Biomedical subjects

Christos Davatzikos

Publications and source records attributed to Christos Davatzikos.

At least 19 recordsLinked to original sources

PubMed2026

Sex-specific biological aging clocks across organs and omics.

Sex differentially shapes aging, neurodevelopment and neurodegenerative diseases such as Alzheimer's disease (AD). However, most biological aging clocks (artificial intelligence-predicted age minus chronological age) were trained on sex-pooled samples and implicitly assume sex invariance.Here we developed 38 sex-specific biological aging clocks across 15 organ systems. We first demonstrate the importance of sex-stratified training for constructing sex-specific healthy normative references and then reveal marked divergence between female and male clocks. Key genetic parameters and Mendelian randomization results indicate that organ-specific aging liability and its relationships to cardiometabolic, endocrine and mental traits are configured differently in females and males. Proteomic analyses identify distinct, organ-resolved synaptic, immune, vascular and metabolic networks that differentially track female and male biological aging. In longitudinal survival analyses, sex-specific clocks predict whole-body systemic diseases and all-cause mortality in a sex-dependent and organ-dependent manner. Further analyses reveal sex-dependent associations between the brain aging clock and cognitive decline trajectory during a preclinical AD clinical trial. Sex-stratified clocks may offer distinct value by defining biological age against sex-appropriate normative references and revealing sex-dependent genetic, molecular and clinical signatures that pooled models may obscure. Meanwhile, sex-pooled and sex-interaction approaches remain valuable, as human aging and disease also share fundamental biological similarities between females and males. Together, these findings reveal sex-specific biological aging signatures in aging, AD and systemic health, highlighting the need for explicitly sex-stratified modeling approaches.

Journal Article↗

PubMed2024

Sex and APOE ε4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer's disease.

INTRODUCTION: The effects of sex and apolipoprotein E (APOE)-Alzheimer's disease (AD) risk factors-on white matter microstructure are not well characterized. METHODS: Diffusion magnetic resonance imaging data from nine well-established longitudinal cohorts of aging were free water (FW)-corrected and harmonized. This dataset included 4741 participants (age = 73.06 ± 9.75) with 9671 imaging sessions over time. FW and FW-corrected fractional anisotropy (FAFWcorr) were used to assess differences in white matter microstructure by sex and APOE ε4 carrier status. RESULTS: Sex differences in FAFWcorr in projection tracts and APOE ε4 differences in FW limbic and occipital transcallosal tracts were most pronounced. DISCUSSION: There are prominent differences in white matter microstructure by sex and APOE ε4 carrier status. This work adds to our understanding of disparities in AD. Additional work to understand the etiology of these differences is warranted. HIGHLIGHTS: Sex and apolipoprotein E (APOE) ε4 carrier status relate to white matter microstructural integrity. Females generally have lower free water-corrected fractional anisotropy compared to males. APOE ε4 carriers tended to have higher free water than non-carriers.

Humans↗

PubMed2006

Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging.

We report evidence that computer-based high-dimensional pattern classification of magnetic resonance imaging (MRI) detects patterns of brain structure characterizing mild cognitive impairment (MCI), often a prodromal phase of Alzheimer's disease (AD). Ninety percent diagnostic accuracy was achieved, using cross-validation, for 30 participants in the Baltimore Longitudinal Study of Aging. Retrospective evaluation of serial scans obtained during prior years revealed gradual increases in structural abnormality for the MCI group, often before clinical symptoms, but slower increase for individuals remaining cognitively normal. Detecting complex patterns of brain abnormality in very early stages of cognitive impairment has pivotal importance for the detection and management of AD.

Aged↗

PubMed2006

Simulating deformations of MR brain images for validation of atlas-based segmentation and registration algorithms.

Simulated deformations and images can act as the gold standard for evaluating various template-based image segmentation and registration algorithms. Traditional deformable simulation methods, such as the use of analytic deformation fields or the displacement of landmarks followed by some form of interpolation, are often unable to construct rich (complex) and/or realistic deformations of anatomical organs. This paper presents new methods aiming to automatically simulate realistic inter- and intra-individual deformations. The paper first describes a statistical approach to capturing inter-individual variability of high-deformation fields from a number of examples (training samples). In this approach, Wavelet-Packet Transform (WPT) of the training deformations and their Jacobians, in conjunction with a Markov random field (MRF) spatial regularization, are used to capture both coarse and fine characteristics of the training deformations in a statistical fashion. Simulated deformations can then be constructed by randomly sampling the resultant statistical distribution in an unconstrained or a landmark-constrained fashion. The paper also describes a model for generating tissue atrophy or growth in order to simulate intra-individual brain deformations. Several sets of simulated deformation fields and respective images are generated, which can be used in the future for systematic and extensive validation studies of automated atlas-based segmentation and deformable registration methods. The code and simulated data are available through our Web site.

Algorithms↗

PubMed2006

Statistical representation of high-dimensional deformation fields with application to statistically constrained 3D warping.

This paper proposes a 3D statistical model aiming at effectively capturing statistics of high-dimensional deformation fields and then uses this prior knowledge to constrain 3D image warping. The conventional statistical shape model methods, such as the active shape model (ASM), have been very successful in modeling shape variability. However, their accuracy and effectiveness typically drop dramatically in high-dimensionality problems involving relatively small training datasets, which is customary in 3D and 4D medical imaging applications. The proposed statistical model of deformation (SMD) uses wavelet-based decompositions coupled with PCA in each wavelet band, in order to more accurately estimate the pdf of high-dimensional deformation fields, when a relatively small number of training samples are available. SMD is further used as statistical prior to regularize the deformation field in an SMD-constrained deformable registration framework. As a result, more robust registration results are obtained relative to using generic smoothness constraints on deformation fields, such as Laplacian-based regularization. In experiments, we first illustrate the performance of SMD in representing the variability of deformation fields and then evaluate the performance of the SMD-constrained registration, via comparing a hierarchical volumetric image registration algorithm, HAMMER, with its SMD-constrained version, referred to as SMD+HAMMER. This SMD-constrained deformable registration framework can potentially incorporate various registration algorithms to improve robustness and stability via statistical shape constraints.

Algorithms↗

PubMed2006

Deformable registration of brain tumor images via a statistical model of tumor-induced deformation.

An approach to the deformable registration of three-dimensional brain tumor images to a normal brain atlas is presented. The approach involves the integration of three components: a biomechanical model of tumor mass-effect, a statistical approach to estimate the model's parameters, and a deformable image registration method. Statistical properties of the sought deformation map from the atlas to the image of a tumor patient are first obtained through tumor mass-effect simulations on normal brain images. This map is decomposed into the sum of two components in orthogonal subspaces, one representing inter-individual differences in brain shape, and the other representing tumor-induced deformation. For a new tumor case, a partial observation of the sought deformation map is obtained via deformable image registration and is decomposed into the aforementioned spaces in order to estimate the mass-effect model parameters. Using this estimate, a simulation of tumor mass-effect is performed on the atlas image in order to generate an image that is similar to tumor patient's image, thereby facilitating the atlas registration process. Results for a real tumor case and a number of simulated tumor cases indicate significant reduction in the registration error due to the presented approach as compared to the direct use of deformable image registration.

Algorithms↗

PubMed2006

Puberty-related influences on brain development.

Puberty is a time of striking changes in cognition and behavior. To indirectly assess the effects of puberty-related influences on the underlying neuroanatomy of these behavioral changes we will review and synthesize neuroimaging data from typically developing children and adolescents and from those with anomalous hormone or sex chromosome profiles. The trajectories (size by age) of brain morphometry differ between boys and girls, with girls generally reaching peak gray matter thickness 1-2 years earlier than boys. Both boys and girls with congenital adrenal hyperplasia (characterized by high levels of intrauterine testosterone), have smaller amygdala volume but the brain morphometry of girls with CAH did not otherwise significantly differ from controls. Subjects with XXY have gray matter reductions in the insula, temporal gyri, amygdala, hippocampus, and cingulate-areas consistent with the language-based learning difficulties common in this group.

Adolescent↗

PubMed2006

Simulation of tissue atrophy using a topology preserving transformation model.

We propose a method to simulate atrophy and other similar volumetric change effects on medical images. Given a desired level of atrophy, we find a dense warping deformation that produces the corresponding levels of volumetric loss on the labeled tissue using an energy minimization strategy. Simulated results on a real brain image indicate that the method generates realistic images of tissue loss. The method does not make assumptions regarding the mechanics of tissue deformation, and provides a framework where a pre-specified pattern of atrophy can readily be simulated. Furthermore, it provides exact correspondences between images prior and posterior to the atrophy that can be used to evaluate provisional image registration and atrophy quantification algorithms.

Algorithms↗

PubMed2006

Hippocampus volume loss due to chronic heavy drinking.

OBJECTIVE: No clear consensus exists regarding the effect of sustained, heavy drinking on hippocampal volume. Our prior work hypothesized significantly lowered total hippocampus volumes in heavy chronically drinking alcohol-dependent (AD) subjects compared with light-drinking nondependent control subjects matched for age and gender. METHOD: Using a series of applicable exclusion criteria culled from previous published studies, we measured hippocampal volumes from MRI scan data acquired on a 3T scanner and subjected those data to automated volume analysis blind to the drinking history. RESULTS: Comparison with AD test (n=8) and non-AD control (n=8) subjects found significant lessening in total (p=0.020) and left (p=0.010) hippocampal volumes with a near-significant difference on the right (p=0.051). Linear regression demonstrated that neither total brain volume nor intracranial volume affected the hippocampus measures. CONCLUSIONS: These data support the view that heavy drinking exerts a unique and selectively injurious effect on the hippocampus. Further study in larger samples must verify this in a search for possible mechanisms of injury.

Adult↗

PubMed2006

Hypercortisolism in alcohol dependence and its relation to hippocampal volume loss.

OBJECTIVE: The effects of hypercortisolism on hippocampal volume have not been studied in heavy drinkers. Prior work suggested increased hypothalamic-pituitary-adrenal activity in relation to lowered total hippocampus volume (THV) in heavy-drinking alcohol-dependent (AD) subjects. The present study hypothesized the following: (1) that chronic heavy-drinking subjects would demonstrate significantly higher salivary cortisol concentrations than light-drinking control subjects and (2) that data from the whole sample group would present an inverse relationship between cortisol concentration and THV. METHOD: In carefully selected test and control subject groups matched for age, gender, and ethnicity, we measured salivary cortisol samples at waking, waking + 30 minutes, noon, and 4 PM on the day of magnetic resonance imaging of the brain. We next compared mean cortisol concentrations between groups and assessed the statistical association between cortisol concentration and hippocampus volume measures. RESULTS: Comparison of AD test subjects (n = 8) and non-AD control subjects (n = 8) found significantly higher cortisol concentrations at both morning sampling times (mean [SD] at waking: 0.49 [0.23] vs 0.24 [0.14] microg/dl, p = .012; at waking + 30 minutes: 0.57 [0.37] vs 0.28 [0.11] microg/dl, p = 0.043). Controlling for intracranial volume, there was a significant inverse correlation between waking cortisol concentration and THV (p = .007) in the total sample group (N = 16). However, when analyzed separately, only the control group maintained a strong, inverse association (p = .025). There was no association among the heavy drinking subjects. CONCLUSIONS: These early data in a small sample support the view that chronic heavy drinking results in high salivary cortisol concentrations. What remains unclear is whether hypercortisolism exerts a selectively injurious effect that results in observed hippocampus volume loss. Further research in larger groups using more frequent, monitored sampling must address the following: (1) whether this finding can be replicated and (2) if replicated, whether the lack of an association between low hippocampal volumes and high cortisol levels may indicate an extent of injury beyond which a normal association of the two may be lost.

Adult↗

PubMed2005

CLASSIC: consistent longitudinal alignment and segmentation for serial image computing.

This paper proposes a temporally consistent and spatially adaptive longitudinal MR brain image segmentation algorithm, referred to as CLASSIC, which aims at obtaining accurate measurements of rates of change of regional and global brain volumes from serial MR images. The algorithm incorporates image-adaptive clustering, spatiotemporal smoothness constraints, and image warping to jointly segment a series of 3-D MR brain images of the same subject that might be undergoing changes due to development, aging, or disease. Morphological changes, such as growth or atrophy, are also estimated as part of the algorithm. Experimental results on simulated and real longitudinal MR brain images show both segmentation accuracy and longitudinal consistency.

Algorithms↗

PubMed2005

Spatiotemporal maturation patterns of murine brain quantified by diffusion tensor MRI and deformation-based morphometry.

Highly heterogeneous spatiotemporal patterns of maturation of the murine brain during the first 80 postnatal days were examined by high-dimensional deformation-based morphometry applied to high-resolution diffusion tensor MRIs. The maturation profile revealed a sharp contrast between tissue anisotropy changes in the cortex and in major white-matter fibers. Radially oriented tissue anisotropy was measured during the first postnatal week in cortical regions, reflecting the underlying columnar organization of the cortex. Subsequently, tissue anisotropy reduced rapidly, potentially reflecting the growth of randomly oriented dendritic trees that reduce tissue coorientation. Distinct anisotropy patterns were also observed along layer I of the cortex and were attributed to thin fibers oriented parallel to the outer surface. Last, spatially complex patterns of maturation were measured in all major axonal pathways and in the hippocampus, caudate putamen, and cerebellum. This analysis provides a framework for quantifying normative maturation patterns against which phenotypes of mice of different genetic and environmental backgrounds can be contrasted.

Age Factors↗

PubMed2005

Quantification of facial expressions using high-dimensional shape transformations.

We present a novel methodology for quantitative analysis of changes in facial display as the intensity of an emotion evolves from neutral to peak expression. The face is modeled as a combination of regions and their boundaries. An expression change in a face is characterized and quantified through a combination of non-rigid (elastic) deformations, i.e., expansions and contractions of these facial regions. After elastic interpolation, this yields a geometry-based high-dimensional 2D shape transformation, which is used to register regions defined on subjects (i.e., faces with expression) to those defined on the reference template face (a neutral face). This shape transformation produces a vector-valued deformation field and is used to define a scalar valued regional volumetric difference (RVD) function, which characterizes and quantifies the facial expression. The approach is applied to a standardized database consisting of single images of professional actors expressing emotions at predefined intensities. We perform a detailed analysis of the deformations generated and the regional volumetric differences computed for expressions. We were able to quantify subtle changes in expression that can distinguish the intended emotions. A model for the average expression of specific emotions was also constructed using the RVD maps. This method can be applied in basic and clinical investigations of facial affect and its neural substrates.

Adolescent↗

PubMed2005

Whole-brain morphometric study of schizophrenia revealing a spatially complex set of focal abnormalities.

CONTEXT: Neuroanatomic abnormalities in schizophrenia may underlie behavioral manifestations. Characterization of such abnormalities is required for interpreting functional data. Frontotemporal abnormalities have been documented by using predetermined region-of-interest approaches, but deformation-based morphometry permits examination of the entire brain. OBJECTIVES: To perform whole-brain analyses of structural differences between patients with schizophrenia and controls, to examine sex and medication effects, and to apply a high-dimensional nonlinear pattern classification technique to quantify the degree of separation between patients and controls, thereby testing the potential of this new technique as an aid to diagnostic procedures. DESIGN: Whole-brain morphologic analysis using high-dimensional shape transformations. SETTING: Schizophrenia Research Center, University of Pennsylvania Medical Center. PARTICIPANTS: Neuroleptic-naïve and previously treated patients with DSM-IV schizophrenia (n = 69) and sociodemographically matched controls (n = 79). MAIN OUTCOME MEASURES: Gray matter, white matter, and ventricular cerebrospinal fluid volumes in the brain. RESULTS: Magnetic resonance images showed reduced gray matter and increased ventricular cerebrospinal fluid volumes in patients with schizophrenia in the whole brain and in specific foci: the hippocampus and adjacent white matter, the cingulate and orbitofrontal cortex, the frontotemporal and parietotemporal areas, and the occipital areas near the lingual gyrus. The classifier had a mean classification accuracy of 81.1% for men and women combined (82% for women and 85% for men, when each group was treated separately), determined via cross-validation. CONCLUSIONS: This study confirms previous findings of reduced frontotemporal volumes and suggests new hypotheses, especially involving occipital association and speech production areas. It also suggests finer localization of volume reduction in the hippocampus and other limbic structures and in the frontal lobe. Pattern classification showed high sensitivity and specificity for the diagnosis of schizophrenia, suggesting the potential utility of magnetic resonance imaging as a diagnostic aid.

Adult↗

PubMed2005

Classification of structural images via high-dimensional image warping, robust feature extraction, and SVM.

This paper presents a method for classification of medical images, using machine learning and deformation-based morphometry. A morphological representation of the anatomy of interest is first obtained using high-dimensional template warping, from which regions that display strong correlations between morphological measurements and the classification (clinical) variable are extracted using a watershed segmentation, taking into account the regional smoothness of the correlation map which is estimated by a cross-validation strategy in order to achieve robustness to outliers. A Support Vector Machine-Recursive Feature Elimination (SVM-RFE) technique is then used to rank computed features from the extracted regions, according to their effect on the leave-one-out error bound. Finally, SVM classification is applied using the best set of features, and it is tested using leave-one-out. The results from a group of 61 brain images of female normal controls and schizophrenia patients demonstrate not only high classification accuracy (91.8%) and steep ROC curves, but also exceptional stability with respect to the number of selected features and the SVM kernel size.

Algorithms↗

PubMed2005

Finite element modeling of brain tumor mass-effect from 3D medical images.

Motivated by the need for methods to aid the deformable registration of brain tumor images, we present a three-dimensional (3D) mechanical model for simulating large non-linear deformations induced by tumors to the surrounding encephalic tissues. The model is initialized with 3D radiological images and is implemented using the finite element (FE) method. To simulate the widely varying behavior of brain tumors, the model is controlled by a number of parameters that are related to variables such as the bulk tumor location, size, mass-effect, and peri-tumor edema extent. Model predictions are compared to real brain tumor-induced deformations observed in serial-time MRI scans of a human subject and 3 canines with surgically transplanted gliomas. Results indicate that the model can reproduce the real deformations with an accuracy that is similar to that of manual placement of landmark points to which the model is compared.

Algorithms↗

PubMed2005

Using the fast marching method to extract curves with given global properties.

Curves are often used as anatomical features to match surfaces that represent biological objects, such as the human brain. Automated and semi-automated methods for extracting these curves usually rely on local properties of the surfaces such as the mean surface curvature without considering the global appearance of the curves themselves. These methods may require additional human intervention, and sometimes produce erroneous results. In this paper, we present an algorithm that is based on the fast marching method (FMM) to extract weighted geodesic curves. Instead of directly using the local image properties as a weight function, we use the surface properties, together with the global properties of the curves, to compute a weight function. This weight function is then used by the FMM to extract curves between given points. The general framework can be used to extract curves with different global properties. The resulting curves are guaranteed to be weighted geodesic curves without cusps usually introduced by intermediate points through which the curves are forced to pass. We show some results on both a simulated image and a highly convoluted human brain cortical surface.

Algorithms↗

PubMed2005

Deformable registration of brain tumor images via a statistical model of tumor-induced deformation.

An approach to deformable registration of three-dimensional brain tumor images to a normal brain atlas is presented. The approach involves the integration of three components: a biomechanical model of tumor mass-effect, a statistical approach to estimate the model's parameters, and a deformable image registration method. Statistical properties of the desired deformation map are first obtained through tumor mass-effect simulations on normal brain images. This map is decomposed into the sum of two components in orthogonal subspaces, one representing inter-individual differences, and the other involving tumor-induced deformation. For a new tumor case, a partial observation of the desired deformation map is obtained via deformable image registration and is decomposed into the aforementioned spaces in order to estimate the mass-effect model parameters. Using this estimate, a simulation of tumor mass-effect is performed on the atlas to generate an image that is more similar to brain tumor image, thereby facilitating the atlas registration process. Results for a real and a simulated tumor case indicate significant reduction in the registration error due to the presented approach as compared to the direct use of deformable image registration.

Algorithms↗