ArticlePLoS computational biology2019
Parameter estimation and identifiability in a neural population model for electro-cortical activity.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
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- Bayesian Inference of a Spectral Graph Model for Brain Oscillations.bioRxiv : the preprint server for biology · 2023Article
- Global nonlinear approach for mapping parameters of neural mass models.PLoS computational biology · 2023Article
- Dynamic causal modelling shows a prominent role of local inhibition in alpha power modulation in higher visual cortex.PLoS computational biology · 2022Article
- Optimal control methods for nonlinear parameter estimation in biophysical neuron models.PLoS computational biology · 2022Article
- Towards an efficient validation of dynamical whole-brain models.Scientific reports · 2022Article
- What Models and Tools can Contribute to a Better Understanding of Brain Activity?Frontiers in network physiology · 2022Article
- Structure-function models of temporal, spatial, and spectral characteristics of non-invasive whole brain functional imaging.Frontiers in neuroscience · 2022Review
- Multimodal parameter spaces of a complex multi-channel neuron model.Frontiers in systems neuroscience · 2022Article
- Computational Evidence for a Competitive Thalamocortical Model of Spikes and Spindle Activity in Rolandic Epilepsy.Frontiers in computational neuroscience · 2021Article
- Estimation of neuron parameters from imperfect observations.PLoS computational biology · 2020Article
- Inferring a simple mechanism for alpha-blocking by fitting a neural population model to EEG spectra.PLoS computational biology · 2020Article
- Dynamics-informed priors (DIP) for neural mass modelling.Imaging neuroscience (Cambridge, Mass.)Article
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4 authors.
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Abstract
Electroencephalography (EEG) provides a non-invasive measure of brain electrical activity. Neural population models, where large numbers of interacting neurons are considered collectively as a macroscopic system, have long been used to understand features in EEG signals. By tuning dozens of input parameters describing the excitatory and inhibitory neuron populations, these models can reproduce prominent features of the EEG such as the alpha-rhythm. However, the inverse problem, of directly estimating the parameters from fits to EEG data, remains unsolved. Solving this multi-parameter non-linear fitting problem will potentially provide a real-time method for characterizing average neuronal properties in human subjects. Here we perform unbiased fits of a 22-parameter neural population model to EEG data from 82 individuals, using both particle swarm optimization and Markov chain Monte Carlo sampling. We estimate how much is learned about individual parameters by computing Kullback-Leibler divergences between posterior and prior distributions for each parameter. Results indicate that only a single parameter, that determining the dynamics of inhibitory synaptic activity, is directly identifiable, while other parameters have large, though correlated, uncertainties. We show that the eigenvalues of the Fisher information matrix are roughly uniformly spaced over a log scale, indicating that the model is sloppy, like many of the regulatory network models in systems biology. These eigenvalues indicate that the system can be modeled with a low effective dimensionality, with inhibitory synaptic activity being prominent in driving system behavior.
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