Effects of Cholinergic Neuromodulation on Thalamocortical Rhythms During NREM Sleep: A Model Study

Li, Qiang; Song, Jiang-Ling; Li, Sihui; Westover, M. Brandon; Zhang, Rui

doi:10.3389/fncom.2019.00100

Cited by 4 publications

(7 citation statements)

References 62 publications

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“…In terms of the NMSE of 24 varying DBS frequencies {2. 5,5,7.5,10,15,20,30,40,50,60,70,80,90,100,110,120,130,140,150,160,170,180,190,and 200Hz}, the mean-field method is significantly larger than this work (ANOVA, p = 8.99 × 10 -9 ).…”

Section: Figure 4 Comparison Of the Rate Model With The Mean-field Mo...contrasting

confidence: 55%

“…The copyright holder for this preprint (which this version posted July 2, 2022. ; https://doi.org/10.1101/2022.06.28.497834 doi: bioRxiv preprint 5,7.5,10,15,20,30,40,50,60,70,80,90,100,110,120,130,140,150,160,170,180,190,and 200Hz}, the mean-field method is significantly larger than this work (ANOVA, p = 8.99 × 10 -9 ). These shortcomings show that the mean-field method with Equation ( 12) is not appropriate for our scenario; possible explanations of such inappropriateness are shown in Discussion.…”

Section: Figure 4 Comparison Of the Rate Model With The Mean-field Mo...mentioning

confidence: 55%

“…The commonly used mean-field method to obtain the firing rate is through a transfer function from the average membrane potential of the modelled brain region [2][16][40]. Typically, the sigmoid transfer function is widely used [18][2][50][40]. We constructed a typical mean-field method in Method (E) and compared with our rate model in Results ; we saw that our rate model was much better than the mean-field method in Vim synthetic data.…”

Section: Discussionmentioning

confidence: 99%

“…𝑟 | is the baseline firing rate of the modelled nuclei. We confine 𝑟 | in biological constraints, based on experimental and synthetic data from both human and mammalian recordings.For Vim, 𝑟 | ∈[10,50] Hz[10][38]; for Rt, 𝑟 | ∈[3,40] Hz[18][10]; for SNr, 𝑟 | ∈[40, 120] Hz[18][10]; for STN, 𝑟 | ∈ [5, 100] Hz[10]…”

mentioning

confidence: 99%

“…For both optimization methods, we used the estimated model parameters to replicate the firing rate for 24 observed and unobserved DBS frequencies in 0~200 Hz, including {2 5,5,. 7.5,10,15,20,30,40,50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, and 200Hz} (for STN and SNr, the maximum is 100Hz and 50Hz, respectively). We used the normalized mean squared error (NMSE) to measure the error between the estimated firing rate and the reference firing rate computed by PSTH.…”

mentioning

confidence: 99%

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Modelling Instantaneous Firing Rate of DBS Target Neuronal Ensembles in Basal Ganglia and Thalamus

Tian

Murphy

Steiner

et al. 2022

Preprint

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Parkinson's disease (PD) is the second most common neurodegenerative disorder symptom, and is closely related to the dysfunction of the basal ganglia-thalamocortical network. Deep Brain Stimulation (DBS) is an effective treatment for suppressing PD motor symptoms; however, the underlying mechanisms of DBS remain elusive. A recent study demonstrated that different substructures of the basal ganglia and thalamus respond differently to various frequencies of DBS. Despite the capability of existing models in qualitatively interpreting experimental data, there are very few unified computational models that quantitatively capture the dynamics of the neuronal activity of varying stimulated substructures - including subthalamic nucleus (STN), substantia nigra pars reticulate (SNr) and ventralis intermedius (Vim) - across different DBS frequencies. Here, we developed a novel mathematical model to represent the firing rate of nuclei receiving DBS (microstimulation), including STN, SNr and Vim - across different DBS frequencies. In our model, the DBS pulses are filtered through a synapse model and a transfer function to formulate the firing rate variability. To consistently fit the model in varying frequencies of DBS, we developed a novel parameter optimization method based on the concatenated data from all DBS frequencies. Our model accurately reproduces the firing rates observed and calculated from both experimental and synthetic data. The optimal model parameters are consistent across different DBS frequencies, and this consistency conforms to the relatively static synaptic structures in short durations of DBS. Our model can be potentially implemented in navigating the DBS parameter space and improves DBS clinical effects.

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Section: Figure 4 Comparison Of the Rate Model With The Mean-field Mo...contrasting

confidence: 55%

Section: Figure 4 Comparison Of the Rate Model With The Mean-field Mo...mentioning

confidence: 55%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Modelling Instantaneous Firing Rate of DBS Target Neuronal Ensembles in Basal Ganglia and Thalamus

Tian

Murphy

Steiner

et al. 2022

Preprint

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Model-based prediction of muscarinic receptor function from auditory mismatch negativity responses

et al. 2021

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Corticothalamic modelling of sleep neurophysiology with applications to mobile EEG

Morshedzadeh,

Kadak,

Bastiaens

et al. 2024

Preprint

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Recent developments in mathematical modelling of EEG data enable estimation and tracking of otherwise-inaccessible neurophysiological parameters over the course of a night’s sleep. Likewise, advancements in wearable electronics have enabled easier & more affordable at-home collection of sleep EEG data. The convergence of these two advances, namely neurophysiological modelling for mobile sleep EEG, has the potential to significantly improve sleep assessments in research and the clinic. However, this subject area has received limited attention in existing literature. To address this, we used an established mathematical model of the corticothalamic system to analyze EEG power spectra from 5 datasets, spanning from in-lab, research-grade systems to at-home mobile EEG devices. In the present work, we compare the convergent and divergent features of the data and the estimated physiological model parameters. While data quality and characteristics differ considerably, several key patterns consistent with previous theoretical and empirical work are observed. During the transition from lighter to deeper NREM stages, i) the exponent of the aperiodic (1/f) spectral component is increased, ii) bottom-up thalamocortical drive is reduced, iii) corticocortical connection strengths are increased. This effect, which we observe in healthy individuals across all 5 datasets, is interestingly absent in individuals taking SSRI antidepressants, suggesting possible effects of ascending neuromodulatory systems on corticothalamic oscillations. Our results provide a proof-of-principle for the utility and feasibility of this physiological modelling-based approach to analyzing data from mobile EEG devices, providing a mechanistic measure of brain physiology during sleep at home or in the lab.

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Effects of Cholinergic Neuromodulation on Thalamocortical Rhythms During NREM Sleep: A Model Study

Cited by 4 publications

References 62 publications

Modelling Instantaneous Firing Rate of DBS Target Neuronal Ensembles in Basal Ganglia and Thalamus

Modelling Instantaneous Firing Rate of DBS Target Neuronal Ensembles in Basal Ganglia and Thalamus

Model-based prediction of muscarinic receptor function from auditory mismatch negativity responses

Corticothalamic modelling of sleep neurophysiology with applications to mobile EEG

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