Gamma Oscillations and Neural Field DCMs Can Reveal Cortical Excitability and Microstructure

Dimitris Pinotsis,Karl Friston

doi:10.3934/neuroscience.2014.1.18

Abstract

This paper shows how gamma oscillations can be combined with neural population models and dynamic causal modeling (DCM) to distinguish among alternative hypotheses regarding cortical excitability and microstructure. This approach exploits inter-subject variability and trial-specific effects associated with modulations in the peak frequency of gamma oscillations. Neural field models are used to evaluate model evidence and obtain parameter estimates using invasive and non-invasive gamma recordings. Our overview comprises two parts: in the first part, we use neural fields to simulate neural activity and distinguish the effects of post synaptic filtering on predicted responses in terms of synaptic rate constants that correspond to different timescales and distinct neurotransmitters. We focus on model predictions of conductance and convolution based field models and show that these can yield spectral responses that are sensitive to biophysical properties of local cortical circuits like synaptic kinetics and filtering; we also consider two different mechanisms for this filtering: a nonlinear mechanism involving specific conductances and a linear convolution of afferent firing rates producing post synaptic potentials. In the second part of this paper, we use neural fields quantitatively—to fit empirical data recorded during visual stimulation. We present two studies of spectral responses obtained from the visual cortex during visual perception experiments: in the first study, MEG data were acquired during a task designed to show how activity in the gamma band is related to visual perception, while in the second study, we exploited high density electrocorticographic (ECoG) data to study the effect of varying stimulus contrast on cortical excitability and gamma peak frequency.

Highlights

This paper shows how recordings of gamma oscillations—under different experimental conditions or from different subjects—can be combined with a class of population models called neural fields and dynamic causal modeling (DCM) to distinguish among alternative hypotheses regarding cortical structure and function
We present two studies of spectral responses obtained from the visual cortex during visual perception experiments: in the first study, MEG data were acquired during a task designed to show how activity in the gamma band is related to visual perception
We have considered neural field models in the light of a Bayesian framework for evaluating model evidence and obtaining parameter estimates using invasive and non-invasive recordings of gamma oscillations

Summary

Introduction

This paper shows how recordings of gamma oscillations—under different experimental conditions or from different subjects—can be combined with a class of population models called neural fields and dynamic causal modeling (DCM) to distinguish among alternative hypotheses regarding cortical structure and function This approach exploits inter-subject variability and trial-specific effects associated with modulations in the peak frequency of gamma oscillations. The functional specialization of visual (and auditory) cortex is reflected in its patchy or modular organization—in which local cortical structures share common response properties This organization may be mediated by a patchy distribution of horizontal intrinsic connections that can extend up to 8 mm, linking neurons with similar receptive fields: see e.g. Bayesian model comparison of dynamic causal models that embody different hypotheses about contrast-specific changes in the connectivity architectures that underlie receptive fields and induced responses

Neural fields predict visually induced oscillations

Explaining putative mechanisms for stimulus-specific gamma peak variability

Findings

Conclusion