Gene expression associated with individual variability in intrinsic functional connectivity

Liangfang Li, Yongbin Wei, Jinbo Zhang, Junji Ma, Yangyang Yi, Yue Gu, Liman Man Wai Li, Ying Lin, Zhengjia Dai*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

It has been revealed that intersubject variability (ISV) in intrinsic functional connectivity (FC) is associated with a wide variety of cognitive and behavioral performances. However, the underlying organizational principle of ISV in FC and its related gene transcriptional profiles remain unclear. Using resting-state fMRI data from the Human Connectome Project (299 adult participants) and microarray gene expression data from the Allen Human Brain Atlas, we conducted a transcription-neuroimaging association study to investigate the spatial configurations of ISV in intrinsic FC and their associations with spatial gene transcriptional profiles. We found that the multimodal association cortices showed the greatest ISV in FC, while the unimodal cortices and subcortical areas showed the least ISV. Importantly, partial least squares regression analysis revealed that the transcriptional profiles of genes associated with human accelerated regions (HARs) could explain 31.29% of the variation in the spatial distribution of ISV in FC. The top-related genes in the transcriptional profiles were enriched for the development of the central nervous system, neurogenesis and the cellular components of synapse. Moreover, we observed that the effect of gene expression profile on the heterogeneous distribution of ISV in FC was significantly mediated by the cerebral blood flow configuration. These findings highlighted the spatial arrangement of ISV in FC and their coupling with variations in transcriptional profiles and cerebral blood flow supply.

Original languageEnglish
Article number118743
Pages (from-to)1-11
Number of pages11
JournalNeuroImage
Volume245
Early online date17 Nov 2021
DOIs
Publication statusPublished - 15 Dec 2021

Bibliographical note

Funding Information:
Funding: This work was supported by the National Natural Science Foundation of China (NSFC) (No. 61772569 , 71701219 ), the Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515012148 ), and the Fundamental Research Funds for the Central Universities (No. 19wkzd20 ).

Funding Information:
Data of the current study were provided by a public dataset Human Connectome Project (HCP). HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS). HCP data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California.

Funding Information:
Funding: This work was supported by the National Natural Science Foundation of China (NSFC) (No. 61772569, 71701219), the Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515012148), and the Fundamental Research Funds for the Central Universities (No. 19wkzd20). Data of the current study were provided by a public dataset Human Connectome Project (HCP). HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS). HCP data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California. Conceptualization: Liangfang Li, Yongbin Wei, Zhengjia Dai; Formal analysis: Liangfang Li, Jinbo Zhang; Funding acquisition: Zhengjia Dai; Investigation: Liangfang Li, Junji Ma, Yangyang Yi; Methodology: Liangfang Li, Yongbin Wei, Jinbo Zhang, Zhengjia Dai; Writing ? original draft: Liangfang Li, Zhengjia Dai; Writing ? review & editing: Liangfang Li, Yongbin Wei, Jinbo Zhang, Junji Ma, Yangyang Yi, Yue Gu, Liman Man Wai Li, Ying Lin, Zhengjia Dai. Data of the current study were obtained from a public dataset Human Connectome Project (HCP). The HCP project was approved by the Institutional Review Board of Washington University in St. Louis. All participants had provided written informed content. The HCP data is publicly available in the database of Human Connectome Project: https://db.humanconnectome.org/data/projects/HCP_1200. The microarray gene expression data is publicly available in Allen Human Brain Atlas dataset: http://human.brain-map.org/static/download. The cerebral blood flow map can be obtained from Satterthwaite et al. (2014). We also uploaded de-identified FC matrices of available participants (https://zenodo.org/record/5607652) in the present study. Detailed documented code for measures calculation, statistical analysis, and figure generation was available with this article (https://github.com/LiangfangLi/ISVGeneExpression).

Publisher Copyright:
© 2021

Funding

Funding: This work was supported by the National Natural Science Foundation of China (NSFC) (No. 61772569 , 71701219 ), the Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515012148 ), and the Fundamental Research Funds for the Central Universities (No. 19wkzd20 ). Data of the current study were provided by a public dataset Human Connectome Project (HCP). HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS). HCP data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California. Funding: This work was supported by the National Natural Science Foundation of China (NSFC) (No. 61772569, 71701219), the Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515012148), and the Fundamental Research Funds for the Central Universities (No. 19wkzd20). Data of the current study were provided by a public dataset Human Connectome Project (HCP). HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS). HCP data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California. Conceptualization: Liangfang Li, Yongbin Wei, Zhengjia Dai; Formal analysis: Liangfang Li, Jinbo Zhang; Funding acquisition: Zhengjia Dai; Investigation: Liangfang Li, Junji Ma, Yangyang Yi; Methodology: Liangfang Li, Yongbin Wei, Jinbo Zhang, Zhengjia Dai; Writing ? original draft: Liangfang Li, Zhengjia Dai; Writing ? review & editing: Liangfang Li, Yongbin Wei, Jinbo Zhang, Junji Ma, Yangyang Yi, Yue Gu, Liman Man Wai Li, Ying Lin, Zhengjia Dai. Data of the current study were obtained from a public dataset Human Connectome Project (HCP). The HCP project was approved by the Institutional Review Board of Washington University in St. Louis. All participants had provided written informed content. The HCP data is publicly available in the database of Human Connectome Project: https://db.humanconnectome.org/data/projects/HCP_1200. The microarray gene expression data is publicly available in Allen Human Brain Atlas dataset: http://human.brain-map.org/static/download. The cerebral blood flow map can be obtained from Satterthwaite et al. (2014). We also uploaded de-identified FC matrices of available participants (https://zenodo.org/record/5607652) in the present study. Detailed documented code for measures calculation, statistical analysis, and figure generation was available with this article (https://github.com/LiangfangLi/ISVGeneExpression).

FundersFunder number
Guangdong Basic and Applied Basic Research Foundation2019A1515012148
National Institute of Mental Health
National Institute of Neurological Disorders and Stroke
National Institute of Dental and Craniofacial Research
University of Southern California
National Natural Science Foundation of China61772569, 71701219
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities19wkzd20
Fundamental Research Funds for the Central Universities

    Keywords

    • Functional connectivity
    • Gene expression
    • Intersubject variability
    • Metabolism
    • Resting-state fMRI

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