Cerebellar-Cerebral Functional Interactions and Associations with Cognitive and Behavioral Measures
Poster No:
1237
Submission Type:
Abstract Submission
Authors:
Xiaoqi Li1, Weiyan Yin1, Chia-Hsin Chen2, Yiding Gui1, Tengfei Li1, Hongtu Zhu1, Weili Lin1
Institutions:
1University of North Carolina at Chapel Hill, Chapel Hill, NC, 2University of North Carolina at Chapel Hill, Biomedical Research Imaging Center, Chapel Hill, NC
First Author:
Co-Author(s):
Chia-Hsin Chen
University of North Carolina at Chapel Hill, Biomedical Research Imaging Center
Chapel Hill, NC
University of North Carolina at Chapel Hill, Biomedical Research Imaging Center
Chapel Hill, NC
Introduction:
he potential links between screen time and physical activities with cognitive development in children have been reported (Bustamante, 2018; Walsh et al., 2018). Neuroimaging studies further revealed the potential neural substrates underpinning the observed associations (Paulus et al., 2019). However, previous neuroimaging studies largely focused on the cerebrum. While the cerebellum has traditionally been associated with motor control, emerging evidence highlights its involvement in higher-order cognitive processes (Buckner, 2013; Schmahmann, 2019). Therefore, we hypothesized that cerebral-cerebellar functional connections also play key roles in the associations and, potentially, serve as mediators in relation between screen time/physical activities and cognitive outcomes.
Methods:
Resting fMRI data from 7,018 participants in the ABCD study was included (Casey et al., 2018). The cerebrum was parcellated into seven canonical functional networks using the Yeo 7 atlas (Yeo et al., 2011) whereas the cerebellum was parcellated using the Automated Anatomical Labeling (AAL) atlas (Tzourio-Mazoyer et al., 2002). FCs between 7 cortical networks and cerebellar regions were computed using correlation analyses of the BOLD time-series signals and z-transformed. Linear mixed-effects models were employed to assess the associations between cerebral-cerebellar FCs and cognitive outcomes, physical activity, and screen time. The NIH Toolbox Cognitive Battery scores were used as cognitive outcomes (Luciana et al., 2018). Health-related behaviors were assessed using self-reported measures of physical activity days, muscle strengthening days, and screen time (Barch et al., 2018). Covariates included sex, age, maternal education, and economic resource index. Mediation analyses were conducted to determine whether cerebral-cerebellar FCs mediated the relationship between screen time/physical activity and cognitive performance. All reported p-values were corrected for multiple comparisons.
Results:
Consistent with the previous studies (Bustamante, 2018; Walsh et al., 2018), increased screen time was associated with lower cognitive performance, picture vocabulary (β=–0.27, p=1.4e-7), reading (β=–0.27, p=9.4e-7), and fluid composite scores (β=–0.27, p=1.2e-6)), while physical activities were positively associated with picture vocabulary (β=0.17, p=5.5e-3) and total composite scores (β=0.18, p=2.9e-12). More importantly, significant associations with cerebral-cerebellar FCs were also observed; the Default Mode Network (DMN)-Vermis VIII and DMN-Vermis IV_V, for instance, are positively associated with picture sequences (p=0.005; p=0.01) and fluid composite scores (p=0.004; p=0.01), respectively (Figure 1). Dorsal Attention Network (DorsAttn)-Region VIII (p=0.014) and DorsAttn-Vermis IX (p=0.0025) are positively associated with card sorting score. A higher level of physical activities enhanced Limbic-Crus II (p=0.016) and Control-Vermis III (p=0.016), respectively. Interestingly, no associations between screen time and cerebral-cerebellar FCs were observed.
Mediation analyses revealed 23 significant cerebral-cerebellar FCs mediators after Bonferroni correction. Of the 23, 16 FC pairs were between the DMN and cerebellar regions; five were negatively with screen time, while the remaining were positively with physical active days or muscle strengthening days (Fig. 2). Three FC pairs were linked to DorsAttn, while three other to Control, and one to Somatomotor Network (SomMot).
Mediation analyses revealed 23 significant cerebral-cerebellar FCs mediators after Bonferroni correction. Of the 23, 16 FC pairs were between the DMN and cerebellar regions; five were negatively with screen time, while the remaining were positively with physical active days or muscle strengthening days (Fig. 2). Three FC pairs were linked to DorsAttn, while three other to Control, and one to Somatomotor Network (SomMot).
·Figure 1. Circular chord diagrams highlighting cerebellar region – cerebral network pairs significantly associated with each behavioral or cognitive measure.
·Figure 2. Circular chord diagrams illustrating cerebral-cerebellar FCs that significantly mediate relationships between behavioral and cognitive outcomes
Conclusions:
These findings highlight the important roles of cerebral-cerebellar functional interaction in the study of how screen time and physical activity are associated with neurodevelopment in pediatric subjects. Importantly, our findings suggest that although no significant associations were observed between screen time and cerebral-cerebellar FCs, they served as mediators, which in turn adversely affects cognitive development in children.
Emotion, Motivation and Social Neuroscience:
Social Cognition 2
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making
Higher Cognitive Functions Other
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling
Keywords:
Cerebellum
Cognition
Data analysis
Development
FUNCTIONAL MRI
Informatics
Modeling
MRI
PEDIATRIC
Sub-Cortical
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
Provide references using APA citation style.
Buckner, R. L. (2013). The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging. Neuron, 80(3), 807–815. https://doi.org/10.1016/j.neuron.2013.10.044
Bustamante, E. E. (2018). Convergent influences of lifestyle behaviour on neurocognitive development in children. The Lancet. Child & Adolescent Health, 2(11), 766–767. https://doi.org/10.1016/S2352-4642(18)30305-5
Casey, B. J., et al.. (2018). The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Developmental Cognitive Neuroscience, 32, 43–54. https://doi.org/10.1016/j.dcn.2018.03.001
Luciana, M., et al.. (2018). Adolescent neurocognitive development and impacts of substance use: Overview of the adolescent brain cognitive development (ABCD) baseline neurocognition battery. Developmental Cognitive Neuroscience, 32, 67–79. https://doi.org/10.1016/j.dcn.2018.02.006
Paulus, M. P., et al.. (2019). Screen media activity and brain structure in youth: Evidence for diverse structural correlation networks from the ABCD study. NeuroImage, 185, 140–153. https://doi.org/10.1016/j.neuroimage.2018.10.040
Schmahmann, J. D. (2019). The cerebellum and cognition. Neuroscience Letters, 688, 62–75. https://doi.org/10.1016/j.neulet.2018.07.005
Tzourio-Mazoyer, N., et al.. (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. https://doi.org/10.1006/nimg.2001.0978
Walsh, J. J., et al.. (2018). Associations between 24 hour movement behaviours and global cognition in US children: A cross-sectional observational study. The Lancet Child & Adolescent Health, 2(11), 783–791. https://doi.org/10.1016/S2352-4642(18)30278-5
Yeo, B. T. T., et al.. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. https://doi.org/10.1152/jn.00338.2011