Two Axes of White Matter Tract Development
Poster No:
1010
Submission Type:
Abstract Submission
Authors:
Audrey Luo1, Steven Meisler1, Valerie Sydnor2, Aaron Alexander-Bloch1, Joëlle Bagautdinova1, Deanna Barch3, Dani Bassett1, Christos Davatzikos4, Alexandre Franco5, Jeff Goldsmith6, Raquel Gur1, Ruben Gur1, Fengling Hu1, Marc Jaskir1, Greg Kiar7, Arielle Keller8, Bart Larsen9, Allyson Mackey1, Michael Milham7, David Roalf1, Golia Shafiei1, Russell Shinohara1, Leah Somerville10, Sarah Weinstein11, Jason Yeatman12, Matthew Cieslak1, Ariel Rokem13, Theodore Satterthwaite1
Institutions:
1University of Pennsylvania, Philadelphia, PA, 2University of Pittsburgh, Pittsburgh, PA, 3Washington University, Saint Louis, MO, 4Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 5Nathan Kline Institute, Orangeburg, NY, 6Columbia University, New York, NY, 7Child Mind Institute, New York, NY, 8University of Connecticut, Mansfield, CT, 9University of Minnesota, Minneapolis, MN, 10Harvard University, Cambridge, MA, 11Temple University, Philadelphia, PA, 12Stanford University, Stanford, CA, 13University of Washington, Seattle, CA
First Author:
Co-Author(s):
Introduction:
White matter (WM) undergoes protracted changes in youth, supporting communication between spatially distributed cortices. Prior WM development studies have typically averaged microstructure within tracts, obscuring variability along their length. This is a major limitation in studying WM development, as myelination has been shown to vary along axons, which can impact neural transmission (Tomassy, 2014). Further, cortical development occurs hierarchically along the sensorimotor-association (S-A) axis (Luo, 2024; Sydnor, 2023), which may interact with WM development via activity-dependent plasticity (Hines, 2015). Using three large-scale datasets (total N=2,722), we show that WM development aligns with two distinct axes: 1) WM develops along a deep-to-peripheral axis within tracts, and 2) development at the tract periphery aligns with the cortical hierarchy defined by the S-A axis.
Methods:
We used diffusion MRI data from the Philadelphia Neurodevelopmental Cohort (PNC; N= 1,101; ages 8-23; discovery), Human Connectome Project: Development (HCP-D; N=568; ages 8-22; replication), and Healthy Brain Network (HBN; N=1,053; ages 5-22; generalization). Images were processed with QSIPrep and QSIRecon(Cieslak, 2021), using MRtrix3(Tournier, 2012) for tractography and pyAFQ for bundle segmentation(Kruper, 2021). Mean diffusivity (MD) was measured at 100 equidistant nodes along each tract. To model linear and non-linear associations between age and MD at each node, we fit generalized additive models, with age as a smooth term and sex and head motion as covariates. We computed age effect magnitude as the absolute difference in adjusted R2 between a full and reduced model without age. Age of maturation was defined as the earliest age at which the rate of developmental change was no longer significant. To assess if age effects were enriched in peripheral compared to deep WM, we used network enrichment significance testing (Weinstein, 2024). To study peripheral WM maturation, we identified cortical endpoints for each tract and parcellated each dataset's tract-to-cortex probability map with the HCP-MMP atlas. We compared age of maturation of WM adjacent to each endpoint to its S-A axis rank. A one-tailed t-test assessed whether tracts with larger age of maturation differences between endpoints had greater S-A rank differences. Significance was assessed via spin test permutation of the t-statistic based on spun S-A axis maps. Parcel-level cortical maps were created by averaging age of maturation maps across endpoints for each dataset. Spearman's correlations quantified the association between maturation age of matured endpoints and S-A axis rank. Significance was assessed using spin tests (Alexander-Bloch, 2018).
Results:
WM microstructural development occurs along a deep-to-peripheral axis in all datasets (Fig. 1). The MD age effect magnitude varies continuously along callosal tracts, association tracts, and corticospinal tracts. In nearly all tracts across datasets, age effects were significantly enriched in peripheral WM compared to deep WM (QFDR < 0.05). Peripheral WM in tracts connecting homologous cortical endpoints follows similar developmental patterns (Fig. 2a) but diverges for non-homologous endpoints (Fig. 2b). For each tract, cortical endpoints at opposite ends of the cortical hierarchy show greater differences in age of maturation (Fig. 2c, PNC: t(4.3) = -4.4, pspin = 0.003; HCP-D: t(5.03) = -16.4, pspin < 0.0001; HBN: t(7.3) = -2.0, pspin = 0.0172). Collapsing across endpoints, peripheral WM age of maturation significantly aligns with the S-A axis (Fig. 2d, PNC: r = 0.63, pspin < 0.0001; HCP-D: r = 0.43, pspin = 0.00015; HBN: r = 0.44, pspin < 0.0001).
·Figure 1
·Figure 2
Conclusions:
These results define two axes of WM development in youth. Deep WM regions may myelinate earlier to insulate a given tract from others carrying different neuronal signals. In contrast, peripheral WM development occurs along the cortical hierarchy and may dynamically interact with cortical development.
Lifespan Development:
Early life, Adolescence, Aging
Normal Brain Development: Fetus to Adolescence 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Normal Development
White Matter Anatomy, Fiber Pathways and Connectivity 2
Novel Imaging Acquisition Methods:
Diffusion MRI
Keywords:
Development
Modeling
Open Data
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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Provide references using APA citation style.
Cieslak, M., et al. (2021), QSIPrep: An integrative platform for preprocessing and reconstructing diffusion MRI data. Nature Methods, 18(7), Article 7.
Hines, J.H., et al. (2015), Neuronal activity biases axon selection for myelination in vivo. Nature Neuroscience, 18(5), 683–689.
Kruper, J., et al. (2021), Evaluating the Reliability of Human Brain White Matter Tractometry. Aperture Neuro, 1(1).
Luo, A.C., et al. (2024), Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy. Nature Communications, 15(1), 3511.
Sydnor, V.J., et al. (2023), Intrinsic activity development unfolds along a sensorimotor–association cortical axis in youth. Nature Neuroscience, 26(4), 638-649.
Tomassy, G.S., et al. (2014), Distinct Profiles of Myelin Distribution Along Single Axons of Pyramidal Neurons in the Neocortex. Science, 344(6181), 319–324.
Tournier, J.D., et al. (2012), MRtrix: Diffusion tractography in crossing fiber regions. International Journal of Imaging Systems and Technology, 22(1), 53–66.
Weinstein, S.M., et al. (2024), Network Enrichment Significance Testing in Brain-Phenotype Association Studies. Human Brain Mapping, 45(8), e26714.