A neuromarker for deficit syndrome in schizophrenia from a combination of structural and functional magnetic resonance imaging.
Ju GaoRongtao JiangXiaowei TangJiu ChenMiao YuChao ZhouXiang WangHongying ZhangChengbing HuangYong YangXiaobin ZhangZaixu CuiXiangrong ZhangPublished in: CNS neuroscience & therapeutics (2023)
The present study demonstrated that local properties of brain regions extracted from multimodal imaging data could distinguish DS from NDS with a machine learning-based approach and confirmed the relationship between distinctive features and the negative symptoms subdomain. These findings may improve the identification of potential neuroimaging signatures and improve the clinical assessment of the deficit syndrome.
Keyphrases
- magnetic resonance imaging
- machine learning
- bipolar disorder
- big data
- high resolution
- computed tomography
- case report
- artificial intelligence
- electronic health record
- pain management
- genome wide
- gene expression
- depressive symptoms
- climate change
- resting state
- deep learning
- risk assessment
- brain injury
- blood brain barrier
- human health
- subarachnoid hemorrhage
- multiple sclerosis
- magnetic resonance
- sleep quality
- functional connectivity
- diffusion weighted imaging