scholarly journals Inferring a simple mechanism for alpha-blocking by fitting a neural population model to EEG spectra

2020 ◽  
Vol 16 (4) ◽  
pp. e1007662
Author(s):  
Agus Hartoyo ◽  
Peter J. Cadusch ◽  
David T. J. Liley ◽  
Damien G. Hicks
Keyword(s):  
Population Model ◽  
Neural Population ◽  
Eeg Spectra ◽  
Alpha Blocking ◽  
Simple Mechanism

scholarly journals Inferring a simple mechanism for alpha-blocking by fitting a neural population model to EEG spectra

2020 ◽  
Author(s):  
Agus Hartoyo ◽  
Peter J. Cadusch ◽  
David T. J. Liley ◽  
Damien G. Hicks
Keyword(s):  
Population Model ◽  
Alpha Rhythm ◽  
Neural Population ◽  
Parameter Estimates ◽  
Inhibitory Input ◽  
Alpha Oscillations ◽  
Eeg Spectra ◽  
Eyes Closed ◽  
Alpha Blocking ◽  
Eyes Open

AbstractAlpha blocking, a phenomenon where the alpha rhythm is reduced by attention to a visual, auditory, tactile or cognitive stimulus, is one of the most prominent features of human electroencephalography (EEG) signals. Here we identify a simple physiological mechanism by which opening of the eyes causes attenuation of the alpha rhythm. We fit a neural population model to EEG spectra from 82 subjects, each showing different degrees of alpha blocking upon opening of their eyes. Although it is notoriously difficult to estimate parameters from fitting such models, we show that, by regularizing the differences in parameter estimates between eyes-closed and eyes-open states, we can reduce the uncertainties in these differences without significantly compromising fit quality. From this emerges a parsimonious explanation for the spectral changes between states: Just a single parameter, pei, corresponding to the strength of a tonic, excitatory input to the inhibitory population, is sufficient to explain the reduction in alpha rhythm upon opening of the eyes. When comparing parameter estimates across different subjects we find that the inferred differential change in pei for each subject increases monotonically with the degree of alpha blocking observed. In contrast, other parameters show weak or negligible differential changes that do not scale with the degree of alpha attenuation in each subject. Thus most of the variation in alpha blocking across subjects can be attributed to the strength of a tonic afferent signal to the inhibitory cortical population.Author summaryOne of the most striking features of the human electroencephalogram (EEG) is the presence of neural oscillations in the range of 8-13 Hz. It is well known that attenuation of these alpha oscillations, a process known as alpha blocking, arises from opening of the eyes, though the cause has remained obscure. In this study we infer the mechanism underlying alpha blocking by fitting a neural population model to EEG spectra from 82 different individuals. Although such models have long held the promise of being able to relate macroscopic recordings of brain activity to microscopic neural parameters, their utility has been limited by the difficulty of inferring these parameters from fits to data. Our approach is to fit both eyes-open and eyes-closed EEG spectra together, minimizing the number of parameter changes required to transition from one spectrum to the other. Surprisingly, we find that there is just one parameter, the external input to the inhibitory neurons in cortex, that is responsible for attenuating the alpha oscillations. We demonstrate how the strength of this inhibitory input scales monotonically with the degree of alpha blocking observed over all 82 subjects.

scholarly journals Optimal Control of an Epileptic Neural Population Model

2014 ◽  
Vol 47 (3) ◽  
pp. 3116-3121 ◽  
Author(s):  
Justin Ruths ◽  
Peter Neal Taylor ◽  
Justin Dauwels
Keyword(s):  
Optimal Control ◽  
Population Model ◽  
Neural Population

scholarly journals A comparative analysis of a firing-rate model and a conductance-based neural population model

2007 ◽  
Vol 369 (1-2) ◽  
pp. 31-36 ◽  
Author(s):  
Anton V. Chizhov ◽  
Serafim Rodrigues ◽  
John R. Terry
Keyword(s):  
Comparative Analysis ◽  
Firing Rate ◽  
Population Model ◽  
Neural Population ◽  
Rate Model

scholarly journals Parameter estimation and identifiability in a neural population model for electro-cortical activity

2018 ◽  
Author(s):  
Agus Hartoyo ◽  
Peter J. Cadusch ◽  
David T. J. Liley ◽  
Damien G. Hicks
Keyword(s):  
Real Time ◽  
Population Model ◽  
Alpha Rhythm ◽  
Population Models ◽  
Neural Population ◽  
Eeg Signals ◽  
Macroscopic System ◽  
Non Invasive ◽  
Eeg Data ◽  
Input Parameters

AbstractElectroencephalography (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 inhibition, 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 inhibition being prominent in driving system behavior.Author summaryElectroencephalography (EEG), where electrodes are used to measure electric potential on the outside of the scalp, provides a simple, non-invasive way to study brain activity. Physiological interpretation of features in EEG signals has often involved use of collective models of neural populations. These neural population models have dozens of input parameters to describe the properties of inhibitory and excitatory neurons. Being able to estimate these parameters by direct fits to EEG data holds the promise of providing a real-time non-invasive method of inferring neuronal properties in different individuals. However, it has long been impossible to fit these nonlinear, multi-parameter models effectively. Here we describe fits of a 22-parameter neural population model to EEG spectra from 82 different subjects, all exhibiting alpha-oscillations. We show how only one parameter, that describing inhibitory dynamics, is constrained by the data, although all parameters are correlated. These results indicate that inhibition plays a central role in the generation and modulation of the alpha-rhythm in humans.

scholarly journals Parameter estimation and identifiability in a neural population model for electro-cortical activity

2019 ◽  
Vol 15 (5) ◽  
pp. e1006694 ◽  
Author(s):  
Agus Hartoyo ◽  
Peter J. Cadusch ◽  
David T. J. Liley ◽  
Damien G. Hicks
Keyword(s):  
Parameter Estimation ◽  
Population Model ◽  
Cortical Activity ◽  
Neural Population
Sign in / Sign up

Export Citation Format

Share Document