Functional connectome through the human life span

1269 

Abstract Submission 

Lianglong Sun¹, Tengda Zhao¹, Xinyuan Liang¹, Mingrui Xia¹, Qiongling Li¹, Xuhong Liao², Gaolang Gong¹, Qian Wang¹, Chenxuan Pang¹, Qian Yu¹, Yong He¹

¹State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, ²School of Systems Science, Beijing Normal University, Beijing, China

Lianglong Sun  
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

Tengda Zhao  
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

Xinyuan Liang  
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

Mingrui Xia  
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

Qiongling Li  
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

Xuhong Liao  
School of Systems Science, Beijing Normal University
Beijing, China

Gaolang Gong  
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

Qian Wang  
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

Chenxuan Pang  
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

Qian Yu  
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

Yong He  
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

The emergence, development, and aging of the intrinsic connectome architecture enables the dynamic reorganization of functional specialization and integration throughout the lifespan, contributing to continuous changes in human cognition and behavior (Zuo et al., 2017). Understanding the spatiotemporal growth process of the typical functional connectome is critical for elucidating network-level developmental principles in healthy individuals and for pinpointing periods of heightened vulnerability or potential. The growth chart framework provides an invaluable tool for charting normative reference curves in the human brain (Bethlehem et al., 2022; Rutherford et al., 2022). However, the normative growth trajectory of the functional brain connectome across the human lifespan remains unknown.

We employed a comprehensive data quality control framework that combined automated assessment tools and expert manual review to assess both structural and functional images across all 45,525 scans. The final sample included 33,809 scans (N = 32,328) with high-quality images from 119 sites (Fig. 1a). Using the standardized and highly uniform processing pipeline, we obtained the surface-based preprocessed BOLD signals in fsaverage4 space for each individual. We then constructed a vertex-wise functional connectome matrix by calculating the Pearson correlation coefficient between the time courses of each vertex. We examined the individual connectome at the whole-brain, system, and regional levels, harmonizing all measures across sites. Guided by the WHO recommendation (Borghi et al., 2006), we used GAMLSS to elucidate the age-related nonlinear trajectories for healthy populations, with sex and in-scanner head motion as fixed effects. To assess the rate of change and inflection points, we calculated first derivatives of the trajectories. By proposing a Gaussian-weighted iterative age-specific group atlas generation approach, we established a set of continuous growth atlases with accurate system correspondences across the life course.

The lifespan curve of global mean functional connectivity (Fig. 1c) showed a nonlinear increase from 32 postmenstrual weeks onward, peaking at 40.0 years (95% CI 39.4-40.5), followed by a nonlinear decline. The peak of the increased rate of growth occurred at 17.8 years (95% CI 14.8-20.0), while the maximum rate of decline was observed at 57.4 years (95% CI 55.8-59.9). Global variance in whole-brain functional connectivity (Fig. 1d) also showed a nonlinear growth pattern, peaking in adulthood at 34.7 years (95% CI 32.4-37.2), with maximum rates of increase and decline occurring at 32 postmenstrual weeks and 59.0 years (95% CI 57.4-62.9), respectively. Consistent with the developmental pattern of the age-specific atlas (Fig. 2a, 2b), the normative growth trajectories showed that the similarity of the individualized atlas to the reference increased from 32 postmenstrual weeks, peaked at 31.7 years (95% CI 30.7-32.6), remained stable until 54.1 years (95% CI 53.7-54.6), and then continuously declined at an accelerated rate until 80 years of age (Fig. 2c). Whole-brain system segregation across all systems peaked at 24.7 years (95% CI 23.0-26.0) and showed a more pronounced accelerated decline around the sixth decade of life (Fig. 2d). Different networks manifested heterochronous growth patterns (Fig. 2e). Lifespan growth of functional connectivity at the regional level reveals a spatial gradient pattern (Fig. 2f, 2g).

Through systematic analysis at the whole-brain, system, and regional levels, we charted the multiscale, nonlinear trajectories of functional connectome and revealed previously unidentified key growth milestones. We created the lifespan age-specific atlases, serving as a foundational resource for future research on brain network development and aging.

Early life, Adolescence, Aging

Lifespan Development Other ¹

Connectivity (eg. functional, effective, structural) ²

fMRI Connectivity and Network Modeling

Segmentation and Parcellation

FUNCTIONAL MRI

Other - lifespan, brain chart, brain atlas, connectomics