White Matter Microstructure As Candidate Brain Phenotypes of Autism

Friday, May 12, 2017: 12:00 PM-1:40 PM
Golden Gate Ballroom (Marriott Marquis Hotel)
J. Villaruz1, D. C. Dean1, B. G. Travers1, A. A. Freeman1, B. A. Zielinski2, M. D. Prigge2, J. S. Anderson2, E. D. Bigler3, N. Lange4, S. J. Schrodi5, M. Leppert6, N. Matsunami7, A. L. Alexander1 and J. E. Lainhart1, (1)University of Wisconsin - Madison, Madison, WI, (2)University of Utah, Salt Lake City, UT, (3)Brigham Young University, Provo, UT, (4)McLean Hospital, Cambridge, MA, (5)Marshfield Clinic Research Foundation, Marshfield, WI, (6)University of Utah, Department of Human Genetics, Salt Lake City, UT, (7)University of Utah School of Medicine, Salt Lake City, UT
Background: Autism spectrum disorder (ASD) is a highly heterogeneous condition that vastly influences brain structure and development. However, despite the effort to identify brain-based causes of ASD, the mechanisms that underlie the associated neurobiological alterations remain unknown. Though the heterogeneity of neuroimaging findings has made it challenging to pinpoint specific brain-related phenotypes of ASD, identifying such features may be informative to the clinical approach of ASD. We can compare the distributions of neuroimaging-based measures from ASD individuals to those of typically developing individuals and single out brain regions that differ between these two groups. Diffusion tensor imaging (DTI) is an integral tool for studying neurobiological change and has been used to associate ASD with microstructural variations in specific white matter tracts. Thus, DTI measures may be worthwhile to distinguish brain phenotypes of ASD.

Objectives: We examined distributions of white matter microstructure of individuals with ASD and typically developing controls (TDC) as initial steps to identify brain based phenotypes of ASD.

Methods: Participants were recruited across two sites, University of Utah and University of Wisconsin-Madison, and involved 140 ASD and 85 TDC males between the ages of 3 and 42-years-old. In total, 497 (320 ASD, 177 TDC) longitudinal DTI datasets were collected. Images were corrected for distortion and head motion, while maps of FA, MD, AD, and RD were estimated. Images were then aligned to a population-specific template and mean diffusion parameters were extracted from 46 major white matter tracts, as defined by the JHU ICBM-DTI-81 template. The bilateral stria terminalis was not included in these analyses due to lack of reliable fit across participants. T-tests were performed to compare the distributions from ASD and TDC individuals using combined data and data separated by collection site.

Results: In the combined data, the ASD group had significantly different (p<0.05) white matter microstructure in 21 major tracts compared to TDC. FA in the splenium, genu, and body of the corpus callosum were the most significantly different (p = 7.51*10-8, p = 1.79*10-6, and p = 3.80*10-5, respectively). Utah data revealed 25 tracts that had significantly different (p<0.05) microstructure in the ASD group while Wisconsin data only showed significantly different (p<0.05) microstructure in 5 tracts. In Utah data, FA was most significantly different (p=1.36*10-8) in the splenium of the corpus callosum, while in Wisconsin data it was most significantly different (p=0.011) in the middle cerebellar peduncle.

Conclusions: Our results suggest that measures derived from diffusion imaging may be informative to the development of brain-based phenotypes of ASD. In particular, the splenium of the corpus callosum may be an ideal candidate for such a brain phenotype. Although the findings from the two sites are similar, the discrepancies are likely due to the larger sample size and increased number of time points in the Utah data, making it more sensitive to overall group effects. Future analyses will investigate differences in MD, RD, and AD between groups, the effect of covariates such as age, and whether candidate imaging phenotypes begin to establish possible subgroups of ASD.