Corticocortical coherence

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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

External links

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. doi:10.1016/0014-4886(63)90042-6. ISSN 0014-4886. PMID 20191690.
  2. ^ Sklar, B.; Hanley, J.; Simmons, W. W. (1972-12-15). "An EEG experiment aimed toward identifying dyslexic children". Nature. 240 (5381): 414–416. Bibcode:1972Natur.240..414S. doi:10.1038/240414a0. ISSN 0028-0836. PMID 4564321. S2CID 4177284.
  3. ^ Lei, Xu; Wu, Taoyu; Valdes-Sosa, Pedro (2015-01-01). "Incorporating priors for EEG source imaging and connectivity analysis". Frontiers in Neuroscience. 9: 284. doi:10.3389/fnins.2015.00284. ISSN 1662-453X. PMC 4539512. PMID 26347599.
  4. ^ Ramírez, Rey R.; Wipf, David; Baillet, Sylvain (2010-01-01). Chaovalitwongse, Wanpracha; Pardalos, Panos M.; Xanthopoulos, Petros (eds.). Computational Neuroscience. Springer Optimization and Its Applications. Springer New York. pp. 127–155. doi:10.1007/978-0-387-88630-5_8. ISBN 9780387886299.
  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. doi:10.1109/RBME.2008.2008233. ISSN 1937-3333. PMC 2903760. PMID 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. Bibcode:2012PLoSO...730135D. doi:10.1371/journal.pone.0030135. ISSN 1932-6203. PMC 3280242. PMID 22355308.
  7. ^ Mehrkanoon, Saeid; Breakspear, Michael; Britz, Juliane; Boonstra, Tjeerd W. (2014-09-17). "Intrinsic Coupling Modes in Source-Reconstructed Electroencephalography". Brain Connectivity. 4 (10): 812–825. doi:10.1089/brain.2014.0280. ISSN 2158-0014. PMC 4268557. PMID 25230358.
  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
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