Biomedical subjects

Michael I Miller

Publications and source records attributed to Michael I Miller.

At least 19 recordsLinked to original sources

PubMed2006

Estimating linear cortical magnification in human primary visual cortex via dynamic programming.

Human primary visual cortex is organized retinotopically, with adjacent locations in cortex representing adjacent locations on the retina. The spatial sampling in cortex is highly nonuniform: the amount of cortex devoted to a unit area of retina decreases with increasing retinal eccentricity. This sampling property can be quantified by the linear cortical magnification factor, which is expressed in terms of millimeters of cortex per degree of visual angle. In this paper, we present a new method using dynamic programming and fMRI retinotopic eccentricity mapping to estimate the linear cortical magnification factor in human primary visual cortex (V1). We localized cortical activity while subjects viewed each of seven stationary contrast- reversing radial checkerboard rings of equal thickness that tiled the visual field from 1.62 to 12.96 degrees of eccentricity. Imaging data from all epochs of each ring were contrasted with data from fixation epochs on a subject-by-subject basis. The resulting t statistic maps were then superimposed on a local coordinate system constructed from the gray/white matter boundary surface of each individual subject's occipital lobe, separately for each ring. Smoothed maps of functional activity on the cortical surface were constructed using orthonormal bases of the Laplace-Beltrami operator that incorporate the geometry of the cortical surface. This allowed us to stably track the ridge of maximum activation due to each ring via dynamic programming optimization over all possible paths on the cortical surface. We estimated the linear cortical magnification factor by calculating geodesic distances between activation ridges on the cortical surface in a population of five normal subjects. The reliability of these estimates was assessed by comparing results based on data from one quadrant to those based on data from the full hemifield along with a split-half reliability analysis.

Brain Mapping↗

PubMed2005

Evidence of structural remodeling in the dyssynchronous failing heart.

Ventricular remodeling of both geometry and fiber structure is a prominent feature of several cardiac pathologies. Advances in MRI and analytical methods now make it possible to measure changes of cardiac geometry, fiber, and sheet orientation at high spatial resolution. In this report, we use diffusion tensor imaging to measure the geometry, fiber, and sheet architecture of eight normal and five dyssynchronous failing canine hearts, which were explanted and fixed in an unloaded state. We apply novel computational methods to identify statistically significant changes of cardiac anatomic structure in the failing and control heart populations. The results demonstrate significant regional differences in geometric remodeling in the dyssynchronous failing heart versus control. Ventricular chamber dilatation and reduction in wall thickness in septal and some posterior and anterior regions are observed. Primary fiber orientation showed no significant change. However, this result coupled with the local wall thinning in the septum implies an altered transmural fiber gradient. Further, we observe that orientation of laminar sheets become more vertical in the early-activated septum, with no significant change of sheet orientation in the late-activated lateral wall. Measured changes in both fiber gradient and sheet structure will affect both the heterogeneity of passive myocardial properties as well as electrical activation of the ventricles.

Animals↗

PubMed2005

Abnormalities of hippocampal surface structure in very mild dementia of the Alzheimer type.

To better define the pattern of hippocampal deformity early in the course of Alzheimer's disease, we compared the pattern of hippocampal surface variation in subjects with very mild dementia of the Alzheimer type (DAT) and nondemented subjects. The surface of the hippocampus was divided a priori on a neuroanatomical template into three zones approximating the locations of underlying subfields [Csernansky, J.G., Wang, L., Swank, J., Miller, J.P., Gado, M., McKeel, D., Miller, M.I., Morris, J.C., 2005. Preclinical detection of Alzheimer's disease: hippocampal shape and volume predict dementia onset in the elderly. NeuroImage 25, 783--792]; i.e., a lateral zone (LZ) approximating the CA1 subfield, a superior zone (SZ) approximating the combined CA2, CA3, CA4 subfields and the gyrus dentatus (GD), and an inferior-medial zone (IMZ) approximating the subiculum. Large-deformation high-dimensional brain mapping (HDBM-LD) was used to generate the hippocampal surfaces of all subjects and to register the surface zones across subjects. Average variations within each zone were calculated for the subjects with very mild DAT as compared to the average of the nondemented subjects. After correcting for multiple comparisons, the very mild DAT subjects showed significant inward variation in the left and right LZ, the left and right IMZ, but not in the left and right SZ as compared to nondemented subjects. In logistic regression analyses, inward variation of the left and right LZ or IMZ by 0.1 mm relative to the average of the nondemented subjects increased the odds of the subject being a very mild DAT subject (range-1.18 to 1.57) rather than being a nondemented subject. The odds ratios for the left and right SZ were not significant. These results represent a replication of our previous findings [Csernansky, J.G., Wang, L., Joshi, S., Miller, J.P., Gado, M., Kido, D., McKeel, D., Morris, J.C., Miller, M.I., 2000. Early DAT is distinguished from aging by high-dimensional mapping of the hippocampus. Neurology 55, 1636--1643.] and suggest that inward deformities of the hippocampal surface in proximity to the CA1 subfield and subiculum can be used to distinguish subjects with very mild DAT from nondemented subjects.

Activities of Daily Living↗

PubMed2005

Increasing the power of functional maps of the medial temporal lobe by using large deformation diffeomorphic metric mapping.

The functional magnetic resonance imagery responses of declarative memory tasks in the medial temporal lobe (MTL) are examined by using large deformation diffeomorphic metric mapping (LDDMM) to remove anatomical variations across subjects. LDDMM is used to map the structures of the MTL in multiple subjects into extrinsic atlas coordinates; these same diffeomorphic mappings are used to transfer the corresponding functional data activation to the same extrinsic coordinates. The statistical power in the averaged LDDMM mapped signals is significantly increased over conventional Talairach-Tournoux averaging. Activation patterns are highly localized within the MTL. Whereas the present demonstration has been aimed at enhancing alignment within the MTL, this technique is general and can be applied throughout the brain.

Brain Mapping↗

PubMed2005

DTI tractography based parcellation of white matter: application to the mid-sagittal morphology of corpus callosum.

Morphology of the corpus callosum (CC) at the mid-sagittal level has been a target of extensive studies. However, the lack of internal structures and its polymorphism make it a challenging task to quantitatively analyze shape differences among subjects. In this paper, diffusion tensor Imaging (DTI) and tract tracing technique were applied to incorporate cortical connectivity information to the morphological study. The CC was parcellated into six major subdivisions based on trajectories to different cortical areas. This subdivision was performed for eight normal subjects and one stroke patient. The parcellated CCs of the normal subjects were normalized for morphological analysis. When comparing the stroke patient to the normal population, we detected significant atrophy in the motor and sensory areas of the patient CC, in line with the clinical deficits. This approach provides a new tool to investigate callosal morphology and functional relationships.

Adult↗

PubMed2005

Mapping postnatal mouse brain development with diffusion tensor microimaging.

While mouse brain development has been extensively studied using histology, quantitative characterization of morphological changes is still a challenging task. This paper presents how developing brain structures can be quantitatively characterized with magnetic resonance diffusion tensor microimaging coupled with techniques of computational anatomy. High resolution diffusion tensor images of ex vivo postnatal mouse brains provide excellent contrasts to reveal the evolutions of mouse forebrain structures. Using anatomical landmarks defined on diffusion tensor images, tissue level growth patterns of mouse brains were quantified. The results demonstrate the use of these techniques to three-dimensionally and quantitatively characterize brain growth.

Algorithms↗

PubMed2005

Magnetic resonance diffusion tensor microimaging reveals a role for Bcl-x in brain development and homeostasis.

A new technique based on diffusion tensor imaging and computational neuroanatomy was developed to efficiently and quantitatively characterize the three-dimensional morphology of the developing brains. The technique was used to analyze the phenotype of conditional Bcl-x knock-out mice, in which the bcl-x gene was deleted specifically in neurons of the cerebral cortex and hippocampus beginning at embryonic day 13.5 as cells became postmitotic. Affected brain regions and associated axonal tracts showed severe atrophy in adult Bcl-x-deficient mice. Longitudinal studies revealed that these phenotypes are established by regressive processes that occur primarily during the first postnatal week, whereas neurogenesis and migration showed no obvious abnormality during embryonic stages. Specific families of white matter tracts that once formed normally during the embryonic stages underwent dramatic degeneration postnatally. Thus, this technique serves as a powerful tool to efficiently localize temporal and spatial manifestation of morphological phenotype.

Animals↗

PubMed2005

A stochastic model for studying the laminar structure of cortex from MRI.

The human cerebral cortex is a laminar structure about 3 mm thick, and is easily visualized with current magnetic resonance (MR) technology. The thickness of the cortex varies locally by region, and is likely to be influenced by such factors as development, disease and aging. Thus, accurate measurements of local cortical thickness are likely to be of interest to other researchers. We develop a parametric stochastic model relating the laminar structure of local regions of the cerebral cortex to MR image data. Parameters of the model include local thickness, and statistics describing white, gray and cerebrospinal fluid (CSF) image intensity values as a function of the normal distance from the center of a voxel to a local coordinate system anchored at the gray/white matter interface. Our fundamental data object, the intensity-distance histogram (IDH), is a two-dimensional (2-D) generalization of the conventional 1-D image intensity histogram, which indexes voxels not only by their intensity value, but also by their normal distance to the gray/white interface. We model the IDH empirically as a marked Poisson process with marking process a Gaussian random field model of image intensity indexed against normal distance. In this paper, we relate the parameters of the IDH model to the local geometry of the cortex. A maximum-likelihood framework estimates the parameters of the model from the data. Here, we show estimates of these parameters for 10 volumes in the posterior cingulate, and 6 volumes in the anterior and posterior banks of the central sulcus. The accuracy of the estimates is quantified via Cramer-Rao bounds. We believe that this relatively crude model can be extended in a straightforward fashion to other biologically and theoretically interesting problems such as segmentation, surface area estimation, and estimating the thickness distribution in a variety of biologically relevant contexts.

Algorithms↗

PubMed2005

Large deformation diffeomorphic metric mapping of vector fields.

This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRIs) through the large deformation diffeomorphic metric mapping of vector fields, focusing on the fiber orientations, considered as unit vector fields on the image volume. We study a suitable action of diffeomorphisms on such vector fields, and provide an extension of the Large Deformation Diffeomorphic Metric Mapping framework to this type of dataset, resulting in optimizing for geodesics on the space of diffeomorphisms connecting two images. Existence of the minimizers under smoothness assumptions on the compared vector fields is proved, and coarse to fine hierarchical strategies are detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI heart images.

Algorithms↗

PubMed2005

Clutter invariant ATR.

One of the central problems in Automated Target Recognition is to accommodate the infinite variety of clutter in real military environments. The principle focus of our paper is on the construction of metric spaces where the metric measures the distance between objects of interest invariant to the infinite variety of clutter. Such metrics are formulated using second-order random field models. Our results indicate that this approach significantly improves detection/classification rates of targets in clutter.

Algorithms↗

PubMed2005

Measuring and mapping cardiac fiber and laminar architecture using diffusion tensor MR imaging.

The ventricular myocardium is known to exhibit a complex spatial organization, with fiber orientation varying as a function of transmural location. It is now well established that diffusion tensor magnetic resonance imaging (DTMRI) may be used to measure this fiber orientation at high spatial resolution. Cardiac fibers are also known to be organized in sheets with surface orientation varying throughout the ventricles. This article reviews results on use of DTMRI for measuring ventricular fiber orientation, as well as presents new results providing strong evidence that the tertiary eigenvector of the diffusion tensor is aligned locally with the cardiac sheet surface normal. Considered together, these data indicate that DTMRI may be used to reconstruct both ventricular fiber and sheet organization. This article also presents the large deformation diffeomorphic metric mapping (LDDMM) algorithm and shows that this algorithm may be used to bring ensembles of imaged and reconstructed hearts into correspondence (e.g., registration) so that variability of ventricular geometry, fiber, and sheet orientation may be quantified. Ventricular geometry and fiber structure is known to be remodeled in a range of disease processes; however, descriptions of this remodeling have remained subjective and qualitative. We anticipate that use of DTMRI for reconstruction of ventricular anatomy coupled with application of the LDDMM method for image volume registration will enable the detection and quantification of changes in cardiac anatomy that are characteristic of specific disease processes in the heart. Finally, we show that epicardial electrical mapping and DTMRI imaging may be performed in the same hearts. The anatomic data may then be used to simulate electrical conduction in a computational model of the very same heart that was mapped electrically. This facilitates direct comparison and testing of model versus experimental results and opens the door to quantitative measurement, modeling, and analysis of the ways in which remodeling of ventricular microanatomy influences electrical conduction in the heart.

Diffusion Magnetic Resonance Imaging↗

PubMed2004

Validation of semiautomated methods for quantifying cingulate cortical metrics in schizophrenia.

This paper validates semiautomated methods for reconstructing cortical surfaces of the cingulate gyrus from high-resolution magnetic resonance (MR) images. Bayesian segmentation was used to delineate the image voxels into five tissue types: cerebrospinal fluid (CSF), gray matter (GM), white matter (WM), and partial volumes of CSF/GM and GM/WM; the tissues were then recalibrated as CSF, GM, and WM via the Neyman-Pearson Likelihood Ratio Test. To generate cortical surfaces at the interface of GM and WM, the thresholds between the tissue types were first used to reassign partial volume voxels to CSF, GM, and WM with minimum error (that varied from 0.06 to 0.15 for the 10 subjects). Next, topology-correct cortical surfaces were generated and validated with almost all surface vertices lying within one voxel (0.5 mm) of hand contours. Dynamic programming was used to delineate and extract the cingulate gyrus from the cortical surfaces based on its gyral and sulcal boundaries. The intraclass correlation coefficient for surface area obtained by two raters for all 10 surfaces was 0.82. In addition, by repeating the entire procedure three times in one subject, we obtained a coefficient of variation of 0.0438 for surface area.

Adult↗

PubMed2004

Computational cardiac anatomy using MRI.

Ventricular geometry and fiber orientation may undergo global or local remodeling in cardiac disease. However, there are as yet no mathematical and computational methods for quantifying variation of geometry and fiber orientation or the nature of their remodeling in disease. Toward this goal, a landmark and image intensity-based large deformation diffeomorphic metric mapping (LDDMM) method to transform heart geometry into common coordinates for quantification of shape and form was developed. Two automated landmark placement methods for modeling tissue deformations expected in different cardiac pathologies are presented. The transformations, computed using the combined use of landmarks and image intensities, yields high-registration accuracy of heart anatomies even in the presence of significant variation of cardiac shape and form. Once heart anatomies have been registered, properties of tissue geometry and cardiac fiber orientation in corresponding regions of different hearts may be quantified.

Algorithms↗

PubMed2004

Computational anatomy: shape, growth, and atrophy comparison via diffeomorphisms.

Computational anatomy (CA) is the mathematical study of anatomy I in I = I(alpha) o G, an orbit under groups of diffeomorphisms (i.e., smooth invertible mappings) g in G of anatomical exemplars I(alpha) in I. The observable images are the output of medical imaging devices. There are three components that CA examines: (i) constructions of the anatomical submanifolds, (ii) comparison of the anatomical manifolds via estimation of the underlying diffeomorphisms g in G defining the shape or geometry of the anatomical manifolds, and (iii) generation of probability laws of anatomical variation P(.) on the images I for inference and disease testing within anatomical models. This paper reviews recent advances in these three areas applied to shape, growth, and atrophy.

Algorithms↗

PubMed2004

Computational anatomy and neuropsychiatric disease: probabilistic assessment of variation and statistical inference of group difference, hemispheric asymmetry, and time-dependent change.

Three components of computational anatomy (CA) are reviewed in this paper: (i) the computation of large-deformation maps, that is, for any given coordinate system representations of two anatomies, computing the diffeomorphic transformation from one to the other; (ii) the computation of empirical probability laws of anatomical variation between anatomies; and (iii) the construction of inferences regarding neuropsychiatric disease states. CA utilizes spatial-temporal vector field information obtained from large-deformation maps to assess anatomical variabilities and facilitate the detection and quantification of abnormalities of brain structure in subjects with neuropsychiatric disorders. Neuroanatomical structures are divided into two types: subcortical structures-gray matter (GM) volumes enclosed by a single surface-and cortical mantle structures-anatomically distinct portions of the cerebral cortical mantle layered between the white matter (WM) and cerebrospinal fluid (CSF). Because of fundamental differences in the geometry of these two types of structures, image-based large-deformation high-dimensional brain mapping (HDBM-LD) and large-deformation diffeomorphic metric matching (LDDMM) were developed for the study of subcortical structures and labeled cortical mantle distance mapping (LCMDM) was developed for the study of cortical mantle structures. Studies of neuropsychiatric disorders using CA usually require the testing of hypothesized group differences with relatively small numbers of subjects per group. Approaches that increase the power for testing such hypotheses include methods to quantify the shapes of individual structures, relationships between the shapes of related structures (e.g., asymmetry), and changes of shapes over time. Promising preliminary studies employing these approaches to studies of subjects with schizophrenia and very mild to mild Alzheimer's disease (AD) are presented.

Algorithms↗

PubMed2004

Abnormalities of thalamic volume and shape in schizophrenia.

OBJECTIVE: Postmortem and neuroimaging studies of schizophrenia have reported deficits in the volume of the thalamus and its component nuclei. However, the pattern of shape change associated with such volume loss has not been investigated. In this study, alterations in thalamic volume, shape, and symmetry were compared in subjects with and without schizophrenia. METHOD: T(1)-weighted magnetic resonance scans were collected in 52 schizophrenia and 65 comparison subjects matched for age, gender, race, and parental socioeconomic status. High-dimensional (large-deformation) brain mapping was used to assess thalamic morphology. RESULTS: Significant differences in thalamic volume, deformities of thalamic shape at the anterior and posterior extremes of the structure, and a significant exaggeration of thalamic asymmetry (i.e., left smaller than right) were found in the schizophrenia subjects. After covarying for total cerebral volume, the difference in thalamic volume became insignificant. When information about thalamic shape was combined with previously collected information about hippocampal shape, the discrimination between schizophrenia patients and comparison subjects was improved. CONCLUSIONS: Thalamic volume was smaller than normal in schizophrenia patients, but only proportionate to reductions in reduced total cerebral volume. The presence of changes in thalamic shape and asymmetry suggest greater pathologic involvement of individual nuclei at its anterior and posterior extremes of the thalamic complex.

Adult↗

PubMed2003

Hippocampal deformities in the unaffected siblings of schizophrenia subjects.

BACKGROUND: Neuroanatomical abnormalities have been reported in schizophrenia subjects and their relatives and may be related to genetic vulnerability. The objective of this study was to further elucidate hippocampal deformities as a marker of genetic vulnerability for schizophrenia. METHODS: Magnetic resonance scans were collected in 13 pairs of schizophrenics and their unaffected siblings from families with multiple affected members, in 12 schizophrenics from families without another affected member, and in 10 healthy controls. Hippocampal volume and shape were compared using large-deformation high-dimensional brain mapping. RESULTS: Decreases in hippocampal volume, covaried for total cerebral volume, were observed in the schizophrenia subjects from families with multiple affected members, as well as in their unaffected siblings. Shape analysis demonstrated that both groups of schizophrenia subjects, as well as the unaffected siblings, could be distinguished from the controls; however, no significant difference in hippocampal shape was found between the schizophrenia subjects and their unaffected siblings. Visualization of the pattern of hippocampal shape deformity in both groups of schizophrenia subjects and in the unaffected siblings showed inward deformities of the head of the hippocampus. CONCLUSIONS: Deformations of hippocampal anatomy may be related to the genetic vulnerability of acquiring schizophrenia.

Adult↗

PubMed2003

Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging.

Rates of hippocampal volume loss have been shown to distinguish subjects with dementia of the Alzheimer type (DAT) from nondemented controls. In this study, we obtained magnetic resonance scans in 18 subjects with very mild DAT (CDR 0.5) and 26 age-matched nondemented controls (CDR 0) 2 years apart. Large-deformation high-dimensional brain mapping was used to quantify and compare changes in hippocampal shape as well as volume in the two groups of subjects. Hippocampal volume loss over time was significantly greater in the CDR 0.5 subjects (left = 8.3%, right = 10.2%) than in the CDR 0 subjects (left = 4.0%, right = 5.5%) (ANOVA, F = 7.81, P = 0.0078). We used singular-value decomposition and logistic regression models to quantify hippocampal shape change across time within individuals, and this shape change in the CDR 0.5 and CDR 0 subjects was found to be significantly different (Wilks's lambda, P = 0.014). Further, at baseline, CDR 0.5 subjects, in comparison to CDR 0 subjects, showed inward deformation over 38% of the hippocampal surface; after 2 years this difference grew to 47%. Also, within the CDR 0 subjects, shape change between baseline and follow-up was largely confined to the head of the hippocampus and subiculum, while in the CDR 0.5 subjects, shape change involved the lateral body of the hippocampus as well as the head region and subiculum. These results suggest that different patterns of hippocampal shape change in time as well as different rates of hippocampal volume loss distinguish very mild DAT from healthy aging.

Aged↗