Developmental frequency landscape of spontaneous brain activity from childhood to adolescence
Poster No:
978
Submission Type:
Abstract Submission
Authors:
Zhu-Qing Gong1, Xi-Nian Zuo2
Institutions:
1Beijing Normal University, Beijing, Beijing, 2State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, Beijing
First Author:
Co-Author:
Xi-Nian Zuo
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, Beijing
Introduction:
Neuronal activity involves neural oscillations across diverse frequency bands, each with distinct functional properties and measurable at various scales (Buzsaki and Draguhn, 2004). Blood oxygen level-dependent (BOLD) oscillations, recorded via fMRI, encompass six slow frequency bands, from slow-1 to slow-6 (Zuo et al., 2010). In healthy adults, BOLD oscillations show hierarchical organization across both frequency and spatial domains, with lower frequencies linked to higher-order processes such as integration and complexity (Gong and Zuo, 2023). Childhood and adolescence are critical periods for brain development, characterized by extensive functional reorganization (Dong et al., 2021; 2024). However, the developmental trajectory of BOLD oscillation organization during this period remains unclear. This study aims to characterize the developmental patterns of functional organization across multiple frequency bands from childhood to adolescence. We also explored the relationship between intelligence and the development of multi-band brain functions by grouping children based on intelligence scores.
Methods:
We analyzed resting-state fMRI data from 381 children (6–18 years) from the Chinese Color Nest Project (CCNP) (Fan et al., 2023). The data were divided into annual age groups. After preprocessing, BOLD oscillations were decomposed into three frequency bands: slow-3 (0.082–0.200 Hz), slow-4 (0.031–0.082 Hz), and slow-5 (0.013–0.031 Hz). Functional connectivity gradient analysis was performed for each age group and frequency band. To track developmental changes, we calculated spatial correlations of gradient distributions between age groups for each frequency band. Additionally, children were grouped by intelligence scores: low (<-1 SD), medium (±1 SD), and high (>+1 SD). Multi-band functional connectivity gradient patterns were mapped for each intelligence group across all ages.
Results:
We focused on the first two gradients, which explain most of the variance. Across frequency bands, gradient development can be divided into three stages. In stage 1, the first gradient showed functional segmentation of sensory and motor regions, while the second gradient reflected integration from primary to associative regions. Stage 2 exhibited mixed transitions for both gradients, and stage 3 showed reversed gradient distributions. Overall, the first gradient matured earlier than the second, suggesting that functional segmentation develops continuously even after functional integration matures. The three frequency bands showed different developmental rates. For the first gradient, slow-3 matured first, followed by slow-5 and slow-4. For the second gradient, slow-5 matured first, then slow-3, and slow-4 last. These results align with the known roles of these frequency bands in sensory integration, executive functions, and self-related functions (Gong and Zuo, 2023). Fig. 2 shows multi-band gradient distributions at age 12 for each intelligence group. Children in the low-intelligence group showed slow-3 and slow-5 gradients remaining in stage 1 until age 16. In contrast, high-intelligence children entered stage 3 by age 6. Slow-4 gradients did not show consistent trends across groups. Interestingly, the medium-intelligence group consistently reached stage 3 in all frequency bands by age 12.
·Fig. 1 Developmental stages of the first (A) and second (B) gradient at different slow bands.
·Fig. 2 Patterns of multi-band cortical gradients at age 12 across the three IQ groups.
Conclusions:
This study maps the developmental patterns of BOLD oscillations across frequency bands, revealing both phased and continuous changes in brain function. The divergent developmental trajectories across frequency bands provide evidence of the frequency specificity of brain functions. Furthermore, the differences in developmental stages across frequency bands are partially explained by intelligence scores.
Higher Cognitive Functions:
Higher Cognitive Functions Other 2
Lifespan Development:
Early life, Adolescence, Aging 1
Keywords:
Development
FUNCTIONAL MRI
Other - Frequency; Intelligence; FC Gradient
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?
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:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Dong, H.-M., et al. (2023). "Shifting gradients of macroscale cortical organization mark the transition from childhood to adolescence." Proceedings of the National Academy of Sciences of the United States of America 118(28): e2024448118.
Dong, H.-M., et al. (2024). "Ventral attention network connectivity is linked to cortical maturation and cognitive ability in childhood." Nature Neuroscience 27: 2009–2020.
Fan, X.-R., et al. (2023). "A longitudinal resource for population neuroscience of school-age children and adolescents in China." Scientific Data 10(1).
Gong, Z.-Q. and X.-N. Zuo (2023). "Connectivity gradients in spontaneous brain activity at multiple frequency bands." Cerebral Cortex 33(17): 9718-9728.
Zuo, X. N., et al. (2010). "The oscillating brain: Complex and reliable." Neuroimage 49(2): 1432-1445.