Neuronal Entropy-Rate Feature of Entopeduncular Nucleus in Rat Model of Parkinson's Disease

Approximate Entropy of Spiking Series Reveals Different Dynamical States in Cortical Assemblies

Self-organized criticality theory proved that information transmission and computational performances of neural networks are optimal in critical state. By using recordings of the spontaneous activity originated by dissociated neuronal assemblies coupled to Micro-Electrode Arrays (MEAs), we tested this hypothesis using Approximate Entropy (ApEn) as a measure of complexity and information transfer. We analysed 60 min of electrophysiological activity of three neuronal cultures exhibiting either sub-critical, critical or super-critical behaviour. The firing patterns on each electrode was studied in terms of the inter-spike interval (ISI), whose complexity was quantified using ApEn. We assessed that in critical state the local complexity (measured in terms of ApEn) is larger than in sub- and super-critical conditions (mean ± std, ApEn about 0.93 ± 0.09, 0.66 ± 0.18, 0.49 ± 0.27, for the cultures in critical, sub-critical and super-critical state, respectively—differences statistically significant). Our estimations were stable when considering epochs as short as 5 min (pairwise cross-correlation of spatial distribution of mean ApEn of 94 ± 5%). These preliminary results indicate that ApEn has the potential of being a reliable and stable index to monitor local information transmission in a neuronal network during maturation. Thus, ApEn applied on ISI time series appears to be potentially useful to reflect the overall complex behaviour of the neural network, even monitoring a single specific location.

Burrowing behaviour of rats: Strain differences and applicability as well-being parameter after intracranial surgery

In mice, burrowing is considered a species-typical parameter for assessing well-being, while this is less clear in rats. This exploratory study evaluated burrowing behaviour in three rat strains during training and in the direct postoperative phase after complex intracranial surgery in different neuroscience rat models established at Hannover Medical School or Aachen University Hospital. Male Crl:CD (SD; n = 18), BDIX/UlmHanZtm (BDIX; n = 8) and RjHan:WI (Wistar; n = 35) rats were individually trained to burrow gravel out of a tube on four consecutive days. Thereafter, BDIX rats were subjected to intracranial injection of BT4Ca cells and tumour resection (rat glioma model), SD rats to injection of 6-hydroxydopamine (6-OHDA) or vehicle (rat Parkinson's disease model) and Wistar rats to endovascular perforation or sham surgery (rat subarachnoid haemorrhage (SAH) model). Burrowing was retested on the day after surgery. During training, BDIX rats burrowed large amounts (mean of 2370 g on the fourth day), while SD and Wistar rats burrowed less gravel (means of 846 and 520 g, respectively). Burrowing increased significantly during training only in Wistar rats. Complex surgery, that is, tumour resection (BDIX), 6-OHDA injection (SD) and endovascular perforation or sham surgery for SAH (Wistar) significantly reduced burrowing and body weight, while simple stereotactic injection of tumour cells or vehicle did not affect burrowing. Despite the training, burrowing differed between the strains. In the direct postoperative phase, burrowing was reduced after complex surgery, indicating reduced well-being. Reduced burrowing was accompanied with postoperative weight loss, a validated and recognised quantitative measure for severity assessment.

The effect of 5-HT1A receptor agonists on the entopeduncular nucleus is modified in 6-hydroxydopamine-lesioned rats

BACKGROUND AND PURPOSE

l-DOPA prolonged treatment leads to disabling motor complications as dyskinesia that could be decreased by drugs acting on 5-HT1A receptors. Since the internal segment of the globus pallidus, homologous to the entopeduncular nucleus in rodents, seems to be involved in the etiopathology of l-DOPA-induced dyskinesia, we investigated whether the entopeduncular nucleus is modulated by the 5-HT1A receptor partial and full agonists, buspirone, and 8-hydroxy-2-(di-n-propylamino)-tetralin (8-OH-DPAT) in control and 6-hydroxydopamine (6-OHDA)-lesioned rats with or without long-term l-DOPA treatment.

EXPERIMENTAL APPROACH

Extracellular single-unit electrocorticogram and local field potential recordings under anaesthesia, immunostaining assays and optogenetic manipulation coupled to electrophysiological recordings were performed.

KEY RESULTS

Systemic buspirone reduced the entopeduncular nucleus firing rate in the sham animals and burst activity in the 6-OHDA-lesioned rats (with or without l-DOPA treatment), while local administration reduced entopeduncular nucleus activity in all the groups, regardless of DA integrity. Systemic 8-OH-DPAT also induced inhibitory effects only in the sham animals. Effects triggered by buspirone and 8-OH-DPAT were reversed by the 5-HT1A receptor antagonist, WAY-100635. Neither buspirone nor 8-OH-DPAT modified the low-frequency oscillatory activity in the entopeduncular nucleus or its synchronization with the motor cortex. Buspirone did not alter the response induced by subthalamic nucleus opto-stimulation in the entopeduncular nucleus.

CONCLUSION AND IMPLICATIONS

Systemic 5-HT1A receptor activation elicits different effects on the electrophysiological properties of the entopeduncular nucleus depending on the integrity of the nigrostriatal pathway and it does not alter the relationship between subthalamic nucleus and entopeduncular nucleus neuron activity.

Parkinsonism Differently Affects the Single Neuronal Activity in the Primary and Supplementary Motor Areas in Monkeys: An Investigation in Linear and Nonlinear Domains

The changes in neuronal firing activity in the primary motor cortex (M1) and supplementary motor area (SMA) were compared in monkeys rendered parkinsonian by treatment with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. The neuronal dynamic was characterized using mathematical tools defined in different frameworks (rate, oscillations or complex patterns). Then, and for each cortical area, multivariate and discriminate analyses were further performed on these features to identify those important to differentiate between the normal and the pathological neuronal activity. Our results show a different order in the importance of the features to discriminate the pathological state in each cortical area which suggests that the M1 and the SMA exhibit dissimilarities in their neuronal alterations induced by parkinsonism. Our findings highlight the need for multiple mathematical frameworks to best characterize the pathological neuronal activity related to parkinsonism. Future translational studies are warranted to investigate the causal relationships between cortical region-specificities, dominant pathological hallmarks and symptoms.

Local field potential dynamics in the primate cortex in relation to parkinsonism reveled by machine learning: A comparison between the primary motor cortex and the supplementary area

The present study compares the cortical local field potentials (LFPs) in the primary motor cortex (M1) and the supplementary motor area (SMA) of non-human primates rendered Parkinsonian with administration of dopaminergic neurotoxin, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. The dynamic of the LFPs was investigated under several mathematical frameworks and machine learning was used to discriminate the recordings based on these features between healthy, parkinsonian with off-medication and parkinsonian with on-medication states. The importance of each feature in the discrimination process was further investigated. The dynamic of the LFPs in M1 and SMA was affected regarding its variability (time domain analysis), oscillatory activities (frequency domain analysis) and complex patterns (non-linear domain analysis). Machine learning algorithms achieved accuracy near 0.90 for comparisons between conditions. The TreeBagger algorithm provided best accuracy. The relative importance of these features differed with the cortical location, condition and treatment. Overall, the most important features included beta oscillation, fractal dimension, gamma oscillation, entropy and asymmetry of amplitude fluctuation. The importance of features in discriminating between normal and pathological states, and on- or off-medication states depends on the pair-comparison and it is region-specific. These findings are discussed regarding the refinement of current models for movement disorders and the development of on-demand therapies.

Neuromechanical Modelling of Articulatory Movements from Surface Electromyography and Speech Formants

Speech articulation is produced by the movements of muscles in the larynx, pharynx, mouth and face. Therefore speech shows acoustic features as formants which are directly related with neuromotor actions of these muscles. The first two formants are strongly related with jaw and tongue muscular activity. Speech can be used as a simple and ubiquitous signal, easy to record and process, either locally or on e-Health platforms. This fact may open a wide set of applications in the study of functional grading and monitoring neurodegenerative diseases. A relevant question, in this sense, is how far speech correlates and neuromotor actions are related. This preliminary study is intended to find answers to this question by using surface electromyographic recordings on the masseter and the acoustic kinematics related with the first formant. It is shown in the study that relevant correlations can be found among the surface electromyographic activity (dynamic muscle behavior) and the positions and first derivatives of the first formant (kinematic variables related to vertical velocity and acceleration of the joint jaw and tongue biomechanical system). As an application example, it is shown that the probability density function associated to these kinematic variables is more sensitive than classical features as Vowel Space Area (VSA) or Formant Centralization Ratio (FCR) in characterizing neuromotor degeneration in Parkinson’s Disease.

On the Motion of Spikes: Turbulent-Like Neuronal Activity in the Human Basal Ganglia

Neuronal signals are usually characterized in terms of their discharge rate, a description inadequate to account for the complex temporal organization of spike trains. Complex temporal properties, which are characteristic of neuronal systems, can only be described with the appropriate, complex mathematical tools. Here, I apply high order structure functions to the analysis of neuronal signals recorded from parkinsonian patients during functional neurosurgery, recovering multifractal properties. To achieve an accurate model of such multifractality is critical for understanding the basal ganglia, since other non-linear properties, such as entropy, depend on the fractal properties of complex systems. I propose a new approach to the study of neuronal signals: to study spiking activity in terms of the velocity of spikes, defining it as the inverse function of the instantaneous frequency. I introduce a neural field model that includes a non-linear gradient field, representing neuronal excitability, and a diffusive term to consider the physical properties of the electric field. Multifractality is present in the model for a range of diffusion coefficients, and multifractal temporal properties are mirrored into space. The model reproduces the behavior of human basal ganglia neurons and shows that it is like that of turbulent fluids. The results obtained from the model predict that passive electric properties of neuronal activity, including ephaptic coupling, are far more relevant to the human brain than what is usually considered: passive electric properties determine the temporal and spatial organization of neuronal activity in the neural tissue.

Vowel Articulation Dynamic Stability Related to Parkinson's Disease Rating Features: Male Dataset

Neurodegenerative pathologies as Parkinson's Disease (PD) show important distortions in speech, affecting fluency, prosody, articulation and phonation. Classically, measurements based on articulation gestures altering formant positions, as the Vocal Space Area (VSA) or the Formant Centralization Ratio (FCR) have been proposed to measure speech distortion, but these markers are based mainly on static positions of sustained vowels. The present study introduces a measurement based on the mutual information distance among probability density functions of kinematic correlates derived from formant dynamics. An absolute kinematic velocity associated to the position of the jaw and tongue articulation gestures is estimated and modeled statistically. The distribution of this feature may differentiate PD patients from normative speakers during sustained vowel emission. The study is based on a limited database of 53 male PD patients, contrasted to a very selected and stable set of eight normative speakers. In this sense, distances based on Kullback-Leibler divergence seem to be sensitive to PD articulation instability. Correlation studies show statistically relevant relationship between information contents based on articulation instability to certain motor and nonmotor clinical scores, such as freezing of gait, or sleep disorders. Remarkably, one of the statistically relevant correlations point out to the time interval passed since the first diagnostic. These results stress the need of defining scoring scales specifically designed for speech disability estimation and monitoring methodologies in degenerative diseases of neuromotor origin.

A Novel Wavelet Transform-Homogeneity Model for Sudden Cardiac Death Prediction Using ECG Signals

Sudden cardiac death (SCD) is one of the main causes of death among people. A new methodology is presented for predicting the SCD based on ECG signals employing the wavelet packet transform (WPT), a signal processing technique, homogeneity index (HI), a nonlinear measurement for time series signals, and the Enhanced Probabilistic Neural Network classification algorithm. The effectiveness and usefulness of the proposed method is evaluated using a database of measured ECG data acquired from 20 SCD and 18 normal patients. The proposed methodology presents the following significant advantages: (1) compared with previous works, the proposed methodology achieves a higher accuracy using a single nonlinear feature, HI, thus requiring low computational resource for predicting an SCD onset in real-time, unlike other methodologies proposed in the literature where a large number of nonlinear features are used to predict an SCD event; (2) it is capable of predicting the risk of developing an SCD event up to 20 min prior to the onset with a high accuracy of 95.8%, superseding the prior 12 min prediction time reported recently, and (3) it uses the ECG signal directly without the need for transforming the signal to a heart rate variability signal, thus saving time in the processing.

Dynamic Neural State Identification in Deep Brain Local Field Potentials of Neuropathic Pain

In neuropathic pain, the neurophysiological and neuropathological function of the ventro-posterolateral nucleus of the thalamus (VPL) and the periventricular gray/periaqueductal gray area (PVAG) involves multiple frequency oscillations. Moreover, oscillations related to pain perception and modulation change dynamically over time. Fluctuations in these neural oscillations reflect the dynamic neural states of the nucleus. In this study, an approach to classifying the synchronization level was developed to dynamically identify the neural states. An oscillation extraction model based on windowed wavelet packet transform was designed to characterize the activity level of oscillations. The wavelet packet coefficients sparsely represented the activity level of theta and alpha oscillations in local field potentials (LFPs). Then, a state discrimination model was designed to calculate an adaptive threshold to determine the activity level of oscillations. Finally, the neural state was represented by the activity levels of both theta and alpha oscillations. The relationship between neural states and pain relief was further evaluated. The performance of the state identification approach achieved sensitivity and specificity beyond 80% in simulation signals. Neural states of the PVAG and VPL were dynamically identified from LFPs of neuropathic pain patients. The occurrence of neural states based on theta and alpha oscillations were correlated to the degree of pain relief by deep brain stimulation. In the PVAG LFPs, the occurrence of the state with high activity levels of theta oscillations independent of alpha and the state with low-level alpha and high-level theta oscillations were significantly correlated with pain relief by deep brain stimulation. This study provides a reliable approach to identifying the dynamic neural states in LFPs with a low signal-to-noise ratio by using sparse representation based on wavelet packet transform. Furthermore, it may advance closed-loop deep brain stimulation based on neural states integrating multiple neural oscillations.

Subthalamic neural entropy is a feature of freezing of gait in freely moving people with Parkinson's disease

The goal of this study was to investigate subthalamic (STN) neural features of Freezers and Non-Freezers with Parkinson's disease (PD), while freely walking without freezing of gait (FOG) and during periods of FOG, which were better elicited during a novel turning and barrier gait task than during forward walking.

METHODS

Synchronous STN local field potentials (LFPs), shank angular velocities, and ground reaction forces were measured in fourteen PD subjects (eight Freezers) off medication, OFF deep brain stimulation (DBS), using an investigative, implanted, sensing neurostimulator (Activa® PC+S, Medtronic, Inc.). Tasks included standing still, instrumented forward walking, stepping in place on dual forceplates, and instrumented walking through a turning and barrier course.

RESULTS

During locomotion without FOG, Freezers showed lower beta (13-30Hz) power (P=0.036) and greater beta Sample Entropy (P=0.032), than Non-Freezers, as well as greater gait asymmetry and arrhythmicity (P<0.05 for both). No differences in alpha/beta power and/or entropy were evident at rest. During periods of FOG, Freezers showed greater alpha (8-12Hz) Sample Entropy (P<0.001) than during walking without FOG.

CONCLUSIONS

A novel turning and barrier course was superior to FW in eliciting FOG. Greater unpredictability in subthalamic beta rhythms was evident during stepping without freezing episodes in Freezers compared to Non-Freezers, whereas greater unpredictability in alpha rhythms was evident in Freezers during FOG. Non-linear analysis of dynamic neural signals during gait in freely moving people with PD may yield greater insight into the pathophysiology of FOG; whether the increases in STN entropy are causative or compensatory remains to be determined. Some beta LFP power may be useful for rhythmic, symmetric gait and DBS parameters, which completely attenuate STN beta power may worsen rather than improve FOG.

Structure Function Revisited: A Simple Tool for Complex Analysis of Neuronal Activity

Neural systems are characterized by their complex dynamics, reflected on signals produced by neurons and neuronal ensembles. This complexity exhibits specific features in health, disease and in different states of consciousness, and can be considered a hallmark of certain neurologic and neuropsychiatric conditions. To measure complexity from neurophysiologic signals, a number of different nonlinear tools of analysis are available. However, not all of these tools are easy to implement, or able to handle clinical data, often obtained in less than ideal conditions in comparison to laboratory or simulated data. Recently, the temporal structure function emerged as a powerful tool for the analysis of complex properties of neuronal activity. The temporal structure function is efficient computationally and it can be robustly estimated from short signals. However, the application of this tool to neuronal data is relatively new, making the interpretation of results difficult. In this methods paper we describe a step by step algorithm for the calculation and characterization of the structure function. We apply this algorithm to oscillatory, random and complex toy signals, and test the effect of added noise. We show that: (1) the mean slope of the structure function is zero in the case of random signals; (2) oscillations are reflected on the shape of the structure function, but they don't modify the mean slope if complex correlations are absent; (3) nonlinear systems produce structure functions with nonzero slope up to a critical point, where the function turns into a plateau. Two characteristic numbers can be extracted to quantify the behavior of the structure function in the case of nonlinear systems: (1). the point where the plateau starts (the inflection point, where the slope change occurs), and (2). the height of the plateau. While the inflection point is related to the scale where correlations weaken, the height of the plateau is related to the noise present in the signal. To exemplify our method we calculate structure functions of neuronal recordings from the basal ganglia of parkinsonian and healthy rats, and draw guidelines for their interpretation in light of the results obtained from our toy signals.

Dopaminergic Modulation of Synaptic Integration and Firing Patterns in the Rat Entopeduncular Nucleus

Dopamine is known to differentially modulate the impact of cortical input to the striatum between the direct and indirect pathways of the basal ganglia (BG). However, the role of extrastriatal dopamine receptors (DRs) in BG information processing is less clear. To investigate the role of extrastriatal DRs, we studied their distribution and function in one of the output nuclei of the BG of the rodent, the entopeduncular nucleus (EP). qRT-PCR indicated that all DR subtypes were expressed by EP neurons, suggesting that both D₁-like receptors (D1LRs) and D₂-like receptors (D2LRs) were likely to affect information processing in the EP. Whole-cell recordings revealed that striatal inputs to the EP were potentiated by D1LRs whereas pallidal inputs to the EP were depressed by D2LRs. Changes to the paired-pulse ratio of inputs to the EP suggested that dopaminergic modulation of striatal inputs is mediated by postsynaptic receptors, and that of globus pallidus-evoked inputs is mediated by presynaptic receptors. We show that these changes in synaptic efficacy changed the information content of EP neuron firing. Overall, the findings suggest that the dopaminergic system affects the passage of feedforward information through the BG by modulating input divergence in the striatum and output convergence in the EP.SIGNIFICANCE STATEMENT The entopeduncular nucleus (EP), one of the basal ganglia (BG) output nuclei, is an important station in information processing in BG. However, it remains unclear how EP neurons encode information and how dopamine affects this process. This contrasts with the well established role of dopamine in the striatum, which is known to redistribute cortical input between the direct and indirect pathways. Here we show that, in symmetry with the striatum, dopamine controls the rebalancing of information flow between the two pathways in the EP. Specifically, we demonstrate that dopamine regulates EP activity by differentially modulating striatal and pallidal GABAergic inputs. These results call for a reassessment of current perspectives on BG information processing by highlighting the functional role of extrastriatal dopamine receptors.

Parkinsonian Balance Deficits Quantified Using a Game Industry Board and a Specific Battery of Four Paradigms

This study describes a cost-effective screening protocol for parkinsonism based on combined objective and subjective monitoring of balance function. Objective evaluation of balance function was performed using a game industry balance board and an automated analyses of the dynamic of the center of pressure in time, frequency, and non-linear domains collected during short series of stand up tests with different modalities and severity of sensorial deprivation. The subjective measurement of balance function was performed using the Dizziness Handicap Inventory questionnaire. Principal component analyses on both objective and subjective measurements of balance function allowed to obtained a specificity and selectivity for parkinsonian patients (vs. healthy subjects) of 0.67 and 0.71 respectively. The findings are discussed regarding the relevance of cost-effective balance-based screening system as strategy to meet the needs of broader and earlier screening for parkinsonism in communities with limited access to healthcare.