Nematollah Batmanghelich

Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline

Spatial patterns of brain atrophy in mild cognitive impairment (MCI) and Alzheimers disease (AD) were measured via methods of computational neuroanatomy. These patterns were spatially complex and involved many brain regions. In addition to the hippocampus and the medial temporal lobe gray matter, a number of other regions displayed significant atrophy, including orbitofrontal and medial-prefrontal grey matter, cingulate (mainly posterior), insula, uncus, and temporal lobe white matter. Approximately 2/3 of the MCI group presented patterns of atrophy that overlapped with AD, whereas the remaining 1/3 overlapped with cognitively normal individuals, thereby indicating that some, but not all, MCI patients have significant and extensive brain atrophy in this cohort of MCI patients. Importantly, the group with AD-like patterns presented much higher rate of MMSE decline in follow-up visits; conversely, pattern classification provided relatively high classification accuracy (87%) of the individuals that presented relatively higher MMSE decline within a year from baseline. High-dimensional pattern classification, a nonlinear multivariate analysis, provided measures of structural abnormality that can potentially be useful for individual patient classification, as well as for predicting progression and examining multivariate relationships in group analyses.

Generative-Discriminative Basis Learning for Medical Imaging

This paper presents a novel dimensionality reduction method for classification in medical imaging. The goal is to transform very high-dimensional input (typically, millions of voxels) to a low-dimensional representation (small number of constructed features) that preserves discriminative signal and is clinically interpretable. We formulate the task as a constrained optimization problem that combines generative and discriminative objectives and show how to extend it to the semi-supervised learning (SSL) setting. We propose a novel large-scale algorithm to solve the resulting optimization problem. In the fully supervised case, we demonstrate accuracy rates that are better than or comparable to state-of-the-art algorithms on several datasets while producing a representation of the group difference that is consistent with prior clinical reports. Effectiveness of the proposed algorithm for SSL is evaluated with both benchmark and medical imaging datasets. In the benchmark datasets, the results are better than or comparable to the state-of-the-art methods for SSL. For evaluation of the SSL setting in medical datasets, we use images of subjects with mild cognitive impairment (MCI), which is believed to be a precursor to Alzheimers disease (AD), as unlabeled data. AD subjects and normal control (NC) subjects are used as labeled data, and we try to predict conversion from MCI to AD on follow-up. The semi-supervised extension of this method not only improves the generalization accuracy for the labeled data (AD/NC) slightly but is also able to predict subjects which are likely to converge to AD.

A General and Unifying Framework for Feature Construction, in Image-Based Pattern Classification

This paper presents a general and unifying optimization framework for the problem of feature extraction and reduction for high-dimensional pattern classification of medical images. Feature extraction is often an ad hoc and case-specific task. Herein, we formulate it as a problem of sparse decomposition of images into a basis that is desired to possess several properties: 1) Sparsity and local spatial support, which usually provides good generalization ability on new samples, and lends itself to anatomically intuitive interpretations; 2) good discrimination ability, so that projection of images onto the optimal basis yields discriminant features to be used in a machine learning paradigm; 3) spatial smoothness and contiguity of the estimated basis functions. Our method yields a parts-based representation, which warranties that the image is decomposed into a number of positive regional projections. A non-negative matrix factorization scheme is used, and a numerical solution with proven convergence is used for solution. Results in classification of Alzheimers patients from the ADNI study are presented.

Joint modeling of imaging and genetics

We propose a unified Bayesian framework for detecting genetic variants associated with a disease while exploiting image-based features as an intermediate phenotype. Traditionally, imaging genetics methods comprise two separate steps. First, image features are selected based on their relevance to the disease phenotype. Second, a set of genetic variants are identified to explain the selected features. In contrast, our method performs these tasks simultaneously to ultimately assign probabilistic measures of relevance to both genetic and imaging markers. We derive an efficient approximate inference algorithm that handles high dimensionality of imaging genetic data. We evaluate the algorithm on synthetic data and show that it outperforms traditional models. We also illustrate the application of the method on ADNI data.

An Integrated Framework for High Angular Resolution Diffusion Imaging–Based Investigation of Structural Connectivity

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 subjects 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.

Regularized tensor factorization for multi-modality medical image classification

This paper presents a general discriminative dimensionality reduction framework for multi-modal image-based classification in medical imaging datasets. The major goal is to use all modalities simultaneously to transform very high dimensional image to a lower dimensional representation in a discriminative way. In addition to being discriminative, the proposed approach has the advantage of being clinically interpretable. We propose a framework based on regularized tensor decomposition. We show that different variants of tensor factorization imply various hypothesis about data. Inspired by the idea of multi-view dimensionality reduction in machine learning community, two different kinds of tensor decomposition and their implications are presented. We have validated our method on a multi-modal longitudinal brain imaging study. We compared this method with a publically available classification software based on SVM that has shown state-of-the-art classification rate in number of publications.

Probabilistic Modeling of Imaging, Genetics and Diagnosis

We propose a unified Bayesian framework for detecting genetic variants associated with disease by exploiting image-based features as an intermediate phenotype. The use of imaging data for examining genetic associations promises new directions of analysis, but currently the most widely used methods make sub-optimal use of the richness that these data types can offer. Currently, image features are most commonly selected based on their relevance to the disease phenotype. Then, in a separate step, a set of genetic variants is identified to explain the selected features. In contrast, our method performs these tasks simultaneously in order to jointly exploit information in both data types. The analysis yields probabilistic measures of clinical relevance for both imaging and genetic markers. We derive an efficient approximate inference algorithm that handles the high dimensionality of image and genetic data. We evaluate the algorithm on synthetic data and demonstrate that it outperforms traditional models. We also illustrate our method on Alzheimers Disease Neuroimaging Initiative data.

Unsupervised discovery of emphysema subtypes in a large clinical cohort

Emphysema is one of the hallmarks of Chronic Obstructive Pulmonary Disorder (COPD), a devastating lung disease often caused by smoking. Emphysema appears on Computed Tomography (CT) scans as a variety of textures that correlate with disease subtypes. It has been shown that the disease subtypes and textures are linked to physiological indicators and prognosis, although neither is well characterized clinically. Most previous computational approaches to modeling emphysema imaging data have focused on supervised classification of lung textures in patches of CT scans. In this work, we describe a generative model that jointly captures heterogeneity of disease subtypes and of the patient population. We also describe a corresponding inference algorithm that simultaneously discovers disease subtypes and population structure in an unsupervised manner. This approach enables us to create image-based descriptors of emphysema beyond those that can be identified through manual labeling of currently defined phenotypes. By applying the resulting algorithm to a large data set, we identify groups of patients and disease subtypes that correlate with distinct physiological indicators.

Application of trace-norm and low-rank matrix decomposition for computational anatomy

We propose a generative model to distinguish normal anatomical variations from abnormal deformations given a group of images with normal and abnormal subjects. We assume that abnormal subjects share common factors which characterize the abnormality. These factors are hard to discover due to large variance of normal anatomical differences. Assuming that the deformation fields are parametrized by their stationary velocity fields, these factors constitute a low-rank subspace (abnormal space) that is corrupted by high variance normal anatomical differences. We assume that these normal anatomical variations are not correlated. We form an optimization problem and propose an efficient iterative algorithm to recover the low-rank subspace. The algorithm iterates between image registration and the decomposition steps and hence can be seen as a group-wise registration algorithm. We apply our method on synthetic and real data and discover abnormality of the population that cannot be recovered by some of the well-known matrix decompositions (e.g. Singular Value Decomposition).

On non-linear characterization of tissue abnormality by constructing disease manifolds

Tissue deterioration as induced by disease can be viewed as a continuous change of tissue from healthy to diseased and hence can be modeled as a non-linear manifold with completely healthy tissue at one end of the spectrum and fully abnormal tissue such as lesions, being on the other end. The ability to quantify this tissue deterioration as a continuous score of tissue abnormality will help determine the degree of disease progression and treatment effects. We propose a semi-supervised method for determining such an abnormality manifold, using multi-parametric magnetic resonance features incorporated into a support vector machine framework in combination with manifold regularization. The position of a tissue voxel on this spatially and temporally smooth manifold, determines its degree of abnormality. We apply the framework towards the characterization of tissue abnormality in brains of multiple sclerosis patients followed longitudinally, to obtain a voxel-wise score of abnormality called the tissue abnormality map, thereby obtaining a voxel-wise measure of disease progression.