Diffusion based abnormality markers of pathology: toward learned diagnostic prediction of ASD.

New CAR Research Sheds Light on

 

Universal Screening for Autism in Toddlers

TitleDiffusion based abnormality markers of pathology: toward learned diagnostic prediction of ASD.
Publication TypeJournal Article
Year of Publication2011
AuthorsIngalhalikar, M, Parker, D, Bloy, L, Roberts, TPL, Verma, R
JournalNeuroimage
Volume57
Issue3
Pagination918-27
Date Published2011 Aug 01
ISSN1095-9572
KeywordsArtificial Intelligence, Brain, Child, Child Development Disorders, Pervasive, Diffusion Magnetic Resonance Imaging, Female, Humans, Male, Sensitivity and Specificity
Abstract

This paper presents a paradigm for generating a quantifiable marker of pathology that supports diagnosis and provides a potential biomarker of neuropsychiatric disorders, such as autism spectrum disorder (ASD). This is achieved by creating high-dimensional nonlinear pattern classifiers using support vector machines (SVM), that learn the underlying pattern of pathology using numerous atlas-based regional features extracted from diffusion tensor imaging (DTI) data. These classifiers, in addition to providing insight into the group separation between patients and controls, are applicable on a single subject basis and have the potential to aid in diagnosis by assigning a probabilistic abnormality score to each subject that quantifies the degree of pathology and can be used in combination with other clinical scores to aid in diagnostic decision. They also produce a ranking of regions that contribute most to the group classification and separation, thereby providing a neurobiological insight into the pathology. As an illustrative application of the general framework for creating diffusion based abnormality classifiers we create classifiers for a dataset consisting of 45 children with ASD (mean age 10.5 ± 2.5 yr) as compared to 30 typically developing (TD) controls (mean age 10.3 ± 2.5 yr). Based on the abnormality scores, a distinction between the ASD population and TD controls was achieved with 80% leave one out (LOO) cross-validation accuracy with high significance of p<0.001, ~84% specificity and ~74% sensitivity. Regions that contributed to this abnormality score involved fractional anisotropy (FA) differences mainly in right occipital regions as well as in left superior longitudinal fasciculus, external and internal capsule while mean diffusivity (MD) discriminates were observed primarily in right occipital gyrus and right temporal white matter.

DOI10.1016/j.neuroimage.2011.05.023
Alternate JournalNeuroimage
PubMed ID21609768
PubMed Central IDPMC3152443
Grant ListR01 DC008871 / DC / NIDCD NIH HHS / United States
R01-MH079938 / MH / NIMH NIH HHS / United States
R01 MH092862 / MH / NIMH NIH HHS / United States
R01-MH092862 / MH / NIMH NIH HHS / United States
R01 MH092862-01 / MH / NIMH NIH HHS / United States
R01 MH079938 / MH / NIMH NIH HHS / United States
R01-DC008871 / DC / NIDCD NIH HHS / United States
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