Evaluation of different measures of functional connectivity using a neural mass model

Neuroimage. 2004 Feb;21(2):659-73. doi: 10.1016/j.neuroimage.2003.10.006.


We use a neural mass model to address some important issues in characterising functional integration among remote cortical areas using magnetoencephalography or electroencephalography (MEG or EEG). In a previous paper [Neuroimage (in press)], we showed how the coupling among cortical areas can modulate the MEG or EEG spectrum and synchronise oscillatory dynamics. In this work, we exploit the model further by evaluating different measures of statistical dependencies (i.e., functional connectivity) among MEG or EEG signals that are mediated by neuronal coupling. We have examined linear and nonlinear methods, including phase synchronisation. Our results show that each method can detect coupling but with different sensitivity profiles that depended on (i) the frequency specificity of the interaction (broad vs. narrow band) and (ii) the nature of the coupling (linear vs. nonlinear). Our analyses suggest that methods based on the concept of generalised synchronisation are the most sensitive when interactions encompass different frequencies (broadband analyses). In the context of narrow-band analyses, mutual information was found to be the most sensitive way to disclose frequency-specific couplings. Measures based on generalised synchronisation and phase synchronisation are the most sensitive to nonlinear coupling. These different sensitivity profiles mean that the choice of coupling measures can have dramatic effects on the cortical networks identified. We illustrate this using a single-subject MEG study of binocular rivalry and highlight the greater recovery of statistical dependencies among cortical areas in the beta band when mutual information is used.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Beta Rhythm
  • Brain Mapping
  • Cerebral Cortex / physiology*
  • Cortical Synchronization*
  • Dendrites / physiology
  • Dominance, Cerebral / physiology
  • Electroencephalography*
  • Evoked Potentials / physiology
  • Fourier Analysis
  • Humans
  • Linear Models
  • Magnetoencephalography*
  • Mathematical Computing
  • Models, Neurological*
  • Nerve Net / physiology*
  • Neurons / physiology
  • Nonlinear Dynamics
  • Pattern Recognition, Visual / physiology
  • Psychomotor Performance / physiology
  • Signal Processing, Computer-Assisted*
  • Statistics as Topic
  • Synaptic Transmission / physiology
  • Vision Disparity / physiology