Biomedical subjects

E H Herskovits

Publications and source records attributed to E H Herskovits.

8 recordsLinked to original sources

PubMed2001

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, warranty 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.

Brain↗

PubMed2001

Accuracy for detection of simulated lesions: comparison of fluid-attenuated inversion-recovery, proton density--weighted, and T2-weighted synthetic brain MR imaging.

OBJECTIVE: The objective of our study was to determine the effects of MR sequence (fluid-attenuated inversion-recovery [FLAIR], proton density--weighted, and T2-weighted) and of lesion location on sensitivity and specificity of lesion detection. MATERIALS AND METHODS: We generated FLAIR, proton density-weighted, and T2-weighted brain images with 3-mm lesions using published parameters for acute multiple sclerosis plaques. Each image contained from zero to five lesions that were distributed among cortical-subcortical, periventricular, and deep white matter regions; on either side; and anterior or posterior in position. We presented images of 540 lesions, distributed among 2592 image regions, to six neuroradiologists. We constructed a contingency table for image regions with lesions and another for image regions without lesions (normal). Each table included the following: the reviewer's number (1--6); the MR sequence; the side, position, and region of the lesion; and the reviewer's response (lesion present or absent [normal]). We performed chi-square and log-linear analyses. RESULTS: The FLAIR sequence yielded the highest true-positive rates (p < 0.001) and the highest true-negative rates (p < 0.001). Regions also differed in reviewers' true-positive rates (p < 0.001) and true-negative rates (p = 0.002). The true-positive rate model generated by log-linear analysis contained an additional sequence-location interaction. The true-negative rate model generated by log-linear analysis confirmed these associations, but no higher order interactions were added. CONCLUSION: We developed software with which we can generate brain images of a wide range of pulse sequences and that allows us to specify the location, size, shape, and intrinsic characteristics of simulated lesions. We found that the use of FLAIR sequences increases detection accuracy for cortical-subcortical and periventricular lesions over that associated with proton density- and T2-weighted sequences.

Adult↗

PubMed2000

Quantitative 1H MR spectroscopic imaging in early Rett syndrome.

OBJECTIVE: To determine cerebral regional concentrations of N-acetyl aspartate (NAA), total choline (Cho), and total creatine (Cr) in Rett syndrome (RS) using 1H magnetic resonance spectroscopic imaging (MRSI). BACKGROUND: The biochemical defect underlying RS is unknown. Because in vivo MRSI can detect important cerebral metabolites, MRSI has a potential to reveal impairment of regional cerebral metabolism in RS noninvasively. METHODS: High-resolution, multislice 1H MRSI was carried out in 17 girls with RS. The control group consisted of nine healthy children. RESULTS: In patients with RS, average Cho concentration was 12% higher (p < 0.005) and average NAA concentration 11% lower (p < 0.0001) compared with the control group. Regional metabolic differences included significantly lower NAA concentration in the frontal gray and white matter, insula, and hippocampus in RS; no difference in regional Cho and Cr concentrations were found. A 20 to 38% higher Cho:NAA ratio in frontal and parietal gray and white matter, insular gray matter, and hippocampus (p < 0.05) and a 14 to 47% lower NAA:Cr ratio in frontal cortical gray matter, parietal and temporal white matter, insula, and putamen (p < 0.05) were found in subjects with RS compared with controls. Patients with seizures had higher average concentrations of Cho, Cr, and NAA compared with those without seizures (8-19%, p < 0.05). CONCLUSION: Metabolic impairment in RS involves both gray and white matter and particularly involves frontal and parietal lobes and the insular cortex. Loss of NAA most likely reflects reduced neuronal and dendritic tree size; increased Cho concentration may result from gliosis.

Analysis of Variance↗

PubMed2000

A simulator for evaluating methods for the detection of lesion-deficit associations.

Although much has been learned about the functional organization of the human brain through lesion-deficit analysis, the variety of statistical and image-processing methods developed for this purpose precludes a closed-form analysis of the statistical power of these systems. Therefore, we developed a lesion-deficit simulator (LDS), which generates artificial subjects, each of which consists of a set of functional deficits, and a brain image with lesions; the deficits and lesions conform to predefined distributions. We used probability distributions to model the number, sizes, and spatial distribution of lesions, to model the structure-function associations, and to model registration error. We used the LDS to evaluate, as examples, the effects of the complexities and strengths of lesion-deficit associations, and of registration error, on the power of lesion-deficit analysis. We measured the numbers of recovered associations from these simulated data, as a function of the number of subjects analyzed, the strengths and number of associations in the statistical model, the number of structures associated with a particular function, and the prior probabilities of structures being abnormal. The number of subjects required to recover the simulated lesion-deficit associations was found to have an inverse relationship to the strength of associations, and to the smallest probability in the structure-function model. The number of structures associated with a particular function (i.e., the complexity of associations) had a much greater effect on the performance of the analysis method than did the total number of associations. We also found that registration error of 5 mm or less reduces the number of associations discovered by approximately 13% compared to perfect registration. The LDS provides a flexible framework for evaluating many aspects of lesion-deficit analysis.

Brain↗

PubMed2000

An architecture for a brain-image database.

The widespread availability of methods for noninvasive assessment of brain structure has enabled researchers to investigate neuroimaging correlates of normal aging, cerebrovascular disease, and other processes; we designate such studies as image-based clinical trials (IBCTs). We propose an architecture for a brain-image database, which integrates image processing and statistical operators, and thus supports the implementation and analysis of IBCTs. The implementation of this architecture is described and results from the analysis of image and clinical data from two IBCTs are presented. We expect that systems such as this will play a central role in the management and analysis of complex research data sets.

Aged↗

PubMed1999

Is the spatial distribution of brain lesions associated with closed-head injury predictive of subsequent development of attention-deficit/hyperactivity disorder? Analysis with brain-image database.

PURPOSE: To determine whether there is an association between the spatial distribution of lesions detected at magnetic resonance (MR) imaging of the brain in children after closed-head injury and the development of secondary attention-deficit/hyperactivity disorder (ADHD). MATERIALS AND METHODS: Data obtained from 76 children without prior history of ADHD were analyzed. MR images were obtained 3 months after closed-head injury. After manual delineation of lesions, images were registered to the Talairach coordinate system. For each subject, registered images and secondary ADHD status were integrated into a brain-image database, which contains depiction (visualization) and statistical analysis software. Using this database, we assessed visually the spatial distributions of lesions and performed statistical analysis of image and clinical variables. RESULTS: Of the 76 children, 15 developed secondary ADHD. Depiction of the data suggested that children who developed secondary ADHD had more lesions in the right putamen than children who did not develop secondary ADHD; this impression was confirmed statistically. After Bonferroni correction, we could not demonstrate significant differences between secondary ADHD status and lesion burdens for the right caudate nucleus or the right globus pallidus. CONCLUSION: Closed-head injury-induced lesions in the right putamen in children are associated with subsequent development of secondary ADHD. Depiction software is useful in guiding statistical analysis of image data.

Adolescent↗

PubMed1991

Algorithms for Bayesian belief-network precomputation.

Bayesian belief networks provide an intuitive and concise means of representing probabilistic relationships among the variables in expert systems. A major drawback to this methodology is its computational complexity. We present an introduction to belief networks, and describe methods for precomputing, or caching, part of a belief network based on metrics of probability and expected utility. These algorithms are examples of a general method for decreasing expected running time for probabilistic inference. We first present the necessary background, and then present algorithms for producing caches based on metrics of expected probability and expected utility. We show how these algorithms can be applied to a moderately complex belief network, and present directions for future research.

Algorithms↗

PubMedDate not supplied

A brain image database for structure/function analysis.

BACKGROUND AND PURPOSE: Lesion-deficit-based structure-function analysis has traditionally been empirical and nonquantitative. Our purpose was to establish a new brain image database (BRAID) that allows the statistical correlation of brain functional measures with anatomic lesions revealed by clinical brain images. METHODS: Data on 303 participants in the MR Feasibility Study of the Cardiovascular Health Study were tested for lesion/deficit correlations. Functional data were derived from a limited neurologic examination performed at the time of the MR examination. Image data included 3D lesion descriptions derived from the MR examinations by hand segmentation. MR images were normalized in-plane using local, linear Talairach normalization. A database was implemented to support spatial data structures and associated geometric and statistical operations. The database stored the segmented lesions, patient functional scores, and several anatomic atlases. Lesion-deficit association was sought by contingency testing (chi2-test) for every possible combination of each neurologic variable and each labeled atlas structure. Significant associations that confirmed accepted lesion-deficit relationships were sought. RESULTS: Two-hundred thirty-five infarctlike lesions in 117 subjects were viewed collectively after mapping into Talairach cartesian coordinates. Anatomic structures most strongly correlated with neurologic deficits tended to be situated in anatomically appropriate areas. For example, infarctlike lesions associated with visual field defects were correlated with structures in contralateral occipital structures, including the optic radiations and occipital gyri. CONCLUSION: Known lesion-deficit correlations can be established by a database using a standard coordinate system for representing spatial data and incorporating functional and structural data together with appropriate query mechanisms. Improvements and further applications of this methodology may provide a powerful technique for uncovering new structure-function relationships.

Brain↗