Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives.
Jing GuoChangyi HeHuimiao SongHuiwu GaoShi YaoShan-Shan DongTie-Lin YangPublished in: Neuroscience bulletin (2024)
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.
Keyphrases
- bipolar disorder
- machine learning
- magnetic resonance
- randomized controlled trial
- white matter
- resting state
- high resolution
- deep learning
- magnetic resonance imaging
- dna methylation
- multiple sclerosis
- mass spectrometry
- genome wide
- systematic review
- computed tomography
- artificial intelligence
- quality improvement
- copy number
- functional connectivity
- meta analyses