Frontoparietal Control Network Development and Executive Function
Poster No:
1200
Submission Type:
Abstract Submission
Authors:
Jing Cong1,2,3, Xiaoyu Xu1,2,3, Hang Yang1,2, Zaixu Cui1,2
Institutions:
1Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China, 2Chinese Institute for Brain Research, Beijing, China, 3State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
First Author:
Jing Cong
Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College|Chinese Institute for Brain Research|State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College|Chinese Institute for Brain Research|State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
Co-Author(s):
Xiaoyu Xu
Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College|Chinese Institute for Brain Research|State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College|Chinese Institute for Brain Research|State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
Hang Yang
Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College|Chinese Institute for Brain Research
Beijing, China|Beijing, China
Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College|Chinese Institute for Brain Research
Beijing, China|Beijing, China
Zaixu Cui
Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College|Chinese Institute for Brain Research
Beijing, China|Beijing, China
Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College|Chinese Institute for Brain Research
Beijing, China|Beijing, China
Introduction:
The frontoparietal control network (FPCN), comprising regions such as the dorsolateral prefrontal cortex and posterior parietal cortex, plays a pivotal role in brain development and executive function (EF) (Keller, 2023; Marek, 2018; Menon, 2022). However, the functional connectivity (FC) between specific subregions of the frontal and parietal regions within the FPCN remains insufficiently understood. To address these gaps, our study focuses on the FC linking the frontal and parietal subregions within the FPCN, aiming to elucidate their roles in neurodevelopment and EF.
Methods:
We utilized the functional MRI (fMRI) data from two datasets: we acquired 366 participants (171 males, ages 8.1–21.9 years) from the public Human Connectome Project in Development (HCP-D) (Somerville et al., 2018), and recruited 179 typically developing participants (88 males, ages 6.5–22.3 years) in Beijing, which is a part of the Youth Executive Function and Neurodevelopment (YEN) dataset. The FPCN was defined according to the Yeo atlas (Yeo, 2011). Based on this definition, we parcellated lateral frontal and parietal regions of interest (ROIs) using both the 100- and 400-parcel Schaefer atlas (Schaefer, 2018). For each participant, FC was calculated by computing Pearson correlation coefficients between the time series of frontal and parietal ROIs within the same hemisphere. To investigate developmental effects on FC, we applied generalized additive models (GAM) (Wood, 2004) separately to each dataset, with age as the smooth term and controlling sex, and mean framewise displacement (FD). Additionally, GAMs were also used to examine the relationships between FC and EF, controlling the smooth term of age, sex, and FD.
Results:
Using the Schaefer 100-parcel atlas, we found 5 edges in the HCP-D dataset (Figure 1E) and 6 edges in the YEN dataset (Figure 1I) with significant age-related developmental effects (P < 0.05, FDR adjusted), with 3 edges overlapping between two datasets. While using the Schaefer 400 parcels atlas, we identified 18 edges in the HCP-D dataset (Figure 1F) and 40 edges in the YEN dataset (Figure 1J) exhibiting significant developmental effects (P < 0.05, FDR adjusted), with 5 edges consistently observed across both datasets.
We further refined the FC of the FPCN by subdividing it into more specific FCs using the finer parcellations of the 400-parcel atlas. Interestingly, among the FCs that exhibited significant developmental effects in the 100-parcel atlas, only a subset of connections between specific subregions retained significant developmental effects in the 400-parcel atlas. This suggests that the developmental effects observed in the coarser parcellations are primarily driven by the FCs between these specific subregions identified in the finer parcellations. Examples were presented in Figure 1G, H, K, L. These findings suggest that finer parcellations, allow for a more precise identification of subregional connectivity developmental patterns within networks, highlighting the heterogeneity of within-network edges.
Additionally, we found several connections that were specifically associated with cognitive flexibility across both parcellation (Figure 1C, D).
We further refined the FC of the FPCN by subdividing it into more specific FCs using the finer parcellations of the 400-parcel atlas. Interestingly, among the FCs that exhibited significant developmental effects in the 100-parcel atlas, only a subset of connections between specific subregions retained significant developmental effects in the 400-parcel atlas. This suggests that the developmental effects observed in the coarser parcellations are primarily driven by the FCs between these specific subregions identified in the finer parcellations. Examples were presented in Figure 1G, H, K, L. These findings suggest that finer parcellations, allow for a more precise identification of subregional connectivity developmental patterns within networks, highlighting the heterogeneity of within-network edges.
Additionally, we found several connections that were specifically associated with cognitive flexibility across both parcellation (Figure 1C, D).
Conclusions:
Overall, our findings reveal that specific FC within the FPCN exhibited significant developmental effects and cognitive association. By identifying these specific connections, we establish a foundation for guiding targeted neuromodulation strategies aimed at regulating brain activity, enhancing EF, and ultimately fostering healthy cognitive development.
Lifespan Development:
Early life, Adolescence, Aging 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Keywords:
Cognition
Development
FUNCTIONAL MRI
MRI
Other - Frontoparietal Control Network
1|2Indicates the priority used for review
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Provide references using APA citation style.
Marek, S. (2018). The frontoparietal network: function, electrophysiology, and importance of individual precision mapping. Dialogues in clinical neuroscience, 20(2), 133-140.
Menon, V. (2022). The role of PFC networks in cognitive control and executive function. Neuropsychopharmacology, 47(1), 90-103.
Schaefer, A. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral Cortex, 28(9), 3095-3114.
Somerville, L. H. (2018). The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5–21 year olds. Neuroimage, 183, 456-468.
Wood, S. N. (2004). Stable and efficient multiple smoothing parameter estimation for generalized additive models. Journal of the American Statistical Association, 99(467), 673-686.
Yeo, B.T. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125-1165.