Brain network mechanisms of visual perceptual organization in schizophrenia and bipolar disorder

Abstract

Visual shape completion is a canonical perceptual process that integrates spatially distributed edge information into unified representations of objects. People with schizophrenia show difficulty in discriminating completed shapes but the brain networks and functional connections underlying this perceptual difference remain poorly understood. Also unclear is whether similar neural differences arise in bipolar disorder or vary across the schizo-bipolar spectrum. To address these topics, we scanned (fMRI) people with schizophrenia, bipolar disorder, or no psychiatric illness during rest and during a task in which they discriminated configurations that formed or failed to form completed shapes (illusory and fragmented condition, respectively). Illusory/fragmented task activation differences ('modulations'), resting-state functional connectivity, and multivariate pattern differences were identified on the cortical surface using 360 predefined parcels and 12 functional networks composed of such parcels. Brain activity flow mapping was used to evaluate the likely involvement of resting-state connections for shape completion. Dorsal attention network modulations distinguished people with schizophrenia (AUCs>.85) and could trans-diagnostically predict cognitive disorganization severity. Activity flow over functional connections from the dorsal attention network could predict secondary visual network modulations in each group, except among those with schizophrenia. Task modulations among patients were more heterogeneous and dispersed over a larger number of networks compared to controls. In summary, abnormal dorsal attention network activity emerges during perceptual organization in schizophrenia and may be related to improper attention-related feedback into secondary visual areas. Patients with either disorder may compensate for abnormal perception by an idiosyncratic recruitment of regions across multiple non-visual networks.

Fractal dimension analysis of resting state functional networks in schizophrenia from EEG signals

“…Our results showed that FDI could be a promising feature to be used in classifiers based on EEG measures, especially when computed for the SAN network (AUC = 0.83). These results reinforce the hypothesis that cognitive networks—but not visual networks—would be differentially active in schizophrenia ( Keane et al, 2022 ). Moreover, FDI as a feature could add a broader perspective when analyzing complexity in current machine learning classifiers which are based on linear and non-linear measures computed directly on the EEG signal, because the input EEG signal is used in FDI to extract the brain activations over time, which allows for analyzing 3D and 4D complexity through fractal dimension.…”

Steady state visual evoked potentials in schizophrenia: A review

“…The spectral signatures related to the processing of visual information are altered in people living with schizophrenia (Sz) (Green et al, 2003 ; Spencer et al, 2003 , 2004 , 2008 ; Wynn et al, 2005 ; Uhlhaas et al, 2006 ; Grützner et al, 2013 ). Notably, these changes are accompanied by changes in visual processing (Dakin et al, 2005 ; Kantrowitz et al, 2009 ; Horton and Silverstein, 2011 ; Keane et al, 2014 , 2022 ; Schallmo et al, 2015 ), and visual aberrations are related, even prodromally, to the severity of clinical symptoms (Phillipson and Harris, 1985 ; Uhlhaas and Mishara, 2007 ; Keane et al, 2018 ).…”