PHASE CORRELATION AMONG RHYTHMS PRESENT AT DIFFERENT FREQUENCIES - SPECTRAL METHODS, APPLICATION TO MICROELECTRODE RECORDINGS FROM VISUAL-CORTEX AND FUNCTIONAL IMPLICATIONS

In classical EEG analysis rhythms with different frequencies occuring at separable regions and states of the brain are analysed. Rhythms in different frequency bands have often been assumed to be independent an d their occurrence was interpreted as a sign of different functional o perations. Independence has scarcely been proved because of conceptual and computational difficulties. It is, on the other hand, probable th at 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 frequen cy bands and gamma-activities (30-90 Hz), because the latter have been analysed in our laboratory in great detail and had properties suggest ing 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 freq uency components at considerable power. Such multiple spectral peaks a re known from many cortical and subcortical structures. Despite their well known occurrence, coupling among different frequencies has not be en established, apart from harmonic components. For the present invest igation we extended existing analytical tools to detect non-linear cor relations among signal pairs at any frequency (including incommensurat e ones). These methods were applied to multiple microelectrode recordi ngs from visual cortical areas 17 and 18 of anesthetized cats and V1 o f awake monkeys. In particular, we assessed non-linear correlations by means of higher order spectral analysis of multi-unit spike activitie s (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. Fir st, the frequency content of short (approximate to 250 ms) sliding win dow 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 combina tion of frequency pairs. Second, non-linear (quadratic) phase coupling between different frequencies was assessed by the methods of bispectr um and bicoherence. We found phase correlations at different frequenci es in the visual cortex of the cat and monkey. These couplings were si gnificant in about 60% of the investigated MUA and LFP recordings, inc luding several cases of coupling among incommensurate (i.e. non-harmon ic) frequencies. Significant phase correlations were present: (1) with in the gamma-frequency range; (2) between gamma- and low frequency ran ges (1-30 Hz, including alpha- and beta-rhythms); and (3) within the l ow frequency range. Phase correlations depended, in most cases, on spe cific visual stimulation. We discuss the possible functional significa nce 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 linki ng across different temporal and spatial scales provided by coherent o scillations at high and low frequencies; (2) linking of visual cortica l representations (high frequencies) to subcortical centers (low frequ encies) like the thalamus and hippocampus; and (3) temporal segmentati on of the sustained stream of incoming visual information into separat e frames at different temporal resolutions in order to prevent percept ual smearing due to shifting retinal images. These proposals are, at p resent, merely speculative. However, they can, in principle, be proved by microelectrode recordings from trained behaving animals. (C) 1997 Elsevier Science B.V.