Brain Network Mechanisms of Cognitive Impairments in Major Psychiatric Disorders and Their Clinical Applications

Previous and our studies have shown that cognitive impairments are core symptoms of three major psychiatric disorders-schizophrenia, bipolar disorder, and major depressive disorder, and are associated with underlying brain dysfunction. However, the specific brain networks involved in cognitive impairments (cognitive impairment brain networks) in these disorders, as well as whether their neuroimaging features can be applied to cognitive assessment, diagnosis, and precision treatment, remain unclear. This study aims to identify cognitive impairment brain networks using publicly available large-scale datasets and clinical research, and to explore whether the neuroimaging features of these networks can be utilized for cognitive assessment, diagnosis, and treatment response prediction. First, a "sensitive cognitive assessment model for major psychiatric disorders" will be established through meta-analysis based on sensitive scales. Second, cognitive impairment brain networks will be identified using publicly available large-scale datasets combined with the lesion network mapping method, and their validity will be examined by assessing their non-randomness, reproducibility, symptom specificity, and disease specificity. Third, cognitive assessment and diagnostic models will be developed based on neuroimaging features of these networks. Finally, a combination of cross-sectional and longitudinal study designs will be used in a clinical trial to validate the identified networks and models, and a treatment response prediction model will be established based on the neuroimaging features of cognitive impairment brain networks. This study will advance the understanding of the neurophysiological mechanisms underlying cognitive impairment in major psychiatric disorders, promote the application of neuroimaging in psychiatric diagnosis and treatment, and improve traditional diagnostic, therapeutic, and cognitive assessment approaches.