Neural mass models describing possible origin of the excessive beta oscillations correlated with Parkinsonian state

Liu, Chen; Zhu, Yulin; Liu, Fei; Wang, Jiang; Li, Yong; Deng, Bin; Fietkiewicz, Chris; Loparo, Kenneth A.

doi:10.1016/j.neunet.2017.01.011

Cited by 29 publications

(26 citation statements)

References 42 publications

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“…The strength of synaptic connections between the cortex and STN, between the STN and GPe, and the thalamus cortex were increased to induce beta oscillations within the network and the STN LFP, similar to that observed experimentally (West et al, 2018). Previous simulation studies have similarly increased the strength of connections between nuclei to simulate the effects of dopamine depletion on basal ganglia network activity in PD resulting in the emergence of oscillatory activity in conductance-based models (Rubin and Terman, 2004;Humphries et al, 2006;Hahn and McIntyre, 2010;Kang and Lowery, 2013;Kumaravelu et al, 2016) and mean-field models (Nevado-Holgado et al, 2010;Davidson et al, 2016;Liu et al, 2017bLiu et al, , 2020.…”

Section: Network Structuresupporting

confidence: 60%

Simulation of Closed-Loop Deep Brain Stimulation Control Schemes for Suppression of Pathological Beta Oscillations in Parkinson’s Disease

2020

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Section: Network Structuresupporting

confidence: 60%

Simulation of Closed-Loop Deep Brain Stimulation Control Schemes for Suppression of Pathological Beta Oscillations in Parkinson’s Disease

2020

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“…This check is important for understanding the development of neurodegenerative processes and the formation of compensatory neurophysiological mechanisms in Parkinson's disease, since the frequency ranges of mu and beta correspond to the work of different functional systems in the brain [12,13,14,15]). We have developed a special algorithm that eliminates from the analysis all beta wave trains in EEG signal that were observed simultaneously with the mu wave trains.…”

Section: Introductionmentioning

confidence: 99%

EEG Beta Wave Trains Are Not the Second Harmonic of Mu Wave Trains in Parkinson’s Disease Patients

Sushkova¹,

Morozov²,

Gabova³

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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The goal of this study is development of a novel signal processing and analysis method for detailed investigation of the time-frequency dynamics of brain cortex electrical activity. The idea of our method of electroencephalograms (EEG) analyzing is in that we consider EEG signal as a composition of so-called wave trains. The wave train term is used to denote a signal localized in time, frequency, and space. We consider the wave train as a typical component of EEG, but not as a special kind of EEG signals.In contrast to papers devoted to detecting wave trains of one or two specific types, such as alpha spindles and sleep spindles, we analyze any kind of wave trains in a wide frequency band. Using this method, we have found three interesting frequency areas where differences were detected between a group of Parkinson's disease (PD) patients and a control group of healthy volunteers. The goal of this work is to check whether the regularities in the mu and beta frequency bands are independent ones, that is, the beta wave trains observed in the analysis were not the second harmonics of the mu wave trains.We have developed a special algorithm that eliminates from the analysis all beta wave trains in EEG signal that were observed simultaneously with the mu wave trains. Analysis of a real experimental data set processed by this algorithm has confirmed that the beta frequency band regularity is separate from the mu frequency band regularity. Moreover, a new significant difference between the left hand tremor and right hand tremor Parkinson's disease patients was discovered.

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“…The GPi then projects into the thalamus and the brainstem. This seemingly simple model is successful at describing the pathophysiological processes, such as those involved in Parkinson's Disease (Liu, et al, 2017). Gurney, et al, 2001).…”

Section: Models Of the Basal Gangliamentioning

confidence: 99%

“…These field models are often expanded into the distribution's moments, giving a second order ordinary differential equation for each moment . Basal ganglia field models of the first moment, the mean firing rate, have been developed and are known as neural mass models (Liu, et al, 2017). These models have been used with great success for analysis of movement disorders that are treated with DBS.…”

Section: Models Of the Basal Gangliamentioning

confidence: 99%

“…The GPi then projects into the thalamus and the brainstem, but this feature is not included in this paper. This seemingly simple model is successful at describing the pathophysiological processes, such as those involved in Parkinson's Disease (Liu, et al, 2017). A neural mass model involves modelling groups of neurons by using the Fokker-Plank equation to describe how the distribution of firing rates for a given group of neurons evolve over time and how different groups interact (Deco, et al, 2008).…”

Section: Basal Ganglia Modelmentioning

confidence: 99%

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Investigation of using firing time renewal processes for modelling and analysis of single point microelectrode recordings of the subthalamic nucleus

Weegink¹

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Microelectrode recordings (MER) involve insertion of an electrode, approximately 50 micrometers at the tip, into deep brain structures and recording the electrical activity. It is commonly used in surgeries to accurately determine the location of a target for deep brain stimulation (DBS). A common target for this type of procedure is the subthalamic nucleus (STN), identified by a unique spiking pattern and a change in the background noise level compared to surrounding structures. This change in background noise level indicates that the noise is composed of neuronal sources. This thesis aims to develop a model to determine what extent the volume of neurons around the electrode contribute to an MER. The potential usefulness of the model as a biomarker for the behaviour of the STN is then explored. The model for the STN MER involves simulating thousands of neurons by assuming they follow a renewal process. This assumption requires that the timing between a single neuron's spikes (inter-spike interval-ISI) are independent and identically distributed (IID). The model is tuned to intraoperative recordings to determine the best simulation parameters. To investigate the usefulness of the model as a biomarker of STN behaviour, the IID assumption is relaxed by introducing synchronization between neurons or changing their spike timings to be driven by a dynamical model of the basal ganglia. Fitting the parameters of the renewal process model to these extended models is then used to see if they can reliably describe the new behaviour. The results show firstly that a volume of ~1mm 3 , or on the order of 10,000 neurons, are required to simulate an STN MER that best describes patient data. This result indicates that the background noise of MERs is in fact partly caused by neuronal sources. The speed of the renewal model, faster than real time, allowed many simulations (Ο(10 6)) with different parameters to be created for tuning and verification of the model. The Weibull distribution is used as a parameterized ISI distribution for the renewal model and was found to reliably describe the simulations when the IID assumptions were relaxed. The first example of this was when neurons had synchronized firing times. In this case the Weibull distribution reliably fit the underlying ISI distribution for different realizations of the same simulation parameters. Finally, the thesis shows that using a neural mass model of the basal ganglia to generate the STN firing times, that the renewal model can differentiate between certain cortical inputs. This motivates future investigation into using the parameters from fitting a renewal model to an MER as biomarkers.

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Neural mass models describing possible origin of the excessive beta oscillations correlated with Parkinsonian state

Cited by 29 publications

References 42 publications

Simulation of Closed-Loop Deep Brain Stimulation Control Schemes for Suppression of Pathological Beta Oscillations in Parkinson’s Disease

Simulation of Closed-Loop Deep Brain Stimulation Control Schemes for Suppression of Pathological Beta Oscillations in Parkinson’s Disease

EEG Beta Wave Trains Are Not the Second Harmonic of Mu Wave Trains in Parkinson’s Disease Patients

Investigation of using firing time renewal processes for modelling and analysis of single point microelectrode recordings of the subthalamic nucleus

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