Network Structural Consistency Reveals the Underlying Regularity of Human Brain Functional Networks
Poster No:
1431
Submission Type:
Abstract Submission
Authors:
Haoda Ren1, Qianyuan Tang1
Institutions:
1Department of Physics, Hong Kong Baptist University, Kowloon, Hong Kong
First Author:
Co-Author:
Introduction:
The functional network of the human brain is a highly ordered organization, characterized by properties such as small-worldness, rich-club structures, and the exist of hub nodes (Van den Heuvel,2011). Previous studies have attempted to identify key nodes or edges in resting-state functional brain networks and further explore their architectural relationships (Seguin, C.,2023). However, the relationship between direct and indirect pathways in network wiring remains unclear, particularly whether there is a consistent relationship at the node level. In this study, we propose a novel method called "network structural consistency" based on network predictability theory and apply it to resting-state functional magnetic resonance imaging (fMRI) data to address this issue.
Methods:
In link prediction, the predictability of brain networks reflects how effectively direct connections can be represented through higher-order neighbors, indicating network structural consistency (Kovács,2019; Lü, L.,2015). We hypothesized that functional connectivity patterns follow heterogeneous principles characterizable through higher-order neighbor relationships. To test this, we evaluated regional deviations from global connectivity patterns by incorporating higher-order indirect links as predictors and using least squares estimation (LSE) to predict original functional connectivity. We quantified prediction accuracy through Pearson similarity between predicted and original functional connections, enabling identification of heterogeneous nodes and connections. The methodology was validated using resting-state fMRI data from 700 healthy adults (HCP dataset) and subsequently applied to the NYU dataset to examine differences between ASD patients and controls.
Results:
We analyzed resting-state fMRI data from 700 healthy adults from the HCP dataset (Van Essen,2013; Schaefer,2018). The results showed that functional connections predicted using only second-order neighbor information achieved a similarity of over 0.85 with the original functional connections. The prediction similarity increases continuously as the number of higher-order neighbor predictors increases. The original functional network was predicted using order 1 to 12 neighbor information with similarity exceeding 0.99. Additionally, we found that at lower-order neighbor information levels, regions with the greatest deviations in structural consistency from the whole-brain network were concentrated in the somatomotor networks (SMN) and default mode networks (DMN)(Yeo, B. T.,2011), displaying a distinct separation pattern (Margulies,2016). As higher-order neighbors information were introduced, this separation pattern gradually weaken, eventually stabilizing into a consistent pattern that primarily reflected segregation within higher-order systems. This suggests that the differences in connectivity between the SMN and DMN are embedded in low-order neighbor information.
Furthermore, we investigated the impact of autism spectrum disorder (ASD) on brain functional connectivity patterns using the NYU dataset (Di Martino,2014), which included 59 health controls (HC) and 54 ASD patients. The results showed that in low-order neighbor network information, structural consistency differences in ASD were significantly pronounced in the visual networks and somatomotor networks.
Furthermore, we investigated the impact of autism spectrum disorder (ASD) on brain functional connectivity patterns using the NYU dataset (Di Martino,2014), which included 59 health controls (HC) and 54 ASD patients. The results showed that in low-order neighbor network information, structural consistency differences in ASD were significantly pronounced in the visual networks and somatomotor networks.
·The correlation between the predicted functional network A* (with different maximum polynomial terms N) and the original functional network A across different datasets.
·The correction between A* with different N in HCP dataset.The distribution of structural consistency in the brain region nodes when N=2,6,11,16 is indicated below.
Conclusions:
The realization of human brain function relies not only on segregation between brain regions but also on effective integration across different systems. In this study, we combined link prediction theories with fMRI data to reveal the organizational principles of structural consistency in resting-state functional networks. We discovered the relationship between higher-order neighbor connections and direct connections, further elucidating the impact of autism on network structural consistency. This study provides new insights into the organizational principles of human brain and offers implications for understanding the mechanisms underlying mental disorders.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Keywords:
Autism
MRI
Other - Network structural consistency
1|2Indicates the priority used for review
Abstract Information
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