Global Signal Topography Alterations and Gene Expression in Major Depressive Disorder
Poster No:
507
Submission Type:
Abstract Submission
Authors:
Huaijin Gao1, Rui Qian1, Wen Zhu1, Chengjiaao Liao1, Dan Wu1, Zhiyong Zhao1
Institutions:
1Zhejiang University, Hangzhou, Zhejiang
First Author:
Co-Author(s):
Introduction:
The global signal (GS) refers to the average signal of the gray matter voxels, reflecting an overall fluctuation of the global BOLD activity[1]. Conventionally, GS has been regarded as a non-neuronal signal, but recent evidence demonstrated its link to human cognition[2] and clinical diseases[1].
Transcription-neuroimaging association analyses have been used to uncover the genes associated with imaging phenotypes in the brain. Previous studies established connections between gene expression and functional changes in individuals with major depressive disorder (MDD) [3,4]. However, GS topography alteration in MDD patients and its genetic basis remain unclear. This study combined the Chinese REST-meta-MDD database and Allen Human Brain Atlas (AHBA) gene data to investigate the MDD-related alterations in GS topography and their associations with gene expression.
Transcription-neuroimaging association analyses have been used to uncover the genes associated with imaging phenotypes in the brain. Previous studies established connections between gene expression and functional changes in individuals with major depressive disorder (MDD) [3,4]. However, GS topography alteration in MDD patients and its genetic basis remain unclear. This study combined the Chinese REST-meta-MDD database and Allen Human Brain Atlas (AHBA) gene data to investigate the MDD-related alterations in GS topography and their associations with gene expression.
Methods:
Resting-state fMRI data of 821 MDD patients and 757 NCs from the REST-meta-MDD consortium were screened and divided into three paired subgroups: 177 recurrent MDD (RMDD) and 392 NCs, 227 first-episode drug-naïve (FEDN) MDD and 388 NCs, and 117 FEDN and 72 RMDD.
A standardized DPARSF processing parameters [5] was used to preprocess individual-level MRI image. Then, GS and averaged time series of each region were extracted for each subject by using a whole-brain grey matter mask and a Dos-160 atlas[6] respectively, and their Pearson correlation coefficients (GSCORR) were calculated as the indicator of GS topography[7].
We used linear mixed models to compare regional GSCORR differences between groups. Then, partial least squares (PLS) regression analysis was performed to detect the relationship between GSCORR alterations and gene expression from the AHBA[8] data. Finally, the genes were ranked based on their weights, and a Gene Ontology (GO) enrichment analysis was conducted using GOrilla (http://cbl-gorilla.cs.technion.ac.il). Benjamini-Hochberg false discovery rate (FDR) correction was used to control false discoveries.
A standardized DPARSF processing parameters [5] was used to preprocess individual-level MRI image. Then, GS and averaged time series of each region were extracted for each subject by using a whole-brain grey matter mask and a Dos-160 atlas[6] respectively, and their Pearson correlation coefficients (GSCORR) were calculated as the indicator of GS topography[7].
We used linear mixed models to compare regional GSCORR differences between groups. Then, partial least squares (PLS) regression analysis was performed to detect the relationship between GSCORR alterations and gene expression from the AHBA[8] data. Finally, the genes were ranked based on their weights, and a Gene Ontology (GO) enrichment analysis was conducted using GOrilla (http://cbl-gorilla.cs.technion.ac.il). Benjamini-Hochberg false discovery rate (FDR) correction was used to control false discoveries.
Results:
Total MDD and RMDD both showed decreased GSCORR in the temporal lobe, precentral gyrus, parietal lobe, and post occipital sulcus compared with NC. RMDD also showed decreased GSCORR in the ventral prefrontal cortex and inferior temporal lobe compared with NC and FEDN (Figure 1). No significant difference was observed between FEDN and NC.
In the PLS regression analysis, the first gene component (PLS1), with the largest explained variance, showed distinct expression patterns in two MMD subtypes. Specifically, the PLS1 exhibited high expression in lateral cerebellum and precuneus for FEDN, and high expression in inferior parietal lobe and prefrontal cortex and low expression in middle insula for RMDD. Regional gene expressions of PLS1 were positively correlated with GS topography alterations in both FEDN and RMDD. Moreover, the PLS1 exhibited enrichment in biological processes related to learning or memory and neurotransmitter receptor activity in RMDD but not FEDN (Figure 2), and was enriched in biological processes related to synaptic transmission and neuron projection development in two MDD subgroups.
In the PLS regression analysis, the first gene component (PLS1), with the largest explained variance, showed distinct expression patterns in two MMD subtypes. Specifically, the PLS1 exhibited high expression in lateral cerebellum and precuneus for FEDN, and high expression in inferior parietal lobe and prefrontal cortex and low expression in middle insula for RMDD. Regional gene expressions of PLS1 were positively correlated with GS topography alterations in both FEDN and RMDD. Moreover, the PLS1 exhibited enrichment in biological processes related to learning or memory and neurotransmitter receptor activity in RMDD but not FEDN (Figure 2), and was enriched in biological processes related to synaptic transmission and neuron projection development in two MDD subgroups.
Conclusions:
This study revealed decreased GSCORR in MDD-related regions in RMDD compared with NC and FEDN, which were not observed in FEDN compared to NC, suggesting these alterations may be associated with depression severity [5]. Consistent with previous studies [9], transcription-neuroimaging analyses exhibited an association between GSCORR changes and genes enriched in chemical synaptic transmission and neuron projection development. Moreover, the difference between FEDN and RMDD in GSCORR alterations might be associated with specific biological processes such as learning or memory and neurotransmitter receptor activity, which agreed with prior findings[10]. In summary, our findings suggest that first-episode and recurrent MDD show different alterations in GS topography, which may be supported by distinct molecular basis.
·Figure 1 GSCORR differences between groups. The size and color (red/blue) of the node represents the t-value, and the change (increase/decrease) of GSCORR in former than latter groups, respectively.
·Figure 2 Association between GSCORR alterations and gene expression. Differences between FEDN and RMDD in PLS1 enrichment were involved in biological process of learning or memory and neurotransmitter
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Genetics:
Transcriptomics 2
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling
Neuroinformatics and Data Sharing:
Brain Atlases
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
FUNCTIONAL MRI
Psychiatric Disorders
Other - Transcriptomics
1|2Indicates the priority used for review
Provide references using author date format
2 Chen, Xiaodan (2018), “Topological Analyses of Functional Connectomics: A Crucial Role of Global Signal Removal, Brain Parcellation, and Null Models.” Human Brain Mapping 39 (11): 4545–64.
3 Xue, Kaizhong (2022), “Local Dynamic Spontaneous Brain Activity Changes in First-Episode, Treatment-Naïve Patients with Major Depressive Disorder and Their Associated Gene Expression Profiles.” Psychological Medicine 52 (11): 2052–61.
4 Zhu, Wenshuang (2023), “Genes Associated with Spontaneous Brain Activity Changes in Clinically Different Patients with Major Depressive Disorder: A Transcription‐neuroimaging Association Study.” CNS Neuroscience & Therapeutics, June, cns.14311.
5 Yan, Chao-Gan (2019), “Reduced Default Mode Network Functional Connectivity in Patients with Recurrent Major Depressive Disorder.” Proceedings of the National Academy of Sciences 116 (18): 9078–83.
6 Dosenbach, Nico U. F. 2010. “Prediction of Individual Brain Maturity Using fMRI.” Science (New York, N.Y.) 329 (5997): 1358–61.
7 Power, Jonathan D. (2012), “Spurious but Systematic Correlations in Functional Connectivity MRI Networks Arise from Subject Motion.” NeuroImage 59 (3): 2142–54.
8 Hawrylycz, Michael J. 2012. “An Anatomically Comprehensive Atlas of the Adult Human Brain Transcriptome.” Nature 489 (7416): 391–99.
9 Howard, David M. (2019), “Genome-Wide Meta-Analysis of Depression Identifies 102 Independent Variants and Highlights the Importance of the Prefrontal Brain Regions.” Nature Neuroscience 22 (3): 343–52.
10 Xia, Mingrui (2022), “Connectome Gradient Dysfunction in Major Depression and Its Association with Gene Expression Profiles and Treatment Outcomes.” Molecular Psychiatry 27 (3): 1384–93.