Individual Variability in the Structural Connectivity Architecture of the Human Brain

The human brain exhibits a high degree of individual variability in both its structure and function, which underlies intersubject differences in cognition and behavior. It was previously shown that functional connectivity is more variable in the heteromodal association cortex but less variable in th...

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Published inThe Journal of neuroscience Vol. 45; no. 5; p. e2139232024
Main Authors Huang (黄伟杰), Weijie, Chen (陈豪杰), Haojie, Liu (刘桢钊), Zhenzhao, Dong (董心怡), Xinyi, Feng (冯国政), Guozheng, Liu (刘广芳), Guangfang, Yang (杨奡偲), Aocai, Zhang (张占军), Zhanjun, Shmuel, Amir, Su (苏里), Li, Ma (马国林), Guolin, Shu (舒妮), Ni
Format Journal Article
LanguageEnglish
Published United States Society for Neuroscience 29.01.2025
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Online AccessGet full text
ISSN0270-6474
1529-2401
1529-2401
DOI10.1523/JNEUROSCI.2139-23.2024

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Abstract The human brain exhibits a high degree of individual variability in both its structure and function, which underlies intersubject differences in cognition and behavior. It was previously shown that functional connectivity is more variable in the heteromodal association cortex but less variable in the unimodal cortices. Structural connectivity (SC) is the anatomical substrate of functional connectivity, but the spatial and temporal patterns of individual variability in SC (IVSC) remain largely unknown. In the present study, we discovered a detailed and robust chart of IVSC obtained by applying diffusion MRI and tractography techniques to 1,724 adults (770 males and 954 females) from multiple imaging datasets. Our results showed that the SC exhibited the highest and lowest variability in the limbic regions and the unimodal sensorimotor regions, respectively. With increased age, higher IVSC was observed across most brain regions. Moreover, the specific spatial distribution of IVSC is related to the cortical laminar differentiation and myelination content. Finally, we proposed a modified ridge regression model to predict individual cognition and generated idiographic brain mapping, which was significantly correlated with the spatial pattern of IVSC. Overall, our findings further contribute to the understanding of the mechanisms of individual variability in brain SC and link to the prediction of individual cognitive function in adult subjects.
AbstractList The human brain exhibits a high degree of individual variability in both its structure and function, which underlies intersubject differences in cognition and behavior. It was previously shown that functional connectivity is more variable in the heteromodal association cortex but less variable in the unimodal cortices. Structural connectivity (SC) is the anatomical substrate of functional connectivity, but the spatial and temporal patterns of individual variability in SC (IVSC) remain largely unknown. In the present study, we discovered a detailed and robust chart of IVSC obtained by applying diffusion MRI and tractography techniques to 1,724 adults (770 males and 954 females) from multiple imaging datasets. Our results showed that the SC exhibited the highest and lowest variability in the limbic regions and the unimodal sensorimotor regions, respectively. With increased age, higher IVSC was observed across most brain regions. Moreover, the specific spatial distribution of IVSC is related to the cortical laminar differentiation and myelination content. Finally, we proposed a modified ridge regression model to predict individual cognition and generated idiographic brain mapping, which was significantly correlated with the spatial pattern of IVSC. Overall, our findings further contribute to the understanding of the mechanisms of individual variability in brain SC and link to the prediction of individual cognitive function in adult subjects.
The human brain exhibits high degree of individual variability in both its structure and function, which underlies inter-subject differences in cognition and behavior. It was previously shown that functional connectivity is more variable in the hetero-modal association cortex but less variable in the unimodal cortices. Structural connectivity is the anatomical substrate of functional connectivity, but the spatial and temporal patterns of individual variability in structural connectivity (IVSC) remain largely unknown. In the present study, we discovered a detailed and robust chart of IVSC obtained by applying diffusion MRI and tractography techniques to 1724 adults (770 males and 954 females) from multiple imaging datasets. Our results showed that the structural connectivity exhibited the highest and lowest variability in the limbic regions and the unimodal sensorimotor regions, respectively. With increased age, higher IVSC was observed across most brain regions. Moreover, the specific spatial distribution of IVSC is related to the cortical laminar differentiation and myelination content. Finally, we proposed a modified ridge regression model to predict individual cognition and generated idiographic brain mapping, which was significantly correlated with the spatial pattern of IVSC. Overall, our findings further contribute to the understanding of the mechanisms of individual variability in brain structural connectivity and link to the prediction of individual cognitive function in adult subjects.Significance Statement White matter connectivity between grey matter regions plays an important role in integrating information from distributed regions when individuals performing complex cognitive functions. Unique white matter connectivity is the neuroanatomical identity of each individual. The authors systematically explored the spatio-temporal pattern of individual variability in structural connectivity, and the results show higher variability in structural connectivity relates to higher neuroplasticity. In addition, this study reveals that structural connectivity involved in executive function and attention task is different among individuals and highlights the importance of individualized statistical methods for mapping neural pathway of complex cognition.The human brain exhibits high degree of individual variability in both its structure and function, which underlies inter-subject differences in cognition and behavior. It was previously shown that functional connectivity is more variable in the hetero-modal association cortex but less variable in the unimodal cortices. Structural connectivity is the anatomical substrate of functional connectivity, but the spatial and temporal patterns of individual variability in structural connectivity (IVSC) remain largely unknown. In the present study, we discovered a detailed and robust chart of IVSC obtained by applying diffusion MRI and tractography techniques to 1724 adults (770 males and 954 females) from multiple imaging datasets. Our results showed that the structural connectivity exhibited the highest and lowest variability in the limbic regions and the unimodal sensorimotor regions, respectively. With increased age, higher IVSC was observed across most brain regions. Moreover, the specific spatial distribution of IVSC is related to the cortical laminar differentiation and myelination content. Finally, we proposed a modified ridge regression model to predict individual cognition and generated idiographic brain mapping, which was significantly correlated with the spatial pattern of IVSC. Overall, our findings further contribute to the understanding of the mechanisms of individual variability in brain structural connectivity and link to the prediction of individual cognitive function in adult subjects.Significance Statement White matter connectivity between grey matter regions plays an important role in integrating information from distributed regions when individuals performing complex cognitive functions. Unique white matter connectivity is the neuroanatomical identity of each individual. The authors systematically explored the spatio-temporal pattern of individual variability in structural connectivity, and the results show higher variability in structural connectivity relates to higher neuroplasticity. In addition, this study reveals that structural connectivity involved in executive function and attention task is different among individuals and highlights the importance of individualized statistical methods for mapping neural pathway of complex cognition.
Author Chen (陈豪杰), Haojie
Zhang (张占军), Zhanjun
Shmuel, Amir
Shu (舒妮), Ni
Dong (董心怡), Xinyi
Yang (杨奡偲), Aocai
Ma (马国林), Guolin
Feng (冯国政), Guozheng
Liu (刘广芳), Guangfang
Su (苏里), Li
Liu (刘桢钊), Zhenzhao
Huang (黄伟杰), Weijie
AuthorAffiliation 1 School of Systems Science, Beijing Normal University , Beijing 100875, China
4 Department of Radiology, China-Japan Friendship Hospital , Beijing 100029, China
5 BABRI Centre, Beijing Normal University , Beijing 100875, China
3 School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University , Beijing 100871, China
7 Department of Neuroscience, Neuroscience Institute, Insigneo Institute for In Silico Medicine, University of Sheffield , Sheffield S10 2HQ, United Kingdom
2 State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University , Beijing 100875, China
6 Montreal Neurological Institute, McGill University , Montreal, Quebec H3A 2B4, Canada
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– name: 6 Montreal Neurological Institute, McGill University , Montreal, Quebec H3A 2B4, Canada
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Keywords structural connectivity
individual variability
transcription
machine learning
dMRI
Language English
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Author contributions: W.H. and N.S. designed research; W.H., H.C., Z.L., X.D., G.F., G.L., A.Y., Z.Z., L.S. and G.M. performed research; W.H. analyzed data; W.H., A.S. and N.S. wrote the paper.
We thank all the volunteers for their participation in the study. This work was supported by the STI2030-Major Projects (2022ZD0213300, 2021ZD0200500), National Natural Science Foundation of China (32271145, 81871425, 81971585, 82271953, 82301608), Fundamental Research Funds for the Central Universities (2017XTCX04), the Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning (CNLZD2101, CNLYB2001), Key Research and Development Program of Hebei Provincial Department of Science and Technology (223777112D), Science and Technology Research and Development Plan of Chengde (202109A057), and Hebei Provincial Government-funded outstanding talent project.
The authors declare no competing financial interests.
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Snippet The human brain exhibits a high degree of individual variability in both its structure and function, which underlies intersubject differences in cognition and...
The human brain exhibits high degree of individual variability in both its structure and function, which underlies inter-subject differences in cognition and...
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StartPage e2139232024
SubjectTerms Adolescent
Adult
Age composition
Aged
Brain
Brain - anatomy & histology
Brain - diagnostic imaging
Brain - physiology
Brain architecture
Brain Mapping
Cognition
Cognitive ability
Diffusion Magnetic Resonance Imaging
Diffusion Tensor Imaging
Female
Functional anatomy
Humans
Individuality
Male
Middle Aged
Myelination
Nerve Net - anatomy & histology
Nerve Net - physiology
Neural networks
Neural Pathways - anatomy & histology
Neural Pathways - physiology
Neuroimaging
Regression models
Sensorimotor system
Spatial distribution
Structure-function relationships
Temporal lobe
Variability
Young Adult
Title Individual Variability in the Structural Connectivity Architecture of the Human Brain
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