精神分裂症(面向对象编程)
心理学
功能磁共振成像
认知
感知
认知心理学
视觉感受
神经科学
任务(项目管理)
神经影像学
精神科
经济
管理
作者
Brian P. Keane,Bart Krekelberg,Ravi D. Mill,Steven M. Silverstein,Judy Thompson,Megan R. Serody,Deanna M. Barch,Michael W. Cole
摘要
Abstract Visual shape completion is a canonical perceptual organization 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 brain network differences in schizophrenia occur in related illnesses or vary with illness features transdiagnostically. To address these topics, we scanned (functional magnetic resonance imaging, 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). 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. Illusory/fragmented task activation differences (‘modulations’) in the dorsal attention network (DAN) could distinguish people with schizophrenia from the other groups (AUCs > .85) and could transdiagnostically predict cognitive disorganization severity. Activity flow over functional connections from the DAN could predict secondary visual network modulations in each group, except in schizophrenia. The secondary visual network was strongly and similarly modulated in each group. Task modulations were dispersed over more networks in patients compared to controls. In summary, DAN activity during visual perceptual organization is distinct in schizophrenia, symptomatically relevant, and potentially related to improper attention‐related feedback into secondary visual areas.
科研通智能强力驱动
Strongly Powered by AbleSci AI