Schizophrenia is a complex and serious brain disorder.Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes(IDPs)to investigate the etiology of psychiat...Schizophrenia is a complex and serious brain disorder.Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes(IDPs)to investigate the etiology of psychiatric disorders.IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities.In this review,we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics.We first described IDPs through their phenotypic classification and neuroimaging genomics.Secondly,we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials.Thirdly,considering the genetic evidence of IDPs,we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization.Finally,we discussed machine learning as an optimum approach for validating biomarkers.Together,future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.展开更多
基金Science Fund for Distinguished Young Scholars of Shaanxi Province(2021JC-02)Innovation Capability Support Program of Shaanxi Province(2022TD-44)+3 种基金Key Research and Development Project of Shaanxi Province(2022GXLH-01-22)National Natural Science Foundation of China(82101601)China Postdoctoral Science Foundation(2023T160517,2021M702612)Fundamental Research Funds for the Central Universities.
文摘Schizophrenia is a complex and serious brain disorder.Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes(IDPs)to investigate the etiology of psychiatric disorders.IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities.In this review,we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics.We first described IDPs through their phenotypic classification and neuroimaging genomics.Secondly,we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials.Thirdly,considering the genetic evidence of IDPs,we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization.Finally,we discussed machine learning as an optimum approach for validating biomarkers.Together,future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.
