The diagnosis of autism spectrum disorder (ASD) is currently based on behavior and developmental history of the child. With the development of advanced forms of diffusion-weighted magnetic resonance imaging (DW-MRI), it is expected that imaging will elucidate pathology-induced and neuro-developmental changes in white matter (WM) architecture, and provide diagnostic and predictive anatomical biomarkers.
We aim at developing computational methods for processing and analysis of high angular resolution diffusion imaging data that has been fitted with higher order diffusion models (HOMs). Compared to the tensor model in diffusion tensor imaging (DTI), HOMs provide a much richer understanding of pathology-based connectivity changes in complex WM regions, as well as a quantification of the degree of abnormality of WM. These imaging measures when correlated with clinical measures of symptom severity will provide additional insight into the pathology and its progression, thus making this project very clinically significant. Understanding such complex WM regions is expected to aid in the study of ASD, deficits in which can be linked with WM abnormalities and disruptions in structural connectivity via fiber tracts. The advances in acquisition of data that can be fitted with HOMs in turn calls for novel automated tools for analyzing such data, as existing methods developed for tensors are inapplicable to HOMs. We propose to achieve this by the following specific aims:
In Aim 1, we will define local and global measures from HOMs and use these to obtain a feature-based algorithm for deformable registration of HOM images preparing them for subsequent analysis.
In Aim 2, we will develop and validate an integrated framework for population statistics of HOMs using a combination of voxel-based, manifold-based and tract-based analysis.
In Aim 3, we will design high- dimensional multivariate pattern classifiers using HOM features, to obtain spatial patterns of brain abnormality and assign an abnormality to each brain.
In Aim 4, we will apply the methods developed in Aims 1 - 3 to a large database of ASD patients and demographically balanced typically developing volunteers and identify patient-control differences and correlate with clinical ratings of symptom severity in patients. The quantification of patterns of group differences and connectivity disruptions are expected to provide insight into the deficits observed in autism such as impaired social interactions, impaired language and communication and stereotypical, restricted and repetitive behaviors. The use of HOMs that has never been attempted before in literature to study ASD, with most of the work limited to the analysis of anisotropy and diffusivity measures computed from DTI data. We expect that upon successful completion of the project, we have developed a general and comprehensive, mathematically consistent and computationally efficient processing and analysis paradigm for large population studies using HOMs that will help identify and quantify complex patterns of connectivity changes induced by pathology.

Public Health Relevance

This project aims at developing computational methods for analyzing diffusion MRI data fitted with higher order models that uniquely characterize complex white matter regions, affected in Autism Spectrum Disorder (ASD). These well validated methods will be applied to the analysis of an ASD population to produce a quantification of abnormalities in brain connectivity and white matter integrity. Correlation with clinical diagnostic measures will provide an image-based link to deficits observed in autism such as impaired social interactions, language and communication and restricted and repetitive behaviors, and hence aid in prognosis and in studying disease progression. Project narrative This project aims at developing computational methods for analyzing diffusion MRI data fitted with higher order models that uniquely characterize complex white matter regions, affected in Autism Spectrum Disorder (ASD). These well validated methods will be applied to the analysis of an ASD population to produce a quantification of abnormalities in brain connectivity and white matter integrity. Correlation with clinical diagnostic measures will provide an image-based link to deficits observed in autism such as impaired social interactions, language and communication and restricted and repetitive behaviors, and hence aid in prognosis and in studying disease progression.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
5R01MH092862-05
Application #
8722957
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Cavelier, German
Project Start
Project End
Budget Start
Budget End
Support Year
5
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Pennsylvania
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Caruyer, Emmanuel; Bloy, Luke; Tunç, Birkan et al. (2014) A comparative study of 16 tractography algorithms for the corticospinal tract: reproducibility and subject-specificity. Proc Int Soc Magn Reson Med Sci Meet Exhib Int Soc Magn Reson M 2014:
Rathi, Y; Michailovich, O; Laun, F et al. (2014) Multi-shell diffusion signal recovery from sparse measurements. Med Image Anal 18:1143-56
Tunç, Birkan; Parker, William A; Ingalhalikar, Madhura et al. (2014) Automated tract extraction via atlas based Adaptive Clustering. Neuroimage 102 Pt 2:596-607
Ingalhalikar, Madhura; Parker, William A; Bloy, Luke et al. (2014) Creating multimodal predictors using missing data: classifying and subtyping autism spectrum disorder. J Neurosci Methods 235:1-9
Savadjiev, Peter; Rathi, Yogesh; Bouix, Sylvain et al. (2014) Fusion of white and gray matter geometry: a framework for investigating brain development. Med Image Anal 18:1349-60
Westin, Carl-Fredrik; Szczepankiewicz, Filip; Pasternak, Ofer et al. (2014) Measurement tensors in diffusion MRI: generalizing the concept of diffusion encoding. Med Image Comput Comput Assist Interv 17:209-16
Ghanbari, Yasser; Smith, Alex R; Schultz, Robert T et al. (2014) Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding. Med Image Anal 18:1337-48
Kim, Junghoon; Parker, Drew; Whyte, John et al. (2014) Disrupted structural connectome is associated with both psychometric and real-world neuropsychological impairment in diffuse traumatic brain injury. J Int Neuropsychol Soc 20:887-96
Tunc, Birkan; Ghanbari, Yasser; Smith, Alex R et al. (2014) PUNCH: Population Characterization of Heterogeneity. Neuroimage 98:50-60
Ingalhalikar, Madhura; Smith, Alex; Parker, Drew et al. (2014) Sex differences in the structural connectome of the human brain. Proc Natl Acad Sci U S A 111:823-8

Showing the most recent 10 out of 21 publications