Phase correlation among rhythms present at different frequencies: spectral methods, application to microelectrode recordings from visual cortex and functional implications

Int J Psychophysiol. 1997 Jun;26(1-3):171-89. doi: 10.1016/s0167-8760(97)00763-0.


In classical EEG analysis rhythms with different frequencies occurring at separable regions and states of the brain are analysed. Rhythms in different frequency bands have often been assumed to be independent and their occurrence was interpreted as a sign of different functional operations. Independence has scarcely been proved because of conceptual and computational difficulties. It is, on the other hand, probable that different rhythmic brain processes are coupled because of the broad recurrent connectivity among brain structures. We, therefore, set out to find interactions among rhythmic signals at different frequencies. We were particularly interested in interactions between lower frequency bands and gamma-activities (30-90 Hz), because the latter have been analysed in our laboratory in great detail and had properties suggesting their involvement in perceptual feature linking. Fast oscillations occurred synchronized in a stimulus-specific way in the visual cortex of cat and monkey. Their presence was often accompanied by lower frequency components at considerable power. Such multiple spectral peaks are known from many cortical and subcortical structures. Despite their well known occurrence, coupling among different frequencies has not been established, apart from harmonic components. For the present investigation we extended existing analytical tools to detect non-linear correlations among signal pairs at any frequency (including incommensurate ones). These methods were applied to multiple microelectrode recordings from visual cortical areas 17 and 18 of anesthetized cats and V1 of awake monkeys. In particular, we assessed non-linear correlations by means of higher order spectral analysis of multi-unit spike activities (MUA) and local slow wave field potentials (LFP, 1-120 Hz) recorded with microelectrodes. Non-linear correlations among signal components at different frequencies were investigated in the following steps. First, the frequency content of short (approximately 250 ms) sliding window signal epochs was analyzed for simultaneously occurring rhythms of significant power at different frequencies. This was done by a newly developed method derived from the trispectrum using separate averaging of the products of short-epoch power spectra for any possible combination of frequency pairs. Second, non-linear (quadratic) phase coupling between different frequencies was assessed by the methods of bispectrum and bicoherence. We found phase correlations at different frequencies in the visual cortex of the cat and monkey. These couplings were significant in about 60% of the investigated MUA and LFP recordings, including several cases of coupling among incommensurate (i.e. non-harmonic) frequencies. Significant phase correlations were present: (1) within the gamma-frequency range; (2) between gamma- and low frequency ranges (1-30 Hz, including alpha- and beta-rhythms); and (3) within the low frequency range. Phase correlations depended, in most cases, on specific visual stimulation. We discuss the possible functional significance of phase correlations among high and low frequencies by including proposals from previous work about potential roles of single-frequency rhythms of the EEG. Our suggestions include: (1) visual feature linking across different temporal and spatial scales provided by coherent oscillations at high and low frequencies; (2) linking of visual cortical representations (high frequencies) to subcortical centers (low frequencies) like the thalamus and hippocampus; and (3) temporal segmentation of the sustained stream of incoming visual information into separate frames at different temporal resolutions in order to prevent perceptual smearing due to shifting retinal images. These proposals are, at present, merely speculative. However, they can, in principle, be proved by microelectrode recordings from trained behaving animals.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Animals
  • Cats
  • Electroencephalography*
  • Humans
  • Microelectrodes
  • Models, Biological
  • Visual Cortex / physiology*