Structural covariances of prefrontal subregions selectively associate with dopamine-related gene coexpression and schizophrenia.
Huaigui LiuWei LiNana LiuJie TangLixin SunJiayuan XuYuan JiYingying XieHao DingZhaoxiang YeChunshui YuWen QinPublished in: Cerebral cortex (New York, N.Y. : 1991) (2023)
Evidence highlights that dopamine (DA) system dysregulation and prefrontal cortex (PFC) dysfunction may underlie the pathophysiology of schizophrenia. However, the associations among DA genes, PFC morphometry, and schizophrenia have not yet been fully clarified. Based on the brain gene expression dataset from Allen Human Brain Atlas and structural magnetic resonance imaging data (NDIS = 1727, NREP = 408), we first identified 10 out of 22 PFC subregions whose gray matter volume (GMV) covariance profiles were reliably associated with their DA genes coexpression profiles, then four out of the identified 10 PFC subregions demonstrated abnormally increased GMV covariance with the hippocampus, insula, and medial frontal areas in schizophrenia patients (NCASE = 100; NCONTROL = 102). Moreover, based on a schizophrenia postmortem expression dataset, we found that the DA genes coexpression of schizophrenia was significantly reduced between the middle frontal gyrus and hippocampus, in which 21 DA genes showed significantly unsynchronized expression changes, and the 21 genes' brain expression were enriched in brain activity invoked by working memory, reward, speech production, and episodic memory. Our findings indicate the DA genes selectively regulate the structural covariance of PFC subregions by their coexpression profiles, which may underlie the disrupted GMV covariance and impaired cognitive functions in schizophrenia.
Keyphrases
- bipolar disorder
- working memory
- prefrontal cortex
- genome wide
- genome wide identification
- gene expression
- functional connectivity
- poor prognosis
- magnetic resonance imaging
- resting state
- bioinformatics analysis
- dna methylation
- genome wide analysis
- network analysis
- end stage renal disease
- uric acid
- transcription factor
- white matter
- transcranial direct current stimulation
- magnetic resonance
- binding protein
- machine learning
- ejection fraction
- copy number
- long non coding rna
- big data
- artificial intelligence
- single cell
- data analysis
- patient reported
- diffusion weighted imaging