Meanfield modeling of propofol-induced changes in spontaneous EEG rhythms

Hindriks, Rikkert; Putten, Michel Johannes Antonius Maria van

doi:10.1016/j.neuroimage.2012.02.042

Cited by 76 publications

(97 citation statements)

References 61 publications

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“…Note that there is still spatial dependence based on the incoming connections over the white matter tracts in the network (Equation (2a)). Values for all constants can be found in Supporting Information, Table S1 and are the same parameters as used to simulate resting‐state activity in previous studies (Hindriks & van Putten 2012, 2013; Robinson et al, 2002), with the only exception that we increased

v_{e i}

to account for the extra excitatory input from the network. Simulations based on the cortico‐thalamic mean‐field model were performed using the Euler–Maruyama method with an integration time step of 1 × 10 −4 .…”

Section: Methodsmentioning

confidence: 99%

Explaining the heterogeneity of functional connectivity findings in multiple sclerosis: An empirically informed modeling study

Tewarie

Steenwijk

Brookes

et al. 2018

Human Brain Mapping

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To understand the heterogeneity of functional connectivity results reported in the literature, we analyzed the separate effects of grey and white matter damage on functional connectivity and networks in multiple sclerosis. For this, we employed a biophysical thalamo‐cortical model consisting of interconnected cortical and thalamic neuronal populations, informed and amended by empirical diffusion MRI tractography data, to simulate functional data that mimic neurophysiological signals. Grey matter degeneration was simulated by decreasing within population connections and white matter degeneration by lowering between population connections, based on lesion predilection sites in multiple sclerosis. For all simulations, functional connectivity and functional network organization are quantified by phase synchronization and network integration, respectively. Modeling results showed that both cortical and thalamic grey matter damage induced a global increase in functional connectivity, whereas white matter damage induced an initially increased connectivity followed by a global decrease. Both white and especially grey matter damage, however, induced a decrease in network integration. These empirically informed simulations show that specific topology and timing of structural damage are nontrivial aspects in explaining functional abnormalities in MS. Insufficient attention to these aspects likely explains contradictory findings in multiple sclerosis functional imaging studies so far.

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v_{e i}

Section: Methodsmentioning

confidence: 99%

Explaining the heterogeneity of functional connectivity findings in multiple sclerosis: An empirically informed modeling study

Tewarie

Steenwijk

Brookes

et al. 2018

Human Brain Mapping

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“…Extending the model to include inhibition of inhibition is justified because previous anesthesia modelling studies have suggested that inhibition of the inhibitory population by itself at the cortical level is important for describing the EEG changes linked to anesthesia (Bojak & Liley, 2005;Hindriks & van Putten, 2012). Two types of extensions were considered.…”

Section: Error Analysis Of Extensions Of the Jr Modelmentioning

confidence: 99%

“…In particular, neural mass or neural field model (Deco et al, 2008) approaches that attempt to take into account the meso-/macro-scale neurophysiology involved in the generation of EEG at rest and during anesthesia (Bojak & Liley, 2005;McCarthy et al, 2008;Ching et al, 2010;Steyn-Ross et al, 2012;Hindriks & van Putten, 2012;Hutt, 2013;20 Hutt & Buhry, 2014) present an interesting alternative to tracking the anesthetic brain state, while simultaneously allowing for estimation of underlying physiological variables that may have clinical significance.…”

Section: Introductionmentioning

confidence: 99%

“…Given that neural mass models are generally nonlinear and related noise sources can be considered to be Gaussian white noise (Liley et al, 2002;Nunez & Srinivasan, 2006), this paper presents the application of the unscented Kalman filter (UKF) to state and parameter estimation of a neural mass model of population activity in 35 cerebral cortex. The goals are (1) to evaluate whether the parameter estimations can be used to efficiently track anes-2 thetic brain states during anesthesia in humans using the gamma-amino-butyric-acid-A (GABA-A) receptor agonist propofol (Rudolph & Antkowiak, 2004), and (2) to evaluate if the approach can simultaneously provide information about the key changes in inhibitory post-synaptic potential (IPSP) amplitude and rate constant parameters known to vary with the level of propofol concentration (Collins, 1988;Bieda & MacIver, 2004;Wang et al, 2004;Ying & 40 Goldstein, 2005;McCarthy et al, 2008;Wang, 2009;Hindriks & van Putten, 2012;Hutt, 2013). Such information may help anesthesiologists monitor the depth of anesthesia as well as understand the patient's underlying inhibitory systems during anesthesia and, once such methods have matured, provide improvements in general anesthesia and surgery outcomes.…”

mentioning

confidence: 99%

“…Although there are several 55 other neural models that have been specifically developed to describe the EEG for different anesthetics (Bojak & Liley, 2005;McCarthy et al, 2008;Ching et al, 2010;Steyn-Ross et al, 2012;Hindriks & van Putten, 2012;Hutt, 2013;Hutt & Buhry, 2014) this paper focuses on the JR model (and extensions of this model that include inhibition of inhibition) to provide a benchmark for future studies with more complex models in order to find the simplest neural mass model that is accurate, informative and efficient enough for clinical application.…”

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confidence: 99%

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Neural mass model-based tracking of anesthetic brain states

et al. 2016

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Neural mass model-based tracking of brain states from electroencephalographic signals holds the promise of simultaneously tracking brain states while inferring underlying physiological changes in various neuroscientific and clinical applications. Here, neural mass model-based tracking of brain states using the unscented Kalman filter applied to estimate parameters of the Jansen-Rit cortical population model is evaluated through the application of propofol-based anesthetic state monitoring. In particular, 15 subjects underwent propofol anesthesia induction from awake to anesthetised while behavioural responsiveness was monitored and frontal electroencephalographic signals were recorded.The unscented Kalman filter Jansen-Rit model approach applied to frontal electroencephalography achieved reasonable testing performance for classification of the anesthetic brain state (sensitivity: 0.51; chance sensitivity: 0.17; nearest neighbor sensitivity 0.75) when compared to approaches based on linear (autoregressive moving average) modelling (sensitivity 0.58; nearest neighbor sensitivity: 0.91) and a high performing standard depth of anesthesia monitoring measure, Higuchi Fractal Dimension (sensitivity: 0.50; nearest neighbor sensitivity: 0.88). Moreover, it was found that the unscented Kalman filter based parameter estimates of the inhibitory postsynaptic potential amplitude varied in the physiologically expected direction with increases in propofol concentration, while the estimates of the inhibitory postsynaptic potential rate constant did not. These results combined with analysis of monotonicity of parameter estimates, error analysis of parameter estimates, and observability analysis of the Jansen-Rit model, along with considerations of extensions of the Jansen-Rit model, suggests that the Jansen-Rit model combined with unscented Kalman filtering provides a valuable benckmark for future real-time brain state tracking studies. This is especially true for studies of more complex, but still computationally efficient, neural models of anesthesia that can more accurately track the anesthetic brain state, while simultaneously inferring underlying physiological changes that can potentially provide useful clinical information.

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Epilepsy: Computational Models

Gils¹,

Drongelen²

2022

Encyclopedia of Computational Neuroscience

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Meanfield modeling of propofol-induced changes in spontaneous EEG rhythms

Cited by 76 publications

References 61 publications

Explaining the heterogeneity of functional connectivity findings in multiple sclerosis: An empirically informed modeling study

Explaining the heterogeneity of functional connectivity findings in multiple sclerosis: An empirically informed modeling study

Neural mass model-based tracking of anesthetic brain states

Epilepsy: Computational Models

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