Tracking complexity drops in intrinsic brain activity during neurodevelopment
Poster No:
988
Submission Type:
Abstract Submission
Authors:
Haoluo Liang1, Kristoffer Madsen2, Congying Chu3, Lingzhong Fan4
Institutions:
1Institute of Automation,Chinese Academy of Sciences, Beijing, China, 2Department of Applied Mathematics and Computer Science, Technical University of Denmark, Copenhagen, Denmark, 3Institute of Automation, Chinese Academy of Sciences, Beijing, China, 4Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Aca, Beijing, China
First Author:
Co-Author(s):
Kristoffer Madsen
Department of Applied Mathematics and Computer Science, Technical University of Denmark
Copenhagen, Denmark
Department of Applied Mathematics and Computer Science, Technical University of Denmark
Copenhagen, Denmark
Lingzhong Fan
Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Aca
Beijing, China
Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Aca
Beijing, China
Introduction:
Understanding the development of brain function from a dynamic perspective is crucial (Faghiri et al., 2018). However, the dynamic patterns of intrinsic brain activity during neurodevelopment have not been thoroughly explored. Recent advancements in information theory provide effective methods for measuring information within time series data, particularly by identifying events of complexity drop over time (Krohn et al., 2023). Furthermore, research on disorders such as schizophrenia has shown significant reductions in complexity in specific brain regions, suggesting that lower complexity in brain functional activity may be related to lower information processing capability (Liu et al., 2024).
Building on this evidence, we hypothesize that complexity drops in dynamic intrinsic brain activity are indicative of information suppression processes. Investigating these drops can offer insights into how functional complexity relates to brain maturation. Therefore, in our study, we quantified the frequency of complexity drops (fdrop) to capture the dynamics of intrinsic brain activity and analyzed its developmental patterns during neurodevelopment.
Building on this evidence, we hypothesize that complexity drops in dynamic intrinsic brain activity are indicative of information suppression processes. Investigating these drops can offer insights into how functional complexity relates to brain maturation. Therefore, in our study, we quantified the frequency of complexity drops (fdrop) to capture the dynamics of intrinsic brain activity and analyzed its developmental patterns during neurodevelopment.
Methods:
This study used fMRI data from the Human Connectome Project: Development (HCP-D) involving 621 participants aged 8–21 years (335 females) (Harms et al., 2018). Data were preprocessed with Human Connectome Project's minimal preprocessing pipelines (Glasser et al., 2013), bandpass filtered (0.01–0.1 Hz), and z-scored per vertex. To assess the complexity time series, we applied sliding windows (48 seconds, 2.4-second steps) and calculated weighted permutation entropy (WPE) for each vertex. Drop frequency (fdrop) was identified using scipy's find_peaks() function (Virtanen et al., 2020) and averaged across regions. A Generalized Additive Model (GAM) assessed the effects of age and developmental trajectories on regional fdrop, controlling for inner-scanner motion and sex. The impact of age was determined by comparing R² values of models with and without the age term. Developmental patterns from ages 8 to 21 were analyzed in 168 monthly intervals by computing Spearman correlations between the sensorimotor-association (S-A) axis (Sydnor et al., 2021) and the GAM model's first derivative across the ages. We generated 1,000 GAM models per region by posterior sampling to derive correlation distributions and used median correlations with 95% credible intervals to assess the alignment of fdrop developmental changes with the S-A axis.
Results:
As illustrated in Figure 1 panels A and B, our study identified a significant decrease in fdrop as the brain matures (ANOVA F = 21.29, p < 0.001), indicating enhanced information processing capacity. Notably, the decline in fdrop within unimodal networks is steeper than in transmodal networks, highlighting distinct developmental patterns across different brain regions (Figure 1C). Further analysis using GAM for each region revealed that the development of fdrop is systematically organized along the S-A axis (r = 0.325, pspin = 0.026) corrected for multiple comparisons using a spin test, as shown in Figure 2A,B. Specifically, regions with lower rankings along the S-A axis, closer to the sensorimotor pole, exhibit larger reductions in fdrop. This relationship becomes increasingly pronounced as the brain matures (Figure 2D–F).
Conclusions:
We found that complexity drop frequency decreases during neurodevelopment, with the rate of decline influenced by the S-A axis. Regions near the sensorimotor pole show a faster reduction. We also found that the developmental rate progressively aligns with the S-A axis, indicating that the S-A axis constrains the dynamics of intrinsic brain activity during neurodevelopment. These results enhance our understanding of brain development and offer biomarkers for tracking neurodevelopment.
Lifespan Development:
Early life, Adolescence, Aging 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Normal Development
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
Data analysis
Development
FUNCTIONAL MRI
Other - Complexity
1|2Indicates the priority used for review
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Harms, M. P., Somerville, L. H., Ances, B. M., Andersson, J., Barch, D. M., Bastiani, M., ... & Yacoub, E. (2018). Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects. Neuroimage, 183, 972-984.
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