Oscillations In Basal Ganglia Research Articles

Structural constraints on the emergence of oscillations in multi-population neural networks

Oscillations arise in many real-world systems and are associated with both functional and dysfunctional states. Whether a network can oscillate can be estimated if we know the strength of interaction between nodes. But in real-world networks (in particular in biological networks) it is usually not possible to know the exact connection weights. Therefore, it is important to determine the structural properties of a network necessary to generate oscillations. Here, we provide a proof that uses dynamical system theory to prove that an odd number of inhibitory nodes and strong enough connections are necessary to generate oscillations in a single cycle threshold-linear network. We illustrate these analytical results in a biologically plausible network with either firing-rate based or spiking neurons. Our work provides structural properties necessary to generate oscillations in a network. We use this knowledge to reconcile recent experimental findings about oscillations in basal ganglia with classical findings.

Structural constraints on the emergence of oscillations in multi-population neural networks.

Oscillations arise in many real-world systems and are associated with both functional and dysfunctional states. Whether a network can oscillate can be estimated if we know the strength of interaction between nodes. But in real-world networks (in particular in biological networks) it is usually not possible to know the exact connection weights. Therefore, it is important to determine the structural properties of a network necessary to generate oscillations. Here, we provide a proof that uses dynamical system theory to prove that an odd number of inhibitory nodes and strong enough connections are necessary to generate oscillations in a single cycle threshold-linear network. We illustrate these analytical results in a biologically plausible network with either firing-rate based or spiking neurons. Our work provides structural properties necessary to generate oscillations in a network. We use this knowledge to reconcile recent experimental findings about oscillations in basal ganglia with classical findings.

Parkinsonian oscillations and their suppression by closed-loop deep brain stimulation based on fuzzy concept

This paper provides an adaptive closed-loop strategy for suppressing the pathological oscillations of the basal ganglia based on a variable universe fuzzy algorithm. The pathological basal ganglia oscillations in the theta (4–9 Hz) and beta (12–35 Hz) frequency bands have been demonstrated to be associated with the tremor and rigidity/bradykinesia symptoms in Parkinson’s disease (PD). Although the clinical application of open-loop deep brain stimulation (DBS) is effective, the stimulation waveform with the fixed parameters cannot be self-adjusted as the disease progresses, and thus the stimulation effects go poor. To deal with this difficult problem, a variable universe fuzzy closed-loop strategy is proposed to modulate different PD states. We establish a cortico-basal ganglia-thalamocortical network model to simulate pathological oscillations and test the control effect. The results suggest that the proposed closed-loop control strategy can accommodate the variation of brain states and symptoms, which may become an alternative method to administrate the symptoms in PD.

Adaptive closed-loop control strategy inhibiting pathological basal ganglia oscillations

The presence of pathological basal ganglia oscillations in the beta (12–35 Hz) frequency band is associated with Parkinson’s disease (PD). Suppressing the abnormal beta rhythm can effectively alleviate prominent PD movement disorders such as bradykinesia and rigidity. Brain stimulations, such as deep brain stimulation or transcranial stimulation, are effective therapeutic methods in managing the beta rhythm. However, electrostimulation using the current open-loop paradigm for stimulation is not optimal, especially when the controlled system experiences a substantial unknown disturbance. In this work we propose an adaptive radial basis function (ARBF) neural network strategy to achieve closed-loop brain stimulation based on real-time observed neural oscillation feedback. The underlying system is assumed to be an unknown nonlinear system, and the closed-loop strategy adaptively modulates the stimulation signal to cope with the abnormal neuronal discharge fluctuations, so as to eliminate the beta rhythm of the STN-GPe network. The proposed ARBF neural network closed-loop scheme is tested in a neural mass model composed of the subthalamic nucleus and external globus pallidus. It is shown that the performance of the ARBF controller is robust, including when internal synaptic connections within the basal ganglia network are enhanced to endogenously impact pathological conditions, and also when pathological oscillations are induced by exogenous cortical inputs. Simulation results demonstrate the effectiveness of the proposed closed-loop neuromodulation pattern based on an ARBF neural network. This work may help to develop DBS control systems with adaptive optimization and less network complexity.

Resting-State Low-Frequency Cerebellar Oscillations Can Be Abnormal in Parkinson's Disease.

Recent anatomical studies have shown connections between the basal ganglia and the cerebellum. The basal ganglia and cerebellum are major subcortical structures that influence motor and cognitive functions. Recent neuroimaging and animal studies have suggested the role of the cerebellum in the pathophysiology of Parkinson's disease. However, the role of cerebellar oscillations in PD has not been studied. Here, we recruited 75 PD and 39 healthy control subjects to collect cerebellar EEG during a resting-state condition. We followed the recently published protocols to collect cerebellar oscillations. Relative spectral power was computed in the delta, theta, beta, and gamma frequency bands. Further, we performed classifier methods to differentiate PD subjects from controls. We observed significantly increased theta-band (4-7Hz) relative power in the cerebellar electrodes in PD subjects compared to controls. We also found differences in different frequency bands between mid-cerebellar and nearby mid-occipital EEG signals. Classification analysis using mid-cerebellartheta relative power showed differentiation between PD and control groups. Our results demonstrate that in addition to established cortical and basal ganglia oscillations, abnormal cerebellar oscillations in the theta-band may also participate in the underlying pathophysiology of PD. We suggest that low-frequency cerebellar oscillations may be a potential target for non-invasive neuromodulation to improve PD symptoms.

Can pallidal deep brain stimulation generate high-frequency oscillations in basal ganglia in dystonia? A case report

Can pallidal deep brain stimulation generate high-frequency oscillations in basal ganglia in dystonia? A case report

Intrinsic neural network dynamics in catatonia.

Catatonia is a transnosologic psychomotor syndrome with high prevalence in schizophrenia spectrum disorders (SSD). There is mounting neuroimaging evidence that catatonia is associated with aberrant frontoparietal, thalamic and cerebellar regions. Large‐scale brain network dynamics in catatonia have not been investigated so far. In this study, resting‐state fMRI data from 58 right‐handed SSD patients were considered. Catatonic symptoms were examined on the Northoff Catatonia Rating Scale (NCRS). Group spatial independent component analysis was carried out with a multiple analysis of covariance (MANCOVA) approach to estimate and test the underlying intrinsic components (ICs) in SSD patients with (NCRS total score ≥ 3; n = 30) and without (NCRS total score = 0; n = 28) catatonia. Functional network connectivity (FNC) during rest was calculated between pairs of ICs and transient changes in connectivity were estimated using sliding windowing and clustering (to capture both static and dynamic FNC). Catatonic patients showed increased static FNC in cerebellar networks along with decreased low frequency oscillations in basal ganglia (BG) networks. Catatonic patients had reduced state changes and dwelled more in a state characterized by high within‐network correlation of the sensorimotor, visual, and default‐mode network with respect to noncatatonic patients. Finally, in catatonic patients according to DSM‐IV‐TR (n = 44), there was a significant correlation between increased within FNC in cortico‐striatal state and NCRS motor scores. The data support a neuromechanistic model of catatonia that emphasizes a key role of disrupted sensorimotor network control during distinct functional states.

Is there a single beta oscillation band interfering with movement in Parkinson's disease?

Beta oscillations in basal ganglia are considered to contribute to motor dysfunction in Parkinson's disease (PD). However, there is a high variety in frequency borders for beta oscillations between studies, which complicates the comparison and interpretation of results. Here we aimed to study the homogeneity of oscillations in the broad "beta" range (8-30Hz) and their implication to motor functioning in PD. For this purpose, we recorded local field potentials (LFP) in the subthalamic nucleus (STN) during 34 deep brain stimulation surgeries. We identified spectral features of LFP recordings in the range 8-30Hz to search for candidate sub-regions of stable oscillations and assessed their association with clinical scores on the contralateral side of the body and sensitivity to motor tests. Lower frequency oscillations (8-16Hz) had a significant positive association with bradykinesia score. During voluntary movements, we observed a significant increase in LFP power in the 12-16Hz range and a decrease in the 18-26Hz range. We may conclude that the 8-30Hz oscillation range includes oscillations with different functional features-sensitivity and responsiveness to movement, and clinical symptoms, which should be taken into account in further studies of beta oscillations association with PD pathophysiology. These data assume the coexistence of several frequency domains within beta range that are modulated in different ways under dopaminergic regulation and motor processing in human STN.

Brain Network Oscillations During Gait in Parkinson's Disease.

Human bipedal walking is a complex motor task that requires supraspinal control for balance and flexible coordination of timing and scaling of many muscles in different environment. Gait impairments are a hallmark of Parkinson’s disease (PD), reflecting dysfunction of cortico-basal ganglia-brainstem circuits. Recent studies using implanted electrodes and surface electroencephalography have demonstrated gait-related brain oscillations in the basal ganglia and cerebral cortex. Here, we review the physiological and pathophysiological roles of (1) basal ganglia oscillations, (2) cortical oscillations, and (3) basal ganglia-cortical interactions during walking. These studies extend a novel framework for movement of disorders where specific patterns of abnormal oscillatory synchronization in the basal ganglia thalamocortical network are associated with specific signs and symptoms. Therefore, we propose that many gait dysfunctions in PD arise from derangements in brain network, and discuss potential therapies aimed at restoring gait impairments through modulation of brain network in PD.

Uncoupling the roles of firing rates and spike bursts in shaping the STN-GPe beta band oscillations.

The excess of 15-30 Hz (β-band) oscillations in the basal ganglia is one of the key signatures of Parkinson's disease (PD). The STN-GPe network is integral to generation and modulation of β band oscillations in basal ganglia. However, the role of changes in the firing rates and spike bursting of STN and GPe neurons in shaping these oscillations has remained unclear. In order to uncouple their effects, we studied the dynamics of STN-GPe network using numerical simulations. In particular, we used a neuron model, in which firing rates and spike bursting can be independently controlled. Using this model, we found that while STN firing rate is predictive of oscillations, GPe firing rate is not. The effect of spike bursting in STN and GPe neurons was state-dependent. That is, only when the network was operating in a state close to the border of oscillatory and non-oscillatory regimes, spike bursting had a qualitative effect on the β band oscillations. In these network states, an increase in GPe bursting enhanced the oscillations whereas an equivalent proportion of spike bursting in STN suppressed the oscillations. These results provide new insights into the mechanisms underlying the transient β bursts and how duration and power of β band oscillations may be controlled by an interplay of GPe and STN firing rates and spike bursts.

Basal ganglia oscillations as biomarkers for targeting circuit dysfunction in Parkinson's disease.

Oscillations are a naturally occurring phenomenon in highly interconnected dynamical systems. However, it is thought that excessive synchronized oscillations in brain circuits can be detrimental for many brain functions by disrupting neuronal information processing. Because synchronized basal ganglia oscillations are a hallmark of Parkinson's disease (PD), it has been suggested that aberrant rhythmic activity associated with symptoms of the disease could be used as a physiological biomarker to guide pharmacological and electrical neuromodulatory interventions. We here briefly review the various manifestations of basal ganglia oscillations observed in human subjects and in animal models of PD. In this context, we also review the evidence supporting a pathophysiological role of different oscillations for the suppression of voluntary movements as well as for the induction of excessive motor activity. In light of these findings, it is discussed how oscillations could be used to guide a more precise targeting of dysfunctional circuits to obtain improved symptomatic treatment of PD.

Significance and Translational Value of High-Frequency Cortico-Basal Ganglia Oscillations in Parkinson's Disease.

The mechanisms and significance of basal ganglia oscillations is a fundamental research question engaging both clinical and basic investigators. In Parkinson’s disease (PD), neural activity in basal ganglia nuclei is characterized by oscillatory patterns that are believed to disrupt the dynamic processing of movement-related information and thus generate motor symptoms. Beta-band oscillations associated with hypokinetic states have been reviewed in several excellent previous articles. Here we focus on faster oscillatory phenomena that have been reported in association with a diverse range of motor states. We review the occurrence of different types of fast oscillations and the evidence supporting their pathophysiological role. We also provide a general discussion on the definition, possible mechanisms, and translational value of synchronized oscillations of different frequencies in cortico-basal ganglia structures. Revealing how oscillatory phenomena are caused and spread in cortico-basal ganglia-thalamocortical networks will offer a key to unlock the neural codes of both motor and non-motor symptoms in PD. In preclinical therapeutic research, recording of oscillatory neural activities holds the promise to unravel mechanisms of action of current and future treatments.

Incremental stability of delayed neural fields: a unifying framework for endogenous and exogenous sources of pathological oscillations

Neural fields are integro-differential equations that have been extensively used to model spatiotemporal evolution of neocortical areas (see [1] for a detailed review). Time-delayed neural fields have also been a matter of investigation since they take into account axonal delays [2]. On the other hand, time-delay finite dimensional systems have been used in models of Parkinson's disease: delays have been shown to play a possible role in the generation of pathological neural oscillations linked to motor symptoms of Parkinson disease in a firing-rate model of basal ganglia [3,4]. Nonetheless, these models fail at rendering the spatial distribution of the neural activity of the populations involved. Two possible mechanisms for the onset of pathological oscillations in basal ganglia have been investigated in the literature. The first one, the endogenous mechanism, hypothesizes that dopamine depletion tends to increase the synaptic gains between the excitatory neurons of the subthalamic nucleus (STN) and the inhibitory neurons of the external segment of globus pallidus (GPe), thus generating an instability that translates into sustained oscillations. The second one, the exogenous mechanism, explains these oscillations onset by a diffusion of spontaneous oscillations from external structures (such as Striatum) to the GPe-STN network [5]. The main goal of this work is to deepen this analysis by providing theoretical conditions under which a network of time-delayed neural field equations is incrementally stable. We believe that incremental stability constitutes an instrumental framework to investigate both the mechanisms evoked above. Indeed, by considering constant inputs to the basal ganglia, incremental stability ensures convergence to a unique equilibrium configuration, thus ruling out the possibility of endogenous mechanism for oscillations onset. On the other hand, incremental stability guarantees entrainability to periodic inputs (meaning convergence to a T-periodic solution in response to any T-periodic input), and can thus be useful to unravel the mechanism of pathological diffusion from external structures in the exogenous scenario. Relying on the Razumikhin-Lyapunov approach here we derive these sufficient conditions for incremental stability of delayed neural fields. This theoretical framework thus complements the Krasovskii-Lyapunov approach already used in the literature to address the stability of delayed neural fields equations [6]. Simulations confirm our theoretical expectations and demonstrate that interconnected neural fields can exhibit sustained oscillations, according to either the endogenous or the exogenous mechanism, depending on the strength of the synaptic weights between the excitatory (STN) and the inhibitory (GPe) populations. The derived theoretical results thus seem to constitute a fertile ground for further investigations based on experimental data, to discriminate between the endogenous and the exogenous hypotheses for Parkinsonian sustained oscillations in the STN-GPe network.

Mechanism of parkinsonian neuronal oscillations in the primate basal ganglia: some considerations based on our recent work.

Accumulating evidence suggests that abnormal neuronal oscillations in the basal ganglia (BG) contribute to the manifestation of parkinsonian symptoms. In this article, we would like to summarize our recent work on the mechanism underlying abnormal oscillations in the parkinsonian state and discuss its significance in pathophysiology of Parkinson’s disease. We recorded neuronal activity in the BG of parkinsonian monkeys treated with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. Systemic administration of L-DOPA alleviated parkinsonian motor signs and decreased abnormal neuronal oscillations (8–15 Hz) in the internal (GPi) and external (GPe) segments of the globus pallidus and the subthalamic nucleus (STN). Inactivation of the STN by muscimol (GABAA receptor agonist) injection also ameliorated parkinsonian signs and suppressed GPi oscillations. The blockade of glutamatergic inputs to the STN by local microinjection of a mixture of 3-(2-carboxypiperazin-4-yl)-propyl-1-phosphonic acid (glutamatergic NMDA receptor antagonist) and 1,2,3,4-tetrahydro-6-nitro-2,3-dioxo-benzo[f]quinoxaline-7-sulfonamide (glutamatergic AMPA/kainate receptor antagonist) suppressed neuronal oscillations in the STN. STN oscillations were also attenuated by the blockade of GABAergic neurotransmission from the GPe to the STN by muscimol inactivation of the GPe. These results suggest that cortical glutamatergic inputs to the STN and reciprocal GPe-STN interconnections are both important for the generation and amplification of the oscillatory activity of GPe and STN neurons in the parkinsonian state. The oscillatory activity in the STN is subsequently transmitted to the GPi and may contribute to manifestation of parkinsonian symptoms.

Analysis of delay-induced basal ganglia oscillations: the role of external excitatory nuclei

Basal ganglia are interconnected deep brain structures involved in movement generation. Their persistent beta-band oscillations (13–30 Hz) are known to be linked to Parkinson’s disease motor symptoms. In this paper, we provide conditions under which these oscillations may occur, by explicitly considering the role of the pedunculopontine nucleus (PPN). We analyse the existence of equilibria in the associated firing-rate dynamics and study their stability by relying on a delayed multiple-input/multiple-output (MIMO) frequency analysis. Our analysis suggests that the PPN has an influence on the generation of pathological beta-band oscillations. These results are illustrated by simulations that confirm numerically the analytic predictions of our two main theorems.

Fine temporal structure of neural synchronization

While neural synchronization is widely observed in neuroscience, neural oscillations are rarely in perfect synchrony and go in and out of phase in time. Since this synchrony is not perfect, the same synchrony strength may be achieved with markedly different temporal patterns of activity (roughly speaking oscillations may go out of the phase-locked state for many short episodes or few long episodes). Recent developments in the time-series analysis allowed for the investigation of the temporal variations of phase-locking with a high temporal resolution [1]. Provided that there is some average level of phase-locking is present, one can follow oscillations from cycle to cycle and to observe if the phase difference is close to the preferred phase lag or not [1]. Recent study [2] of neural oscillations in basal ganglia in Parkinson's disease revealed the patterning of neural phase-locking: the synchronized state was interrupted by numerous but mostly short desynchronization states. Here we study neural oscillations recorded by EEG in alpha and beta frequency bands in a large sample of healthy human subjects at rest and during the execution of a simple motor task. While the phase-locking strength depends on many factors, dynamics of synchrony has a very specific temporal pattern: synchronous states are interrupted by frequent, but short desynchronization episodes. The probability for a desynchronization episode to occur decreased with its duration (see Figure ​Figure1).1). The modes and medians of distributions of desynchronization durations were always just one cycle of oscillations. Figure 1 (A) distribution of desynchronization durations and (B) mean desynchronization duration at the beta band for all EEG electrodes at rest (Baseline) and during motor task (Task) as well as for C3 and C4 electrodes (Task C3-C4). Similar temporal patterning of synchrony in different brain areas in different states may suggest that i) this type of patterning is a generic phenomenon in the brain, ii) it may have some functional advantages for oscillating neural networks receiving, processing, and transmitting information, iii) it may be grounded in some general properties of neuronal networks calling for the development of appropriate nonlinear dynamical theory. To further investigate these conjectures we numerically studied a system of two and three coupled simple neuronal models (of Morris-Lecar type) and showed that fast kinetic of ionic conductances leads to the emergence of short desynchronized events when the coupling strength is below the full synchronization threshold. We further show that coupled neural oscillators exhibiting short desynchronizations require smaller values of synaptic connections between them of weaker common synaptic input to induce specified levels of synchrony strength than oscillators of the same frequency exhibiting more prolong desynchronizations. The results may suggests that whenever a (partially) synchronous cell assembly must be formed to facilitate some function, short desynchronization dynamics may allow for efficient formation and break-up of such an assembly.

P7 Basal ganglia oscillations during gait in Parkinsonian patients

P7 Basal ganglia oscillations during gait in Parkinsonian patients

A basis for the pathological oscillations in basal ganglia

aDepartment of Physiology, Faculty of Medicine bFaculty of Dentistry, University of Toronto, Toronto, Canada Correspondence to Jonathan O. Dostrovsky, PhD, Department of Physiology, Medical Sciences Building, Room 3302, 1 King's College Circle, University of Toronto, Toronto, Ontario M5S1A8, Canada Tel: +1 416 978 5289; fax: +1 416 978 4940; e-mail: [email protected] Received November 16, 2010 Accepted November 16, 2010

Basal ganglia oscillations during gait in Parkinsonian patients

Introduction: It has been observed that local field potential (LFP) oscillations in the 13–30Hz frequency band are related to the pathophysiology of the basal ganglia of Parkinson's disease. Here we hypothesized that these pathophysiological oscillations modulate during gait in Parkinson's disease where subjects have freezing and non-freezing phenomenon.

Ictal and peri-ictal oscillations in the human basal ganglia in temporal lobe epilepsy

Preictal, ictal, and postictal oscillations in the basal ganglia were analyzed. Five persons with temporal lobe epilepsy who were candidates for surgery had diagonal depth electrodes implanted in the basal ganglia: four of them in the putamen, and one in the pallidum and caudate. Time–frequency and power spectral analyses were used to analyze the EEG. Significant frequency components of 2–10Hz were consistently observed in the basal ganglia. The frequency of this component slowed during seizures. There was a significant ictal increase in power spectral density in all frequency ranges. The changes in the basal ganglia were consistent while seizure activity spread over the cortex, and partially persisted after the clinical seizure ended. They were inconsistent in the period after seizure onset. Seizures originating in the mesiotemporal structures can affect physiological rhythms in the basal ganglia. The basal ganglia did not generate epileptiform EEG activity. An inhibitory role for the basal ganglia during temporal lobe seizures is suggested.