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ABSTRACT: Structural connectivity models hold great promise for expanding what is known about the ways information travels throughout the brain. The physiologic interpretability of structural connectivity models depends heavily on how the connections between regions are quantified. This article presents an integrated structural connectivity framework designed around such an interpretation. The framework provides three measures to characterize the structural connectivity of a subject: (1) the structural connectivity matrix describing the proportion of connections between pairs of nodes, (2) the nodal connection distribution (nCD) characterizing the proportion of connections that terminate in each node, and (3) the connection density image, which presents the density of connections as they traverse through white matter (WM). Individually, each possesses different information concerning the structural connectivity of the individual and could potentially be useful for a variety of tasks, ranging from characterizing and localizing group differences to identifying novel parcellations of the cortex. The efficiency of the proposed framework allows the determination of large structural connectivity networks, consisting of many small nodal regions, providing a more detailed description of a subject's connectivity. The nCD provides a gray matter contrast that can potentially aid in investigating local cytoarchitecture and connectivity. Similarly, the connection density images offer insight into the WM pathways, potentially identifying focal differences that affect a number of pathways. The reliability of these measures was established through a test/retest paradigm performed on nine subjects, while the utility of the method was evaluated through its applications to 20 diffusion datasets acquired from typically developing adolescents.
Brain connectivity. 04/2012; 2(2):69-79.
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ABSTRACT: Pathologies like autism and schizophrenia are a broad set of disorders with multiple etiologies in the same diagnostic category. This paper presents a method for unsupervised cluster analysis using multi-edge similarity graphs that combine information from different modalities. The method alleviates the issues with traditional supervised classification methods that use diagnostic labels and are therefore unable to exploit or elucidate the underlying heterogeneity of the dataset under analysis. The framework introduced in this paper has the ability to employ diverse features that define different aspects of pathology obtained from different modalities to create a multi-edged graph on which clustering is performed. The weights on the multiple edges are optimized using a novel concept of 'holding power' that describes the certainty with which a subject belongs to a cluster. We apply the technique to two separate clinical populations of autism spectrum disorder (ASD) and schizophrenia (SCZ), where the multi-edged graph for each population is created by combining information from structural networks and cognitive scores. For the ASD-control population the method clusters the data into two classes and the SCZ-control population is clustered into four. The two classes in ASD agree with underlying diagnostic labels with 92% accuracy and the SCZ clustering agrees with 78% accuracy, indicating a greater heterogeneity in the SCZ population.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2012; 15(Pt 2):254-61.
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ABSTRACT: The paper presents a method for learning multimodal classifiers from datasets in which not all subjects have data from all modalities. Usually, subjects with a severe form of pathology are the ones failing to satisfactorily complete the study, especially when it consists of multiple imaging modalities. A classifier capable of handling subjects with unequal numbers of modalities prevents discarding any subjects, as is traditionally done, thereby broadening the scope of the classifier to more severe pathology. It also allows design of the classifier to include as much of the available information as possible and facilitates testing of subjects with missing modalities over the constructed classifier. The presented method employs an ensemble based approach where several subsets of complete data are formed and trained using individual classifiers., The output from these classifiers is fused using a weighted aggregation step giving an optimal probabilistic score for each subject. The method is applied to a spatio-temporal dataset for autism spectrum disorders (ASD) (96 patients with ASD and 42 typically developing controls) that consists of functional features from magnetoencephalography (MEG) and structural connectivity features from diffusion tensor imaging (DTI). A clear distinction between ASD and controls is obtained with an average 5-fold accuracy of 83.3% and testing accuracy of 88.4%. The fusion classifier performance is superior to the classification achieved using single modalities as well as multimodal classifier using only complete data (78.3%). The presented multimodal classifier framework is applicable to all modality combinations.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2012; 15(Pt 3):468-75.
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ABSTRACT: Most diffusion imaging studies have used subject registration to an atlas space for enhanced quantification of anatomy. However, standard diffusion tensor atlases lack information in regions of fiber crossing and are based on adult anatomy. The degree of error associated with applying these atlases to studies of children for example has not yet been estimated but may lead to suboptimal results. This paper describes a novel technique for generating population-specific high angular resolution diffusion imaging (HARDI)-based atlases consisting of labeled regions of homogenous white matter. Our approach uses a fiber orientation distribution (FOD) diffusion model and a data driven clustering algorithm. White matter regional labeling is achieved by our automated data driven clustering algorithm that has the potential to delineate white matter regions based on fiber complexity and orientation. The advantage of such an atlas is that it is study specific and more comprehensive in describing regions of white matter homogeneity as compared to standard anatomical atlases. We have applied this state of the art technique to a dataset consisting of adolescent and preadolescent children, creating one of the first examples of a HARDI-based atlas, thereby establishing the feasibility of the atlas creation framework. The white matter regions generated by our automated clustering algorithm have lower FOD variance than when compared to the regions created from a standard anatomical atlas.
NeuroImage 08/2011; 59(4):4055-63. · 5.89 Impact Factor
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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.
NeuroImage 05/2011; 57(3):918-27. · 5.89 Impact Factor
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ABSTRACT: This work presents an automated method for partitioning neuronal white matter (WM) into regions of interest with uniform WM architecture. These regions can then be used to replace atlas-derived regions for any subsequent statistical analysis. The fiber orientation distribution function is used as a model of WM architecture resulting in a voxel similarity function sensitive to both fiber orientations and configurations. The method utilizes the normalized cuts algorithm to partition WM voxels based on this similarity function along with a connected component labeling algorithm to ensure spatial compactness. We illustrate the algorithms ability to discern regions based on both orientation and complexity through its application to a simulated fiber crossing and an in-vivo dataset.
Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging 01/2011;
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ABSTRACT: The paper presents a method for creating abnormality classifiers from high angular resolution diffusion imaging (HARDI) data. We utilized the fiber orientation distribution (FOD) diffusion model to represent the local WM architecture of each subject. The FOD images are then spatially normalized to a common template using a non-linear registration technique. Regions of homogeneous white matter architecture (ROIs) are determined by applying a parcellation algorithm to the population average FOD image. Orientation invariant features of each ROI's mean FOD are determined and concatenated into a feature vector to represent each subject. Principal component analysis (PCA) was used for dimensionality reduction and a linear support vector machine (SVM) classifier is trained on the PCA coefficients. The classifier assigns each test subject a probabilistic score indicating the likelihood of belonging to the patient group. The method was validated using a 5 fold validation scheme on a population containing autism spectrum disorder (ASD) patients and typically developing (TD) controls. A clear distinction between ASD patients and controls was obtained with a 77% accuracy.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2011; 14(Pt 2):234-41.
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ABSTRACT: This article presents a method (DROID) for deformable registration of diffusion tensor (DT) images that utilizes the full tensor information by integrating the intensity and orientation features into a hierarchical matching framework. The intensity features are derived from eigen value based measures that characterize the tensor in terms of its different shape properties, such as, prolateness, oblateness, and sphericity of the tensor. Local spatial distributions of the prolate, oblate, and spherical geometry are used to create an attribute vector called the geometric/intensity feature for matching. The orientation features are the orientation histograms computed from the eigenvectors. These intensity and orientation features are incorporated into a hierarchical deformable registration framework to develop a deformable registration algorithm for DT images. Using orientation features improves the matching of the white matter fiber tracts by taking into account the underlying fiber orientation information. Extensive experiments on simulated and real brain DT data show promising results that makes DROID potentially useful for subsequent group-based analysis of DT images to identify disease-induced and developmental changes in a population. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 99–107, 2010
International Journal of Imaging Systems and Technology 05/2010; 20(2):99 - 107. · 0.78 Impact Factor
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ABSTRACT: The paper presents a method of creating abnormality classifiers learned from Diffusion Tensor Imaging (DTI) data of a population of patients and controls. The score produced by the classifier can be used to aid in diagnosis as it quantifies the degree of pathology. Using anatomically meaningful features computed from the DTI data we train a non-linear support vector machine (SVM) pattern classifier. The method begins with high dimensional elastic registration of DT images followed by a feature extraction step that involves creating a feature by concatenating average anisotropy and diffusivity values in anatomically meaningful regions. Feature selection is performed via a mutual information based technique followed by sequential elimination of the features. A non-linear SVM classifier is then constructed by training on the selected features. The classifier assigns each test subject with a probabilistic abnormality score that indicates the extent of pathology. In this study, abnormality classifiers were created for two populations; one consisting of schizophrenia patients (SCZ) and the other with individuals with autism spectrum disorder (ASD). A clear distinction between the SCZ patients and controls was achieved with 90.62% accuracy while for individuals with ASD, 89.58% classification accuracy was obtained. The abnormality scores clearly separate the groups and the high classification accuracy indicates the prospect of using the scores as a diagnostic and prognostic marker.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2010; 13(Pt 1):558-65.
