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

Darbin, Olivier; Jin, Xingxing; Wrangel, Christof von; Schwabe, Kerstin; Nambu, Atsushi; Naritoku, Dean K.; Krauss, Joachim K.; Alam, Mesbah

doi:10.1142/s0129065715500380

Cited by 19 publications

(21 citation statements)

References 58 publications

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“…Phase synchronization and coupling between multiple oscillations were also found to be associated with pain (Sarnthein and Jeanmonod, 2008 ). Patterns of neural oscillations have also been measured with Lempel-Ziv complexity (Geng et al, 2017 ), coefficients of variance (Little et al, 2012 ), and entropy (Darbin et al, 2016 ). When features in multiple domains are used to encode neural states, the binary coding method may become no longer intuitive or applicable, and non-linear coding with Bayesian and artificial neural network methods would be needed.…”

Section: Discussionmentioning

confidence: 99%

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

Luo

Huang

et al. 2018

Front. Neurosci.

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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.

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Section: Discussionmentioning

confidence: 99%

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

Luo

Huang

et al. 2018

Front. Neurosci.

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“…Other complex and nonlinear temporal properties are also present in basal ganglia spike trains from rodents, primates and patients with movement disorders (Li, Jia et al 2008, Lim, Sanghera et al 2010, Hohlefeld, Huebl et al 2012, Darbin, Adams et al 2013, Hohlefeld, Ehlen et al 2015. The most popular nonlinear measure for the characterization of parkinsonian spike trains is probably entropy, which diminishes due to pharmacological or stimulation treatment, and increases due to alertness (Latora 2000, Lafreniere-Roula, Darbin et al 2010, Lim, Sanghera et al 2010, Alam, Sanghera et al 2015, Darbin, Jin et al 2015. Importantly, entropy can be estimated wrongly if the multifractality of a system is not considered (Costa, Lyra et al 1997, Lyra 1998, Latora 2000.…”

Section: Discussionmentioning

confidence: 99%

“…Other complex and nonlinear temporal properties are also present in basal ganglia spike trains from rodents, primates and patients with movement disorders [23,[60][61][62][63]. The most popular nonlinear measure for the characterization of parkinsonian spike trains is probably entropy, which diminishes due to pharmacological or stimulation treatment, and increases due to alertness [22,23,[64][65][66][67]. Importantly, entropy can be estimated wrongly if the multifractality of a system is not considered [64,68,69].…”

Section: Discussionmentioning

confidence: 99%

On the motion of spikes: a model of multifractality as observed in the neuronal activity of the human basal ganglia

Andres

2017

Preprint

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Neuronal signals are usually characterized in terms of their discharge rate. However, this description is inadequate to account for the complex temporal organization of spike trains. In particular multifractality is a hallmark of the neuronal activity of the human, parkinsonian basal ganglia, which is not accounted for in most models. Here I develop a new conceptualization of neuronal activity, enabling the analysis of spike trains in terms of a velocity field. Firstly, I show that structure functions of increasing order can be used to recover the multifractal spectrum of spike trains obtained from the globus pallidus interna (GPi) of patients with Parkinson's disease. Further, I propose a neural field model to study the observed multifractality. The model describes the motion of spikes in terms of a velocity field, including a diffusive term to consider the physical properties of the electric field that is associated to neuronal activity. As the model is perturbed with colored noise, the following is observed: 1. multifractality is present for a wide range of diffusion coefficients; and 2. multifractal temporal properties are mirrored into space. These results predict that passive electric properties of neuronal activity are far more relevant to the human brain than what has been usually considered.

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“…The fact that S p 1−2 is significantly higher than S p 2−3 in parkinsonian animals is in agreement with previous observations indicating that the basal ganglia of animals with dopamine depletion do not handle well the awakening process (Andres et al, 2014a ). Importantly, as a consequence of the averaging process S p is independent from the frequency of discharge of the neurons by definition, solving a previous controversy about the structure function method (Darbin et al, 2016 ). Regarding the inflection point τ 1 , it was higher in Parkinson's disease than in control neurons with a more pronounced effect at higher alertness levels, but this effect was not statistically significant ( p > 0.05) and needs to be further tested in larger studies.…”

Section: Summary and Conclusion: Guidelines For The Interpretation Ofmentioning

confidence: 91%

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

Nanni

Andres

2017

Front. Hum. Neurosci.

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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.

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

Cited by 19 publications

References 58 publications

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

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

On the motion of spikes: a model of multifractality as observed in the neuronal activity of the human basal ganglia

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

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