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/

Global: Zendy Plus annual

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

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Schizophrenia (SCH) is a severe psychiatric disorder associated with brain connectivity abnormalities, and early diagnosis can significantly reduce the burden on the families of the patients. Though several classification methods have been created to identify SCH, a reliable method is yet to be found. In this study, we explore the performance of multidimensional spatial feature fusion in the recognition of SCH. Using an MRI connectomes dataset, we extract the spatial pattern network (SPN) and diffusion map embedding (DME) features from functional connectivity (FC) and structural connectivity (SC) networks of both schizophrenic patients and healthy subjects, and we use both single mode features and fused features to classify the two groups. Compared to the single mode features, the fused features showed superior performance in classification. By fusing the SPN and DME features of the structural network, we obtained the highest accuracy of 87.50%. Multidimensional spatial feature fusion is promising as a reliable method for the recognition of SCH.

Pattern recognition of schizophrenia based on multidimensional spatial feature fusion