title: Short desynchronization episodes prevail in synchronous dynamics of human brain rhythms.
abstract: Neural synchronization is believed to be critical for many brain functions. It frequently exhibits temporal variability, but it is not known if this variability has a specific temporal patterning. This study explores these synchronization/desynchronization patterns. We employ recently developed techniques to analyze the fine temporal structure of phase-locking to study the temporal patterning of synchrony of the human brain rhythms. We study neural oscillations recorded by electroencephalograms in α and β frequency bands in healthy human subjects at rest and during the execution of a task. While the phase-locking strength depends on many factors, dynamics of synchrony has a very specific temporal pattern: synchronous states are interrupted by frequent, but short desynchronization episodes. The probability for a desynchronization episode to occur decreased with its duration. The transition matrix between synchronized and desynchronized states has eigenvalues close to 0 and 1 where eigenvalue 1 has multiplicity 1, and therefore if the stationary distribution between these states is perturbed, the system converges back to the stationary distribution very fast. The qualitative similarity of this patterning across different subjects, brain states and electrode locations suggests that this may be a general type of dynamics for the brain. Earlier studies indicate that not all oscillatory networks have this kind of patterning of synchronization/desynchronization dynamics. Thus, the observed prevalence of short (but potentially frequent) desynchronization events (length of one cycle of oscillations) may have important functional implications for the brain. Numerous short desynchronizations (as opposed to infrequent, but long desynchronizations) may allow for a quick and efficient formation and break-up of functionally significant neuronal assemblies.
authors.affiliation: Department of Mathematical Sciences and Center for Mathematical Biosciences, Indiana University Purdue University Indianapolis, Indiana 46032, USA. ahnmath@gmail.com
authors.forename: Sungwoo
authors.initials: S
authors.lastname: Ahn
authors.forename: Leonid L
authors.initials: LL
authors.lastname: Rubchinsky
mesh_terms: D001921:Brain / Q000166:cytology / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D058256:Brain Waves; D058407:Electroencephalography Phase Synchronization; D006801:Humans; D008390:Markov Chains; D008959:Models, Neurological; D015233:Models, Statistical; D009043:Motor Activity; D009474:Neurons / Q000378:metabolism*; D009991:Oscillometry; D011336:Probability; D013997:Time Factors
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