Space-time resolved inference-based neurophysiological process imaging: Application to resting-state alpha rhythm

Zhao, Yun; Boley, Mario; Pelentritou, Andria; Karoly, Philippa J.; Freestone, Dean R.; Liu, Yueyang; Muthukumaraswamy, Suresh; Liley, David T. J.; Kuhlmann, Levin

doi:10.1016/j.neuroimage.2022.119592

Cited by 11 publications

(8 citation statements)

References 67 publications

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“…These are important findings because quantifying differences between model output and data is integral to any parameter inference method, including probabilistic frameworks like dynamic causal modelling (DCM) or Kalman filtering. In such methods, recursive updates to posterior distributions depend upon the difference between model predictions and data [ 44 , 45 ], which could evolve differently if the model and data are transformed, for example, between the time and frequency domain [ 46 ]. Care must therefore be taken to caveat inferences, or to check whether they are robust to methodological choices.…”

Section: Discussionmentioning

confidence: 99%

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Global nonlinear approach for mapping parameters of neural mass models

et al. 2023

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Neural mass models (NMMs) are important for helping us interpret observations of brain dynamics. They provide a means to understand data in terms of mechanisms such as synaptic interactions between excitatory and inhibitory neuronal populations. To interpret data using NMMs we need to quantitatively compare the output of NMMs with data, and thereby find parameter values for which the model can produce the observed dynamics. Mapping dynamics to NMM parameter values in this way has the potential to improve our understanding of the brain in health and disease. Though abstract, NMMs still comprise of many parameters that are difficult to constrain a priori. This makes it challenging to explore the dynamics of NMMs and elucidate regions of parameter space in which their dynamics best approximate data. Existing approaches to overcome this challenge use a combination of linearising models, constraining the values they can take and exploring restricted subspaces by fixing the values of many parameters a priori. As such, we have little knowledge of the extent to which different regions of parameter space of NMMs can yield dynamics that approximate data, how nonlinearities in models can affect parameter mapping or how best to quantify similarities between model output and data. These issues need to be addressed in order to fully understand the potential and limitations of NMMs, and to aid the development of new models of brain dynamics in the future. To begin to overcome these issues, we present a global nonlinear approach to recovering parameters of NMMs from data. We use global optimisation to explore all parameters of nonlinear NMMs simultaneously, in a minimally constrained way. We do this using multi-objective optimisation (multi-objective evolutionary algorithm, MOEA) so that multiple data features can be quantified. In particular, we use the weighted horizontal visibility graph (wHVG), which is a flexible framework for quantifying different aspects of time series, by converting them into networks. We study EEG alpha activity recorded during the eyes closed resting state from 20 healthy individuals and demonstrate that the MOEA performs favourably compared to single objective approaches. The addition of the wHVG objective allows us to better constrain the model output, which leads to the recovered parameter values being restricted to smaller regions of parameter space, thus improving the practical identifiability of the model. We then use the MOEA to study differences in the alpha rhythm observed in EEG recorded from 20 people with epilepsy. We find that a small number of parameters can explain this difference and that, counterintuitively, the mean excitatory synaptic gain parameter is reduced in people with epilepsy compared to control. In addition, we propose that the MOEA could be used to mine for the presence of pathological rhythms, and demonstrate the application of this to epileptiform spike-wave discharges.

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Section: Discussionmentioning

confidence: 99%

“…The method we propose is different from probabilistic approaches such as DCM, Kalman filtering, or approximate Bayesian computation [ 21 , 44 , 45 ]. These methods optimise probability distributions for parameters given the data.…”

Section: Discussionmentioning

confidence: 99%

Global nonlinear approach for mapping parameters of neural mass models

et al. 2023

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“…This means the computation of the LSTM model is more complex. We have tested the result when both methods are computed via CPU, the AKF takes about 516 s to run on a onehour recording, while the LSTM model takes about three times longer: about 1720 s. With the utilisation of GPU, the LSTM model is significantly faster, and is able to run on the same recording in 14 s. This suggests the LSTM filter can be scaled up for larger more complex NMMs for more detailed inference based imaging tasks [2], while still providing tractable run times.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, a filter referred to as the Analytical Kalman filter (AKF) is applied. This filter is highly stable and accurate and was developed in prior work [2,10,27] that evolved from deriving the Kalman filter for general nonlinear NMMs using the specific sigmoidal nonlinearity in equation [15]. For the sake of brevity, we refer the reader to these prior works for greater mathematical insight.…”

Section: Analytic Kalman Filtermentioning

confidence: 99%

“…Understanding the brain is one of the most challenging problems of science, engineering and medicine. Currently, due to limitations of existing brain imaging and neurophysiological techniques, many neurophysiological variables cannot be measured directly [1,2]. Moreover, if they can be measured directly, we cannot do so for all neurons in the brain let alone a small brain region.…”

Section: Introductionmentioning

confidence: 99%

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Brain model state space reconstruction using an LSTM neural network

et al. 2023

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ObjectiveKalman filtering has previously been applied to track neural model states and parameters, particularly at the scale relevant to electroencephalography (EEG). However, this approach lacks a reliable method to determine the initial filter conditions and assumes that the distribution of states remains Gaussian. This study presents an alternative, data-driven method to track the states and parameters of neural mass models (NMMs) from EEG recordings using deep learning techniques, specifically a Long Short-Term Memory (LSTM) neural network.ApproachAn LSTM filter was trained on simulated EEG data generated by a neural mass model using a wide range of parameters. With an appopriately customised loss function, the LSTM filter can learn the behaviour of NMMs. As a result, it can output the state vector and parameters of NMMs given observation data as the input.Main ResultsTest results using simulated data yielded correlations with R squared of around 0.99 and verified that the method is robust to noise and can be more accurate than a nonlinear Kalman filter when the initial conditions of the Kalman filter are not accurate. As an example of real-world application, the LSTM filter was also applied to real EEG data that included epileptic seizures, and revealed changes in connectivity strength parameters at the beginnings of seizures.SignificanceTracking the state vector and parameters of mathematical brain models is of great importance in the area of brain modelling, monitoring, imaging and control. This approach has no need to specify the initial state vector and parameters, which is very difficult to do in practice because many of the variables being estimated cannot be measured directly in physiological experiments. This method may be applied using any neural mass model and, therefore, provides a general, novel, efficient approach to estimate brain model variables that are often difficult to measure.

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