MICROSTRUCTURAL FEATURES LINKING WHITE MATTER AND MATHEMATICS IN CHILDHOOD AND ADOLESCENCE
Poster No:
1297
Submission Type:
Abstract Submission
Authors:
Bryce Geeraert1, Kiara Kunimoto1, Marc Lebel1, Catherine Lebel1
Institutions:
1University of Calgary, Calgary, Alberta
First Author:
Co-Author(s):
Introduction:
Mathematics is a complex skill requiring the coordination of distributed gray matter brain regions connected by white matter tracts1. Diffusion imaging (dMRI) studies have revealed a network of white matter tracts that facilitate math processing2, but it remains unclear how the microstructural features of these tracts support math. Additional modalities can provide higher specificity to white matter microstructure. The neurite density index (NDI) and orientation dispersion index (ODI) probe axon packing and coherence, myelin volume fraction (VFm) is sensitive to myelin content, while the g-ratio reports axon diameter to myelin thickness, which relates to communication efficiency in white matter3-5. We combined dMRI with these more specific metrics to evaluate links between white matter microstructural development and math in a longitudinal cohort of adolescents.
Methods:
33 healthy children (16M/17F, 12.7 ± 2.4 years) were scanned on a GE 3T Discovery MR750w scanner, and again after two years (66 datasets total). DTI/NODDI: spin echo EPI, TR/TE = 12s/88ms, 2.2mm isotropic resolution, b=900 & 2000 s/mm2, 30 directions and 5 b0 per shell. McDESPOT: multi-α SPGR, 18° max α, 1.72x0.86x1.7mm res.; IR-SPGR, 2.29x0.86x3.4mm res., 5° α; two bSSFP images with phase of 0° and 180°, 1.72x0.86x1.7 mm resolution, max α 60°, TR/TE = min+0.1ms/min for all. Participants completed WIAT-III CDN subtests for Mathematics and Math Fluency composite scores at both time points6. Following preprocessing, four math-related tracts-the left superior longitudinal (SLF) and inferior longitudinal fasciculi (ILF), corticospinal tract (CST), and the splenium-were segmented in ExploreDTI. Fractional anisotropy (FA), mean diffusivity (MD), NDI, ODI, VFm, and g-ratio maps were produced. Partial correlations between time 1 and annual change in white matter metrics and math scores, controlling for age and gender, were computed per tract. Multiple comparisons were corrected via the false discovery rate (FDR) method7.
Results:
ODI of the SLF and CST correlated to Mathematics in cross-sectional analysis of time 1 data. Longitudinally, annual change in Mathematics correlated with change of NDI in the SLF, MD in the CST, and VFm in the splenium. Math Fluency correlated with VFm in the ILF and splenium, and with g-ratio in the ILF and CST. No significant correlations were observed between FA and math scores in any investigated region (Table 1, Figure 1). Correlations did not survive FDR correction. Follow-up analyses of math subtests suggested white matter microstructure was most closely related to rapid information processing, although some links to higher-order mathematical skills were observed.
·Table 1. Partial correlations between cross-sectional and annual change in imaging metrics and mathematics scores from the WIAT-III CDN, controlling for age at time 1 and parent-reported gender.
·Figure 1. Partial correlations between annual change in white matter microstructure (x axes) and Mathematics or Math Fluency composite scores (y axes), controlling for age and parent-reported gender.
Conclusions:
We applied measures specific to white matter microstructure in a longitudinal cohort to show that links between DTI metrics and math skill are primarily driven by changes in axonal packing and myelin. Our findings suggest axonal packing in the math network supports sophisticated processing and problem solving, while myelin predominantly supports rapid information processing. Correlations were moderate to high magnitude and achieved high power, despite not surviving FDR correction. This, combined with the near absence of correlations between DTI metrics and math, highlights the strength of microstructurally-sensitive metrics to detect subtle relationships that may be missed by traditional methods. While most relationships were such that indications of more mature white matter were positively correlated to math, decreases in myelin content were linked to improvements in mathematics in adolescence. This suggests adolescence is a period of refinement to the already established structural network underlying mathematical processing. These findings shed light on the role of white matter tracts in complex cognitive tasks such as mathematical processing, and may be applied to better understand the biological underpinnings of learning disabilities.
Higher Cognitive Functions:
Higher Cognitive Functions Other 2
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 1
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity
Novel Imaging Acquisition Methods:
Multi-Modal Imaging
Keywords:
Cognition
Development
MRI
Myelin
PEDIATRIC
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Mathematics
1|2Indicates the priority used for review
Provide references using author date format
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5. Stikov, N. et al. In vivo histology of the myelin g-ratio with magnetic resonance imaging. NeuroImage 118, 397–405 (2015).
6. Wechsler D. Wechsler Individual Achievement Test -- Third Edition: Canadian (WIAT-III CDN). (Pearson Canada Assessment Inc, 2010).
7. Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).