The Development of White Matter Tract Functional Segregation during Adolescence
Poster No:
2203
Submission Type:
Abstract Submission
Authors:
Min Lan1, Zaixu Cui2, Suyu Zhong1
Institutions:
1Beijing University of Posts and Telecommunications, Beijing, China, 2Chinese Institute for Brain Research, Beijing, China
First Author:
Co-Author(s):
Introduction:
Due to the advancement of diffusion MRI technology, there are a large number of studies investigating the development of white matter (WM) microstructure during adolescence [7]. However, limited by the unclear physiological significance of WM functional signals, few research has reported on WM functional development. In recent years, more and more studies have demonstrated that WM functional signals reflect the functional activity of specific tracts [1, 2]. In human brain functional connectomes, functional segregation is a fundamental organizational principles, which has been observed to strengthen with age in distinct cortical networks during adolescence [6]. To understand the development of WM tracts functional segregation during adolescence, we characterized the normative development trajectories of functional segregation of WM tracts during adolescence by utilizing the HCP-D dataset and GAMLSS model, and explore whether gender affects the development trajectories of different WM tracts.
Methods:
608 healthy subjects from HCP-development (HCP-D) datasets were included, including structural MRI and resting state fMRI (ages: 8-21) [4]. Data post-processing were performed by xcp_abcd. To quantify the segregation of WM tracts, we calculated a segregation index at WM tract-level [6]. More specifically, the WM tracts were defined by the rICBM_DTI_81_WMPM_90p_FMRIB58 WM atlas [3]. The segregation index was defined as (FCSw - FCSb) / FCSw, where FCSw (within-tract functional connectivity strength) was defined as the averaged FCS among all voxels within tract and FCSb (between-tract functional connectivity strength) was the averaged FCS between this tract and all other tracts. To estimate the normative age-dependent curves for functional segregation of tracts during adolescence, we implemented the GAMLSS [5] using gamlss package in R. Specifically, we constructed the GAMLSS procedure with the segregation index as the dependent variable, age as a smooth term ( B-spline basis function, df = 3), sex and mean frame displacement as other fixed effects, and Johnson's Su (JSU) distribution as the data distribution. Finally, we added sex to form an interaction term with age to explore the sexual differences in development trajectories of functional segregation.
Results:
After excluding tracts with less than 40 voxels and correcting for multiple comparisons, the segregation index of 27 tracts was found to be significantly correlated with age (FDR p < 0.05). Fig. 1A showed the averaged tract segregation value for each age group. The development trajectories and change rate of all the 27 tracts were characterized in Fig. 1B and 1D. After z-score normalization, we can find that the development trajectories present three different trends (Fig. 1E), that is (1) "decreased-increased-decreased" development pattern, (2) "U-shaped"development pattern, (3) continuing decreased development pattern. However, in all the three development trajectories, the value of the segregation index was always greater than zero, indicating that between-tract functional connectivity was always stronger than within-tract functional connectivity. In addition, differentiated development of the WM functional segregation mainly appeared from 15 years old (Fig. 1F). Finally, sexual differences were not found in the functional segregation development trajectories of tracts (Fig. 2) .
Conclusions:
Our study revealed the development of WM tracts' functional segregation with age in adolescence. Specifically, three different development trajectories were found and the differentiated development were mainly appeared in the ages of 15 to 20. Besides, there was no sexual differences in the development trajectory of tract functional segregation. These findings might provide new insights about the WM functional development.
Lifespan Development:
Early life, Adolescence, Aging 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 1
Keywords:
Development
FUNCTIONAL MRI
NORMAL HUMAN
White Matter
Other - Adolescence
1|2Indicates the priority used for review
Provide references using author date format
[2] Gore, J.C. (2019), 'Functional MRI and resting state connectivity in white matter-a mini-review', Magnetic resonance imaging, 63: 1-11.
[3] Oishi, K. (2008), 'Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter', Neuroimage, 43(3): 447-457.
[4] 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.
[5] Stasinopoulos, D.M. (2008), 'Generalized additive models for location scale and shape (GAMLSS) in R', Journal of Statistical Software, 23: 1-46.
[6] Sun L. (2023), 'Functional connectome through the human life span', bioRxiv, 2023: 2023.09. 12.557193.
[7] Tamnes, C.K. (2018), 'Diffusion MRI of white matter microstructure development in childhood and adolescence: Methods, challenges and progress', Developmental cognitive neuroscience, 33: 161-175.