Temporal evolution of oscillations and synchrony in GPi/muscle pairs in Parkinson's disease

Adaptive Stimulations in a Biophysical Network Model of Parkinson’s Disease

Deep brain stimulation (DBS)—through a surgically implanted electrode to the subthalamic nucleus (STN)—has become a widely used therapeutic option for the treatment of Parkinson’s disease and other neurological disorders. The standard conventional high-frequency stimulation (HF) that is currently used has several drawbacks. To overcome the limitations of HF, researchers have been developing closed-loop and demand-controlled, adaptive stimulation protocols wherein the amount of current that is delivered is turned on and off in real-time in accordance with a biophysical signal. Computational modeling of DBS in neural network models is an increasingly important tool in the development of new protocols that aid researchers in animal and clinical studies. In this computational study, we seek to implement a novel technique of DBS where we stimulate the STN in an adaptive fashion using the interspike time of the neurons to control stimulation. Our results show that our protocol eliminates bursts in the synchronized bursting neuronal activity of the STN, which is hypothesized to cause the failure of thalamocortical neurons (TC) to respond properly to excitatory cortical inputs. Further, we are able to significantly decrease the TC relay errors, representing potential therapeutics for Parkinson’s disease.

Amplitude and frequency modulation of subthalamic beta oscillations jointly encode the dopaminergic state in Parkinson's disease

Brain states in health and disease are classically defined by the power or the spontaneous amplitude modulation (AM) of neuronal oscillations in specific frequency bands. Conversely, the possible role of the spontaneous frequency modulation (FM) in defining pathophysiological brain states remains unclear. As a paradigmatic example of pathophysiological resting states, here we assessed the spontaneous AM and FM dynamics of subthalamic beta oscillations recorded in patients with Parkinson's disease before and after levodopa administration. Even though AM and FM are mathematically independent, they displayed negatively correlated dynamics. First, AM decreased while FM increased with levodopa. Second, instantaneous amplitude and instantaneous frequency were negatively cross-correlated within dopaminergic states, with FM following AM by approximately one beta cycle. Third, AM and FM changes were also negatively correlated between dopaminergic states. Both the slow component of the FM and the fast component (i.e. the phase slips) increased after levodopa, but they differently contributed to the AM-FM correlations within and between states. Finally, AM and FM provided information about whether the patients were OFF vs. ON levodopa, with partial redundancy and with FM being more informative than AM. AM and FM of spontaneous beta oscillations can thus both separately and jointly encode the dopaminergic state in patients with Parkinson's disease. These results suggest that resting brain states are defined not only by AM dynamics but also, and possibly more prominently, by FM dynamics of neuronal oscillations.

Transmission of delta band (0.5-4 Hz) oscillations from the globus pallidus to the substantia nigra pars reticulata in dopamine depletion

Parkinson's disease (PD) and animal models of PD feature enhanced oscillations in several frequency bands in the basal ganglia (BG). Past research has emphasized the enhancement of 13-30 Hz beta oscillations. Recently, however, oscillations in the delta band (0.5-4 Hz) have been identified as a robust predictor of dopamine loss and motor dysfunction in several BG regions in mouse models of PD. In particular, delta oscillations in the substantia nigra pars reticulata (SNr) were shown to lead oscillations in motor cortex (M1) and persist under M1 lesion, but it is not clear where these oscillations are initially generated. In this paper, we use a computational model to study how delta oscillations may arise in the SNr due to projections from the globus pallidus externa (GPe). We propose a network architecture that incorporates inhibition in SNr from oscillating GPe neurons and other SNr neurons. In our simulations, this configuration yields firing patterns in model SNr neurons that match those measured in vivo. In particular, we see the spontaneous emergence of near-antiphase active-predicting and inactive-predicting neural populations in the SNr, which persist under the inclusion of STN inputs based on experimental recordings. These results demonstrate how delta oscillations can propagate through BG nuclei despite imperfect oscillatory synchrony in the source site, narrowing down potential targets for the source of delta oscillations in PD models and giving new insight into the dynamics of SNr oscillations.

Identifying and modulating distinct tremor states through peripheral nerve stimulation in Parkinsonian rest tremor

BACKGROUND

Resting tremor is one of the most common symptoms of Parkinson's disease. Despite its high prevalence, resting tremor may not be as effectively treated with dopaminergic medication as other symptoms, and surgical treatments such as deep brain stimulation, which are effective in reducing tremor, have limited availability. Therefore, there is a clinical need for non-invasive interventions in order to provide tremor relief to a larger number of people with Parkinson's disease. Here, we explore whether peripheral nerve stimulation can modulate resting tremor, and under what circumstances this might lead to tremor suppression.

METHODS

We studied 10 people with Parkinson's disease and rest tremor, to whom we delivered brief electrical pulses non-invasively to the median nerve of the most tremulous hand. Stimulation was phase-locked to limb acceleration in the axis with the biggest tremor-related excursion.

RESULTS

We demonstrated that rest tremor in the hand could change from one pattern of oscillation to another in space. Median nerve stimulation was able to significantly reduce (- 36%) and amplify (117%) tremor when delivered at a certain phase. When the peripheral manifestation of tremor spontaneously changed, stimulation timing-dependent change in tremor severity could also alter during phase-locked peripheral nerve stimulation.

CONCLUSIONS

These results highlight that phase-locked peripheral nerve stimulation has the potential to reduce tremor. However, there can be multiple independent tremor oscillation patterns even within the same limb. Parameters of peripheral stimulation such as stimulation phase may need to be adjusted continuously in order to sustain systematic suppression of tremor amplitude.

Different effects of essential tremor and Parkinsonian tremor on multiscale dynamics of hand tremor

OBJECTIVE

Essential tremor (ET) and Parkinsonian tremor (PT) are often clinically misdiagnosed due to the overlapping characteristics of their hand tremor. We aim to examine if ET and PT influence the multiscale dynamics of hand tremor, as quantified using complexity, differently, and if such complexity metric is of promise to help identify ET from PT.

METHODS

Forty-eight participants with PT and 48 with ET performed two 30-second tests within each of the following conditions: sitting while resting arms or outstretching arms horizontally. The hand tremor was captured by accelerometers secured to the dorsum of each hand. The complexity was quantified using multiscale entropy.

RESULTS

Compared to PT group, ET group had lower complexity of both hands across conditions (F > 34.2, p < 0.001). Lower complexity was associated with longer disease duration (r² > 0.15, p < 0.009) in both PT and ET, and within PT, greater Unified Parkinson's Disease Rating Scale-III UPDRS-III scores (r² > 0.18, p < 0.009). Receiver-operating-characteristic curves revealed that the complexity metric can distinguish ET from PT (area-under-the-curve > 0.77, cut-off value = 48 (postural), 49 (resting)), which was confirmed in a separate dataset with ET and PT that were clearly diagnosed in prior work.

CONCLUSIONS

The PT and ET have different effects on hand tremor complexity, and this metric is promising to help the identification of ET and PT, which still needs to be confirmed in future studies.

SIGNIFICANCE

The characteristics of multiscale dynamics of the hand tremor, as quantified by complexity, provides novel insights into the different pathophysiology between ET and PT.

Delta oscillations are a robust biomarker of dopamine depletion severity and motor dysfunction in awake mice

Delta oscillations (0.5-4 Hz) are a robust feature of basal ganglia pathophysiology in patients with Parkinson's disease (PD) in relationship to tremor, but their relationship to other parkinsonian symptoms has not been investigated. While delta oscillations have been observed in mouse models of PD, they have only been investigated in anesthetized animals, suggesting that the oscillations may be an anesthesia artifact and limiting the ability to relate them to motor symptoms. Here, we establish a novel approach to detect spike oscillations embedded in noise to provide the first study of delta oscillations in awake, dopamine-depleted mice. We find that approximately half of neurons in the substantia nigra pars reticulata (SNr) exhibit delta oscillations in dopamine depletion and that these oscillations are a strong indicator of dopamine loss and akinesia, outperforming measures such as changes in firing rate, irregularity, bursting, and synchrony. These oscillations are typically weakened, but not ablated, during movement. We further establish that these oscillations are caused by the loss of D2-receptor activation and do not originate from motor cortex, contrary to previous findings in anesthetized animals. Instead, SNr oscillations precede those in M1 at a 100- to 300-ms lag, and these neurons' relationship to M1 oscillations can be used as the basis for a novel classification of SNr into two subpopulations. These results give insight into how dopamine loss leads to motor dysfunction and suggest a reappraisal of delta oscillations as a marker of akinetic symptoms in PD.NEW & NOTEWORTHY This work introduces a novel method to detect spike oscillations amidst neural noise. Using this method, we demonstrate that delta oscillations in the basal ganglia are a defining feature of awake, dopamine-depleted mice and are strongly correlated with dopamine loss and parkinsonian motor symptoms. These oscillations arise from a loss of D2-receptor activation and do not require motor cortex. Similar oscillations in human patients may be an underappreciated marker and target for Parkinson's disease (PD) treatment.

Waveform changes with the evolution of beta bursts in the human subthalamic nucleus

OBJECTIVE

Phasic bursts of beta band synchronisation have been linked to motor impairment in Parkinson's disease (PD). However, little is known about what terminates bursts.

METHODS

We used the Hilbert-Huang transform to investigate beta bursts in the local field potential recorded from the subthalamic nucleus in nine patients with PD on and off levodopa.

RESULTS

The sharpness of the beta waveform extrema fell as burst amplitude dropped. Conversely, an index of phase slips between waveform extrema, and the power of concurrent theta activity increased as burst amplitude fell. Theta activity was also increased on levodopa when beta bursts were attenuated. These phenomena were associated with reduction in coupling between beta phase and high gamma activity amplitude. We discuss how these findings may suggest that beta burst termination is associated with relative desynchronization of the beta drive, increase in competing theta activity and increased phase slips in the beta activity.

CONCLUSIONS

We characterise the dynamical nature of beta bursts, thereby permitting inferences about underlying activities and, in particular, about why bursts terminate.

SIGNIFICANCE

Understanding the dynamical nature of beta bursts may help point to interventions that can cause their termination and potentially treat motor impairment in PD.

Basal Forebrain Gating by Somatostatin Neurons Drives Prefrontal Cortical Activity

The basal forebrain provides modulatory input to the cortex regulating brain states and cognitive processing. Somatostatin-expressing neurons constitute a heterogeneous GABAergic population known to functionally inhibit basal forebrain cortically projecting cells thus favoring sleep and cortical synchronization. However, it remains unclear if somatostatin cells can regulate population activity patterns in the basal forebrain and modulate cortical dynamics. Here, we demonstrate that somatostatin neurons regulate the corticopetal synaptic output of the basal forebrain impinging on cortical activity and behavior. Optogenetic inactivation of somatostatin neurons in vivo rapidly modified neural activity in the basal forebrain, with the consequent enhancement and desynchronization of activity in the prefrontal cortex, reflected in both neuronal spiking and network oscillations. Cortical activation was partially dependent on cholinergic transmission, suppressing slow waves and potentiating gamma oscillations. In addition, recruitment dynamics was cell type-specific, with interneurons showing similar temporal profiles, but stronger responses than pyramidal cells. Finally, optogenetic stimulation of quiescent animals during resting periods prompted locomotor activity, suggesting generalized cortical activation and increased arousal. Altogether, we provide physiological and behavioral evidence indicating that somatostatin neurons are pivotal in gating the synaptic output of the basal forebrain, thus indirectly controlling cortical operations via both cholinergic and non-cholinergic mechanisms.

Tremor in Parkinson's Disease May Arise from Interactions of Central Rhythms with Spinal Reflex Loop Oscillations

It is commonly believed that tremor, one of the cardinal signs of Parkinson’s disease, is associated with cerebello-thalamo-cortical oscillations set off by the dopamine-depleted basal ganglia networks. The triggering mechanism has been, however, not entirely delineated. Several reports have pointed to the relevance of interactions with peripheral/spinal mechanisms to tremor generation. Investigations of motor unit synchronization and discharge patterns suggested that exaggerated beta-band oscillations may intermittently reach alpha-motoneurons and modulate low-amplitude membrane oscillations due to spinal loop transmission delays. As a result, the spinal reflex loop will oscillate more vigorously and at a lower frequency and, in turn, entrain larger transcortical loops. Motoneurons may thus represent the specific generator “node” in a tremor network encompassing both cerebral and peripheral/spinal recurrent circuits.

Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data

Analysis of spike and local field potential (LFP) data is an essential part of neuroscientific research. Today there exist many open-source toolboxes for spike and LFP data analysis implementing various functionality. Here we aim to provide a practical guidance for neuroscientists in the choice of an open-source toolbox best satisfying their needs. We overview major open-source toolboxes for spike and LFP data analysis as well as toolboxes with tools for connectivity analysis, dimensionality reduction and generalized linear modeling. We focus on comparing toolboxes functionality, statistical and visualization tools, documentation and support quality. To give a better insight, we compare and illustrate functionality of the toolboxes on open-access dataset or simulated data and make corresponding MATLAB scripts publicly available.

Relationship between resting and action tremors in Parkinson's disease

OBJECTIVE

To determine the relationship between resting tremor (RT) and action tremor (AT) in Parkinson's disease (PD) patients.

METHODS

A retrospective study of RT and AT severity was conducted in 100 PD patients. The severity rating for each type of tremor in the upper extremities was assessed. The disparity in tremor severity between extremities for each tremor type was compared to that of the other two to identify commonalities in the laterality of the tremor manifestation.

RESULTS

Overall, RT is predictive of AT on the same side, but not the opposing side of the body. Patients with less intense resting right upper limb (RRU) tremor and moderately intense RRU tremor were significantly more likely to have an action right upper limb (ARU) tremor (-1.53, P = 0.020; -1.88, P = 0.005, respectively). Similarly, patients with less intense resting left upper limb (RLU) tremor and moderately intense RLU tremors were significantly more likely to have an action left upper limb (ALU) tremor (-3.49, P = 0.000; -1.86, P = 0.017, respectively). In addition, RRU and ALU tremors were associated with an increase in RLU and ARU tremors, respectively.

CONCLUSION

Tremors are common findings in PD patients, and often impair quality of life. By identifying and classifying the relationship between resting and ATs in PD patients, our study sheds light onto the importance of better understanding and future management of this debilitating symptomology.

The relative phases of basal ganglia activities dynamically shape effective connectivity in Parkinson's disease

Phase alignment between oscillatory circuits is thought to optimize information flow, but excessive synchrony within the motor circuit may impair network function. Cagnan et al. characterize the processes that underscore excessive synchronization and its termination, as well as their modulation by levodopa, before suggesting interventions that might prevent pathological circuit interactions.

Optimal phase alignment between oscillatory neural circuits is hypothesized to optimize information flow and enhance system performance. This theory is known as communication-through-coherence. The basal ganglia motor circuit exhibits exaggerated oscillatory and coherent activity patterns in Parkinson's disease. Such activity patterns are linked to compromised motor system performance as evinced by bradykinesia, rigidity and tremor, suggesting that network function might actually deteriorate once a certain level of net synchrony is exceeded in the motor circuit. Here, we characterize the processes underscoring excessive synchronization and its termination. To this end, we analysed local field potential recordings from the subthalamic nucleus and globus pallidus of five patients with Parkinson's disease (four male and one female, aged 37–64 years). We observed that certain phase alignments between subthalamic nucleus and globus pallidus amplified local neural synchrony in the beta frequency band while others either suppressed it or did not induce any significant change with respect to surrogates. The increase in local beta synchrony directly correlated with how long the two nuclei locked to beta-amplifying phase alignments. Crucially, administration of the dopamine prodrug, levodopa, reduced the frequency and duration of periods during which subthalamic and pallidal populations were phase-locked to beta-amplifying alignments. Conversely ON dopamine, the total duration over which subthalamic and pallidal populations were aligned to phases that left beta-amplitude unchanged with respect to surrogates increased. Thus dopaminergic input shifted circuit dynamics from persistent periods of locking to amplifying phase alignments, associated with compromised motoric function, to more dynamic phase alignment and improved motoric function. This effect of dopamine on local circuit resonance suggests means by which novel electrical interventions might prevent resonance-related pathological circuit interactions.

Tremor Reduction by Deep Brain Stimulation Is Associated With Gamma Power Suppression in Parkinson's Disease

Objectives

Rest tremor is a cardinal symptom of Parkinson's disease (PD), and is readily suppressed by deep brain stimulation (DBS) of the subthalamic nucleus (STN). The therapeutic effect of the latter on bradykinesia and rigidity has been associated with the suppression of exaggerated beta (13–30 Hz) band synchronization in the vicinity of the stimulating electrode, but there is no correlation between beta suppression and tremor amplitude. In the present study, we investigate whether tremor suppression is related to suppression of activities at other frequencies.

Materials and Methods

We recorded hand tremor and contralateral local field potential (LFP) activity from DBS electrodes during stimulation of the STN in 15 hemispheres in 11 patients with PD. DBS was applied with increasing voltages starting at 0.5 V until tremor suppression was achieved or until 4.5 V was reached.

Results

Tremor was reduced to 48.9% ± 10.9% of that without DBS once stimulation reached 2.5–3 V (t14 = −4.667, p < 0.001). There was a parallel suppression of low gamma (31–45 Hz) power to 92.5% ± 3% (t14 = −2.348, p = 0.034). This was not seen over a band containing tremor frequencies and their harmonic (4–12 Hz), or over the beta band. Moreover, low gamma power correlated with tremor severity (mean r = 0.43 ± 0.14, p = 0.008) within subjects. This was not the case for LFP power in the other two bands.

Conclusions

Our findings support a relationship between low gamma oscillations and PD tremor, and reinforce the principle that the subthalamic LFP is a rich signal that may contain information about the severity of multiple different Parkinsonian features.

Thalamomuscular coherence in essential tremor: hen or egg in the emergence of tremor?

Thalamomuscular coherence in essential tremor (ET) has consistently been detected in numerous neurophysiological studies. Thereby, spatial properties of coherence indicate a differentiated, somatotopic organization; so far, however, little attention has been paid to temporal aspects of this interdependency. Further insight into the relationship between tremor onset and the onset of coherence could pave the way to more efficient deep brain stimulation (DBS) algorithms for tremor. We studied 10 severely affected ET patients (six females, four males) during surgery for DBS-electrode implantation and simultaneously recorded local field potentials (LFPs) and surface electromyographic signals (EMGs) from the extensor and flexor muscles of the contralateral forearm during its elevation. The temporal relationship between the onset of significant wavelet cross spectrum (WCS) and tremor onset was determined. Moreover, we examined the influence of electrode location within one recording depth on this latency and the coincidence of coherence and tremor for depths with strong overall coherence ("tremor clusters") and those without. Data analysis revealed tremor onset occurring 220 ± 460 ms before the start of significant LFP-EMG coherence. Furthermore, we could detect an anterolateral gradient of WCS onset within one recording depth. Finally, the coincidence of tremor and coherence was significantly higher in tremor clusters. We conclude that tremor onset precedes the beginning of coherence. Besides, within one recording depth there is a spread of the tremor signal. This reflects the importance of somatosensory feedback for ET and questions the suitability of thalamomuscular coherence as a biomarker for "closed-loop" DBS systems to prevent tremor emergence.

Control effects of stimulus paradigms on characteristic firings of parkinsonism

Experimental studies have shown that neuron population located in the basal ganglia of parkinsonian primates can exhibit characteristic firings with certain firing rates differing from normal brain activities. Motivated by recent experimental findings, we investigate the effects of various stimulation paradigms on the firing rates of parkinsonism based on the proposed dynamical models. Our results show that the closed-loop deep brain stimulation is superior in ameliorating the firing behaviors of the parkinsonism, and other control strategies have similar effects according to the observation of electrophysiological experiments. In addition, in conformity to physiological experiments, we found that there exists optimal delay of input in the closed-loop GPtrain|M1 paradigm, where more normal behaviors can be obtained. More interestingly, we observed that W-shaped curves of the firing rates always appear as stimulus delay varies. We furthermore verify the robustness of the obtained results by studying three pallidal discharge rates of the parkinsonism based on the conductance-based model, as well as the integrate-and-fire-or-burst model. Finally, we show that short-term plasticity can improve the firing rates and optimize the control effects on parkinsonism. Our conclusions may give more theoretical insight into Parkinson's disease studies.

A point process approach to identifying and tracking transitions in neural spiking dynamics in the subthalamic nucleus of Parkinson's patients

Understanding the role of rhythmic dynamics in normal and diseased brain function is an important area of research in neural electrophysiology. Identifying and tracking changes in rhythms associated with spike trains present an additional challenge, because standard approaches for continuous-valued neural recordings--such as local field potential, magnetoencephalography, and electroencephalography data--require assumptions that do not typically hold for point process data. Additionally, subtle changes in the history dependent structure of a spike train have been shown to lead to robust changes in rhythmic firing patterns. Here, we propose a point process modeling framework to characterize the rhythmic spiking dynamics in spike trains, test for statistically significant changes to those dynamics, and track the temporal evolution of such changes. We first construct a two-state point process model incorporating spiking history and develop a likelihood ratio test to detect changes in the firing structure. We then apply adaptive state-space filters and smoothers to track these changes through time. We illustrate our approach with a simulation study as well as with experimental data recorded in the subthalamic nucleus of Parkinson's patients performing an arm movement task. Our analyses show that during the arm movement task, neurons underwent a complex pattern of modulation of spiking intensity characterized initially by a release of inhibitory control at 20-40 ms after a spike, followed by a decrease in excitatory influence at 40-60 ms after a spike.

A direct relationship between oscillatory subthalamic nucleus-cortex coupling and rest tremor in Parkinson's disease

Electrophysiological studies suggest that rest tremor in Parkinson's disease is associated with an alteration of oscillatory activity. Although it is well known that tremor depends on cortico-muscular coupling, it is unclear whether synchronization within and between brain areas is specifically related to the presence and severity of tremor. To tackle this longstanding issue, we took advantage of naturally occurring spontaneous tremor fluctuations and investigated cerebral synchronization in the presence and absence of rest tremor. We simultaneously recorded local field potentials from the subthalamic nucleus, the magnetoencephalogram and the electromyogram of forearm muscles in 11 patients with Parkinson's disease (all male, age: 52-74 years). Recordings took place the day after surgery for deep brain stimulation, after withdrawal of anti-parkinsonian medication. We selected epochs containing spontaneous rest tremor and tremor-free epochs, respectively, and compared power and coherence between subthalamic nucleus, cortex and muscle across conditions. Tremor-associated changes in cerebro-muscular coherence were localized by Dynamic Imaging of Coherent Sources. Subsequently, cortico-cortical coupling was analysed by computation of the imaginary part of coherency, a coupling measure insensitive to volume conduction. After tremor onset, local field potential power increased at individual tremor frequency and cortical power decreased in the beta band (13-30 Hz). Sensor level subthalamic nucleus-cortex, cortico-muscular and subthalamic nucleus-muscle coherence increased during tremor specifically at tremor frequency. The increase in subthalamic nucleus-cortex coherence correlated with the increase in electromyogram power. On the source level, we observed tremor-associated increases in cortico-muscular coherence in primary motor cortex, premotor cortex and posterior parietal cortex contralateral to the tremulous limb. Analysis of the imaginary part of coherency revealed tremor-dependent coupling between these cortical areas at tremor frequency and double tremor frequency. Our findings demonstrate a direct relationship between the synchronization of cerebral oscillations and tremor manifestation. Furthermore, they suggest the feasibility of tremor detection based on local field potentials and might thus become relevant for the design of closed-loop stimulation systems.

The many roads to tremor

Tremor represents one of the most prominent examples of aberrant synchronisation within the human motor system, and Essential Tremor (ET) is by far the most common tremor disorder. Yet, even within ET there is considerable variation, and patients may have contrasting amounts of postural and intention tremor. Recently, Pedrosa et al. (2013) challenged tremor circuits in a cohort of patients presenting with ET, by applying low-frequency deep brain stimulation within thalamus. This interventional approach provided strong evidence that distinct (yet possibly overlapping) neural substrates are responsible for postural and intention tremor in ET. Intention tremor, and not postural tremor, was exacerbated by low frequency stimulation, and the effect was localised in the region of the ventrolateral thalamus in such a way as to implicate cerebello-thalamic pathways. These results, taken in conjunction with the contemporary literature, reveal that pathological changes exaggerate oscillatory synchrony in selective components of an extensive and distributed motor network, and that synchronisation within these networks is further regulated according to motor state. Through a combination of pathological and more dynamic physiological factors, activity then spills out into the periphery in the form of tremor. The findings of Pedrosa et al. (2013) are timely as they coincide with an emerging notion that tremor may result through selective dysregulation within a broader tremorgenic network.

Short desynchronization episodes prevail in synchronous dynamics of human brain rhythms

Neural synchronization is believed to be critical for many brain functions. It frequently exhibits temporal variability, but it is not known if this variability has a specific temporal patterning. This study explores these synchronization/desynchronization patterns. We employ recently developed techniques to analyze the fine temporal structure of phase-locking to study the temporal patterning of synchrony of the human brain rhythms. We study neural oscillations recorded by electroencephalograms in α and β frequency bands in healthy human subjects at rest and during the execution of a 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. The transition matrix between synchronized and desynchronized states has eigenvalues close to 0 and 1 where eigenvalue 1 has multiplicity 1, and therefore if the stationary distribution between these states is perturbed, the system converges back to the stationary distribution very fast. The qualitative similarity of this patterning across different subjects, brain states and electrode locations suggests that this may be a general type of dynamics for the brain. Earlier studies indicate that not all oscillatory networks have this kind of patterning of synchronization/desynchronization dynamics. Thus, the observed prevalence of short (but potentially frequent) desynchronization events (length of one cycle of oscillations) may have important functional implications for the brain. Numerous short desynchronizations (as opposed to infrequent, but long desynchronizations) may allow for a quick and efficient formation and break-up of functionally significant neuronal assemblies.

Failure of delayed feedback deep brain stimulation for intermittent pathological synchronization in Parkinson's disease

Suppression of excessively synchronous beta-band oscillatory activity in the brain is believed to suppress hypokinetic motor symptoms of Parkinson's disease. Recently, a lot of interest has been devoted to desynchronizing delayed feedback deep brain stimulation (DBS). This type of synchrony control was shown to destabilize the synchronized state in networks of simple model oscillators as well as in networks of coupled model neurons. However, the dynamics of the neural activity in Parkinson's disease exhibits complex intermittent synchronous patterns, far from the idealized synchronous dynamics used to study the delayed feedback stimulation. This study explores the action of delayed feedback stimulation on partially synchronized oscillatory dynamics, similar to what one observes experimentally in parkinsonian patients. We employ a computational model of the basal ganglia networks which reproduces experimentally observed fine temporal structure of the synchronous dynamics. When the parameters of our model are such that the synchrony is unphysiologically strong, the feedback exerts a desynchronizing action. However, when the network is tuned to reproduce the highly variable temporal patterns observed experimentally, the same kind of delayed feedback may actually increase the synchrony. As network parameters are changed from the range which produces complete synchrony to those favoring less synchronous dynamics, desynchronizing delayed feedback may gradually turn into synchronizing stimulation. This suggests that delayed feedback DBS in Parkinson's disease may boost rather than suppress synchronization and is unlikely to be clinically successful. The study also indicates that delayed feedback stimulation may not necessarily exhibit a desynchronization effect when acting on a physiologically realistic partially synchronous dynamics, and provides an example of how to estimate the stimulation effect.

Levodopa-induced dyskinesia is strongly associated with resonant cortical oscillations

The standard pharmacological treatment for Parkinson's disease using the dopamine precursor levodopa is unfortunately limited by gradual development of disabling involuntary movements for which the underlying causes are poorly understood. Here we show that levodopa-induced dyskinesia in hemiparkinsonian rats is strongly associated with pronounced 80 Hz local field potential oscillations in the primary motor cortex following levodopa treatment. When this oscillation is interrupted by application of a dopamine antagonist onto the cortical surface the dyskinetic symptoms disappear. The finding that abnormal cortical oscillations are a key pathophysiological mechanism calls for a revision of the prevailing hypothesis that links levodopa-induced dyskinesia to an altered sensitivity to dopamine only in the striatum. Apart from having important implications for the treatment of Parkinson's disease, the discovered pathophysiological mechanism may also play a role in several other psychiatric and neurological conditions involving cortical dysfunction.

The distributed somatotopy of tremor: a window into the motor system

The posterior ventrolateral thalamus (VLp) plays a crucial role in Parkinson's tremor and in essential tremor: deep brain stimulation (DBS) of the VLp effectively diminishes both tremor types. Previous research has shown tremor oscillations in the VLp, but the spatial extent and somatotopy of these oscillations remained unclear. In this issue of Experimental Neurology, Pedrosa and colleagues measured neuro-muscular coherence at multiple sites in the VLp of patients with essential tremor and Parkinson's disease using implanted DBS electrodes (Pedrosa et al., 2012). They found multiple tremor clusters within the VLp, with spatially distinct tremor clusters for antagonistic muscles, and in many patients also multiple distinct tremor clusters for a single muscle. Interestingly, this group previously showed similar effects for the STN in tremulous Parkinson's disease (Reck et al., 2009, 2010). Together, these studies suggest that the distribution of tremor clusters is a general organizational principle of tremor, being present in two different tremor pathologies, and in two different nodes of the motor system. The presence of multiple tremor clusters also fits with the distributed somatotopy of the healthy motor system. Therefore, a further conclusion of this study could be that tremor is caused by aberrant synchronization within an otherwise healthy network, brought about by different pathophysiological neural triggers.

Potential mechanisms for imperfect synchronization in parkinsonian basal ganglia

Neural activity in the brain of parkinsonian patients is characterized by the intermittently synchronized oscillatory dynamics. This imperfect synchronization, observed in the beta frequency band, is believed to be related to the hypokinetic motor symptoms of the disorder. Our study explores potential mechanisms behind this intermittent synchrony. We study the response of a bursting pallidal neuron to different patterns of synaptic input from subthalamic nucleus (STN) neuron. We show how external globus pallidus (GPe) neuron is sensitive to the phase of the input from the STN cell and can exhibit intermittent phase-locking with the input in the beta band. The temporal properties of this intermittent phase-locking show similarities to the intermittent synchronization observed in experiments. We also study the synchronization of GPe cells to synaptic input from the STN cell with dependence on the dopamine-modulated parameters. Earlier studies showed how the strengthening of dopamine-modulated coupling may lead to transitions from non-synchronized to partially synchronized dynamics, typical in Parkinson's disease. However, dopamine also affects the cellular properties of neurons. We show how the changes in firing patterns of STN neuron due to the lack of dopamine may lead to transition from a lower to a higher coherent state, roughly matching the synchrony levels observed in basal ganglia in normal and parkinsonian states. The intermittent nature of the neural beta band synchrony in Parkinson's disease is achieved in the model due to the interplay of the timing of STN input to pallidum and pallidal neuronal dynamics, resulting in sensitivity of pallidal output to the phase of the arriving STN input. Thus the mechanism considered here (the change in firing pattern of subthalamic neurons through the dopamine-induced change of membrane properties) may be one of the potential mechanisms responsible for the generation of the intermittent synchronization observed in Parkinson's disease.

Tracking tremor frequency in spike trains using the extended kalman filter

Tremor is one of the most disabling symptoms in patients with many movement disorders including Parkinson's disease (PD) and essential tremor (ET). Neural tremor, which is measured by microelectrodes placed near nerve cells or obtained from brain cells during stereotactic neurosurgery, is due to the fluctuation of the firing rate of neurons. The frequency of this neural tremor varies over time and is nonstationary. We describe a frequency tracking method using the extended Kalman filter (EKF) to estimate the instantaneous tremor frequency (ITF) of binary spike trains detected from microelectrode recordings (MER). The results demonstrate that the EKF can accurately track fluctuations in tremor frequency even though the noise in binary spike trains is not Gaussian.

On the origin of tremor in Parkinson's disease

The exact origin of tremor in Parkinson’s disease remains unknown. We explain why the existing data converge on the basal ganglia-thalamo-cortical loop as a tremor generator and consider a conductance-based model of subthalamo-pallidal circuits embedded into a simplified representation of the basal ganglia-thalamo-cortical circuit to investigate the dynamics of this loop. We show how variation of the strength of dopamine-modulated connections in the basal ganglia-thalamo-cortical loop (representing the decreasing dopamine level in Parkinson’s disease) leads to the occurrence of tremor-like burst firing. These tremor-like oscillations are suppressed when the connections are modulated back to represent a higher dopamine level (as it would be the case in dopaminergic therapy), as well as when the basal ganglia-thalamo-cortical loop is broken (as would be the case for ablative anti-parkinsonian surgeries). Thus, the proposed model provides an explanation for the basal ganglia-thalamo-cortical loop mechanism of tremor generation. The strengthening of the loop leads to tremor oscillations, while the weakening or disconnection of the loop suppresses them. The loop origin of parkinsonian tremor also suggests that new tremor-suppression therapies may have anatomical targets in different cortical and subcortical areas as long as they are within the basal ganglia-thalamo-cortical loop.

Intermittent neural synchronization in Parkinson's disease

Motor symptoms of Parkinson's disease are related to the excessive synchronized oscillatory activity in the beta frequency band (around 20Hz) in the basal ganglia and other parts of the brain. This review explores the dynamics and potential mechanisms of these oscillations employing ideas and methods from nonlinear dynamics. We present extensive experimental documentation of the relevance of synchronized oscillations to motor behavior in Parkinson's disease, and we discuss the intermittent character of this synchronization. The reader is introduced to novel time-series analysis techniques aimed at the detection of the fine temporal structure of intermittent phase locking observed in the brains of parkinsonian patients. Modeling studies of brain networks are reviewed, which may describe the observed intermittent synchrony, and we discuss what these studies reveal about brain dynamics in Parkinson's disease. The parkinsonian brain appears to exist on the boundary between phase-locked and nonsynchronous dynamics. Such a situation may be beneficial in the healthy state, as it may allow for easy formation and dissociation of transient patterns of synchronous activity which are required for normal motor behavior. Dopaminergic degeneration in Parkinson's disease may shift the brain networks closer to this boundary, which would still permit some motor behavior while accounting for the associated motor deficits. Understanding the mechanisms of the intermittent synchrony in Parkinson's disease is also important for biomedical engineering since efficient control strategies for suppression of pathological synchrony through deep brain stimulation require knowledge of the dynamics of the processes subjected to control.

Closed-loop deep brain stimulation is superior in ameliorating parkinsonism

Continuous high-frequency deep brain stimulation (DBS) is a widely used therapy for advanced Parkinson's disease (PD) management. However, the mechanisms underlying DBS effects remain enigmatic and are the subject of an ongoing debate. Here, we present and test a closed-loop stimulation strategy for PD in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) primate model of PD. Application of pallidal closed-loop stimulation leads to dissociation between changes in basal ganglia (BG) discharge rates and patterns, providing insights into PD pathophysiology. Furthermore, cortico-pallidal closed-loop stimulation has a significantly greater effect on akinesia and on cortical and pallidal discharge patterns than standard open-loop DBS and matched control stimulation paradigms. Thus, closed-loop DBS paradigms, by modulating pathological oscillatory activity rather than the discharge rate of the BG-cortical networks, may afford more effective management of advanced PD. Such strategies have the potential to be effective in additional brain disorders in which a pathological neuronal discharge pattern can be recognized.

Effects of dopamine depletion on information flow between the subthalamic nucleus and external globus pallidus

Abnormal oscillatory synchrony is increasingly acknowledged as a pathophysiological hallmark of Parkinson's disease, but what promotes such activity remains unclear. We used novel, nonlinear time series analyses and information theory to capture the effects of dopamine depletion on directed information flow within and between the subthalamic nucleus (STN) and external globus pallidus (GPe). We compared neuronal activity recorded simultaneously from these nuclei in 6-hydroxydopamine-lesioned Parkinsonian rats with that in dopamine-intact control rats. After lesioning, both nuclei displayed pronounced augmentations of beta-frequency (∼20 Hz) oscillations and, critically, information transfer between STN and GPe neurons was increased. Furthermore, temporal profiles of the directed information transfer agreed with the neurochemistry of these nuclei, being "excitatory" from STN to GPe and "inhibitory" from GPe to STN. Separation of the GPe population in lesioned animals into "type-inactive" (GP-TI) and "type-active" (GP-TA) neurons, according to definitive firing preferences, revealed distinct temporal profiles of interaction with STN and each other. The profile of GP-TI neurons suggested their output is of greater causal significance than that of GP-TA neurons for the reduced activity that periodically punctuates the spiking of STN neurons during beta oscillations. Moreover, STN was identified as a key candidate driver for recruiting ensembles of GP-TI neurons but not GP-TA neurons. Short-latency interactions between GP-TI and GP-TA neurons suggested mutual inhibition, which could rhythmically dampen activity and promote anti-phase firing across the two subpopulations. Results thus indicate that information flow around the STN-GPe circuit is exaggerated in Parkinsonism and further define the temporal interactions underpinning this.

Milestones in tremor research

Tremor is one of the most frequent movement disorders and covers a wide spectrum of entities summarized in the 1998 consensus statement of the Movement Disorder Society. Essential tremor and Parkinson tremor are most common and are also the most thoroughly studied. Major progress has occurred in the clinical semiology, neuroimaging, epidemiology, and pathophysiology of tremors. Pathology and genetic research are rapidly growing fields of study. Recently described tremor entities include orthostatic tremor, dystonic tremor, cortical tremor, and thalamic tremor. Treatment research methodology has improved substantially, but few double-blind controlled trials have been published. Deep brain stimulation is the most effective treatment for most tremors but is reserved for advanced cases.

Pharmacological and physiological characterization of the tremulous jaw movement model of parkinsonian tremor: potential insights into the pathophysiology of tremor

Tremor is a cardinal symptom of parkinsonism, occurring early on in the disease course and affecting more than 70% of patients. Parkinsonian resting tremor occurs in a frequency range of 3-7 Hz and can be resistant to available pharmacotherapy. Despite its prevalence, and the significant decrease in quality of life associated with it, the pathophysiology of parkinsonian tremor is poorly understood. The tremulous jaw movement (TJM) model is an extensively validated rodent model of tremor. TJMs are induced by conditions that also lead to parkinsonism in humans (i.e., striatal DA depletion, DA antagonism, and cholinomimetic activity) and reversed by several antiparkinsonian drugs (i.e., DA precursors, DA agonists, anticholinergics, and adenosine A(2A) antagonists). TJMs occur in the same 3-7 Hz frequency range seen in parkinsonian resting tremor, a range distinct from that of dyskinesia (1-2 Hz), and postural tremor (8-14 Hz). Overall, these drug-induced TJMs share many characteristics with human parkinsonian tremor, but do not closely resemble tardive dyskinesia. The current review discusses recent advances in the validation of the TJM model, and illustrates how this model is being used to develop novel therapeutic strategies, both surgical and pharmacological, for the treatment of parkinsonian resting tremor.

Detecting the temporal structure of intermittent phase locking

This study explores a method to characterize the temporal structure of intermittent phase locking in oscillatory systems. When an oscillatory system is in a weakly synchronized regime away from a synchronization threshold, it spends most of the time in parts of its phase space away from the synchronization state. Therefore characteristics of dynamics near this state (such as its stability properties and Lyapunov exponents or distributions of the durations of synchronized episodes) do not describe the system's dynamics for most of the time. We consider an approach to characterize the system dynamics in this case by exploring the relationship between the phases on each cycle of oscillations. If some overall level of phase locking is present, one can quantify when and for how long phase locking is lost, and how the system returns back to the phase-locked state. We consider several examples to illustrate this approach: coupled skewed tent maps, the stability of which can be evaluated analytically; coupled Rössler and Lorenz oscillators, undergoing through different intermittency types on the way to phase synchronization; and a more complex example of coupled neurons. We show that the obtained measures can describe the differences in the dynamics and temporal structure of synchronization and desynchronization events for the systems with a similar overall level of phase locking and similar stability of the synchronized state.

Neural dynamics in parkinsonian brain: the boundary between synchronized and nonsynchronized dynamics

Synchronous oscillatory dynamics is frequently observed in the human brain. We analyze the fine temporal structure of phase-locking in a realistic network model and match it with the experimental data from Parkinsonian patients. We show that the experimentally observed intermittent synchrony can be generated just by moderately increased coupling strength in the basal ganglia circuits due to the lack of dopamine. Comparison of the experimental and modeling data suggest that brain activity in Parkinson's disease resides in the large boundary region between synchronized and nonsynchronized dynamics. Being on the edge of synchrony may allow for easy formation of transient neuronal assemblies.

Fine temporal structure of beta oscillations synchronization in subthalamic nucleus in Parkinson's disease

Synchronous oscillatory dynamics in the beta frequency band is a characteristic feature of neuronal activity of basal ganglia in Parkinson's disease and is hypothesized to be related to the disease's hypokinetic symptoms. This study explores the temporal structure of this synchronization during episodes of oscillatory beta-band activity. Phase synchronization (phase locking) between extracellular units and local field potentials (LFPs) from the subthalamic nucleus (STN) of parkinsonian patients is analyzed here at a high temporal resolution. We use methods of nonlinear dynamics theory to construct first-return maps for the phases of oscillations and quantify their dynamics. Synchronous episodes are interrupted by less synchronous episodes in an irregular yet structured manner. We estimate probabilities for different kinds of these "desynchronization events." There is a dominance of relatively frequent yet very brief desynchronization events with the most likely desynchronization lasting for about one cycle of oscillations. The chances of longer desynchronization events decrease with their duration. The observed synchronization may primarily reflect the relationship between synaptic input to STN and somatic/axonal output from STN at rest. The intermittent, transient character of synchrony even on very short time scales may reflect the possibility for the basal ganglia to carry out some informational function even in the parkinsonian state. The dominance of short desynchronization events suggests that even though the synchronization in parkinsonian basal ganglia is fragile enough to be frequently destabilized, it has the ability to reestablish itself very quickly.

A model of pathological oscillations in the basal ganglia and deep brain stimulation in Parkinson's disease

A model of pathological oscillatory activity within the basal ganglia in Parkinson's disease is presented. Synchronous oscillatory activity of the subthalamic nucleus (STN) and globus pallidus external (GPe) is simulated at both the parkinsonian tremor (3-9) Hz and beta (15-30) Hz frequencies. The model extends previous models to incorporate cortical inputs to the subthalamic nucleus through the 'hyperdirect' pathway, changes in coupling between STN neurons due to dopamine depletion and the plateau potential generating capacity of STN neurons. The effect of deep brain stimulation (DBS) across a range of frequencies on the oscillatory activity is examined. High frequency DBS attenuates synchronous oscillatory activity within the STN-GPe network at both the tremor and beta frequencies. The efficacy of the DBS input in quenching the pathological oscillations is shown to vary systematically with the frequency, pulse width and amplitude of the applied stimulus.

Revealing neuronal functional organization through the relation between multi-scale oscillatory extracellular signals

The spatial organization of neuronal elements and their connectivity make up the substrate underlying the information processing carried out in the networks they form. Conventionally, anatomical findings make the initial structure which later combines with superimposed neurophysiological information to create a functional organization map. The most common neurophysiological measure is the single neuron spike train extracted from an extracellular recording. This single neuron firing pattern provides valuable clues on information processing in a given brain area; however, it only gives a sparse and focal view of this process. Even with the increase in number of simultaneously recorded neurons, inference on their large-scale functional organization remains problematic. We propose a method of utilizing additional information derived from the same extracellular recording to generate a more comprehensive picture of neuronal functional organization. This analysis is based on the relationship between the oscillatory activity of single neurons and their neighboring neuronal populations. Two signals that reflect the multiple scales of neuronal populations are used to complement the single neuron spike train: (1) the high-frequency background unit activity representing the spiking activity of small localized sub-populations and (2) the low-frequency local field potential that represents the synaptic input to a larger global population. The three coherences calculated between pairs of these three signals arising from a single source of extracellular recording are then used to infer mosaic representations of the functional neuronal organization. We demonstrate this methodology on experimental data and on simulated leaky integrate-and-fire neurons.

Tremor

PURPOSE OF REVIEW

Tremor continuously attracts the attention of clinicians and basic researchers in search of pathophysiological, molecular and genetic mechanisms of the oscillatory activity.

RECENT FINDINGS

A widespread dynamic network of cortical and subcortical oscillators taking part in tremor generation intermittently has been postulated. Essential tremor is accompanied by functional deficits but may also occur along with subtle cerebellar changes. According to recent epidemiological studies there may be a link of essential tremor with Parkinson's disease. Many of the epidemiologic studies suffer from small cohorts, small effects or the lack of a definite test for essential tremor leaving the diagnosis a pure clinical one. A very recent large genome-wide association study has revealed that the LINGO1 gene is associated with an increased risk for essential tremor. Topiramate is becoming the best-established second line treatment for essential tremor. Targets for deep brain stimulation in the grey matter below the ventral intermediate nucleus of the thalamus seem to be most effective.

SUMMARY

New concepts of the central origin of tremors stimulate the search for new therapeutic targets for tremor suppression outside the basal ganglia and thalamus (e.g. cortex). The role of structural neurodegenerative changes in essential tremor remains an open question. Further studies on specific subgroups of patients are necessary.

Akineto-rigid vs. tremor syndromes in Parkinsonism

PURPOSE OF REVIEW

Akinesia, rigidity and low-frequency rest tremor are the three cardinal motor signs of Parkinson's disease and some Parkinson's disease animal models. However, cumulative evidence supports the view that akinesia/rigidity vs. tremor reflect different pathophysiological phenomena in the basal ganglia. Here, we review the recent physiological literature correlating abnormal neural activity in the basal ganglia with Parkinson's disease clinical symptoms.

RECENT FINDINGS

The subthalamic nucleus of Parkinson's disease patients is characterized by oscillatory activity in the beta-frequency (approximately 15 Hz) range. However, Parkinson's disease tremor is not strictly correlated with the abnormal synchronous oscillations of the basal ganglia. On the other hand, akinesia and rigidity are better correlated with the basal ganglia beta oscillations.

SUMMARY

The abnormal basal ganglia output leads to akinesia and rigidity. Parkinson's disease tremor most likely evolves as a downstream compensatory mechanism.

Cortical correlates of the basic and first harmonic frequency of Parkinsonian tremor

OBJECTIVE

It has been hypothesized that the basic and first harmonic frequency of Parkinsonian tremor are somewhat independent oscillations the biological basis of which remains unclear.

METHODS

We recorded 64-channel EEG in parallel with EMG of the forearm muscles most affected by rest tremor in 21 PD patients. EMG power spectrum, corticomuscular coherence spectra and EEG power spectra for each EEG electrode were calculated. The dynamics of the coherence and relative EMG and EEG power at the basic (tremor) frequency were calculated by a sliding, overlapping window analysis. Corticomuscular delays and direction of interaction were analysed by the maximizing coherence method for narrow band signals.

RESULTS

The contralateral EEG electrodes with maximal coherence were different for the basic and first harmonic frequency. The dynamical coherence curves showed non-parallel time courses for the two frequencies. The mean EEG-EMG and EMG-EEG delays were all around 15-20ms but significantly longer for the first harmonic than for the basic frequency.

CONCLUSIONS

Our data indicate different cortical representations and corticomuscular interaction of the basic and first harmonic frequencies of Parkinsonian tremor.

SIGNIFICANCE

Separate central generators seem to contribute to the tremor via different pathways. Further studies on this complex tremor network are warranted.

A novel segmentation, mutual information network framework for EEG analysis of motor tasks

Background

Monitoring the functional connectivity between brain regions is becoming increasingly important in elucidating brain functionality in normal and disease states. Current methods of detecting networks in the recorded electroencephalogram (EEG) such as correlation and coherence are limited by the fact that they assume stationarity of the relationship between channels, and rely on linear dependencies. In contrast to diseases of the brain cortex (e.g. Alzheimer's disease), with motor disorders such as Parkinson's disease (PD) the EEG abnormalities are most apparent during performance of dynamic motor tasks, but this makes the stationarity assumption untenable.

Methods

We therefore propose a novel EEG segmentation method based on the temporal dynamics of the cross-spectrogram of the computed Independent Components (ICs). We then utilize mutual information (MI) as the metric for determining also nonlinear statistical dependencies between EEG channels. Graphical theoretical analysis is then applied to the derived MI networks. The method was applied to EEG data recorded from six normal subjects and seven PD subjects off medication. One-way analysis of variance (ANOVA) tests demonstrated statistically significant difference in the connectivity patterns between groups.

Results

The results suggested that PD subjects are unable to independently recruit different areas of the brain while performing simultaneous tasks compared to individual tasks, but instead they attempt to recruit disparate clusters of synchronous activity to maintain behavioral performance.

Conclusion

The proposed segmentation/MI network method appears to be a promising approach for analyzing the EEG recorded during dynamic behaviors.

Parafascicular thalamic nucleus activity in a rat model of Parkinson's disease

Parkinson's disease is associated with increased oscillatory firing patterns in basal ganglia output, which are thought to disrupt thalamocortical activity. However, it is unclear how specific thalamic nuclei are affected by these changes in basal ganglia activity. The thalamic parafascicular nucleus (PFN) receives input from basal ganglia output nuclei and directly projects to the subthalamic nucleus (STN), striatum and cortex; thus basal ganglia-mediated changes on PFN activity may further impact basal ganglia and cortical functions. To investigate the impact of increased oscillatory activity in basal ganglia output on PFN activity after dopamine cell lesion, PFN single-unit and local field potential activities were recorded in neurologically intact (control) rats and in both non-lesioned and dopamine lesioned hemispheres of unilateral 6-hydroxydopamine lesioned rats anesthetized with urethane. Firing rates were unchanged 1-2 weeks after lesion; however, significantly fewer spontaneously active PFN neurons were evident. Firing pattern assessments after lesion showed that a larger proportion of PFN spike trains had 0.3-2.5 Hz oscillatory activity and significantly fewer spike trains exhibited low threshold calcium spike (LTS) bursts. In paired recordings, more PFN-STN spike oscillations were significantly correlated, but as these oscillations were in-phase, results are inconsistent with feedforward control of PFN activity by inhibitory oscillatory basal ganglia output. Furthermore, the decreased incidence of LTS bursts is incompatible with inhibitory basal ganglia output inducing rebound bursting in PFN after dopamine lesion. Together, results show that robust oscillatory activity observed in basal ganglia output nuclei after dopamine cell lesion does not directly drive changes in PFN oscillatory activity.

Increased Gamma Oscillatory Activity in the Subthalamic Nucleus During Tremor in Parkinson's Disease Patients

Rest tremor is one of the main symptoms in Parkinson's disease (PD), although in contrast to rigidity and akinesia, the severity of the tremor does not correlate well with the degree of dopamine deficiency or the progression of the disease. Studies suggest that akinesia in PD patients is related to abnormal increased beta (15–30 Hz) and decreased gamma (35–80 Hz) synchronous oscillatory activity in the basal ganglia. Here we investigated the dynamics of oscillatory activity in the subthalamic nucleus (STN) during tremor. We used two adjacent microelectrodes to simultaneously record neuronal firing and local field potential (LFP) activity in nine PD patients who exhibited resting tremor during functional neurosurgery. We found that neurons exhibiting oscillatory activity at tremor frequency are located in the dorsal region of STN, where neurons with beta oscillatory activity are observed, and that their activity is coherent with LFP oscillations in the beta frequency range. Interestingly, in 85% of the 58 sites examined, the LFP exhibited increased oscillatory activity in the low gamma frequency range (35–55 Hz) during periods with stronger tremor. Furthermore, in 17 of 26 cases where two LFPs were recorded simultaneously, their coherence in the gamma range increased with increased tremor. When averaged across subjects, the ratio of the beta to gamma coherence was significantly lower in periods with stronger tremor compared with periods of no or weak tremor. These results suggest that resting tremor in PD is associated with an altered balance between beta and gamma oscillations in the motor circuits of STN.

Cortical local and long-range synchronization interplay in human absence seizure initiation

Brain activity relies on transient, fluctuating interactions between segregated neuronal populations. Synchronization within a single and between distributed neuronal clusters reflects the dynamics of these cooperative patterns. Thus absence epilepsy can be used as a model for integrated, large-scale investigation of the emergence of pathological collective dynamics in the brain. Indeed, spike-wave discharges (SWD) of an absence seizure are thought to reflect abnormal cortical hypersynchronization. In this paper, we address two questions: how and where do SWD arise in the human brain? Therefore, we explored the spatio-temporal dynamics of interactions within and between widely distributed cortical sites using magneto-encephalographic recordings of spontaneous absence seizures. We then extracted, from their time-frequency analysis, local synchronization of cortical sources and long-range synchronization linking distant sites. Our analyses revealed a reproducible sequence of 1) long-range desynchronization, 2) increased local synchronization and 3) increased long-range synchronization. Although both local and long-range synchronization displayed different spatio-temporal profiles, their cortical projection within an initiation time window overlap and reveal a multifocal fronto-central network. These observations contradict the classical view of sudden generalized synchronous activities in absence epilepsy. Furthermore, they suggest that brain states transition may rely on multi-scale processes involving both local and distant interactions.