Multivariate characterization of white matter heterogeneity in autism spectrum disorder

D C Dean 3rd; N Lange; B G Travers; M B Prigge; N Matsunami; K A Kellett; A Freeman; K L Kane; N Adluru; D P M Tromp; D J Destiche; D Samsin; B A Zielinski; P T Fletcher; J S Anderson; A L Froehlich; M F Leppert; E D Bigler; J E Lainhart; A L Alexander

doi:10.1016/j.nicl.2017.01.002

Multivariate characterization of white matter heterogeneity in autism spectrum disorder

Neuroimage Clin. 2017 Jan 6:14:54-66. doi: 10.1016/j.nicl.2017.01.002. eCollection 2017.

Authors

D C Dean 3rd¹, N Lange², B G Travers³, M B Prigge⁴, N Matsunami⁵, K A Kellett⁶, A Freeman¹, K L Kane¹, N Adluru¹, D P M Tromp⁷, D J Destiche¹, D Samsin¹, B A Zielinski⁸, P T Fletcher⁹, J S Anderson¹⁰, A L Froehlich¹¹, M F Leppert⁵, E D Bigler¹², J E Lainhart⁷, A L Alexander¹³

Affiliations

¹ Waisman Center, University of Wisconsin-Madison, Madison, WI, USA.
² Department of Psychiatry, Harvard School of Medicine, Boston, MA, USA; Child and Adolescent Psychiatry, McLean Hospital, Belmont, MA, USA.
³ Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Occupational Therapy Program, Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, USA.
⁴ Department of Radiology, University of Utah, Salt Lake City, UT, USA; Department of Pediatrics, University of Utah and Primary Children's Medical Center, Salt Lake City, UT, USA.
⁵ Department of Human Genetics, University of Utah, Salt Lake City, UT, USA.
⁶ Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA.
⁷ Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA.
⁸ Department of Pediatrics, University of Utah and Primary Children's Medical Center, Salt Lake City, UT, USA; Department of Neurology, University of Utah, Salt Lake City, UT, USA.
⁹ Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; School of Computing, University of Utah, Salt Lake City, UT, USA.
¹⁰ Department of Radiology, University of Utah, Salt Lake City, UT, USA; Interdepartmental Program in Neuroscience, University of Utah, Salt Lake City, UT, USA.
¹¹ School of Computing, University of Utah, Salt Lake City, UT, USA.
¹² Department of Psychology, Brigham Young University, Provo, UT, USA; Neuroscience Center, Brigham Young University, Provo, UT 84602, USA.
¹³ Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA.

Abstract

The complexity and heterogeneity of neuroimaging findings in individuals with autism spectrum disorder has suggested that many of the underlying alterations are subtle and involve many brain regions and networks. The ability to account for multivariate brain features and identify neuroimaging measures that can be used to characterize individual variation have thus become increasingly important for interpreting and understanding the neurobiological mechanisms of autism. In the present study, we utilize the Mahalanobis distance, a multidimensional counterpart of the Euclidean distance, as an informative index to characterize individual brain variation and deviation in autism. Longitudinal diffusion tensor imaging data from 149 participants (92 diagnosed with autism spectrum disorder and 57 typically developing controls) between 3.1 and 36.83 years of age were acquired over a roughly 10-year period and used to construct the Mahalanobis distance from regional measures of white matter microstructure. Mahalanobis distances were significantly greater and more variable in the autistic individuals as compared to control participants, demonstrating increased atypicalities and variation in the group of individuals diagnosed with autism spectrum disorder. Distributions of multivariate measures were also found to provide greater discrimination and more sensitive delineation between autistic and typically developing individuals than conventional univariate measures, while also being significantly associated with observed traits of the autism group. These results help substantiate autism as a truly heterogeneous neurodevelopmental disorder, while also suggesting that collectively considering neuroimaging measures from multiple brain regions provides improved insight into the diversity of brain measures in autism that is not observed when considering the same regions separately. Distinguishing multidimensional brain relationships may thus be informative for identifying neuroimaging-based phenotypes, as well as help elucidate underlying neural mechanisms of brain variation in autism spectrum disorders.

Keywords: Autism spectrum disorder; Brain variability; Diffusion tensor imaging; Mahalanobis distance; White matter microstructure.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Adolescent
Adult
Anisotropy
Autism Spectrum Disorder / diagnostic imaging*
Child
Child, Preschool
Diffusion Magnetic Resonance Imaging
Female
Humans
Image Processing, Computer-Assisted
Longitudinal Studies
Male
Neural Pathways / diagnostic imaging*
White Matter / diagnostic imaging*
Young Adult

Abstract

Publication types

MeSH terms

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