Classification in DTI using shapes of white matter tracts

Adluru, Nagesh; Hinrichs, Chris; Chung, Moo K.; Lee, Jee Eun; Singh, Vikas; Bigler, Erin D.; Lange, Nicholas; Lainhart, Janet E.; Alexander, Andrew L.

doi:10.1109/iembs.2009.5333386

Cited by 18 publications

(21 citation statements)

References 21 publications

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“…While this might not be an important distinction for the long rope-like tract groups that are typical of tract dissections towards distinct functional areas (e.g., the corticospinal tract projections to the primary motor cortex ), it could be particularly valuable as a way to investigate variability across related bundles (e.g., the entire fanning geometry of the corticospinal tract, or the entire set of fibers that pass anywhere through the corpus callosum). An alternative approach to utilize shape information is to directly analyze the shape properties, for example, the vertex-wise deformation needed to bring each tract group shape into register (Qiu et al, 2010), the shape “context” contributed by analyzing where a set of streamlines travel beyond a voxel of interest (Adluru et al, 2009), or streamline curvature and torsion (Batchelor et al, 2006). In the context of the present report, these types of metrics can be mapped to the vertex-wise tract locations to provide complementary information to FA.…”

Section: Discussionmentioning

confidence: 99%

Along-tract statistics allow for enhanced tractography analysis

et al. 2012

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Diffusion imaging tractography is a valuable tool for neuroscience researchers because it allows the generation of individualized virtual dissections of major white matter tracts in the human brain. It facilitates between-subject statistical analyses tailored to the specific anatomy of each participant. There is prominent variation in diffusion imaging metrics (e.g., fractional anisotropy, FA) within tracts, but most tractography studies use a “tract-averaged” approach to analysis by averaging the scalar values from the many streamline vertices in a tract dissection into a single point-spread estimate for each tract. Here we describe a complete workflow needed to conduct an along-tract analysis of white matter streamline tract groups. This consists of 1) A flexible MATLAB toolkit for generating along-tract data based on B-spline resampling and compilation of scalar data at different collections of vertices along the curving tract spines, and 2) Statistical analysis and rich data visualization by leveraging tools available through the R platform for statistical computing. We demonstrate the effectiveness of such an along-tract approach over the tract-averaged approach in an example analysis of 10 major white matter tracts in a single subject. We also show that these techniques easily extend to between-group analyses typically used in neuroscience applications, by conducting an along-tract analysis of differences in FA between 9 individuals with fetal alcohol spectrum disorders (FASDs) and 11 typically-developing controls. This analysis reveals localized differences between FASD and control groups that were not apparent using a tract-averaged method. Finally, to validate our approach and highlight the strength of this extensible software framework, we implement 2 other methods from the literature and leverage the existing workflow tools to conduct a comparison study.

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Section: Discussionmentioning

confidence: 99%

Along-tract statistics allow for enhanced tractography analysis

et al. 2012

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“…Three studies examined classification of subjects via DTI. One study had 75.34% average accuracy with specificity and 71.88% sensitivity using anisotropic maps and tracts drawn in the splenium [118]. Another study found 78% accuracy, 77% specificity and 78% sensitivity for classification of ASD subjects using white matter regions of interest [119].…”

Section: Neuroimaging To Assist Diagnostic Classification Of Individumentioning

confidence: 97%

Advancing our understanding of the brain in autism: contribution of functional MRI and diffusion tensor imaging

Libero¹,

Kana²

2013

Imaging in Medicine

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Advancing our understanding of the brain in autism: contribution of functional MRI and diffusion tensor imaging Neuroimaging research in the last two decades has contributed significantly in illuminating our knowledge of the neurobiology of autism spectrum disorders. Using modern brain imaging methods, particularly functional MRI and diffusion tensor imaging, researchers have examined the make-up and functioning of the brain in autism. Findings of widespread functional, anatomical and connectional abnormalities in the brains of individuals with autism spectrum disorders have helped in conceptualizing autism as a systemwide neural disorder. This review explores the results of functional MRI and diffusion tensor imaging studies to characterize the brain organization in autism spectrum disorders, to establish brain-behavior relationship, to apply the knowledge gained from neuroimaging research to diagnostic classification and to design effective treatment for individuals with autism.KEYWORDS: autism n diffusion tensor imaging n effective connectivity n functional connectivity n functional MRI n white matter

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“…General leave-one-out cross-validation accuracies reported are in the high 70% to 80% [17,7,18]. An accuracy of 90% on an independently chosen test sample was reported in [19].…”

Section: Ern Analyses In Autismmentioning

confidence: 99%

Applications of Epsilon Radial Networks in Neuroimage Analyses

Adluru

Chung

Lange

et al. 2011

Advances in Image and Video Technology

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“Is the brain ’wiring’ different between groups of populations?” is an increasingly important question with advances in diffusion MRI and abundance of network analytic tools. Recently, automatic, data-driven and computationally efficient framework for extracting brain networks using tractography and epsilon neighborhoods were proposed in the diffusion tensor imaging (DTI) literature [1]. In this paper we propose new extensions to that framework and show potential applications of such epsilon radial networks (ERN) in performing various types of neuroimage analyses. These extensions allow us to use ERNs not only to mine for topo-physical properties of the structural brain networks but also to perform classical region-of-interest (ROI) analyses in a very efficient way. Thus we demonstrate the use of ERNs as a novel image processing lens for statistical and machine learning based analyses. We demonstrate its application in an autism study for identifying topological and quantitative group differences, as well as performing classification. Finally, these views are not restricted to ERNs but can be effective for population studies using any computationally efficient network-extraction procedures.

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Classification in DTI using shapes of white matter tracts

Cited by 18 publications

References 21 publications

Along-tract statistics allow for enhanced tractography analysis

Along-tract statistics allow for enhanced tractography analysis

Advancing our understanding of the brain in autism: contribution of functional MRI and diffusion tensor imaging

Applications of Epsilon Radial Networks in Neuroimage Analyses

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