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Brain Gene Expression of Insomnia Disorder is Associated with Brain Function, rather than Structure

Poster No:

535 

Submission Type:

Abstract Submission 

Authors:

Haobo Zhang1,2, Haonan Sun1,2, Jiatao Li1,2, Zhangwei Lv1,2, lei xu1,2

Institutions:

1Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China, 2Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing, China

First Author:

Haobo Zhang  
Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University|Key Laboratory of Cognition and Personality of Ministry of Education
Chongqing, China|Chongqing, China

Co-Author(s):

Haonan Sun  
Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University|Key Laboratory of Cognition and Personality of Ministry of Education
Chongqing, China|Chongqing, China
Jiatao Li  
Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University|Key Laboratory of Cognition and Personality of Ministry of Education
Chongqing, China|Chongqing, China
Zhangwei Lv  
Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University|Key Laboratory of Cognition and Personality of Ministry of Education
Chongqing, China|Chongqing, China
lei xu  
Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University|Key Laboratory of Cognition and Personality of Ministry of Education
Chongqing, China|Chongqing, China

Introduction:

The etiology of insomnia disorder (ID) is very complex and potentially influenced by genetic factors (Jansen & Watanabe, 2019; Madrid-Valero et al., 2021). However, the ID is highly polygenic, determined by many combinations of variants in many genes that each individually have a very small effect (Van Someren, 2021). Previous research has highlighted the abnormalities of specific brain regions among ID, including altered brain function and/or structure (Spiegelhalder et al., 2013). In this context, an interesting question is whether the observed abnormalities in brain structure and function, obtained by neuroimaging techniques, are influenced by genetic factors in ID. Therefore, we want compare the different contribution of gene expression in functional and structural neuroimaging in ID, which is an interesting topic, by magnetic resonance imaging (MRI) and Allen Human Brain Atlas (AHBA) (Hawrylycz et al., 2012).

Methods:

The data included 264 ID (58 male, M = 39.08, SD = 15.49) recruited from three hospitals in Chongqing according to DSM-V diagnostic criteria and 129 healthy controls (HC, 101 male, M = 38.91, SD = 14.44). Resting-state functional images and high-resolution structural images were acquired for each subject. After preprocessing, we calculated amplitude of low-frequency fluctuation, fractional ALFF, and regional homogeneity. Then, with human Brainnetome (BN) atlas, three functional measures and gray matter volume (GMV) were calculated for 210 brain cortical regions. For AHBA data, we obtained the Gene Expression after processing according to the guide of previous study (Arnatkeviciute et al., 2019). We used two-sample t-test to assess the differences in ALFF, fALFF, ReHo and GMV between groups. Converting T-values to Cohen's d effect for observing the differences. The p-values were adjusted using the false discover rate (FDR, p < 0.05). We utilized Principal Component Analysis (PCA) to extract the first principal component (funcPC1) among three functional measures. Similarly, the first principal component was extracted from the gene expression matrix (genePC1). Using Spearman's rank correlation analysis to investigate the association between whole brain gene expression and brain abnormalities, and significant ID-related genes. Finally, we annotated the function of ID-related genes by enrichment analysis.

Results:

Compared to the healthy group, the IDs exhibited similar difference patterns in three functional measures, there was an increase in neuronal activity in lateral regions and a decrease in activity in medial regions. Furthermore, PCA was applied to extract the first principal component of effect sizes for three functional measures, which explained 71% variance of functional brain abnormalities in ID. In terms of GMV, decreased GMV in frontal cortex and increased GMV in temporal cortex were observed (Figure 1). The correlation analysis revealed that genePC1 was negatively correlated with funcPC1, but not with GMV differences. Then, it was observed that the gene expression of 1336 genes exhibited significant positive correlations with funcPC1, while gene expression of 1550 genes showed significant negative correlations with funcPC1 (Figure 2). For funcPC1+ genes, we observed enriched pathways such as development, synaptic transmission, and regulation processes. For funcPC1- genes, significant enrichment was mainly found for transport processes and signal transmission.
Supporting Image: Figure1.jpg
Supporting Image: Figure2.jpg
 

Conclusions:

Gene expression is associated with functional, but not structural brain abnormalities in ID. Additionally, ID-related genes are enriched in brain tissue, cortex, cells, and biological pathways. The above findings suggest that abnormal brain gene expression may lead to dysfunction of the HPA axis, neurotransmitter disorders and hyper-arousal in ID. Future studies focusing on the classification of insomnia subtypes should be based on brain structure rather than brain function, which maybe more effective.

Disorders of the Nervous System:

Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1

Genetics:

Transcriptomics 2

Novel Imaging Acquisition Methods:

Anatomical MRI
BOLD fMRI

Keywords:

FUNCTIONAL MRI
MRI
Sleep
STRUCTURAL MRI
Other - Allen Human Brain Atlas, Brain Gene Expression

1|2Indicates the priority used for review

Provide references using author date format

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