A probabilistic framework for a physiological representation of dynamically evolving sleep state

Dadok, Vera M.; Kirsch, Heidi E.; Sleigh, Jamie; Lopour, Beth A.; Szeri, Andrew J.

doi:10.1007/s10827-013-0489-x

Cited by 12 publications

(15 citation statements)

References 37 publications

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“…However the comparison between the “pseudo-EEG” output from the model, and the real EEG obtained from experiments, has always been semi-quantitative at best. Therefore, using the Steyn-Ross model as a basis, following Lopour et al (2011), Dadok et al (2014) developed Bayesian methods to solve the inverse problem of mapping experimentally derived EEG features data back onto the state-space of the model. In this way the association of specific values of model parameters corresponding to each epoch of real EEG might give insight into the underlying neurobiology.…”

Section: Introductionmentioning

confidence: 99%

Emergence from general anesthesia and the sleep-manifold

Hight

Dadok

Szeri

et al. 2014

Front. Syst. Neurosci.

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The electroencephalogram (EEG) during the re-establishment of consciousness after general anesthesia and surgery varies starkly between patients. Can the EEG during this emergence period provide a means of estimating the underlying biological processes underpinning the return of consciousness? Can we use a model to infer these biological processes from the EEG patterns? A frontal EEG was recorded from 84 patients. Ten patients were chosen for state-space analysis. Five showed archetypal emergences; which consisted of a progressive decrease in alpha power and increase peak alpha frequency before return of responsiveness. The five non-archetypal emergences showed almost no spectral EEG changes (even as the volatile general anesthetic decreased) and then an abrupt return of responsiveness. We used Bayesian methods to estimate the likelihood of an EEG pattern corresponding to the position of the patient on a 2-dimensional manifold in a state space of excitatory connection strength vs. change in intrinsic resting neuronal membrane conductivity. We could thus visualize the trajectory of each patient in the state-space during their emergence period. The patients who followed an archetypal emergence displayed a very consistent pattern; consisting of progressive increase in conductivity, and a temporary period of increased connection strength before return of responsiveness. The non-archetypal emergence trajectories remained fixed in a region of phase space characterized by a relatively high conductivity and low connection strength throughout emergence. This unexpected progressive increase in conductivity during archetypal emergence may be due to an abating of the surgical stimulus during this period. Periods of high connection strength could represent forays into dissociated consciousness, but the model suggests all patients reposition near the fold in the state space to take advantage of bi-stable cortical dynamics before transitioning to consciousness.

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Section: Introductionmentioning

confidence: 99%

Emergence from general anesthesia and the sleep-manifold

Hight

Dadok

Szeri

et al. 2014

Front. Syst. Neurosci.

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“…After choosing two parameter regions to investigate, we present a process for mapping ECoG onto either of them via a general procedure similar to that in (Dadok et al 2013). To account for the innate stochasticity and noisiness of this system, we simulate the stochastic cortical model multiple times at each model parameter state to estimate the probability distribution functions of ECoG features at each state.…”

Section: Overview Of Methodsmentioning

confidence: 99%

“…There have been several studies in associating cortical states (including seizures) measured in patients with these types of meso-scale cortical models and neural mass models for both seizures (Kramer et al 2005(Kramer et al , 2007NevadoHolgado et al 2012;Wang et al 2012;Aarabi and He 2014) and sleep (Lopour et al 2011;Dadok et al 2013). In Kramer et al (2005Kramer et al ( , 2007, pathways to seizure regions identified with bifurcation analysis through the particular mesoscale model used in this work were explored qualitatively to determine potential directions through seizure and enumerate seizure regions.…”

Section: Introductionmentioning

confidence: 99%

“…With an application to sleep rather than seizures, in Lopour et al (2011) and Dadok et al (2013), sleep states were associated with sleep physiological parameters in such a meso-scale cortical model. The methods presented in this work most closely resembles the methods used in Dadok et al (2013) with a new application to seizures, an extension to comparing model regions rather than analysis within a single region, and technical improvements including improved sampling, simplified feature selection algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…The methods presented in this work most closely resembles the methods used in Dadok et al (2013) with a new application to seizures, an extension to comparing model regions rather than analysis within a single region, and technical improvements including improved sampling, simplified feature selection algorithm.…”

Section: Introductionmentioning

confidence: 99%

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A probabilistic method for determining cortical dynamics during seizures

et al. 2015

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This work presents a probabilistic method for inferring the parameter ranges in a biologically relevant mathematical model of the cortex most likely to be producing seizures observed in an electrocorticogram (ECoG) signal from a human subject. Additionally, this method produces a probabilistic pathway of the temporal evolution of physiological state in the cortex over the course of individual seizures, leveraging a model of the cortex that describes cortical physiology. We describe ways in which these methods and results offer insights into seizure etiology and have the potential to suggest new treatment options. To directly account for the stochastic and noisy nature of the mathematical model and the ECoG signal, we use a probabilistic Bayesian framework to map features of ECoG segments onto a distribution of likelihoods over physiologically-relevant parameter states. A Hidden Markov Model (HMM) is then introduced to incorporate the belief that cortical physiology has both temporal continuity and also a degree of reproducibility between individual seizures. By inspecting the ratio of likelihoods between HMMs run under two possible parameter regions, both of which produce seizures in the model, we determine which physiological parameter regions are more likely to be causing the observed seizures. We show that between individual seizures, there is consistency in these likelihood ratios between hypothesized regions, in the temporal pathways calculated, and in the separation of seizure from non-seizure time segment likelihood maps.

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Anesthesia, Neural Population Models of

Steyn-Ross

Sleigh

2022

Encyclopedia of Computational Neuroscience

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A probabilistic framework for a physiological representation of dynamically evolving sleep state

Cited by 12 publications

References 37 publications

Emergence from general anesthesia and the sleep-manifold

Emergence from general anesthesia and the sleep-manifold

A probabilistic method for determining cortical dynamics during seizures

Anesthesia, Neural Population Models of

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