Cerebello-cortical Connectivity During Early Brain Development
Poster No:
1264
Submission Type:
Abstract Submission
Authors:
WENJIAO LYU1, Kimhan Thung1, Li Wang1, Weili Lin1, Sahar Ahmad1, Pew-Thian Yap1
Institutions:
1University of North Carolina at Chapel Hill, Chapel Hill, NC
First Author:
Co-Author(s):
Introduction:
The cerebello-cortical system stands out as one of the most crucial networks in the human brain. In comparison to other primates, the expansion of key regions within the human cerebello-cortical system has undergone the highest evolutionary rate, suggesting a pivotal role of the cerebellum in the cognitive evolution of humans [1]. The cerebellum undergoes rapid growth in the late stages of gestation and continues to develop after birth, establishing long-range connections with the brain through the formation of white matter fibers [2]. However, during the early childhood period, when the cerebellum undergoes continuous development, a clear picture of the evolution of cerebello-cortical connectivity remains elusive. Abnormal connections between the cerebellum and the cerebral cortex have been found to be associated with disorders such as autism spectrum disorder and hyperactivity disorder, which tend to manifest in childhood and are associated with cognitive and motor abnormalities [3]. It is therefore crucial to investigate the fundamental patterns of cerebello-cortical connectivity during early childhood. Here, we map the typical developmental patterns of cerebello-cortical connectivity in early children.
Methods:
Over 1000 BCP scans from 285 healthy participants (M/F:137/148), aged between birth and 5 years were collected in this study [4].
The rs-fMRI data were processed using the following steps [5]: head motion correction, EPI distortion correction, brain extraction, registration to structural MRI, high-pass filtering, ICA-AROMA denoising, and co-registration to MNI space.
Then 2 stages of group independent component analysis (GICA) were used to generate the independent components. Based on the anatomical location of the individual components, 26 components were selected from 35 components and further grouped as 9 cortical functional networks (FNs).
Dual regression was used to obtain individual FNs [6]. For each individual non-overlapping FN, a representative eigen time series was computed. For each cerebellar voxel, partial correlations were computed with respect to all the FN eigen time series. Finally, the correlations were converted to z-scores using Fisher r-to-z transform.
SUIT and R were used for visualization.
The rs-fMRI data were processed using the following steps [5]: head motion correction, EPI distortion correction, brain extraction, registration to structural MRI, high-pass filtering, ICA-AROMA denoising, and co-registration to MNI space.
Then 2 stages of group independent component analysis (GICA) were used to generate the independent components. Based on the anatomical location of the individual components, 26 components were selected from 35 components and further grouped as 9 cortical functional networks (FNs).
Dual regression was used to obtain individual FNs [6]. For each individual non-overlapping FN, a representative eigen time series was computed. For each cerebellar voxel, partial correlations were computed with respect to all the FN eigen time series. Finally, the correlations were converted to z-scores using Fisher r-to-z transform.
SUIT and R were used for visualization.
Results:
We identified 26 components and categorized them into nine brain functional networks, namely the Sensorimotor Network (SMN), Default Mode Network (DMN), Salience Network (SN), Executive Control Network (ECN), Visual Network (VIS), Auditory Network (AUD), Dorsal Attention Network (DAN), Ventral Attention Network (VAN), and Limbic Network. Cerebello-cortical functional connectivity maps across months were derived through partial correlation analyses involving all the specified components.
Generally, the connectivity between the SMN and the cerebellum surpasses that of other cortical brain networks. Particularly, connectivity is more pronounced in Lobule I-IV, Lobule V, and Lobule VIII. Robust connectivity has been identified between the visual network and Lobule VI. Connectivity between higher-order functional networks and the cerebellum is typically weaker in early childhood. Around age one, children display cerebello-cortical connectivity patterns similar to those seen in adults (Figure 1). During childhood, the overall strength of cerebello-cortical connectivity exhibits an ascending trend. Connection between the cerebellum and different brain functional networks develops asynchronously and heterogeneously. Certain brain networks demonstrate gender differences in connectivity with the cerebellum (Figure 2).
Generally, the connectivity between the SMN and the cerebellum surpasses that of other cortical brain networks. Particularly, connectivity is more pronounced in Lobule I-IV, Lobule V, and Lobule VIII. Robust connectivity has been identified between the visual network and Lobule VI. Connectivity between higher-order functional networks and the cerebellum is typically weaker in early childhood. Around age one, children display cerebello-cortical connectivity patterns similar to those seen in adults (Figure 1). During childhood, the overall strength of cerebello-cortical connectivity exhibits an ascending trend. Connection between the cerebellum and different brain functional networks develops asynchronously and heterogeneously. Certain brain networks demonstrate gender differences in connectivity with the cerebellum (Figure 2).
·Figure 1. Cerebello-cortical connectivity during early brain development.
·Figure 2. Developmental trajectories of cerebello-cortical functional connectivity.
Conclusions:
We mapped the cerebello-cortical functional connections from 0 to 5 years of age, capturing the asynchronous and heterogenous patterns of development during this early phase of brain development.
Lifespan Development:
Early life, Adolescence, Aging 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 2
Neuroinformatics and Data Sharing:
Brain Atlases
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Cerebellum
Data analysis
Development
FUNCTIONAL MRI
Motor
MRI
NORMAL HUMAN
Vision
1|2Indicates the priority used for review
Provide references using author date format
[2] Volpe (2009), "Cerebellum of the premature infant: rapidly developing, vulnerable, clinically important," Journal of Child Neurology, 24(9), 1085-1104.
[3] ten Donkelaar et al. (2003), "Development and developmental disorders of the human cerebellum," Journal of Neurology, 250, 1025–1036.
[4] Howell et al. (2019), "The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development," NeuroImage, 185, 891-905.
[5] Thung et al. (2022), “Analysis of ICA-AROMA motion denoising on fMRI data in infant cohort,” OHBM.
[6] Beckmann et al. (2009), “Group Comparison of Resting-State FMRI Data Using Multi-Subject ICA and Dual Regression,” NeuroImage, 47(Suppl 1), S148.