Douglas C Dean III1, Nicholas Lange2, Brittany Travers1, Nagesh Adluru1, Do Tromp1, Daniel Destiche1, Abigail Freeman1, Danica Samsin1, Brandon Zielinski3, Molly Prigge3, P.T. Fletcher3, Jeffery Anderson3, Erin Bigler4, Janet Lainhart1, and Andrew Alexander1
1Waisman Center, University of Wisconsin-Madison, Madison, WI, United States, 2McLean Hospital, Boston, MA, United States, 3University of Utah, Salt Lake City, UT, United States, 4Brigham Young University, Provo, UT, United States
Synopsis
To date, the heterogeneity of neuroimaging
findings has made it challenging to identify specific brain-related phenotypes
within autism spectrum disorder (ASD). In particular, a quantitative index of
individual deviation across a set of brain measurements may be informative for
constructing distributions of brain variation and identifying individuals who
may or may not have abnormal brain structure. To this end, we investigated the
use of the Mahalanobis distance to characterize multidimensional brain measures
in individuals with and without ASD and to demonstrate that patterns of brain
features distinguishing individuals with ASD are multidimensional and likely
encompass differing cortical and sub-cortical characteristics.Target Audience
Researchers interested in autism spectrum
disorders (ASD) and the use of diffusion tensor imaging (DTI) in understanding
white matter changes in ASD.
Introduction
Brain imaging findings in children with autism
spectrum disorder (ASD) suggest the disorder is complex and heterogeneous,
making the interpretation of the underlying neurobiology of ASD challenging. Such heterogeneity
may suggest that the neuroanatomy of ASD is not restricted to a single brain
region or network, rather that differences are subtle and more widespread
1.
Thus, consideration of multivariate relationships of the brain may enable
better characterization and discrimination of individuals with ASD. Distinguishing these multidimensional brain
relationships at both the group and individual level are therefore critical as
characterization may be informative for identifying ASD subgroups as well as
understanding individual brain variation in ASD. Here, we propose that the Mahalanobis distance
2,
a multidimensional generalization of a standard score (i.e., z-score for
univariate statistics), may provide an informative index of characterizing
individual brain variation in ASD. The Mahalanobis distance is constructed as
the distance from the typically developing control (TD) sample mean in
multidimensional feature space weighted by the TD covariance. In this study we used longitudinal
trajectories of structural and microstructural brain development to construct
the individual Mahalanobis distances and compare the
distributions of brain variability between ASD and TD individuals.
Methods
MRI Acquisition: Longitudinal data from 148 participants (92 ASD and
56 TD) between 3.1 and 34.5 years of age were used for this study. A total of
213 scans from ASD participants and 123 scans from TD individuals were acquired
on a 3 Tesla Siemens Tim Trio scanner and consisted of structural T1-weighted
(MP-RAGE) and diffusion tensor imaging (DTI) data. Diffusion weighted images
were corrected for distortion and head motion and tensors were subsequently fit
using the robust estimation algorithm (RESTORE
3).
Analysis: Volumetric measures from 16 brain regions were computed
using FreeSurfer [version 5.1
4], following the steps for
longitudinal processing
5, while the mean FA were computed from 21
major white matter tracts
6 and used in subsequent analyses. To
construct the Mahalanobis distance metric, longitudinal trajectories were first
separately modeled for TD and ASD groups using mixed effects modeling. Distances
from the population-averaged TD trajectory were then computed and normalized by
the covariance matrix of fit residuals, forming the Mahalanobis distance.
Results
Fig. 1 shows a visualization of the distributions
of Mahalanobis distances calculated from the ASD and TD groups. Qualitatively,
we notice a differential pattern between individuals with and without ASD. In
particular, the ASD group’s distribution of Mahalanobis distances is shifted rightward,
signifying larger multivariate brain deviation compared to the TD group.
Comparing these distributions quantitatively, we find the mean of the ASD
distribution (1.2125 [0.3792]) to be significantly larger (p<0.0001) than
the mean of the TD distribution (0.4751 [0.2237]). Also of note, the standard
deviation of the Mahalanobis distributions was found to be larger in the ASD
group compared to the TD group, highlighting the heterogeneity across the ASD
population.
Discussion
Previous studies
have consistently indicated both gross and microstructural brain differences in
ASD compared to typical development
7,8. While univariate measures
provide informative insight to specific brain regions and biological mechanisms
implicated in ASD, consideration of the multivariate relationships of the brain
may enable better characterization and discrimination of individuals with ASD. In
this work, we used mixed effects modeling and the Mahalanobis distance to
characterize patterns of multivariate brain measures in ASD and TD. Our results
suggest that that the distribution of Mahalanobis distances in ASD individuals
is larger and more variable compared to TD individuals. These results additionally
suggest that the Mahalanobis distance of differing image modalities may be
valuable for distinguishing individuals with and without ASD as well as identifying
possible subgroups of autism.
Conclusion
In this work, we have sought to examine the
application of the Mahalanobis distance, to assess multivariate brain measures
of ASD and TD brain development. Our results suggest that this measure may provide
an informative metric for characterizing and distinguishing the multivariate
patterns of brain development between ASD and TD individuals. Moreover, our
results raise additional questions in regards to whether specific brain regions
or image modalities are particularly sensitive to observed ASD brain differences.
Acknowledgements
We sincerely thank the children, adolescents, and adults
with autism, the individuals with typical development, and all the families who
participated in this study.
This work was supported by the National Institute of Mental
Health [RO1 MH080826 to JEL, ALA, NL, EDB; RO1 MH084795 to JEL, PTF, NL; RO1
MH097464 to JEL, ML, NL, ALA; K08 MH100609 to BAZ, and KO8 MH092697 to JSA],
the Eunice Kennedy Shriver National Institute of Child Health and Human
Development [T32 HD007489 to DCD, BGT, and P30 HD003352 to the Waisman Center],
the Poelman Foundation [to EDB], the Primary Children’s Foundation [Early
Career Development Award to BAZ], and the Hartwell Foundation [Postdoctoral
Fellowship Award to BGT]. The content is solely the responsibility of the
authors and does not necessarily represent the official views of the National
Institute of Mental Health, the National Institute of Child Health &
Development, or the National Institutes of Health. We thank Zhan Xu, Anne M.
Bartosic, Annahir Cariello, Celeste Knoles, Sam Doran, and Kristine McLaughlin for their contributions to this project.References
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