Untangling cross-frequency coupling in neuroscience
Section snippets
Cross-frequency coupling (CFC): how much is that in real money?
One of the central questions in neuroscience is how neural activity is coordinated across different spatial and temporal scales. An elegant solution to this problem could be that the activity of local neural populations is modulated according to the global neuronal dynamics. As larger populations oscillate and synchronize at lower frequencies and smaller ensembles are active at higher frequencies [1], CFC would facilitate flexible coordination of neural activity simultaneously in time and
Caveats and confounds of the CFC analysis
In this section we concentrate on what we call the classical CFC analysis — it is illustrated and explained in Figure 1. Any result of this analysis can be used to classify different conditions but only as a marker that is devoid of concrete and clear physiological interpretation. To give a physiological interpretation to CFC, one needs to know the set of potential mechanisms responsible for neural coupling. This set of mechanisms is only beginning to emerge (discussed below). We now discuss
Organization of modeling/statistical approaches to CFC
Until now we have focused on what we call the classical approach (see Figure 1) to assess phase–amplitude CFC effects consisting of: first isolating frequency components, second assessing their dependencies, and third computing P-values based on surrogates. We have described how difficult it is at this stage to draw any conclusions about the biophysical mechanisms underlying these measures. However, different frameworks exist to assess relationships among rhythmic processes from experimental
Practical recommendations
As previously discussed we will need progress in several directions to establish phase–amplitude or other types of CFC as a fundamental mechanism in coordinating neuronal activity. Together with experimental and modeling advances, stricter standards in the use of CFC metrics are also necessary. Below we list practical recommendations to avoid some of the mentioned confounds and increase the specificity of the most popular phase–amplitude CFC metric (see Figure 1). Rather than a comprehensive
Conclusions
CFC might be a key mechanism for the coordination of neural dynamics. Several independent research groups have observed CFC and related it to information processing, most notably to learning and memory [4, 5, 6, 7, 8••]. Recently, CFC has also been used to investigate neurological and psychiatric disorders [9, 10, 11•, 12, 13, 14, 15]. Thus, CFC analysis is potentially a promising approach to unravel brain function and some of their pathologies.
In the present manuscript we have reviewed some
Conflict of interest statement
Nothing declared.
References and recommended reading
Papers of particular interest, published within the period of review, have been highlighted as:
• of special interest
•• of outstanding interest
Acknowledgements
This work was supported by the Max Planck Society. RV, VP and MW were supported by LOEWE grant ‘Neuronale Koordination Forschunsgschwerpunkt Frankfurt (NeFF)’. RV also acknowledges the support from the Hertie Foundation and the projects PUT438 financed by the Estonian Research Council and SF0180008s12 of the Estonian Ministry of Science and Education. VP received financial support from the German Ministry for Education and Research (BMBF) via the Bernstein Center for Computational Neuroscience
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