Dysconnection Topography in Schizophrenia Revealed with State-Space Analysis of EEG

Abstract

BackgroundThe dysconnection hypothesis has been proposed to account for pathophysiological mechanisms underlying schizophrenia. Widespread structural changes suggesting abnormal connectivity in schizophrenia have been imaged. A functional counterpart of the structural maps would be the EEG synchronization maps. However, due to the limits of currently used bivariate methods, functional correlates of dysconnection are limited to the isolated measurements of synchronization between preselected pairs of EEG signals.Methods/ResultsTo reveal a whole-head synchronization topography in schizophrenia, we applied a new method of multivariate synchronization analysis called S-estimator to the resting dense-array (128 channels) EEG obtained from 14 patients and 14 controls. This method determines synchronization from the embedding dimension in a state-space domain based on the theoretical consequence of the cooperative behavior of simultaneous time series—the shrinking of the state-space embedding dimension. The S-estimator imaging revealed a specific synchronization landscape in schizophrenia patients. Its main features included bilaterally increased synchronization over temporal brain regions and decreased synchronization over the postcentral/parietal region neighboring the midline. The synchronization topography was stable over the course of several months and correlated with the severity of schizophrenia symptoms. In particular, direct correlations linked positive, negative, and general psychopathological symptoms to the hyper-synchronized temporal clusters over both hemispheres. Along with these correlations, general psychopathological symptoms inversely correlated within the hypo-synchronized postcentral midline region. While being similar to the structural maps of cortical changes in schizophrenia, the S-maps go beyond the topography limits, demonstrating a novel aspect of the abnormalities of functional cooperation: namely, regionally reduced or enhanced connectivity.Conclusion/SignificanceThe new method of multivariate synchronization significantly boosts the potential of EEG as an imaging technique compatible with other imaging modalities. Its application to schizophrenia research shows that schizophrenia can be explained within the concept of neural dysconnection across and within large-scale brain networks.

EEG Power Asymmetry and Functional Connectivity as a Marker of Treatment Effectiveness in DBS Surgery for Depression

“…This synchronization is observed in oscillatory activity recorded by EEG and MEG and refers to a consistent relation between the phases of the oscillatory activity of two brain regions that can be assessed from the measured time series . Recent studies on a host of neurological disorders including Alzheimer's disease (de Haan et al, 2012;Tahaei et al, 2012), Parkinson disease (Babiloni et al, 2011;Bosboom et al, 2009;Stoffers et al, 2008), schizophrenia (Hanslmayr et al, 2012;Hinkley et al, 2011;Jalili et al, 2007) and epileptic seizures (Mormann et al, 2003;Stam et al, 2007a) have demonstrated a disruption in this synchronization in various frequency bands. In MDD, impairments in synchronization in both y and a bands have been reported (Fingelkurts et al, 2007;LinkenkaerHansen et al, 2005).…”

Discriminating early stage AD patients from healthy controls using synchronization analysis of EEG

“…The PSDs were calculated with frequency resolution of 0.125 Hz. Then, these resulting PSDs were, first, averaged across all epochs and then integrated over the delta (1-3 Hz), theta (3-7 Hz), alpha1 (7-9.5 Hz), alpha2 (9.5-13 Hz), beta1 (13)(14)(15)(16)(17)(18)(19)(20), and beta2 (20-30 Hz) bands. To address AD-related changes in EEG power, the individual PSD maps were collected into two groups (AD patients and controls) consisting of seventeen members each.…”

“…The gray background highlights all the sensors included in the analyses. The sensor locations encircled in green exemplify the first and second neighborhoods for the sensor encircled in brown (sensor 73), i.e., the territory considered in the calculation of a single value of MPS[18].…”

Effects of network structure on the synchronizability of nonlinearly coupled Hindmarsh–Rose neurons

“…For example, in many biological neural networks, it has been observed that coupling allows neurons to synchronize each other [1][2][3][4][5][6][7]. Moreover, many brain disorders such as Alzheimer's disease, epilepsy, Parkinson's disease, and schizophrenia have been linked to the abnormal patterns of synchronization in the brain [8][9][10]. Thus, understanding neuronal synchrony is one of the fundamental issues in neuroscience.…”