Tract-Geometry Coupling Underlying the Externalization-Internalization Transition in Youth
Poster No:
956
Submission Type:
Abstract Submission
Authors:
Deying Li1, Yufan Wang1, Congying Chu2, Lingzhong Fan3
Institutions:
1Institute of Automation, Chinese Academy of Science, Beijing, China, 2Institute of Automation, Chinese Academy of Sciences, Beijing, China, 3Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Aca, Beijing, China
First Author:
Co-Author(s):
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:
During adolescence, characterized by rapid neurodevelopment and cognitive-behavioral transformations affecting cognition, personality, and mental health, internalizing or externalizing problematic behaviors arise(Fuhrmann, Knoll et al. 2015, Achenbach, Ivanova et al. 2016, Bethlehem, Seidlitz et al. 2022). Some externalizing behaviors are proposed to precede internalizing behaviors, correlating with various adverse developmental outcomes. Meanwhile, the geometry of the cerebral cortex and neural connectivity are acknowledged to mutually influence and undergo adaptations during developmental processes(Vasung, Lepage et al. 2016, Wu, Palaniyappan et al. 2023). Comprehending the dynamics of cortical geometry and connectivity changes can yield insights into the emergence of cognitive functions and behaviors(Giedd, Blumenthal et al. 1999). Our previous research introduced a robust methodology for seamlessly integrating cerebral cortex geometry and neural connectivity. Building on this, we employed this novel structural indicator that combined both gray matter and white matter measurements to comprehensively understand the interplay between brain structure and mental health, especially the internalizing and externalizing transition in adolescence.
Methods:
HCPD dataset (n=590, age=8-22) and IMAGEN datasets (n=647, age=14-22) were used in this research. The same preprocessing methods was applied to both datasets(Li, Wang et al. 2023). 72 tracts were identified by TractSeg (Wasserthal, Neher et al. 2018). The fiber connection fingerprint results from projecting each fiber onto the white surface(Pang, Aquino et al. 2023). Geometric modes of the white surface mesh were computed for each age and utilized to reconstruct the fiber connection fingerprint. Reconstruction coefficients were then extracted, forming the novel tract-geometry coupling (TGC). GAM were applied to plot the trajectory of each TGC over age.
Behavioral symptoms in IMAGEN participants were assessed using screening questions from DAWBA and SDQ, covering externalizing and internalizing symptoms(Xie, Xiang et al. 2023). PLSCanonical(PLSC) analysis of TGC with internalizing and externalizing behavioral symptoms was conducted. To avoid overfitting, we implemented a train/test design, assigning 90% to the training set and 10% to the test set. This study specifically focuses on the first principal component, possessing the largest explainable variance.
Behavioral symptoms in IMAGEN participants were assessed using screening questions from DAWBA and SDQ, covering externalizing and internalizing symptoms(Xie, Xiang et al. 2023). PLSCanonical(PLSC) analysis of TGC with internalizing and externalizing behavioral symptoms was conducted. To avoid overfitting, we implemented a train/test design, assigning 90% to the training set and 10% to the test set. This study specifically focuses on the first principal component, possessing the largest explainable variance.
Results:
We aimed to address two core questions.
1. What's the developmental pattern of TGC in youth?
We used data from the HCPD and IMAGEN datasets to establish and validate the TGC developmental trajectory. The same method was applied in both datasets. Data from HCPD were analyzed using GAM to model the TGC L1 norm trajectory for each tract(Figure 1A), showing most tracts were significantly age-related. To better observe tract patterns, we ranked them based on the first derivative of GAM(Figure 1B). Tracts with higher L1 norm values, indicating stronger geometry coupling, showed faster growth with age. The detailed trajectories of six example tracts are shown in Figure 1C. Notably, tracts supporting language functions(e.g., UF, MLF, AF) showed growth starting at age 14 and peaking at age 19(Figure 1C, left), a trend also seen in IMAGEN(Figure 1C, right).
2. How does TGC relate to mental health during adolescence?
PLSC was used to link behavioral symptoms with TGC in IMAGEN at ages 14 and 19. TGC was categorized into static TGC and dynamic TGC(Figure 2A). Both static and dynamic TGC were associated with psychiatric symptoms at both ages(Figure 2B). Specifically, static and dynamic TGC were linked to externalizing symptoms at age 14, while only dynamic TGC was associated with internalizing symptoms at age 19(Figure 2C).
1. What's the developmental pattern of TGC in youth?
We used data from the HCPD and IMAGEN datasets to establish and validate the TGC developmental trajectory. The same method was applied in both datasets. Data from HCPD were analyzed using GAM to model the TGC L1 norm trajectory for each tract(Figure 1A), showing most tracts were significantly age-related. To better observe tract patterns, we ranked them based on the first derivative of GAM(Figure 1B). Tracts with higher L1 norm values, indicating stronger geometry coupling, showed faster growth with age. The detailed trajectories of six example tracts are shown in Figure 1C. Notably, tracts supporting language functions(e.g., UF, MLF, AF) showed growth starting at age 14 and peaking at age 19(Figure 1C, left), a trend also seen in IMAGEN(Figure 1C, right).
2. How does TGC relate to mental health during adolescence?
PLSC was used to link behavioral symptoms with TGC in IMAGEN at ages 14 and 19. TGC was categorized into static TGC and dynamic TGC(Figure 2A). Both static and dynamic TGC were associated with psychiatric symptoms at both ages(Figure 2B). Specifically, static and dynamic TGC were linked to externalizing symptoms at age 14, while only dynamic TGC was associated with internalizing symptoms at age 19(Figure 2C).
Conclusions:
Our study mapped the developmental trajectory of TGC in youth and revealed that TGC supports the transition between externalizing and internalizing symptoms during adolescence, particularly the age-dependent TGC.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism)
Lifespan Development:
Early life, Adolescence, Aging 1
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Keywords:
Cortex
Development
Tractography
White Matter
1|2Indicates the priority used for review
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Provide references using APA citation style.
Bethlehem, R. A. I., Seidlitz, J., White, S. R., Vogel, J. W., Anderson, K. M., Adamson, C., . . . Alexander-Bloch, A. F. (2022). Brain charts for the human lifespan. Nature, 604(7906), 525-533. doi:10.1038/s41586-022-04554-y
Fuhrmann, D., Knoll, L. J., & Blakemore, S. J. (2015). Adolescence as a Sensitive Period of Brain Development. Trends Cogn Sci, 19(10), 558-566. doi:10.1016/j.tics.2015.07.008
Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Liu, H., Zijdenbos, A., . . . Rapoport, J. L. (1999). Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci, 2(10), 861-863. doi:10.1038/13158
Li, D., Wang, Y., Ma, L., Shi, W., Lu, Y., Wang, H., . . . Fan, L. (2023). doi:10.1101/2023.09.06.556618
Pang, J. C., Aquino, K. M., Oldehinkel, M., Robinson, P. A., Fulcher, B. D., Breakspear, M., & Fornito, A. (2023). Geometric constraints on human brain function. Nature, 618(7965), 566-574. doi:10.1038/s41586-023-06098-1
Vasung, L., Lepage, C., Rados, M., Pletikos, M., Goldman, J. S., Richiardi, J., . . . Kostovic, I. (2016). Quantitative and Qualitative Analysis of Transient Fetal Compartments during Prenatal Human Brain Development. Front Neuroanat, 10, 11. doi:10.3389/fnana.2016.00011
Wasserthal, J., Neher, P., & Maier-Hein, K. H. (2018). TractSeg - Fast and accurate white matter tract segmentation. Neuroimage, 183, 239-253. doi:10.1016/j.neuroimage.2018.07.070
Wu, X., Palaniyappan, L., Yu, G., Zhang, K., Seidlitz, J., Liu, Z., . . . Zhang, J. (2023). Morphometric dis-similarity between cortical and subcortical areas underlies cognitive function and psychiatric symptomatology: a preadolescence study from ABCD. Mol Psychiatry, 28(3), 1146-1158. doi:10.1038/s41380-022-01896-x
Xie, C., Xiang, S., Shen, C., Peng, X., Kang, J., Li, Y., . . . Consortium, Z. I. B. (2023). A shared neural basis underlying psychiatric comorbidity. Nat Med, 29(5), 1232-1242. doi:10.1038/s41591-023-02317-4