Biomedical subjects

C Davatzikos

Publications and source records attributed to C Davatzikos.

At least 19 recordsLinked to original sources

PubMed2006

Past adult lead exposure is linked to neurodegeneration measured by brain MRI.

OBJECTIVE: To determine whether cumulative lead dose in former organolead workers was associated with MRI measures of white matter lesions (WML) and global and structure-specific brain volumes. METHODS: MRIs, tibia lead, and other measures were obtained from 532 former organolead workers with a mean age of 56 years and a mean of 18 years since last occupational exposure to lead. Cumulative lead dose was measured by tibia lead, obtained by X-ray fluorescence, and expressed as microg lead per gram of bone mineral (microg Pb/g). WML were evaluated using the Cardiovascular Health Study grading scale. A total of 21 global and specific brain regions were evaluated. RESULTS: A total of 36% of individuals had WML grade of 1 to 7 (0 to 9 scale). Increasing peak tibia lead was associated with increasing WML grade (p = 0.004). The adjusted OR for a 1 microg Pb/g increase in tibia lead was 1.042 (95% CI = 1.021, 1.063) for a CHS grade of 5+ (> or = 5 vs < 5). In linear regression, the coefficient for tibia lead was negative for associations with all structures. Higher tibia lead was significantly related to smaller total brain volume, frontal and total gray matter volume, and parietal white matter volume. Of nine smaller specific regions of interest, higher tibia lead was associated with smaller volumes for the cingulate gyrus and insula. CONCLUSIONS: These data suggest that cumulative lead dose is associated with persistent brain lesions, and may explain previous findings of a progressive decline in cognitive function.

Adult↗

PubMed2005

Classifying spatial patterns of brain activity with machine learning methods: application to lie detection.

Patterns of brain activity during deception have recently been characterized with fMRI on the multi-subject average group level. The clinical value of fMRI in lie detection will be determined by the ability to detect deception in individual subjects, rather than group averages. High-dimensional non-linear pattern classification methods applied to functional magnetic resonance (fMRI) images were used to discriminate between the spatial patterns of brain activity associated with lie and truth. In 22 participants performing a forced-choice deception task, 99% of the true and false responses were discriminated correctly. Predictive accuracy, assessed by cross-validation in participants not included in training, was 88%. The results demonstrate the potential of non-linear machine learning techniques in lie detection and other possible clinical applications of fMRI in individual subjects, and indicate that accurate clinical tests could be based on measurements of brain function with fMRI.

Adult↗

PubMed2003

Correlation of corpus callosal morphometry with cognitive and motor function in periventricular leukomalacia.

PURPOSE: We aim to correlate size and shape of corpus callosum with severity of motor and cognitive impairments in children with periventricular leukomalacia (PVL). METHODS: Children with PVL were stratified based on the severity of their motor and cognitive impairments. An age-matched control group was established. The corpus callosum was identified on mid-sagittal T (1)-weighted spin-echo (TR/TE: 550/15) MR images. The shape characteristics of the corpus callosum were measured with respect to a template via a shape transformation. The degree of callosal-shape transformation was quantified by a deformation function, which in turn was compared, using point-wise T-tests, for controls versus patients, diplegic versus quadriplegic patients, and patients with mild versus severe cognitive impairment. RESULTS: 29 children with spastic cerebral palsy and PVL and 32 age-matched controls were identified. In the PVL group, the entire corpus callosum was significantly smaller than in the control group (p value = 0.001). Significant differences existed in the shape of the corpus callosum between patients with diplegic versus quadriplegic and between patients with severe versus mild cognitive impairment. CONCLUSION: Global and regional corpus callosal morphology can be quantified using deformation functions.

Adolescent↗

PubMed2001

Accuracy and sensitivity of detection of activation foci in the brain via statistical parametric mapping: a study using a PET simulator.

Statistical parametric mapping (SPM) is currently the most widely used method for analysis of functional activation images. This paper reports a quantitative evaluation of the sensitivity and accuracy of SPM, using a realistic simulator of PET image formation, which accounted for the main physical processes involved in PET, including attenuation, scatter, randoms, Poisson noise, and limited detector resolution. Activation foci of the brain were simulated by placing spheres of specified activities in particular locations. Using these data, the sensitivity and accuracy of SPM in detecting activation foci was measured for different versions of the SPM spatial normalization method and for an elastic warping method referred to as STAR (spatial transformation algorithm for registration). The STAR method resulted in relatively better registration and hence better detection of the activation foci. A secondary goal of the paper was to evaluate the improvement in detection sensitivity obtained by applying an atlas-based adaptive smoothing method instead of the usual Gaussian filtering method. The results indicate some limitations of statistical parametric mapping, assist in the correct interpretation of the SPM maps, and point to future research directions in functional image analysis.

Adolescent↗

PubMed2001

Voxel-based morphometry using the RAVENS maps: methods and validation using simulated longitudinal atrophy.

Statistical analysis of anatomical maps in a stereotaxic space has been shown to be a useful tool in population-based studies for quantifying local anatomical differences or changes, without a priori assumptions about the location and extent of the regions of interest. This paper presents an extension and validation of a previously published methodology, referred to as RAVENS, for characterizing regional atrophy in the brain. A new method for elastic, volume-preserving spatial normalization, which allows for accurate quantification of very localized atrophy, is used. The RAVENS methodology was tested on images with simulated atrophy within two gyri: precentral and superior temporal. It was found to accurately determine the regions of atrophy, despite their localized nature and the interindividual variability of cortical structures. Moreover, it was found to perform substantially better than the voxel-based morphology method of SPM'99. Improved sensitivity was achieved at the expense of human effort involved in defining a number of sulcal curves that serve as constraints on the 3D elastic warping.

Atrophy↗

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

A framework for predictive modeling of anatomical deformations.

A framework for modeling and predicting anatomical deformations is presented, and tested on simulated images. Although a variety of deformations can be modeled in this framework, emphasis is placed on surgical planning, and particularly on modeling and predicting changes of anatomy between preoperative and intraoperative positions, as well as on deformations induced by tumor growth. Two methods are examined. The first is purely shape-based and utilizes the principal modes of co-variation between anatomy and deformation in order to statistically represent deformability. When a patient's anatomy is available, it is used in conjunction with the statistical model to predict the way in which the anatomy will/can deform. The second method is related, and it uses the statistical model in conjunction with a biomechanical model of anatomical deformation. It examines the principal modes of co-variation between shape and forces, with the latter driving the biomechanical model, and thus predicting deformation. Results are shown on simulated images, demonstrating that systematic deformations, such as those resulting from change in position or from tumor growth, can be estimated very well using these models. Estimation accuracy will depend on the application, and particularly on how systematic a deformation of interest is.

Biomechanical Phenomena↗

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

Optimization of multiple-isocenter treatment planning for linac-based stereotactic radiosurgery.

OBJECTIVE: Computer-assisted treatment planning for linac-based radiosurgery is still an open research problem, especially for multiple-isocenter procedures, primarily due to its high complexity and computational requirements. This paper focuses on the optimization of multiple-isocenter treatment planning for linac systems, and addresses several important issues associated with multiple isocenters, such as dose conformality, homogeneity, and optimization of isocenter position and dose. METHODS: The key idea behind our approach is that the desired dose distribution can be decomposed into a number of fundamental components. In the current paper, an analytical form, the so-called Ellipsoidal Dose Distribution Estimation (EDDE) model, represents each component. We establish ways (arc configurations) to achieve such ellipsoidal doses of arbitrary position, orientation, and size. Since the EDDE model is described by relatively few parameters, it allows very quick estimation of the dose distribution corresponding to a particular isocenter and thus makes the optimization of isocenter position very efficient. It is further used in a framework for optimal treatment planning, in which a number of ellipsoidal dose distributions, each corresponding to a different isocenter, are optimally placed to cover the target while sparing healthy tissue. RESULTS: The general ellipsoidal dose distribution of linac-based radiosurgery is summarized as a mathematical model with the aid of supporting experiments. Comparisons between the EDDE-optimized and clinically implemented plans are made, revealing the superior performance of the former. In addition, a dramatic reduction in planning time is achieved using the EDDE model. CONCLUSION: The proposed EDDE model is a useful and effective dose model in multiple-isocenter treatment planning for linac-based radiosurgery.

Brain Neoplasms↗

PubMed2000

One-year age changes in MRI brain volumes in older adults.

Longitudinal studies indicate that declines in cognition and memory accelerate after age 70 years. The neuroanatomic and neurophysiologic underpinnings of cognitive change are unclear, as there is little information on longitudinal brain changes. We are conducting a longitudinal neuroimaging study of nondemented older participants in the Baltimore Longitudinal Study of Aging. This report focuses on age and sex differences in brain structure measured by magnetic resonance imaging during the first two annual evaluations. Cross-sectional results from 116 participants aged 59-85 years reveal significantly larger ventricular volumes and smaller gray and white matter volumes in older compared with younger participants and in men compared with women. Regional brain volumes show that the effects of age and sex are not uniform across brain regions. Age differences are greatest for the parietal region. Sex differences tend to be larger for frontal and temporal than parietal and occipital regions. Longitudinal analysis demonstrates an increase of 1526 mm(3) in ventricular volume over 1 year, but no detectable change in total or regional brain volumes. Definition of the pattern and rate of longitudinal brain changes will facilitate the detection of pathological brain changes, which may be predictors of dementia.

Age Factors↗

PubMed1999

Nonlinear elastic registration of brain images with tumor pathology using a biomechanical model.

A biomechanical model of the brain is presented, using a finite-element formulation. Emphasis is given to the modeling of the soft-tissue deformations induced by the growth of tumors and its application to the registration of anatomical atlases, with images from patients presenting such pathologies. First, an estimate of the anatomy prior to the tumor growth is obtained through a simulated biomechanical contraction of the tumor region. Then a normal-to-normal atlas registration to this estimated pre-tumor anatomy is applied. Finally, the deformation from the tumor-growth model is applied to the resultant registered atlas, producing an atlas that has been deformed to fully register to the patient images. The process of tumor growth is simulated in a nonlinear optimization framework, which is driven by anatomical features such as boundaries of brain structures. The deformation of the surrounding tissue is estimated using a nonlinear elastic model of soft tissue under the boundary conditions imposed by the skull, ventricles, and the falx and tentorium. A preliminary two-dimensional (2-D) implementation is presented in this paper, and tested on both simulated and patient data. One of the long-term goals of this work is to use anatomical brain atlases to estimate the locations of important brain structures in the brain and to use these estimates in presurgical and radiosurgical planning systems.

Biomechanical Phenomena↗

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↗

PubMed1998

Mapping image data to stereotaxic spaces: applications to brain mapping.

A methodology for spatial normalization of image data is presented. This methodology is based on a map between homologous features of an individual brain and the target brain, which is used to drive a three-dimensional elastic warping transformation. Functional or structural information present in the original, nonnormalized images is preserved during this transformation. In particular, information such as the volume or the total amount of a radioactive agent in any brain region can be calculated directly from the normalized images. Moreover, subtle morphological characteristics of an individual brain are captured by the properties of the spatial transformation applied to that brain. Intersubject or interpopulation comparisons are performed by comparing the corresponding transformations.

Algorithms↗

PubMed1997

Spatial transformation and registration of brain images using elastically deformable models.

The development of algorithms for the spatial transformation and registration of tomographic brain images is a key issue in several clinical and basic science medical applications, including computer-aided neurosurgery, functional image analysis, and morphometrics. This paper describes a technique for the spatial transformation of brain images, which is based on elastically deformable models. A deformable surface algorithm is used to find a parametric representation of the outer cortical surface and then to define a map between corresponding cortical regions in two brain images. Based on the resulting map, a three-dimensional elastic warping transformation is then determined, which brings two images into register. This transformation models images as inhomogeneous elastic objects which are deformed into registration with each other by external force fields. The elastic properties of the images can vary from one region to the other, allowing more variable brain regions, such as the ventricles, to deform more freely than less variable ones. Finally, the framework of prestrained elasticity is used to model structural irregularities, and in particular the ventricular expansion occurring with aging or diseases, and the growth of tumors. Performance measurements are obtained using magnetic resonance images.

Aging↗

PubMed1997

Finding parametric representations of the cortical sulci using an active contour model.

The cortical sulci are brain structures resembling thin convoluted ribbons embedded in three dimensions. The importance of the sulci lies primarily in their relation to the cytoarchitectonic and functional organization of the underlying cortex and in their utilization as features in non-rigid registration methods. This paper presents a methodology for extracting parametric representations of the cerebral sulcus from magnetic resonance images. The proposed methodology is based on deformable models utilizing characteristics of the cortical shape. Specifically, a parametric representation of a sulcus is determined by the motion of an active contour along the medial surface of the corresponding cortical fold. The active contour is initialized along the outer boundary of the brain and deforms toward the deep root of a sulcus under the influence of an external force field, restricting it to lie along the medial surface of the particular cortical fold. A parametric representation of the medial surface of the sulcus is obtained as the active contour traverses the sulcus. Based on the first fundamental form of this representation, the location and degree of an interruption of a sulcus can be readily quantified; based on its second fundamental form, shape properties of the sulcus can be determined. This methodology is tested on magnetic resonance images and it is applied to three medical imaging problems: quantitative morphological analysis of the central sulcus; mapping of functional activation along the primary motor cortex and non-rigid registration of brain images.

Anatomy, Cross-Sectional↗

PubMed1992

Measurement of radiotracer concentration in brain gray matter using positron emission tomography: MRI-based correction for partial volume effects.

Accuracy in in vivo quantitation of brain function with positron emission tomography (PET) has often been limited by partial volume effects. This limitation becomes prominent in studies of aging and degenerative brain diseases where partial volume effects vary with different degrees of atrophy. The present study describes how the actual gray matter (GM) tracer concentration can be estimated using an algorithm that relates the regional fraction of GM to partial volume effects. The regional fraction of GM was determined by magnetic resonance imaging (MRI). The procedure is designated as GM PET. In computer simulations and phantom studies, the GM PET algorithm permitted a 100% recovery of the actual tracer concentration in neocortical GM and hippocampus, irrespective of the GM volume. GM PET was applied in a test case of temporal lobe epilepsy revealing an increase in radiotracer activity in GM that was undetected in the PET image before correction for partial volume effects. In computer simulations, errors in the segmentation of GM and errors in registration of PET and MRI images resulted in less than 15% inaccuracy in the GM PET image. In conclusion, GM PET permits accurate determination of the actual radiotracer concentration in human brain GM in vivo. The method differentiates whether a change in the apparent radiotracer concentration reflects solely an alteration in GM volume or rather a change in radiotracer concentration per unit volume of GM.

Algorithms↗

PubMedDate not supplied

Sex differences in anatomic measures of interhemispheric connectivity: correlations with cognition in women but not men.

A robust sex difference in the splenium of the corpus callosum, reflecting greater interhemispheric connectivity in women, was observed on magnetic resonance images from 114 individuals. In addition, bulbosity of the corpus callosum correlated with better cognitive performance in women but not in men, indicating that the degree of interhemispheric connectivity has different implications for men and women. These findings were based on a new image analysis technique which allows investigation of local variability in brain morphology.

Aged↗

PubMedDate not supplied

A computerized approach for morphological analysis of the corpus callosum.

OBJECTIVE: A new technique for analyzing the morphology of the corpus callosum is presented, and it is applied to a group of elderly subjects. MATERIALS AND METHODS: The proposed approach normalizes subject data into the Talairach space using an elastic deformation transformation. The properties of this transformation are used as a quantitative description of the callosal shape with respect to the Talairach atlas, which is treated as a standard. In particular, a deformation function measures the enlargement/shrinkage associated with this elastic deformation. Intersubject comparisons are made by comparing deformation functions. RESULTS: This technique was applied to eight male and eight female subjects. Based on the average deformation functions of each group, the posterior region of the female corpus callosum was found to be larger than its corresponding region in the males. The average callosal shape of each group was also found, demonstrating visually the callosal shape differences between the two groups in this sample. CONCLUSION: The proposed methodology utilizes the full resolution of the data, rather than relying on global descriptions such as area measurements. The application of this methodology to an elderly group indicated sex-related differences in the callosal shape and size.

Aged↗