High-frequency oscillations (>200Hz) in the human non-parkinsonian subthalamic nucleus

Evolution of interictal activity in models of mesial temporal lobe epilepsy

Interictal activity and seizures are the hallmarks of focal epileptic disorders (which include mesial temporal lobe epilepsy, MTLE) in humans and in animal models. Interictal activity, which is recorded with cortical and intracerebral EEG recordings, comprises spikes, sharp waves and high-frequency oscillations, and has been used in clinical practice to identify the epileptic zone. However, its relation with seizures remains debated. Moreover, it is unclear whether specific EEG changes in interictal activity occur during the time preceding the appearance of spontaneous seizures. This period, which is termed "latent", has been studied in rodent models of MTLE in which spontaneous seizures start to occur following an initial insult (most often a status epilepticus induced by convulsive drugs such as kainic acid or pilocarpine) and may mirror epileptogenesis, i.e., the process leading the brain to develop an enduring predisposition to seizure generation. Here, we will address this topic by reviewing experimental studies performed in MTLE models. Specifically, we will review data highlighting the dynamic changes in interictal spiking activity and high-frequency oscillations occurring during the latent period, and how optogenetic stimulation of specific cell populations can modulate them in the pilocarpine model. These findings indicate that interictal activity: (i) is heterogeneous in its EEG patterns and thus, presumably, in its underlying neuronal mechanisms; and (ii) can pinpoint to the epileptogenic processes occurring in focal epileptic disorders in animal models and, perhaps, in epileptic patients.

Multi-disease Deep Brain Stimulation

Current closed-loop deep brain stimulation (DBS) devices can generally tackle one disorder. This paper presents the design and evaluation of a multi-disease closed-loop DBS device that can sense multiple brain biomarkers, detect a disorder, and adaptively deliver electrical stimulation pulses based on the disease state. The device consists of: (i) a neural sensor, (ii) a controller involving a feature extractor, a disease classifier, and a control strategy, and (iii) neural stimulator. The neural sensor records and processes local field potentials and spikes from within the brain using two low-frequency and high-frequency channels. The feature extractor digitally processes the output of the neural sensor, and extracts five potential biomarkers: alpha, beta, slow gamma, high-frequency oscillations, and spikes. The disease classifier identifies the type of the neurological disorder through an analysis of the biomarkers' amplitude features. The control strategy considers the disease state and supplies the stimulation settings to the neural stimulator. Both the disease classifier and control strategy are based on fuzzy algorithms. The neural stimulator generates electrical stimulation pulses according to the control commands, and delivers them to the target area of the brain. The device can generate current stimulation pulses with specific amplitude, frequency, and duration. The fabricated device has the maximum radius of 15 mm. Its total weight including the circuit board, battery and battery holder is 5.1 g. The performance of the integrated device has been evaluated through six bench and in-vitro experiments. The experimental results are presented, analyzed, and discussed. Six bench and in-vitro experiments were conducted using sinusoidal, normal pre-recorded, and diseased neural signals representing normal, epilepsy, depression and PD conditions. The results obtained through these tests indicate the successful neural sensing, classification, control, and neural stimulating performance.

A Miniature Dual-Biomarker-Based Sensing and Conditioning Device for Closed-Loop DBS

In this paper, a dual-biomarker-based neural sensing and conditioning device is proposed for closing the feedback loop in deep brain stimulation devices. The device explores both local field potentials (LFPs) and action potentials (APs) as measured biomarkers. It includes two channels, each having four main parts: (1) a pre-amplifier with built-in low-pass filter, (2) a ground shifting circuit, (3) an amplifier with low-pass function, and (4) a high-pass filter. The design specifications include miniature-size, light-weight, and 100 dB gain in the LFP and AP channels. This device has been validated through bench and in-vitro tests. The bench tests have been performed using different sinusoidal signals and pre-recorded neural signals. The in-vitro tests have been conducted in the saline solution that mimics the brain environment. The total weight of the device including a 3 V coin battery, and battery holder is 1.2 g. The diameter of the device is 11.2 mm. The device can be used to concurrently sense LFPs and APs for closing the feedback loop in closed-loop deep brain stimulation systems. It provides a tetherless head-mountable platform suitable for pre-clinical trials.

A Programmable Multi-biomarker Neural Sensor for Closed-loop DBS

Most of the current closed-loop DBS devices use a single biomarker in their feedback loop which may limit their performance and applications. This paper presents design, fabrication, and validation of a programmable multi-biomarker neural sensor which can be integrated into closed-loop DBS devices. The device is capable of sensing a combination of low-frequency (7-45 Hz), and high-frequency (200-1000 Hz) neural signals. The signals can be amplified with a digitally programmable gain within the range 50-100 dB. The neural signals can be stored into a local memory for processing and validation. The sensing and storage functions are implemented via a combination of analog and digital circuits involving preamplifiers, filters, programmable post-amplifiers, microcontroller, digital potentiometer, and flash memory. The device is fabricated, and its performance is validated through: (i) bench tests using sinusoidal and pre-recorded neural signals, (ii) in-vitro tests using pre-recorded neural signals in saline solution, and (iii) in-vivo tests by recording neural signals from freely-moving laboratory mice. The animals were implanted with a PlasticsOne electrode, and recording was conducted after recovery from the electrode implantation surgery. The experimental results are presented and discussed confirming the successful operation of the device. The size and weight of the device enable tetherless back-mountable use in pre-clinical trials.

Occurrence of thalamic high frequency oscillations in patients with different tremor syndromes

OBJECTIVE

To assess whether high frequency oscillations (HFOs, >150 Hz), known to occur in basal ganglia nuclei, can be observed in the thalamus.

METHODS

We recorded intraoperative local field potentials from the ventral intermediate nucleus (VIM) of the thalamus in patients with Essential Tremor (N = 16), Parkinsonian Tremor (3), Holmes Tremor (2) and Dystonic Tremor (1) during implantation of electrodes for deep brain stimulation. Recordings were performed with up to five micro/macro-electrodes that were simultaneously advanced to the stereotactic target.

RESULTS

Thalamic HFOs occurred in all investigated tremor syndromes. A detailed analysis of the Essential Tremor subgroup revealed that medial channels recorded HFOs more frequently than other channels. The highest peaks were observed 4 mm above target. Macro- but not microelectrode recordings were dominated by peaks in the slow HFO band (150-300 Hz), which were stable across several depths and channels.

CONCLUSION

HFOs occur in the thalamus and are not specific to any of the tremors investigated. Their spatial distribution is not homogeneous, and their appearance depends on the type of electrode used for recording.

SIGNIFICANCE

The occurrence of HFOs in the thalamus of tremor patients indicates that HFOs are not part of basal ganglia pathophysiology.

Parkinsonian Rest Tremor Is Associated With Modulations of Subthalamic High-Frequency Oscillations

BACKGROUND

High frequency oscillations (>200 Hz) have been observed in the basal ganglia of PD patients and were shown to be modulated by the administration of levodopa and voluntary movement.

OBJECTIVE

The objective of this study was to test whether the power of high-frequency oscillations in the STN is associated with spontaneous manifestation of parkinsonian rest tremor.

METHODS

The electromyogram of both forearms and local field potentials from the STN were recorded in 11 PD patients (10 men, age 58 [9.4] years, disease duration 9.2 [6.3] years). Patients were recorded at rest and while performing repetitive hand movements before and after levodopa intake. High-frequency oscillation power was compared across epochs containing rest tremor, tremor-free rest, or voluntary movement and related to the tremor cycle.

RESULTS

We observed prominent slow (200-300 Hz) and fast (300-400 Hz) high-frequency oscillations. The ratio between slow and fast high-frequency oscillation power increased when tremor became manifest. This increase was consistent across nuclei (94%) and occurred in medication ON and OFF. The ratio outperformed other potential markers of tremor, such as power at individual tremor frequency, beta power, or low gamma power. For voluntary movement, we did not observe a significant difference when compared with rest or rest tremor. Finally, rhythmic modulations of high-frequency oscillation power occurred within the tremor cycle.

CONCLUSIONS

Subthalamic high-frequency oscillation power is closely linked to the occurrence of parkinsonian rest tremor. The balance between slow and fast high-frequency oscillation power combines information on motor and medication state. © 2016 International Parkinson and Movement Disorder Society.

Subthalamic local field potentials in Parkinson's disease and isolated dystonia: An evaluation of potential biomarkers

Local field potentials (LFP) recorded from the subthalamic nucleus in patients with Parkinson's disease (PD) demonstrate prominent oscillations in the beta (13-30 Hz) frequency range, and reduction of beta band spectral power by levodopa and deep brain stimulation (DBS) is correlated with motor symptom improvement. Several features of beta activity have been theorized to be specific biomarkers of the parkinsonian state, though these have rarely been studied in non-parkinsonian conditions. To compare resting state LFP features in PD and isolated dystonia and evaluate disease-specific biomarkers, we recorded subthalamic LFPs from 28 akinetic-rigid PD and 12 isolated dystonia patients during awake DBS implantation. Spectral power and phase-amplitude coupling characteristics were analyzed. In 26/28 PD and 11/12 isolated dystonia patients, the LFP power spectrum had a peak in the beta frequency range, with similar amplitudes between groups. Resting state power did not differ between groups in the theta (5-8 Hz), alpha (8-12 Hz), beta (13-30 Hz), broadband gamma (50-200 Hz), or high frequency oscillation (HFO, 250-350 Hz) bands. Analysis of phase-amplitude coupling between low frequency phase and HFO amplitude revealed significant interactions in 19/28 PD and 6/12 dystonia recordings without significant differences in maximal coupling or preferred phase. Two features of subthalamic LFPs that have been proposed as specific parkinsonian biomarkers, beta power and coupling of beta phase to HFO amplitude, were also present in isolated dystonia, including focal dystonias. This casts doubt on the utility of these metrics as disease-specific diagnostic biomarkers.

Hippocampal sharp wave-ripple: A cognitive biomarker for episodic memory and planning

Sharp wave ripples (SPW-Rs) represent the most synchronous population pattern in the mammalian brain. Their excitatory output affects a wide area of the cortex and several subcortical nuclei. SPW-Rs occur during "off-line" states of the brain, associated with consummatory behaviors and non-REM sleep, and are influenced by numerous neurotransmitters and neuromodulators. They arise from the excitatory recurrent system of the CA3 region and the SPW-induced excitation brings about a fast network oscillation (ripple) in CA1. The spike content of SPW-Rs is temporally and spatially coordinated by a consortium of interneurons to replay fragments of waking neuronal sequences in a compressed format. SPW-Rs assist in transferring this compressed hippocampal representation to distributed circuits to support memory consolidation; selective disruption of SPW-Rs interferes with memory. Recently acquired and pre-existing information are combined during SPW-R replay to influence decisions, plan actions and, potentially, allow for creative thoughts. In addition to the widely studied contribution to memory, SPW-Rs may also affect endocrine function via activation of hypothalamic circuits. Alteration of the physiological mechanisms supporting SPW-Rs leads to their pathological conversion, "p-ripples," which are a marker of epileptogenic tissue and can be observed in rodent models of schizophrenia and Alzheimer's Disease. Mechanisms for SPW-R genesis and function are discussed in this review.

Clinical implications of local field potentials for understanding and treating movement disorders

BACKGROUND

Deep brain stimulation (DBS) for the treatment of movement disorders has provided researchers with an opportunity to record electrical oscillatory activity from electrodes implanted in deep brain structures. Extracellular activity recorded from a population of neurons, termed local field potentials (LFPs), has shed light on the pathophysiology of movement disorders and holds the potential to lead to refinement in existing treatments.

OBJECTIVE

This paper reviews the clinical significance of LFPs recorded from macroelectrodes implanted in basal ganglia and thalamic targets for the treatment of Parkinson's disease, essential tremor and dystonia.

METHODS

Neural population dynamics and subthreshold events, which are undetectable by single-unit recordings, can be examined with frequency band analysis of LFPs (frequency range: 1-250 Hz).

RESULTS

Of clinical relevance, reliable correlations between motor symptoms and components of the LFP power spectrum suggest that LFPs may serve as a biomarker for movement disorders. In particular, Parkinson's rigidity has been shown to correlate with the power of beta oscillations (13-30 Hz), and essential tremor coheres with oscillations of 8-27 Hz. Furthermore, evidence indicates that the optimal contacts for DBS programming can be predicted from the anatomic location of beta and gamma bands (48-200 Hz).

CONCLUSION

LFP analysis has implications for improved electrode targeting and the development of a real-time, individualized, 'closed-loop' stimulation system.

High-frequency oscillations in Parkinson's disease: spatial distribution and clinical relevance

The pathophysiology of Parkinson's disease (PD) has been related to excessive beta band oscillations in the basal ganglia. Recent recordings from the subthalamic nucleus of PD patients showed that beta oscillations show strong cross-frequency coupling with high-frequency oscillations (>200 Hz). However, little is known about the characteristics and functional properties of these oscillations. We studied the spatial distribution of high-frequency oscillations and their relation to PD motor symptoms. We included 10 PD patients in medication OFF who underwent implantation of deep brain stimulation (DBS) electrodes. Intraoperative five-channel microelectrode recordings were performed at 9 to 10 recording sites within the subthalamic nucleus and its immediate surroundings. We found a focal spatial distribution of high-frequency oscillations with highest power 2 mm below the dorsolateral border of the subthalamic nucleus. Within the subthalamic nucleus, power peaked slightly anterior to the DBS target site. In addition, contralateral akinesia/rigidity scores were negatively correlated with high-frequency oscillation power. Our results demonstrate a focal origin of high-frequency oscillations within the subthalamic nucleus and provide further evidence for their functional association with motor state.

Subthalamic nucleus long-range synchronization-an independent hallmark of human Parkinson's disease

Beta-band synchronous oscillations in the dorsolateral region of the subthalamic nucleus (STN) of human patients with Parkinson's disease (PD) have been frequently reported. However, the correlation between STN oscillations and synchronization has not been thoroughly explored. The simultaneous recordings of 2390 multi-unit pairs recorded by two parallel microelectrodes (separated by fixed distance of 2 mm, n = 72 trajectories with two electrode tracks >4 mm STN span) in 57 PD patients undergoing STN deep brain stimulation surgery were analyzed. Automatic procedures were utilized to divide the STN into dorsolateral oscillatory and ventromedial non-oscillatory regions, and to quantify the intensity of STN oscillations and synchronicity. Finally, the synchronicity of simultaneously vs. non-simultaneously recorded pairs were compared using a shuffling procedure. Synchronization was observed predominately in the beta range and only between multi-unit pairs in the dorsolateral oscillatory region (n = 615). In paired recordings between sites in the dorsolateral and ventromedial (n = 548) and ventromedial-ventromedial region pairs (n = 1227), no synchronization was observed. Oscillation and synchronicity intensity decline along the STN dorsolateral-ventromedial axis suggesting a fuzzy border between the STN regions. Synchronization strength was significantly correlated to the oscillation power, but synchronization was no longer observed following shuffling. We conclude that STN long-range beta oscillatory synchronization is due to increased neuronal coupling in the Parkinsonian brain and does not merely reflect the outcome of oscillations at similar frequency. The neural synchronization in the dorsolateral (probably the motor domain) STN probably augments the pathological changes in firing rate and patterns of subthalamic neurons in PD patients.

Delta-mediated cross-frequency coupling organizes oscillatory activity across the rat cortico-basal ganglia network

The brain's ability to integrate different behavioral and cognitive processes relies on its capacity to generate neural oscillations in a cooperative and coordinated manner. Cross-frequency coupling (CFC) has recently been proposed as one of the mechanisms involved in organizing brain activity. Here we investigated the phase-to-amplitude CFC (PA-CFC) patterns of the oscillatory activity in the cortico-basal ganglia network of healthy, freely moving rats. Within-structure analysis detected consistent PA-CFC patterns in the four regions analyzed, with the phase of delta waves modulating the amplitude of activity in the gamma (low-gamma ~50 Hz; high-gamma ~80 Hz) and high frequency ranges (high frequency oscillations HFO, ~150 Hz). Between-structure analysis revealed that the phase of delta waves parses the occurrence of transient episodes of coherence in the gamma and high frequency bands across the entire network, providing temporal windows of coherence between different structures. Significantly, this specific spatio-temporal organization was affected by the action of dopaminergic drugs. Taken together, our findings suggest that delta-mediated PA-CFC plays a key role in the organization of local and distant activities in the rat cortico-basal ganglia network by fine-tuning the timing of synchronization events across different structures.

What basal ganglia changes underlie the parkinsonian state? The significance of neuronal oscillatory activity

One well accepted functional feature of the parkinsonian state is the recording of enhanced beta oscillatory activity in the basal ganglia. This has been demonstrated in patients with Parkinson's disease (PD) and in animal models such as the rat with 6-hydroxydopamine (6-OHDA)-induced lesion and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treated monkeys, all of which are associated with severe striatal dopamine depletion. Neuronal hyper-synchronization in the beta (or any other) band is not present despite the presence of bradykinetic features in the rat and monkey models, suggesting that increased beta band power may arise when nigro-striatal lesion is advanced and that it is not an essential feature of the early parkinsonian state. Similar observations and conclusions have been previously made for increased neuronal firing rate in the subthalamic and globus pallidus pars interna nuclei. Accordingly, it is suggested that early parkinsonism may be associated with dynamic changes in basal ganglia output activity leading to reduced movement facilitation that may be an earlier feature of the parkinsonian state.

Parameter identification for a local field potential driven model of the Parkinsonian subthalamic nucleus spike activity

Several models, with various degrees of complexity have been proposed to model the neuronal activity from different parts of the human brain. We have shown before that various modeling approaches, including a Hammerstein-Wiener (H-W) model, can be used to predict the spike trains from a deep nucleus, the subthalamic nucleus, using the underlying local field potentials. In this article, we present, in depth, the various choices one has to make, and the limitations that they introduce, during the H-W model parameter identification process. From a segment of the recorded data, which contains information about the spike times of a single neuron, we identify and extract the model parameters. We then use those parameters to numerically simulate the spike timing, the rhythm and the inter-spike intervals for the rest of the recording. To assess how well the model fits to the measured data we combine measures of spike train synchrony, namely the Victor-Purpura distance and the Gaussian similarity measure, with time-scale independent train distances. We show that a wise combination of metrics results in models that predict the spikes with temporal accuracy ranging, on average, from 53% to more than 80%, depending on the number of the neurons' spikes recorded. The model's prediction is adequate for estimating accurately the spike rhythm. Quantitative results establish the model's validity as a simple yet biologically plausible model of the spike activity recorded from a deep nucleus inside the human brain.

Mechanisms of physiological and epileptic HFO generation

High frequency oscillations (HFO) have a variety of characteristics: band-limited or broad-band, transient burst-like phenomenon or steady-state. HFOs may be encountered under physiological or under pathological conditions (pHFO). Here we review the underlying mechanisms of oscillations, at the level of cells and networks, investigated in a variety of experimental in vitro and in vivo models. Diverse mechanisms are described, from intrinsic membrane oscillations to network processes involving different types of synaptic interactions, gap junctions and ephaptic coupling. HFOs with similar frequency ranges can differ considerably in their physiological mechanisms. The fact that in most cases the combination of intrinsic neuronal membrane oscillations and synaptic circuits are necessary to sustain network oscillations is emphasized. Evidence for pathological HFOs, particularly fast ripples, in experimental models of epilepsy and in human epileptic patients is scrutinized. The underlying mechanisms of fast ripples are examined both in the light of animal observations, in vivo and in vitro, and in epileptic patients, with emphasis on single cell dynamics. Experimental observations and computational modeling have led to hypotheses for these mechanisms, several of which are considered here, namely the role of out-of-phase firing in neuronal clusters, the importance of strong excitatory AMPA-synaptic currents and recurrent inhibitory connectivity in combination with the fast time scales of IPSPs, ephaptic coupling and the contribution of interneuronal coupling through gap junctions. The statistical behaviour of fast ripple events can provide useful information on the underlying mechanism and can help to further improve classification of the diverse forms of HFOs.

Ketamine-induced oscillations in the motor circuit of the rat basal ganglia

Oscillatory activity can be widely recorded in the cortex and basal ganglia. This activity may play a role not only in the physiology of movement, perception and cognition, but also in the pathophysiology of psychiatric and neurological diseases like schizophrenia or Parkinson's disease. Ketamine administration has been shown to cause an increase in gamma activity in cortical and subcortical structures, and an increase in 150 Hz oscillations in the nucleus accumbens in healthy rats, together with hyperlocomotion.We recorded local field potentials from motor cortex, caudate-putamen (CPU), substantia nigra pars reticulata (SNr) and subthalamic nucleus (STN) in 20 awake rats before and after the administration of ketamine at three different subanesthetic doses (10, 25 and 50 mg/Kg), and saline as control condition. Motor behavior was semiautomatically quantified by custom-made software specifically developed for this setting.Ketamine induced coherent oscillations in low gamma (~ 50 Hz), high gamma (~ 80 Hz) and high frequency (HFO, ~ 150 Hz) bands, with different behavior in the four structures studied. While oscillatory activity at these three peaks was widespread across all structures, interactions showed a different pattern for each frequency band. Imaginary coherence at 150 Hz was maximum between motor cortex and the different basal ganglia nuclei, while low gamma coherence connected motor cortex with CPU and high gamma coherence was more constrained to the basal ganglia nuclei. Power at three bands correlated with the motor activity of the animal, but only coherence values in the HFO and high gamma range correlated with movement. Interactions in the low gamma band did not show a direct relationship to movement.These results suggest that the motor effects of ketamine administration may be primarily mediated by the induction of coherent widespread high-frequency activity in the motor circuit of the basal ganglia, together with a frequency-specific pattern of connectivity among the structures analyzed.

High frequency oscillations in the subthalamic nucleus: a neurophysiological marker of the motor state in Parkinson's disease

Increasing evidence suggests that abnormal oscillatory activity in basal ganglia and cortex plays a pivotal role in the pathophysiology of Parkinson's disease. Recordings of local field potentials from subthalamic nucleus of patients undergoing deep brain stimulation have focused on oscillations occurring at frequencies below 100 Hz in the alpha, beta and gamma range and suggested that, in particular, an increase of beta band oscillations underlies slowing of movement in Parkinson's disease. Recent findings showing that the amplitude of high frequency oscillations (>200 Hz) couples with the phase of beta activity have raised the important question about the role of subthalamic high frequency oscillations in Parkinson's disease. To investigate functional characteristics and clinical relevance of high frequency oscillations, we recorded local field potentials from 18 subthalamic nuclei of 9 akinetic-rigid Parkinsonian patients with implanted deep brain stimulation electrodes and still externalised leads before and after intake of levodopa. We identified two distinct bands of high frequency oscillations, one centred around 250 Hz and another one around 350 Hz that show characteristic levodopa dependent amplitude and coupling behaviours. Administration of levodopa changed the power ratio between the two high frequency bands towards the component centred around 350 Hz in all 18 nuclei under study (p<10(-4)). Moreover, this power ratio correlated significantly with the Unified Parkinson's Disease Rating Scale hemibody akinesia/rigidity subscore (r=0.3618, p=0.015), but interestingly not with beta peak power (p=0.1) suggesting that levodopa induced changes in high frequency and beta oscillations are at least potentially independent of each other. Accordingly, a combined parameter composed of power ratio of high frequency oscillations and beta peak power significantly increased the correlation with the motor state (r=0.45, p=0.004). These results indicate that a shift from slower to faster frequencies of the spectrum greater than 200 Hz represents a prokinetic neurophysiological marker underlying levodopa induced motor improvement in Parkinson's disease.

Coupling between beta and high-frequency activity in the human subthalamic nucleus may be a pathophysiological mechanism in Parkinson's disease

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

Dopamine-dependent high-frequency oscillatory activity in thalamus and subthalamic nucleus of patients with Parkinson's disease

In recent years there has been great interest in oscillatory activity in the brain and in the role of pathological oscillations in the basal ganglia in mediating some of the symptoms of Parkinson's disease (PD). In this study, thalamic and subthalamic nucleus local field potentials were intraoperatively recorded from pairs of closely separated microelectrodes in six PD patients ON and OFF dopaminergic medication. Using correlation and coherence analyses, we found high-frequency oscillatory activity in the 110-170 Hz band in thalamus in patients OFF dopamine. These oscillations were significantly reduced after administration of dopamine-replacement therapy. In contrast, activity in the lower frequencies (the theta and beta ranges) was increased. However, in subthalamic nucleus, we observed an increase in high-frequency oscillatory activity (150-200 Hz), and a reduction of the activity in the low-frequency range after levodopa administration. These findings confirm and extend earlier findings suggesting that in PD there are marked changes in basal ganglia oscillatory activity and that these can be reversed after dopaminergic therapy.

Identifying rhythms of subthalamic neural oscillations in time-frequency domain

We aimed to identify neural oscillations in the time-frequency representation of local field potentials recorded from the subthalamic nucleus. The time-frequency representation was normalised over the global mean and standard deviation global normalisation, or against the baseline period at each frequency, local normalisation. The cross-correlation between beta and gamma oscillations was enhanced by global normalisation. Furthermore, voluntary movement related amplitude changes in the gamma band and frequency modulation in the beta band were revealed by local normalisation. Thus global or local normalisation of time-frequency representation provides a reliable and effective way to identify oscillatory rhythms in subthalamic neural activity by reducing noise and increasing frequency discrimination. It can be used to enhance the detection of obscure or hidden neural oscillations and improve the sensitivity of post-hoc analysis.