Comparative Analysis of Brain Connectivity and Gene Expression Divergence in Chimpanzees and Humans
Presented During:
Wednesday, June 26, 2024: 11:30 AM - 12:45 PM
COEX
Room:
Conference Room E 1
Poster No:
2107
Submission Type:
Abstract Submission
Authors:
Yufan Wang1,2, Luqi Cheng3,4, Deying Li1,2, Yuheng Lu1,2, Yaping Wang1,5, Chaohong Gao1,5, Haiyan Wang1, Congying Chu1, Lingzhong Fan1,2,5,6,7
Institutions:
1Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 2School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China, 3School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China, 4Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou, China, 5Sino-Danish College, University of Chinese Academy of Sciences, Beijing, China, 6CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 7School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao, China
First Author:
Yufan Wang
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|School of Artificial Intelligence, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|School of Artificial Intelligence, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Co-Author(s):
Luqi Cheng
School of Life and Environmental Sciences, Guilin University of Electronic Technology|Research Center for Augmented Intelligence, Zhejiang Lab
Guilin, China|Hangzhou, China
School of Life and Environmental Sciences, Guilin University of Electronic Technology|Research Center for Augmented Intelligence, Zhejiang Lab
Guilin, China|Hangzhou, China
Deying Li
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|School of Artificial Intelligence, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|School of Artificial Intelligence, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Yuheng Lu
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|School of Artificial Intelligence, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|School of Artificial Intelligence, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Yaping Wang
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|Sino-Danish College, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|Sino-Danish College, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Chaohong Gao
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|Sino-Danish College, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|Sino-Danish College, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Congying Chu
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences
Beijing, China
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences
Beijing, China
Lingzhong Fan
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|School of Artificial Intelligence, University of Chinese Academy of Sciences|Sino-Danish College, University of Chinese Academy of Sciences|CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences|School of Health and Life Sciences, University of Health and Rehabilitation Sciences
Beijing, China|Beijing, China|Beijing, China|Beijing, China|Qingdao, China
Brainnetome Center, Institute of Automation, Chinese Academy of Sciences|School of Artificial Intelligence, University of Chinese Academy of Sciences|Sino-Danish College, University of Chinese Academy of Sciences|CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences|School of Health and Life Sciences, University of Health and Rehabilitation Sciences
Beijing, China|Beijing, China|Beijing, China|Beijing, China|Qingdao, China
Introduction:
Anatomical connectivity changes during evolution may underlie functional specialization of the human brain (Thiebaut de Schotten et al. 2022). Although chimpanzees (Pan troglodytes) are crucial as a comparative reference for investigating human brain evolution (Varki et al. 2005), comprehensive connectional analyses between human and chimpanzee brains have been limited due to lack of comparable cross-species brain atlases. Moreover, the underlying genetic association of human-specific brain connectivity also remains unclear. To address these questions, we built the Chimpanzee Brainnetome Atlas (ChimpBNA), investigated cross-species connectivity divergence and examined the associated genetic factors.
Methods:
Data and Preprocessing - 46 chimpanzees were available from the National Chimpanzee Brain Resource. 40 human subjects were available from HCP dataset (Van Essen et al. 2003). Preprocessing was the same as our previous study (Cheng et al. 2021).
Parcellation – Following a connectivity-based parcellation framework used previously with humans (Fan et al. 2016), we performed tractography for each initial ROI and used spectral clustering, resulting in the final parcellation of the whole brain.
Connectivity divergence - We reconstructed homologous white matter tracts for each chimpanzee and human and obtained the connectivity blueprints (Bryant et al. 2020, Mars et al. 2018). We then calculated the KL divergence for each pair of subregions in ChimpBNA and HumanBNA and searched for the minimal value for each subregion in the HumanBNA.
Genetic analysis – We used PLSR to identify the first component of AHBA gene expression (Hawrylycz et al. 2012), and used bootstrapping to calculated Z-score of each gene. Genes with Z-score higher than 3 were input into cell-type and gene enrichment analysis. Finally, we examined gene expression difference between two species in three regions of interest using PsychENCODE database (Sousa et al. 2017).
Parcellation – Following a connectivity-based parcellation framework used previously with humans (Fan et al. 2016), we performed tractography for each initial ROI and used spectral clustering, resulting in the final parcellation of the whole brain.
Connectivity divergence - We reconstructed homologous white matter tracts for each chimpanzee and human and obtained the connectivity blueprints (Bryant et al. 2020, Mars et al. 2018). We then calculated the KL divergence for each pair of subregions in ChimpBNA and HumanBNA and searched for the minimal value for each subregion in the HumanBNA.
Genetic analysis – We used PLSR to identify the first component of AHBA gene expression (Hawrylycz et al. 2012), and used bootstrapping to calculated Z-score of each gene. Genes with Z-score higher than 3 were input into cell-type and gene enrichment analysis. Finally, we examined gene expression difference between two species in three regions of interest using PsychENCODE database (Sousa et al. 2017).
Results:
Using the connectivity profiles, we subdivided the chimpanzee brain in 198 cortical and 44 subcortical regions, thereby building ChimpBNA, the most refined atlas of the chimpanzee brain to date (Fig 1A, 1B).
Leveraging connectivity blueprints, we found that regions with the most different connectivity patterns between species were located at the middle and posterior temporal lobe, especially the pSTS, posterior IPL, anterior Pcun, anterior insula (Fig 2B, C). The connectivity divergence map showed dissimilarity with that of cortical expansion (Wei et al. 2019), indicating that the connectional changes reflect a unique aspect of brain reorganization.
The first component idenfied from the AHBA data significantly correlated with the connectivity divergence map (r=0.39, p<.013), and 1912 genes had a Z-score greater than 3. These genes were highly enriched in excitatory neurons, specifically the L6 and L2-3 IT excitatory neurons (Fig 2D), and related to neuron projection and synapses (Fig 2E).
In addition, 70 of these genes significantly overlapped with HAR-BRAIN genes (Wei et al. 2019) (p<.005). 1459 of 1912 genes overlapped with the PsychENCODE data, and showed significant expression differences in three ROIs (STC: t=15.96; DFC: t=8.16; V1C: t=-2.11, all p<.05) but a more significant effect size in the STC and DFC (STC: Cohen's d=0.46; DFC: Cohen's d=0.27; V1C: Cohen's d=0.03, Fig 2F), indicating a potential association between gene expression differences and connectivity divergence between species.
Leveraging connectivity blueprints, we found that regions with the most different connectivity patterns between species were located at the middle and posterior temporal lobe, especially the pSTS, posterior IPL, anterior Pcun, anterior insula (Fig 2B, C). The connectivity divergence map showed dissimilarity with that of cortical expansion (Wei et al. 2019), indicating that the connectional changes reflect a unique aspect of brain reorganization.
The first component idenfied from the AHBA data significantly correlated with the connectivity divergence map (r=0.39, p<.013), and 1912 genes had a Z-score greater than 3. These genes were highly enriched in excitatory neurons, specifically the L6 and L2-3 IT excitatory neurons (Fig 2D), and related to neuron projection and synapses (Fig 2E).
In addition, 70 of these genes significantly overlapped with HAR-BRAIN genes (Wei et al. 2019) (p<.005). 1459 of 1912 genes overlapped with the PsychENCODE data, and showed significant expression differences in three ROIs (STC: t=15.96; DFC: t=8.16; V1C: t=-2.11, all p<.05) but a more significant effect size in the STC and DFC (STC: Cohen's d=0.46; DFC: Cohen's d=0.27; V1C: Cohen's d=0.03, Fig 2F), indicating a potential association between gene expression differences and connectivity divergence between species.
Conclusions:
In summary, we constructed the fine-grained ChimpBNA based on anatomical connectivity and performed a comparative analysis of chimpanzee and human connectivity profiles. This revealed divergent patterns across species and associated genetic factors. Our findings indicate that connectivity changes in chimpanzees and humans likely reflect functional specialization in evolution and provide key steps toward elucidating diversity of primate brains.
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 1
Neuroinformatics and Data Sharing:
Brain Atlases 2
Novel Imaging Acquisition Methods:
Diffusion MRI
Keywords:
Cross-Species Homologues
MRI
1|2Indicates the priority used for review
Provide references using author date format
Cheng, L. et al., (2021). "Connectional asymmetry of the inferior parietal lobule shapes hemispheric specialization in humans, chimpanzees, and rhesus macaques." Elife 10.
Fan, L. et al., (2016). "The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture." Cereb Cortex 26(8): 3508-3526.
Hawrylycz, M. J. et al., (2012). "An anatomically comprehensive atlas of the adult human brain transcriptome." Nature 489(7416): 391-399.
Mars, R. B. et al., (2018). "Whole brain comparative anatomy using connectivity blueprints." Elife 7.
Sousa, A. M. M. et al., (2017). "Molecular and cellular reorganization of neural circuits in the human lineage." Science 358(6366): 1027-1032.
Thiebaut de Schotten, M. et al., (2022). "The emergent properties of the connected brain." Science 378(6619): 505-510.
Van Essen, D. C. et al., (2013). "The WU-Minn Human Connectome Project: an overview." Neuroimage 80: 62-79.
Varki, A. et al., (2005). "Comparing the human and chimpanzee genomes: searching for needles in a haystack." Genome Res 15(12): 1746-1758.
Wei, Y. et al., (2019). "Genetic mapping and evolutionary analysis of human-expanded cognitive networks." Nat Commun 10(1): 4839.