Edward H. Herskovits

A Bayesian Method for the Induction of Probabilistic Networks from Data

This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.

Anatomy of Spatial Attention: Insights from Perfusion Imaging and Hemispatial Neglect in Acute Stroke

The site of lesion responsible for left hemispatial neglect after stroke has been intensely debated recently. Some studies provide evidence that right angular lesions are most likely to cause left neglect, whereas others indicate that right superior temporal lesions are most likely to cause neglect. We examine two potential accounts of the conflicting results: (1) neglect could result from cortical dysfunction beyond the structural lesion in some studies; and (2) different forms of neglect with separate neural correlates have been included in different proportions in separate studies. To evaluate these proposals, we studied 50 patients with acute right subcortical infarcts using tests of hemispatial neglect and magnetic resonance diffusion-weighted and perfusion-weighted imaging performed within 48 h of onset of symptoms. Left “allocentric” neglect (errors on the left sides of individual stimuli, regardless of location with respect to the viewer) was most strongly associated with hypoperfusion of right superior temporal gyrus (Fishers exact test; p < 0.0001), whereas left “egocentric” neglect (errors on the left of the viewer) was most strongly associated with hypoperfusion of the right angular gyrus (p < 0.0001). Patients without cortical hypoperfusion showed no hemispatial neglect. Because the patients did not have cortical infarcts, our data show that neglect can be caused by hypoperfused dysfunctional tissue not detectable by structural magnetic resonance imaging. Moreover, different forms of neglect were associated with different sites of cortical hypoperfusion. Results help explain conflicting results in the literature and contribute to the understanding of spatial attention and representation in the human brain.

A Bayesian method for constructing Bayesian belief networks from databases

This paper presents a Bayesian method for constructing Bayesian belief networks from a database of cases. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. We relate the methods in this paper to previous work, and we discuss open problems.

An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures

This paper presents a deformable model for automatically segmenting brain structures from volumetric magnetic resonance (MR) images and obtaining point correspondences, using geometric and statistical information in a hierarchical scheme. Geometric information is embedded into the model via a set of affine-invariant attribute vectors, each of which characterizes the geometric structure around a point of the model from a local to a global scale. The attribute vectors, in conjunction with the deformation mechanism of the model, warrant that the model not only deforms to nearby edges, as is customary in most deformable surface models, but also that it determines point correspondences based on geometric similarity at different scales. The proposed model is adaptive in that it initially focuses on the most reliable structures of interest, and gradually shifts focus to other structures as those become closer to their respective targets and, therefore, more reliable. The proposed techniques have been used to segment boundaries of the ventricles, the caudate nucleus, and the lenticular nucleus from volumetric MR images.

Predictive models of autism spectrum disorder based on brain regional cortical thickness

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a wide phenotypic range, often affecting personality and communication. Previous voxel-based morphometry (VBM) studies of ASD have identified both gray- and white-matter volume changes. However, the cerebral cortex is a 2-D sheet with a highly folded and curved geometry, which VBM cannot directly measure. Surface-based morphometry (SBM) has the advantage of being able to measure cortical surface features, such as thickness. The goals of this study were twofold: to construct diagnostic models for ASD, based on regional thickness measurements extracted from SBM, and to compare these models to diagnostic models based on volumetric morphometry. Our study included 22 subjects with ASD (mean age 9.2+/-2.1 years) and 16 volunteer controls (mean age 10.0+/-1.9 years). Using SBM, we obtained regional cortical thicknesses for 66 brain structures for each subject. In addition, we obtained volumes for the same 66 structures for these subjects. To generate diagnostic models, we employed four machine-learning techniques: support vector machines (SVMs), multilayer perceptrons (MLPs), functional trees (FTs), and logistic model trees (LMTs). We found that thickness-based diagnostic models were superior to those based on regional volumes. For thickness-based classification, LMT achieved the best classification performance, with accuracy=87%, area under the receiver operating characteristic (ROC) curve (AUC)=0.93, sensitivity=95%, and specificity=75%. For volume-based classification, LMT achieved the highest accuracy, with accuracy=74%, AUC=0.77, sensitivity=77%, and specificity=69%. The thickness-based diagnostic model generated by LMT included 7 structures. Relative to controls, children with ASD had decreased cortical thickness in the left and right pars triangularis, left medial orbitofrontal gyrus, left parahippocampal gyrus, and left frontal pole, and increased cortical thickness in the left caudal anterior cingulate and left precuneus. Overall, thickness-based classification outperformed volume-based classification across a variety of classification methods.

Neural substrates of visuospatial processing in distinct reference frames: Evidence from unilateral spatial neglect

There is evidence for different levels of visuospatial processing with their own frames of reference: viewer-centered, stimulus-centered, and object-centered. The neural locus of these levels can be explored by examining lesion location in subjects with unilateral spatial neglect (USN) manifest in these reference frames. Most studies regarding the neural locus of USN have treated it as a homogenous syndrome, resulting in conflicting results. In order to further explore the neural locus of visuospatial processes differentiated by frame of reference, we presented a battery of tests to 171 subjects within 48 hr after right supratentorial ischemic stroke before possible structural and/or functional reorganization. The battery included MR perfusion weighted imaging (which shows hypoperfused regions that may be dysfunctional), diffusion weighted imaging (which reveals areas of infarct or dense ischemia shortly after stroke onset), and tests designed to disambiguate between various types of neglect. Results were consistent with a dorsal/ventral stream distinction in egocentric/allocentric processing. We provide evidence that portions of the dorsal stream of visual processing, including the right supramarginal gyrus, are involved in spatial encoding in egocentric coordinates, whereas parts of the ventral stream (including the posterior inferior temporal gyrus) are involved in allocentric encoding.

Neural regions essential for reading and spelling of words and pseudowords

To identify dysfunctional brain regions critical for impaired reading/spelling of words/pseudowords by evaluating acute stroke patients on lexical tests and magnetic resonance imaging, before recovery or reorganization of structure–function relationships.

Towards effective and rewarding data sharing.

Recently issued NIH policy statement and implementation guidelines (National Institutes of Health, 2003) promote the sharing of research data. While urging that “all data should be considered for data sharing” and “data should be made as widely and freely available as possible” the current policy requires only high-direct-cost (>US

Optimized prostate biopsy via a statistical atlas of cancer spatial distribution

500,000/yr) grantees to share research data, starting 1 October 2003. Data sharing is central to science, and we agree that data should be made available.

Structural MRI in Autism Spectrum Disorder

A methodology is presented for constructing a statistical atlas of spatial distribution of prostate cancer from a large patient cohort, and it is used for optimizing needle biopsy. An adaptive-focus deformable model is used for the spatial normalization and registration of 100 prostate histological samples, which were provided by the Center for Prostate Disease Research of the US Department of Defense, resulting in a statistical atlas of spatial distribution of prostate cancer. Based on this atlas, a statistical predictive model was developed to optimize the needle biopsy sites, by maximizing the probability of detecting cancer. Experimental results using cross-validation show that the proposed method can detect cancer with a 99% success rate using seven needles, in these samples.