Corticocortical coherence explained

Corticocortical coherence is referred to the synchrony in the neural activity of different cortical brain areas. The neural activities are picked up by electrophysiological recordings from the brain (e.g. EEG, MEG, ECoG, etc.). It is a method to study the brain's neural communication and function at rest or during functional tasks.

History and basics

Initial applications of spectral analysis for finding the relationship between the EEG recordings from different regions of scalp dates back to 1960's.^[1] Corticocortical coherence has since been extensively studied using EEG and MEG recording for potential diagnostic applications^[2] and beyond.

The exact origins of corticocortical coherence are under active investigation. While the consensus suggests that the functional neural communication between distinct brain sources leads to synchronous activity in those regions (possibly connected by neural tracts, in either direct or indirect way),^{[3] [4] [5]} an alternative explanation emphasises on single focal oscillations that occur at single brain sources that eventually appear connected or synchronous in different scalp or brain source regions.^[6]

Corticocortical coherence has been of special interest in delta, theta, alpha, beta and gamma frequency bands (commonly used for EEG studies).

Methods, mathematics and statistics

Cortico-cortical coherence is commonly studied using bipolar channels of EEG recordings, as well as unipolar channels of EEG or MEG signals; however, unipolar channels are usually used to estimate the brain sources and their connectivity, using electrical source imaging and connectivity analysis.^[7]

A classic and commonly used approach to assess the synchrony between neural signals is to use Coherence.^[8]

Statistical significance of coherence is found as function of number of data segments with assumption of the signals' normal distribution.^[9] Alternatively non-parametric techniques such as bootstrapping can be used.

See also

• Intermuscular coherence
• Corticomuscular coherence

External links

• Neurspec Toolbox for MATLAB

Notes and References

1. Walter. D. O.. 1963-08-01. Spectral analysis for electroencephalograms: mathematical determination of neurophysiological relationships from records of limited duration. Experimental Neurology. 8. 2. 155–181. 0014-4886. 20191690. 10.1016/0014-4886(63)90042-6.
2. Sklar. B.. Hanley. J.. Simmons. W. W.. 1972-12-15. An EEG experiment aimed toward identifying dyslexic children. Nature. 240. 5381. 414–416. 0028-0836. 4564321. 10.1038/240414a0. 1972Natur.240..414S. 4177284.
3. Lei. Xu. Wu. Taoyu. Valdes-Sosa. Pedro. 2015-01-01. Incorporating priors for EEG source imaging and connectivity analysis. Frontiers in Neuroscience. English. 9. 284. 10.3389/fnins.2015.00284. 1662-453X. 4539512. 26347599. free.
4. Book: Computational Neuroscience. limited. Ramírez. Rey R.. Wipf. David. Baillet. Sylvain. 2010-01-01. Springer New York. 9780387886299. Chaovalitwongse. Wanpracha. Springer Optimization and Its Applications. 127–155. en. 10.1007/978-0-387-88630-5_8. Pardalos. Panos M.. Xanthopoulos. Petros.
5. He. B.. Liu. Z.. 2008-01-01. Multimodal Functional Neuroimaging: Integrating Functional MRI and EEG/MEG. IEEE Reviews in Biomedical Engineering. 1. 23–40. 10.1109/RBME.2008.2008233. 1937-3333. 2903760. 20634915.
6. Delorme. Arnaud. Palmer. Jason. Onton. Julie. Oostenveld. Robert. Makeig. Scott. 2012-02-15. Independent EEG Sources Are Dipolar. PLOS ONE. 7. 2. e30135. 10.1371/journal.pone.0030135. 1932-6203. 3280242. 22355308. 2012PLoSO...730135D. free.
7. Mehrkanoon. Saeid. Michael Breakspear. Breakspear. Michael. Britz. Juliane. Boonstra. Tjeerd W.. 2014-09-17. Intrinsic Coupling Modes in Source-Reconstructed Electroencephalography. Brain Connectivity. 4. 10. 812–825. 10.1089/brain.2014.0280. 25230358. 4268557. 2158-0014.
8. Halliday, D. M., Rosenberg, J. R., Amjad, A. M., Breeze, P., Conway, B. A., & Farmer, S. F. (1995). A framework for the analysis of mixed time series/point process data—Theory and application to the study of physiological tremor, single motor unit discharges and electromyograms. Progress in Biophysics and Molecular Biology, 64(2–3), 237–278. http://doi.org/10.1016/S0079-6107(96)00009-0
9. Halliday, D. M., & Rosenberg, J. R. (1999). Time and frequency domain analysis of spike train and time series data. In Modern techniques in neuroscience research (pp. 503–543). Springer. Retrieved from http://doi.org/10.1007/978-3-642-58552-4_18