Poster No:
1002
Submission Type:
Abstract Submission
Authors:
Ke-Ying Wang1,2, Jie Pu1,2, 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:
Ke-Ying Wang
Institute of Psychology, Chinese Academy of Sciences|Department of Psychology, the University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Co-Author(s):
Jie Pu
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 the cerebellum plays a crucial role in a variety of functions, including motor control, affective and cognitive processing (Gaiser et al., 2024). Imaging studies have demonstrated that the cerebellum has extensive connections with the cerebral cortex and the limbic system, modulating the emotional and cognitive responses collaboratively (Choi et al., 2023). Recently, investigating changes in brain connectivity across the human lifespan has become a pivotal topic in neuroscience. However, the findings are inconsistent due to the small sample size and relatively narrow age ranges (Luo et al., 2020). In this study, we examined the lifespan trajectories of the cerebellar-cerebral resting-state functional connectivity (rsFC) in a large Chinese sample.
Methods:
We recruited 3424 participants (4-83 years old) to undertake T1-weighted and resting-state funtional MRI brain scan with a 3T GE MR750 scanner. Functional data preprocessing was conducted using the toolbox for Data Processing & Analysis for Brain Imaging on Surface (DPABISurf)(Yan et al., 2021). T1-weighted image was intensity nonuniformity corrected, skull-stripped, segmented, and recon -all (FreeSurfer 6.0.1) was used to reconstruct brain surfaces. The functional images were co-registered to the T1-weighted image using bbregister (FreeSurfer, Greve & Fischl, 2009) and then slice-time corrected, resampled onto the fsaverage5 surfaces. The Friston 24-parameter (Friston et al., 1996), white matter and cerebrospinal fluid signals were removed through linear regression. Finally, linear detrend and bandpass filter (0.01 - 0.1 Hz) was applied to the normalized functional images. To calculate the cerebellar-cerebral rsFC, BOLD series of 10 cerebellar regions (Lobule_I_II, Lobule III, Lobule_IV_V, Lobule_VI, Crus I, Curs II, Lobule_VIIb, Lobule_VIII, Lobule_ IX, Lobule_X) were calculated by averaging the 26 cerebellar regions based on the anatomical automatic labeling (AAL) template (Tzourio-Mazoyer et al., 2002). For each participant, Pearson's correlations between BOLD series of cerebellar regions and 90 cerebral regions were then calculated and Fisher's r-to-z transformed to measure the cerebellar-cerebral rsFC. Strong rsFC in the whole sample with r > 0.4 were selected for further analysis, in which we applied linear mixed model to examine the predictive effects of age, age2, gender, and their interactions on cerebellar-cerebral rsFC, controlling framework displacement (FD) (Power et al., 2014) and BMI. The significance threshold was set at a Bonferroni-corrected p < .05.
Results:
Overall, 110 cerebello-cerebral rsFCs exhibited age-related changes. Of these, inverted U-shaped relationships were predominantly found between 93 cerebellar-cerebral rsFCs and age, characterized by an early increase in rsFCs between the posterior cerebellum and networks such as the Default Mode Network(DMN), Limbic Network(LN), Visual System(VS), Dorsal Attention Network and Executive Control Network(ECN), followed by a decline in older age. A U-shaped trajectory was found specifically in rsFC between Lobule IV-V and the frontal lobe. A positive linear relatioship was found between 9 cerebellar-cerebral rsFCs, mainly between Lobule IV-VI and DMN and LN. We also found an interaction between gender and age² in the rsFC between Crus I and the right middle temporal pole, with males showing more significantly increase in older stage.
Conclusions:
Our findings highlight the age-related changes in cerebellar-cerebral rsFC across lifespan of a Chinese sample. Inverted U-shaped trajectories were found in most of the strong cerebellar-cerebral rsFCs with DMN, LN, and ECN. These results highlight distinct developmental trajectories of cerebellar-cerebral rsFC across lifespan, reflecting the differentiated maturation of brain networks, which may play important roles in cognitive, emotional, and sensorimotor functions.
Lifespan Development:
Lifespan Development Other 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Task-Independent and Resting-State Analysis
Keywords:
Aging
Cerebellum
Cortex
Development
FUNCTIONAL MRI
NORMAL HUMAN
Sub-Cortical
Univariate
Other - Lifespan; Resting-state functional connectivity
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
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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Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
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Not applicable
Please indicate which methods were used in your research:
Functional MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Other, Please list
-
Data Processing & Analysis for Brain Imaging on Surface (DPABIsurf)
SPM
FSL
Provide references using APA citation style.
Choi, S. Y., Ha, M., Choi, S., Moon, S. Y., Park, S., Kim, M., & Kwon, J. S. (2023). Altered intrinsic cerebellar-cerebral functional connectivity is related to negative symptoms in patients with first-episode psychosis. Schizophrenia Research, 252, 56-63. doi:https://doi.org/10.1016/j.schres.2022.12.041
Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S. J., & Turner, R. (1996). Movement-Related effects in fMRI time-series. Magn Reson Med, 35(3), 346-355. doi:https://doi.org/10.1002/mrm.1910350312
Gaiser, C., van der Vliet, R., de Boer, A. A., Donchin, O., Berthet, P., Devenyi, G. A., ... & Muetzel, R. L. (2024). Population-wide cerebellar growth models of children and adolescents. Nature Communications, 15(1), 2351. doi:https://doi.org/10.1038/s41467-024-46398-2
Greve, D. N., & Fischl, B. (2009). Accurate and robust brain image alignment using boundary-based registration. Neuroimage, 48(1), 63-72. doi:10.1016/j.neuroimage.2009.06.060
Luo, N., Sui, J., Abrol, A., Lin, D., Chen, J., Vergara, V. M., ... & Calhoun, V. D. (2020). Age‐related structural and functional variations in 5,967 individuals across the adult lifespan. Human brain map**, 41(7), 1725-1737. doi:https://doi.org/10.1002/hbm.24905
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 320–341. doi:https://doi.org/10.1016/j.neuroimage.2013.08.048
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., ... & Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1), 273-289. doi:https://doi.org/10.1006/nimg.2001.0978
Yan, C.-G., Wang, X.-D., & Lu, B. (2021). DPABISurf: data processing & analysis for brain imaging on surface. Science Bulletin. doi:https://doi.org/10.1016/j.scib.2021.09.016
No