Transcriptional substrates underlying the maintenance of the brain network in human adult
Poster No:
860
Submission Type:
Abstract Submission
Authors:
Changshuo Wang1,2,3, Kristofferm Madsen4,5, Yuan Zhou1,6,7, Tianzi Jiang3,8,9,10,11
Institutions:
1CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China, 2Sino-Danish Center, University of Chinese Academy of Sciences, Beijing 100190, China, 3Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China, 4Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark, 5Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark, 6Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China, 7The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing 100120, China, 8School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China, 9Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China, 10Research Center for Augmented Intelligence, Artificial Intelligence Research Institute, Zhejiang Lab, Hangzhou, Zhejiang Province 311100, China, 11Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou, Hunan Province 425000, China
First Author:
Changshuo Wang
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences|Sino-Danish Center, University of Chinese Academy of Sciences|Brainnetome Center, Institute of Automation, Chinese Academy of Sciences
Beijing 100101, China|Beijing 100190, China|Beijing 100190, China
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences|Sino-Danish Center, University of Chinese Academy of Sciences|Brainnetome Center, Institute of Automation, Chinese Academy of Sciences
Beijing 100101, China|Beijing 100190, China|Beijing 100190, China
Co-Author(s):
Kristofferm Madsen
Department of Applied Mathematics and Computer Science, Technical University of Denmark|Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Amager and Hvidovre
Kongens Lyngby, Denmark|Copenhagen, Denmark
Department of Applied Mathematics and Computer Science, Technical University of Denmark|Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Amager and Hvidovre
Kongens Lyngby, Denmark|Copenhagen, Denmark
Yuan Zhou
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences|Department of Psychology, University of Chinese Academy of Sciences|The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders
Beijing 100101, China|Beijing 100049, China|Beijing 100120, China
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences|Department of Psychology, University of Chinese Academy of Sciences|The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders
Beijing 100101, China|Beijing 100049, China|Beijing 100120, China
Tianzi Jiang
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|School of Artificial Intelligence, University of Chinese Academy of Sciences|Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation|Research Center for Augmented Intelligence, Artificial Intelligence Research Institute, Zhejiang Lab|Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital
Beijing 100190, China|Beijing 100190, China|Chinese Academy of Sciences, Beijing 100190, China|Hangzhou, Zhejiang Province 311100, China|Yongzhou, Hunan Province 425000, China
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|School of Artificial Intelligence, University of Chinese Academy of Sciences|Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation|Research Center for Augmented Intelligence, Artificial Intelligence Research Institute, Zhejiang Lab|Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital
Beijing 100190, China|Beijing 100190, China|Chinese Academy of Sciences, Beijing 100190, China|Hangzhou, Zhejiang Province 311100, China|Yongzhou, Hunan Province 425000, China
Introduction:
Billions of neurons and their synaptic connections constitute the human brain, forming a complex structural network supporting a variety of behaviors and cognitive functions (Lynn and Bassett 2019). In the adult stage, the brain's network rarely undergoes changes, and even after brain injury, some connections would be repaired to maintain the function of the entire brain network under the regulation of gene activities (Osmanlıoğlu et al. 2020; Low and Cheng 2006). Considering the between-individual conserved topology of brain network, there should be some transcriptional molecules responsible for such maintenance of specific network structure, that is, the robustness and stability of adult brain. In this study, the joint analysis is performed on structural connectivity (SC) and transcriptome data to investigate the transcriptional substrates underlying the maintenance of adult brain network.
Methods:
Based on Human Brainnetome Atlas (HBA), the probabilistic fiber tracking algorithm is performed on the neuroimage data: Human Connectome Project (HCP) S500 dataset, to obtain the region-level SC map. And the whole-brain transcriptome data is extracted from six postmortem brains, provided by the Allen Human Brain Atlas (AHBA) (Hawrylycz et al. 2015). Then, a partial least square regression (PLSR) model is built to project the SC matrix and region-gene transcriptome matrix into the latent space, to study the distribution of the SC-transcriptome gradients across the brain. Genes are ranked according to the PLSR loadings, and high ranked genes are identified as the important genes. The whole framework of the PLSR analysis has been illustrated in the Figure 1. Finally, several enrichment analyses are performed to annotate the key genes selected, including the temporal-specific expression analysis, gene ontology (GO) and disease ontology (DO). The spin test is performed to test the significance of PLSR model to exclude the effect of spatial autocorrelation(Alexander-Bloch et al. 2018). The gene selection process is validified by the bootstrapping resampling to test whether the gene loading is significant above chance. The result of gene enrichment analysis has been corrected for the multiple comparison using Benjamini-Hochberg (BH) method.
·Figure 1. The framework for the PLSR analysis
Results:
The PLSR analysis between SC and gene expression reveals two consistent gradients along the early development axes: the rostral-caudal axis and the medial-lateral axis. Based on PLSR loadings, the key genes for each gradient are identified as two group: positive loading group and negative loading group. Several well-known morphogen-related genes are identified as key genes, such as PAX6, WNT family and NOTCH family genes. According to functional annotation, both gene sets are related with the development of the nervous system. More interestingly, the positive loading genes and negative loading genes exhibit a clear chronological order on the temporal specific expression. The disease ontology associates the key genes with the development-related diseases, including the epilepsy, intellectual disability, and autism spectral disorder.
·Figure 2. The performance of the PLSR model
Conclusions:
In this study, we jointly analyze the transcriptome data and the SC profile to understand the molecular basis underlying the maintenance of brain network in human adult. The topology of adult brain network is significantly fitted by the whole-brain transcriptional expression matrix via a PLSR model. Also, two development-related molecular gradients are found highly correlated with the adult SC network and several transcriptional molecules are found accounting for the whole-brain SC formation. The key genes selected are found associated with the development process and some development-related diseases. Overall, this study provides a clear insight into the coupling between brain network structure and transcriptional activity, and further expand our understanding on how the gene activities support the structure and functions of the brain network.
Genetics:
Genetic Association Studies 1
Transcriptomics 2
Keywords:
ADULTS
Modeling
Multivariate
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Provide references using author date format
Hawrylycz, Michael, Jeremy A Miller, Vilas Menon, David Feng, Tim Dolbeare, Angela L Guillozet-Bongaarts, Anil G Jegga, et al. 2015. ‘Canonical Genetic Signatures of the Adult Human Brain’. Nature Neuroscience 18 (12): 1832–44. https://doi.org/10.1038/nn.4171.
Low, Lawrence K, and Hwai-Jong Cheng. 2006. ‘Axon Pruning: An Essential Step Underlying the Developmental Plasticity of Neuronal Connections’. Philosophical Transactions of the Royal Society B: Biological Sciences 361 (1473): 1531–44. https://doi.org/10.1098/rstb.2006.1883.
Lynn, Christopher W., and Danielle S. Bassett. 2019. ‘The Physics of Brain Network Structure, Function and Control’. Nature Reviews Physics 1 (5): 318–32. https://doi.org/10.1038/s42254-019-0040-8.
Osmanlıoğlu, Yusuf, Jacob A Alappatt, Drew Parker, and Ragini Verma. 2020. ‘Connectomic Consistency: A Systematic Stability Analysis of Structural and Functional Connectivity’. Journal of Neural Engineering 17 (4): 045004. https://doi.org/10.1088/1741-2552/ab947b.