A disturbance of nonlinear interdependence in scalp EEG of subjects with first episode schizophrenia
It has been proposed that schizophrenia arises through a disturbance of coupling between large-scale cortical systems. This “disconnection hypothesis” is tested by applying a measure of dynamical interdependence to scalp EEG data. EEG data were collected from 40 subjects with a first episode of schi...
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Veröffentlicht in: | NeuroImage (Orlando, Fla.) Fla.), 2003-09, Vol.20 (1), p.466-478 |
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Sprache: | eng |
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Zusammenfassung: | It has been proposed that schizophrenia arises through a disturbance of coupling between large-scale cortical systems. This “disconnection hypothesis” is tested by applying a measure of dynamical interdependence to scalp EEG data. EEG data were collected from 40 subjects with a first episode of schizophrenia and 40 matched healthy controls. An algorithm for the detection of dynamical interdependence was applied to six pairs of bipolar electrodes in each subject. The topographic organization of the interdependence was calculated and served as the principle measure of cortical integration. The rate of occurrence of dynamical interdependence did not statistically differ between subject groups at any of the sites. However, the topography across the scalp was significantly different between the two groups. Specifically, nonlinear interdependence tended to occur in larger concurrent “clusters” across the scalp in schizophrenia than in the healthy subjects. This disturbance was reflected most strongly in left intrahemispheric coupling and did not differ significantly according to symptomatology. Medication dose and subject arousal were not observed to be confounding factors. The study of dynamical interdependence in scalp EEG data does not support a straightforward interpretation of the disconnection hypothesis—that there is a decrease in the strength of functional coupling between adjacent cortical regions. Rather, it suggests a dysregulation in the organization of dynamical interactions across supraregional brain systems. |
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ISSN: | 1053-8119 1095-9572 |
DOI: | 10.1016/S1053-8119(03)00332-X |