities support many physiological functions from motor control to cognition. Disruptions of the normal oscillatory brain activities are commonly observed in neurological and psychiatric disorders including epilepsy, Parkinson's disease, Alzheimer's disease, schizophrenia, anxiety/trauma-related disorders, major depressive disorders, and drug addiction. Therefore, these disorders can be considered as common oscillation defects despite having distinct behavioral manifestations and genetic causes. Recent technical advances of neuronal activity recording and analysis have allowed us to study the pathological oscillations of each disorder as a possible biomarker of symptoms. Furthermore, recent advances in brain stimulation technologies enable time- and space-targeted interventions of the pathological oscillations of both neurological disorders and psychiatric disorders as possible targets for regulating their symptoms.

Parkin deficiency perturbs striatal circuit dynamics

Loss-of-function mutations in the parkin-encoding PARK2 gene are a frequent cause of young-onset, autosomal recessive Parkinson's disease (PD). Parkin knockout mice have no nigro-striatal neuronal loss but exhibit abnormalities of striatal dopamine transmission and cortico-striatal synaptic function. How these predegenerative changes observed in vitro affect neural dynamics at the intact circuit level, however, remains hitherto elusive. Here, we recorded from motor cortex, striatum and globus pallidus (GP) of anesthetized parkin-deficient mice to assess cortex-basal ganglia circuit dynamics and to dissect cell type-specific functional connectivity in the presymptomatic phase of genetic PD. While ongoing activity of presumed striatal spiny projection neurons and their downstream counterparts in the GP was not different from controls, parkin deficiency had a differential impact on striatal interneurons: In parkin-mutant mice, tonically active neurons displayed elevated activity levels. Baseline firing rates of transgenic striatal fast spiking interneurons (FSI), on the contrary, were reduced and the correlational structure of the FSI microcircuitry was disrupted. The entire transgenic striatal microcircuit showed enhanced and phase-shifted phase coupling to slow (1-3 Hz) cortical population oscillations. Unexpectedly, local field potentials recorded from striatum and GP of parkin-mutant mice robustly displayed amplified beta oscillations (~22 Hz), phase-coupled to cortex. Parkin deficiency selectively increased spike-field coupling of FSIs to beta oscillations. Our findings suggest that loss of parkin function leads to amplifications of synchronized cortico-striatal oscillations and an intrastriatal reconfiguration of interneuronal circuits. This presymptomatic disarrangement of dynamic functional connectivity may precede nigro-striatal neurodegeneration and predispose to imbalance of striatal outflow accompanying symptomatic PD.

Aberrant features of in vivo striatal dynamics in Parkinson's disease

The striatum plays an important role in learning, selecting, and executing actions. As a major input hub of the basal ganglia, it receives and processes a diverse array of signals related to sensory, motor, and cognitive information. Aberrant neural activity in this area is implicated in a wide variety of neurological and psychiatric disorders. It is therefore important to understand the hallmarks of disrupted striatal signal processing. This review surveys literature examining how in vivo striatal microcircuit dynamics are impacted in animal models of one of the most widely studied movement disorders, Parkinson's disease. The review identifies four major features of aberrant striatal dynamics: altered relative levels of direct and indirect pathway activity, impaired information processing by projection neurons, altered information processing by interneurons, and increased synchrony.