English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

We apply flicker-noise spectroscopy (FNS), a time series analysis methodoperating on structure functions and power spectrum estimates, to study theclinical electroencephalogram (EEG) signals recorded in children/adolescents(11 to 14 years of age) with diagnosed schizophrenia-spectrum symptoms at theNational Center for Psychiatric Health (NCPH) of the Russian Academy of MedicalSciences. The EEG signals for these subjects were compared with the signals fora control sample of chronically depressed children/adolescents. The purpose ofthe study is to look for diagnostic signs of subjects' susceptibility toschizophrenia in the FNS parameters for specific electrodes andcross-correlations between the signals simultaneously measured at differentpoints on the scalp. Our analysis of EEG signals from scalp-mounted electrodesat locations F3 and F4, which are symmetrically positioned in the left andright frontal areas of cerebral cortex, respectively, demonstrates an essentialrole of frequency-phase synchronization, a phenomenon representing specificcorrelations between the characteristic frequencies and phases of excitationsin the brain. We introduce quantitative measures of frequency-phasesynchronization and systematize the values of FNS parameters for the EEG data.The comparison of our results with the medical diagnoses for 84 subjectsperformed at NCPH makes it possible to group the EEG signals into 4 categoriescorresponding to different risk levels of subjects' susceptibility toschizophrenia. We suggest that the introduced quantitative characteristics andclassification of cross-correlations may be used for the diagnosis ofschizophrenia at the early stages of its development.

Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia