Poster No:
1001
Submission Type:
Abstract Submission
Authors:
Jie Pu1,2, Ke-Ying Wang1,2, Yi Wang1,2, Raymond Chan1,2
Institutions:
1Institute of Psychology, Chinese Academy of Sciences, Beijing, China, 2Department of Psychology, the University of Chinese Academy of Sciences, Beijing, China
First Author:
Jie Pu
Institute of Psychology, Chinese Academy of Sciences|Department of Psychology, the University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Co-Author(s):
Ke-Ying Wang
Institute of Psychology, Chinese Academy of Sciences|Department of Psychology, the University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Yi Wang
Institute of Psychology, Chinese Academy of Sciences|Department of Psychology, the University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Raymond Chan
Institute of Psychology, Chinese Academy of Sciences|Department of Psychology, the University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Introduction:
Recent findings suggest that dysfunctions in the cerebellum are associated with various neurodevelopmental disorders (Haldipur et al., 2022). Interaction between the cerebrum and cerebellum has also been identified as critical marker for social and cognitive functions (Van Overwalle, 2024). Structural changes in the cerebellum drive gradients in resting-state fMRI dynamics and mirrors cortical connectivity patterns (X. Liu et al., 2022). Despite the importance of cerebellar-cortical interactions and the observed gradient changes in cerebellar development, few studies have examined the lifespan changes of the structural covariance between cortical regions and the cerebellum. This study examined the structural covariance between cortical volumes and the cerebellum across the lifespan in a Chinese sample.
Methods:
We recruited 3,424 participants (4-83 years old) to undertake the structural brain scan in a 3T GE Discovery MR750 scanner. Anatomical data preprocessing was conducted using the toolbox for Data Processing & Analysis for Brain Imaging on Surface (DPABISurf)(Yan et al., 2021) as follows: using N4BiasFieldCorrection to correct the T1-weighted image for intensity nonuniformity (Tustison et al., 2010); using ANTs for skull-strip (Avants et al., 2009); using fast to segment brain tissues (Zhang et al., 2001); recon-all (FreeSurfer 6.0.1) was used to reconstruct brain surfaces. Finally, regional volumes were estimated based on the Desikan-Killiany atlas (Desikan et al., 2006), including 2 bilateral cerebellar volumes, 68 bilateral cerebral cortical regions and 14 subcortical regions (Thalamus, Caudate, Putamen, Pallidum, Hippocampus, Amygdala, and Accumbens). Individual differential structural covariance networks (Z. Liu et al., 2021) among all the regional volumes (84 by 84 matrix) were calculated for each participant, controlling for total intracranial volume and BMI. Linear mixed models were then performed with age, age², and biological gender, age by gender interactions as regressors and individualized cerebellar-cerebral covariance as dependent variable to characterize age-related structural covariance trajectories. All p-values were corrected for multiple comparisons using the Bonferroni correction.
Results:
In total, 19 regions with linear age-related trajectory of cerebellar structural covariance (SC) were found (β coefficients: -1.93 ~ -1.05). The SC between left cerebellum and cingulate cortex, lingual gyrus and precuneus showed linear decreases with age, SC of the right cerebellum also showed similar changes in these regions. The U-shaped nonlinear trajectories of the cerebellar SC were found in 54 regions (β coefficients: 0.98 ~ 1.85), including bilateral superior/middle frontal gyrus, insula, posterior cingulate, postcentral and supramarginal gyrus, superior parietal lobule, and right NAcc. The SC of left and right cerebellum showed similar patterns in most of the regions except that right cerebellar SC additionally exhibited U-shaped trajectory with bilateral orbitofrontal cortices. Gender differences were found in the left precuneus, with a linear trajectory significant only in males. Nonlinear U-shaped trajectories of bilateral cerebellum were observed in males for the pars opercularis and posterior cingulate, but no significant age-related effects were found in females.
Conclusions:
These findings highlight the distinct age-related changes in the structural covariance between the cerebellum and various brain regions. Linear decreases primarily involve regions associated with self-referential thinking and cognitive attention, while U-shaped changes span broader functional networks. These results suggest that cerebellar and cortical development and aging may follow different trajectories, with important implications for cognitive control, emotional regulation, and sensorimotor functions across the lifespan.
Lifespan Development:
Lifespan Development Other 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Subcortical Structures
Keywords:
Aging
Cerebellum
Cortex
Development
Multivariate
NORMAL HUMAN
STRUCTURAL MRI
Sub-Cortical
Other - Lifespan; Structural Covariance
1|2Indicates the priority used for review
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Please indicate which methods were used in your research:
Structural MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Free Surfer
Other, Please list
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Data Processing & Analysis for Brain Imaging on Surface (DPABIsurf)
Provide references using APA citation style.
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., Albert, M. S., & Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021
Haldipur, P., Millen, K. J., & Aldinger, K. A. (2022). Human Cerebellar Development and Transcriptomics: Implications for Neurodevelopmental Disorders. Annual Review of Neuroscience, 45(Volume 45, 2022), 515–531. https://doi.org/10.1146/annurev-neuro-111020-091953
Liu, X., d’Oleire Uquillas, F., Viaene, A. N., Zhen, Z., & Gomez, J. (2022). A multifaceted gradient in human cerebellum of structural and functional development. Nature Neuroscience, 25(9), 1129–1133. https://doi.org/10.1038/s41593-022-01136-z
Liu, Z., Palaniyappan, L., Wu, X., Zhang, K., Du, J., Zhao, Q., Xie, C., Tang, Y., Su, W., Wei, Y., Xue, K., Han, S., Tsai, S.-J., Lin, C.-P., Cheng, J., Li, C., Wang, J., Sahakian, B. J., Robbins, T. W., … Feng, J. (2021). Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: Individualized structural covariance network analysis. Molecular Psychiatry, 26(12), 7719–7731. https://doi.org/10.1038/s41380-021-01229-4
Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: improved N3 bias correction. IEEE Trans Med Imaging, 29(6), 1310-1320. doi:10.1109/tmi.2010.2046908
Van Overwalle, F. (2024). Social and emotional learning in the cerebellum. Nature Reviews Neuroscience. https://doi.org/10.1038/s41583-024-00871-5
Yan, C.-G., Wang, X.-D., Zuo, X.-N., & Zang, Y.-F. (2016). DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics, 14(3), 339–351. https://doi.org/10.1007/s12021-016-9299-4
Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging, 20(1), 45-57. doi:10.1109/42.906424
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