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Structural connectivity gradient associated with a dichotomy reveals the topographic organization of the macaque insular cortex

Long Cao, Zongchang Du, Yue Cui, Yuanchao Zhang, Yuheng Lu, Baogui Zhang, Yanyan Liu, Xiaoxiao Hou, Xinyi Liu, Luqi Cheng, Kaixin Li, Zhengyi Yang, Lingzhong Fan, View ORCID ProfileTianzi Jiang
doi: https://doi.org/10.1101/2022.03.18.484254
Long Cao
1Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
2Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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Zongchang Du
3School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
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Yue Cui
2Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
4National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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Yuanchao Zhang
1Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
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Yuheng Lu
2Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
4National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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Baogui Zhang
2Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
4National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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Yanyan Liu
2Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
4National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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Xiaoxiao Hou
2Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
4National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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Xinyi Liu
2Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
4National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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Luqi Cheng
5School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
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Kaixin Li
6School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China
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Zhengyi Yang
2Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
4National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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Lingzhong Fan
2Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
4National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
7CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
8Research Center for Augmented Intelligence, Artificial Intelligence Research Institute, Zhejiang Laboratory, Hangzhou 311100, China
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  • For correspondence: jiangtz@nlpr.ia.ac.cn lingzhong.fan@ia.ac.cn
Tianzi Jiang
1Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
2Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
4National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
7CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
8Research Center for Augmented Intelligence, Artificial Intelligence Research Institute, Zhejiang Laboratory, Hangzhou 311100, China
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  • ORCID record for Tianzi Jiang
  • For correspondence: jiangtz@nlpr.ia.ac.cn lingzhong.fan@ia.ac.cn
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Abstract

Histology studies revealed that the macaque insular cortex was characterized by the gradual organizations containing agranular, dysgranular and granular insula. However, no consensus has been reached on the elaborate subdivisions of macaque insula. Until now, no neuroimaging study to our knowledge combining connectivity-based gradients and parcellation has been performed to investigate the topographic organization of the macaque insular cortex. In this study, we used high-resolution ex vivo diffusion-weighted imaging data to explore the macaque insular cortex’s global gradient organization and subdivisions. We found a rostrocaudal organization of the dominant gradient in the macaque insula using a diffusion map embedding. Meanwhile, extracting the 25% top and bottom components from the dominant and second gradient, which explained variance over 60% in total within ten gradients, the connectivity-based parcellation method was performed to subdivide each component into two subregions confirmed by the cross-validation analysis. Furthermore, permutations tests identified that two subregions from each component showed significant differences between their connectivity fingerprints. Finally, we found that the dominant and second gradients were significantly correlated with the T1w/T2w and cortical thickness maps in the macaque insula. Taken together, the global gradients combining the subdivisions examined the topographic organization of the macaque insular cortex based on the structural connectivity, which may contribute to a better understanding of the intricate insular cortex anatomy.

Competing Interest Statement

The authors have declared no competing interest.

  • Abbreviations

    6VR
    area 6 of cortex, ventral part, rostral subdivision
    8AV
    area 8 of cortex, anteroventral part
    36R
    area 36, rostral part
    Acb
    accumbens nucleus
    AK
    auditory koniocortex
    Amyg
    amygdaloid nucleus
    Gu
    gustatory cortex
    INS
    insular cortex
    IPL
    Inferior parietal lobule
    MST
    medial superior temporal area
    PaA
    paraauditory area
    PaI
    parainsular cortex
    Pd
    pallidus
    PE
    parietal area PE
    Pir
    piriform cortex
    PPt
    posterior parietal area
    ProK
    prokoniocortex
    ProM
    promotor
    ReI
    retroinsular area
    ROI
    region of interest
    S2
    secondary somatosensory cortex
    Str
    striatum
    STS
    superior temporal sulcus
    Tha
    thalamus
    TP
    temporopolar area
    TPt
    temporoparietal cortex
  • Copyright 
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    Posted March 20, 2022.
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    Structural connectivity gradient associated with a dichotomy reveals the topographic organization of the macaque insular cortex
    Long Cao, Zongchang Du, Yue Cui, Yuanchao Zhang, Yuheng Lu, Baogui Zhang, Yanyan Liu, Xiaoxiao Hou, Xinyi Liu, Luqi Cheng, Kaixin Li, Zhengyi Yang, Lingzhong Fan, Tianzi Jiang
    bioRxiv 2022.03.18.484254; doi: https://doi.org/10.1101/2022.03.18.484254
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    Structural connectivity gradient associated with a dichotomy reveals the topographic organization of the macaque insular cortex
    Long Cao, Zongchang Du, Yue Cui, Yuanchao Zhang, Yuheng Lu, Baogui Zhang, Yanyan Liu, Xiaoxiao Hou, Xinyi Liu, Luqi Cheng, Kaixin Li, Zhengyi Yang, Lingzhong Fan, Tianzi Jiang
    bioRxiv 2022.03.18.484254; doi: https://doi.org/10.1101/2022.03.18.484254

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