Autism characterized by persistently slower axonal conduction velocity across development
Poster No:
362
Submission Type:
Late-Breaking Abstract Submission
Authors:
Benjamin Newman1, Haylee Ressa1, Zachary Jacokes1, T. Jason Druzgal1, Kevin Pelphrey1, John Van Horn1
Institutions:
1University of Virginia, Charlottesville, VA
First Author:
Co-Author(s):
Introduction:
Autism Spectrum Disorder (ASD) is characterized by social communication differences and repetitive behaviors but the underlying neurology of ASD is still poorly defined and a cellular basis has not been established1. In previous work2, our lab has developed axonal conduction velocity (ACV), an MRI-based metric that describes axonal functional capacity and transmission speed using axonal diameter and myelin thickness. We previously described a cross-sectional cohort of ASD individuals with brain-wide reduced ACV compared to age and sex matched controls. In this study, we examine a longitudinal cohort to determine if reduced conduction velocity in ASD rebounds over the developmental period. We also explore changes in ACV developmental trajectories by sex to examine differences that may underlie observed behavioral differences in girls and boys with ASD.
Methods:
Participants: This study included 82 subjects (40 female, 48%) from Waves 1 and 2 of an NIH-sponsored Autism Centers of Excellence Network, comprising 34 individuals with ASD and 48 neurotypical controls.
Image Acquisition: Diffusion images were acquired with an isotropic voxel size of 2×2×2mm3, 64 non-colinear gradient directions at b=1000 s/mm2, and 1 b=0. T1-weighted MPRAGE images were collected with an FOV of 176x256x256 and an isotropic voxel size of 1x1x1mm3, TE = 3.3.
Image Processing: In accordance with the procedures described in more detail in previous work2 images were processed with MRtrix33, FSL4, and Freesurfer5. Diffusion images were preprocessed, and upsampled to a 1.3mm isotropic resolution. The b=0 image was utilized as a T2-weighted image and resampled to match the resolution of the T1w MPRAGE. SS3T-CSD modeled the fiber orientation distribution and a fixel-based framework was implemented to obtain the fiber density and cross section as a proxy for the axonal volume fraction. These metrics were combined as described by Rushton6, Stikov7 and our prior work2 to calculate aggregate g-ratio and ACV. The mean value of these metrics was measured within each of the 164 regions of the Destrieux Cortical Atlas and 48 regions of the JHU WM Atlas. After FWE correction, no ROI was significant for two or three way interactions between diagnosis, sex, and time-point, so these interactions were discarded. All statistical models were corrected for age, total brain volume, participant IQ, and scanning site.
Image Acquisition: Diffusion images were acquired with an isotropic voxel size of 2×2×2mm3, 64 non-colinear gradient directions at b=1000 s/mm2, and 1 b=0. T1-weighted MPRAGE images were collected with an FOV of 176x256x256 and an isotropic voxel size of 1x1x1mm3, TE = 3.3.
Image Processing: In accordance with the procedures described in more detail in previous work2 images were processed with MRtrix33, FSL4, and Freesurfer5. Diffusion images were preprocessed, and upsampled to a 1.3mm isotropic resolution. The b=0 image was utilized as a T2-weighted image and resampled to match the resolution of the T1w MPRAGE. SS3T-CSD modeled the fiber orientation distribution and a fixel-based framework was implemented to obtain the fiber density and cross section as a proxy for the axonal volume fraction. These metrics were combined as described by Rushton6, Stikov7 and our prior work2 to calculate aggregate g-ratio and ACV. The mean value of these metrics was measured within each of the 164 regions of the Destrieux Cortical Atlas and 48 regions of the JHU WM Atlas. After FWE correction, no ROI was significant for two or three way interactions between diagnosis, sex, and time-point, so these interactions were discarded. All statistical models were corrected for age, total brain volume, participant IQ, and scanning site.
Results:
Mean difference between baseline and follow-up of ACV measured within all the cortical Destrieux ROIs and all JHU WM ROIs are displayed in Figure 1a. In the cortex in male participants there was a significant difference between ASD and the control group (T=2.476, p<0.05) but there was not a significant change overall (T=1.733, p=0.088 n.s.) likely driven by the lack of change in the ASD male group (Fig. 1a). In female participants there was a significant increase between baseline and follow-up in the cortical conduction velocity (T=2.809, p<0.05) but not a significant difference overall between ASD and the control group (T=-1.712, p=0.096, n.s.). In the WM ROIs in male participants there was not a significant change (T=1.452, p=0.15, n.s.) nor between ASD and the control group (T=0.225, p=0.82 n.s.) whereas in the female participants there was a significant change between baseline and followup (T=3.502, p<0.01) but this was not different between the ASD and the control group (T=0.087, p=0.93, n.s.). Changes across all separate ROIs are displayed in Figure 2.
·Figure 1: Violin plots showing mean change in ACV in cortex and WM (A). Plots showing ACV as a function of participant age in both cortex and WM (B).
·Figure 2: Mean change in each individual ROI in the 164 Destrieux Cortical Atlas and 48 JHU WM Atlas ROIs, separated by diagnosis and sex. Blue values indicate increases and red values decreases.
Conclusions:
Here we present longitudinal data from a large developmental ASD cohort. These findings support previous findings of reduced ACV in ASD throughout the developmental period but overall indicate that ACV increases across development similarly in either ASD or non-ASD participants. Decreased ACV in cortex in ASD boys and decreased ACV in WM in ASD girls suggest that axonal development may be differentially disrupted between ASD males and females compared to their non-ASD counterparts.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Lifespan Development:
Early life, Adolescence, Aging
Normal Brain Development: Fetus to Adolescence
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 2
Keywords:
Autism
Computational Neuroscience
Modeling
MRI
Myelin
Neuron
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - microstructure
1|2Indicates the priority used for review
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2. Newman BT, Jacokes Z, Venkadesh S, et al. Conduction velocity, G-ratio, and extracellular water as microstructural characteristics of autism spectrum disorder. Plos one. 2024;19(4):e0301964.
3. Tournier JD, Smith R, Raffelt D, et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage. 2019;202:116137.
4. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. Fsl. Neuroimage. 2012;62(2):782-790.
5. Fischl B. FreeSurfer. Neuroimage. 2012;62(2):774-781.
6. Rushton WAH. A theory of the effects of fibre size in medullated nerve. The Journal of physiology. 1951;115(1):101.
7. Stikov N, Campbell JS, Stroh T, et al. In vivo histology of the myelin g-ratio with magnetic resonance imaging. Neuroimage. 2015;118:397-405.