Brain Structural Connectivity and Pre-Reading Abilities in Children with Prenatal Alcohol Exposure
Poster No:
1032
Submission Type:
Abstract Submission
Authors:
Mohammad Ghasoub1, Meaghan Perdue1, Xiangyu Long1, Claire Donnici1, Preeti Kar1, Deborah Dewey1, Ben Gibbard1, Chris tortorelli1, Catherine Lebel1
Institutions:
1University of Calgary, Calgary, Alberta
First Author:
Co-Author(s):
Introduction:
Worldwide, approximately 10% of people consume alcohol while pregnant. Children with prenatal alcohol exposure (PAE) can go on to develop cognitive deficits including reading disorders. PAE is also associated with a range of structural neural alterations, including abnormal white matter microstructure and altered connectivity. Alterations have been found in brain pathways associated with reading, but very few studies have directly examined the association between structural connectivity and early reading performance in children with PAE. Using graph theory analysis, we examined associations between structural network connectivity in areas of the brain associated with reading/pre-reading in young children, and the extent to which PAE moderates these associations.
Methods:
363 scans from 135 children (53 with PAE, 82 controls) collected longitudinally between ages 3-7 years were examined. Children from both groups completed pre-reading assessments including Phonological Processing and Speeded Naming and underwent a diffusion MRI scan on a GE 3T MR750w system with a 32-channel head coil at each time point. Diffusion tensor imaging (DTI) data were acquired using single-shot spin-echo, echo planar imaging (EPI) sequence of 1.6 × 1.6 × 2.2 mm resolution (resampled to 0.78 × 0.78 × 2.2 mm on scanner), TR = 6,750 ms; TE = 79 ms, 30 gradient encoding directions at b = 750 s/mm, and 5 baselines at b = 0 s/mm2 (4:03 minutes total). DTI preprocessing protocol included flipping/permuting images, as well as signal drift, Gibbs ringing, head motion, and eddy current distortion corrections. Diffusion tensor was calculated to delineate the FA values, and whole brain diffusion tractography was performed using seedpoint resolution = 2 × 2 × 2 mm3, seed fractional anisotropy (FA) threshold = 0.15, fiber length range = 50–500 mm, angle threshold = 30°, step size = 1.
We defined a reading network consisting of 16 brain regions from both hemispheres known from prior work to be associated with reading (Figure 1). We used the mean FA extracted from the whole-brain tractography, and Automated Anatomical Atlas (AAL 90) to generate FA-weighted connectivity matrices. A 16x16 connectivity matrix of regions from both hemispheres as well as 8x8 connectivity matrices from each hemisphere were extracted for analysis. The following graph theory measures of network properties were calculated for each hemisphere and bilaterally using the Brain Connectivity Toolbox: global efficiency, local efficiency, clustering coefficient, and nodal degree. Linear mixed effects models were used to test the associations between graph theory measures and pre-reading assessments as well as the effect of PAE on these associations, accounting for age and sex (fixed effects), and repeated measures within subjects (random effect).
We defined a reading network consisting of 16 brain regions from both hemispheres known from prior work to be associated with reading (Figure 1). We used the mean FA extracted from the whole-brain tractography, and Automated Anatomical Atlas (AAL 90) to generate FA-weighted connectivity matrices. A 16x16 connectivity matrix of regions from both hemispheres as well as 8x8 connectivity matrices from each hemisphere were extracted for analysis. The following graph theory measures of network properties were calculated for each hemisphere and bilaterally using the Brain Connectivity Toolbox: global efficiency, local efficiency, clustering coefficient, and nodal degree. Linear mixed effects models were used to test the associations between graph theory measures and pre-reading assessments as well as the effect of PAE on these associations, accounting for age and sex (fixed effects), and repeated measures within subjects (random effect).
Results:
Children with PAE had significantly lower mean graph theory metrics than controls across all networks (Figure 2). They also had lower Phonological Processing and Speeded Naming scores (p < 0.001). PAE significantly moderated the associations between Phonological Processing and global efficiency in both the bilateral (p= 0.005) and left hemisphere (p= 0.025) networks, as well as nodal degree in the bilateral (p= 0.005) network. No significant associations were found for Speeded Naming.
Conclusions:
Children with PAE had lower pre-reading skills as well as overall reduced structural connectivity properties compared to unexposed children. PAE moderated the associations between Phonological Processing and global efficiency and nodal degree, suggesting that PAE may influence information integration in the reading networks. It is notable that PAE influenced brain-pre-reading relationships in preschool aged children before the beginning of formal reading instructions at school. Further research is required to better understand this moderation effect and its implications for early interventions.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 2
Language:
Reading and Writing 1
Language Other
Lifespan Development:
Lifespan Development Other
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Keywords:
Development
Language
PEDIATRIC
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - PAE; Reading; Connectome
1|2Indicates the priority used for review
Provide references using author date format
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