Lawrence H. Staib

Magnetic resonance imaging-based measurement of hippocampal volume in posttraumatic stress disorder related to childhood physical and sexual abuse—a preliminary report

We have previously reported smaller hippocampal volume and deficits in short-term memory in patients with combat-related posttraumatic stress disorder (PTSD) relative to comparison subjects. The purpose of this study was to compare hippocampal volume in adult survivors of childhood abuse to matched controls. Magnetic resonance imaging was used to measure volume of the hippocampus in adult survivors of childhood abuse (n = 17) and healthy subjects (n = 17) matched on a case-by-case basis for age, sex, race, handedness, years of education, body size, and years of alcohol abuse. All patients met criteria for PTSD secondary to childhood abuse. PTSD patients had a 12% smaller left hippocampal volume relative to the matched controls (p < .05), without smaller volumes of comparison regions (amygdala, caudate, and temporal lobe). The findings were significant after controlling for alcohol, age, and education, with multiple linear regression. These findings suggest that a decrease in left hippocampal volume is associated with abuse-related PTSD.

Boundary finding with parametrically deformable models

Segmentation using boundary finding is enhanced both by considering the boundary as a whole and by using model-based global shape information. The authors apply flexible constraints, in the form of a probabilistic deformable model, to the problem of segmenting natural 2-D objects whose diversity and irregularity of shape make them poorly represented in terms of fixed features or form. The parametric model is based on the elliptic Fourier decomposition of the boundary. Probability distributions on the parameters of the representation bias the model to a particular overall shape while allowing for deformations. Boundary finding is formulated as an optimization problem using a maximum a posteriori objective function. Results of the method applied to real and synthetic images are presented, including an evaluation of the dependence of the method on prior information and image quality. >

Neural correlates of exposure to traumatic pictures and sound in Vietnam combat veterans with and without posttraumatic stress disorder: a positron emission tomography study.

BACKGROUND Patients with posttraumatic stress disorder (PTSD) show a reliable increase in PTSD symptoms and physiological reactivity following exposure to traumatic pictures and sounds. In this study neural correlates of exposure to traumatic pictures and sounds were measured in PTSD. METHODS Positron emission tomography and H2[15O] were used to measure cerebral blood flow during exposure to combat-related and neutral pictures and sounds in Vietnam combat veterans with and without PTSD. RESULTS Exposure to traumatic material in PTSD (but not non-PTSD) subjects resulted in a decrease in blood flow in medial prefrontal cortex (area 25), an area postulated to play a role in emotion through inhibition of amygdala responsiveness. Non-PTSD subjects activated anterior cingulate (area 24) to a greater degree than PTSD patients. There were also differences in cerebral blood flow response in areas involved in memory and visuospatial processing (and by extension response to threat), including posterior cingulate (area 23), precentral (motor) and inferior parietal cortex, and lingual gyrus. There was a pattern of increases in PTSD and decreases in non-PTSD subjects in these areas. CONCLUSIONS The findings suggest that functional alternations in specific cortical and subcortical brain areas involved in memory, visuospatial processing, and emotion underlie the symptoms of patients with PTSD.

Reduced volume of orbitofrontal cortex in major depression.

BACKGROUND Functional neuroimaging studies have implicated dysfunction of orbitofrontal cortex in the symptoms of depression, and a recent postmortem study of depressed patients found reduced density of neurons and glia in this area. The purpose of this study was to measure volume of orbitofrontal cortex and other frontal cortical subregions in patients with major depression. METHODS Magnetic resonance imaging was used to measure volume of the orbitofrontal cortex and other frontal cortical regions in patients with major depression in remission (n = 15) and comparison subjects (n = 20). RESULTS Patients with depression had a statistically significant 32% smaller medial orbitofrontal (gyrus rectus) cortical volume, without smaller volumes of other frontal regions including anterior cingulate Brodmanns area 24 (subgenual gyrus), anterior cingulate Brodmanns area 32, subcallosal gyrus (Brodmanns area 25), or whole brain volume. The findings were significant after statistically controlling for brain size. CONCLUSIONS These findings are consistent with smaller orbitofrontal cortical volume in depression.

Hippocampal volume, memory, and cortisol status in major depressive disorder: effects of treatment.

BACKGROUND Depression has been linked to stress, memory deficits, and hypercortisolemia. However, the relationships between depression, hippocampal structure and function, and cortisol levels are unclear and the effects of antidepressant treatment on the measures are not well studied. METHODS Whole hippocampal volume, performance on verbal and visual declarative memory function and cortisol status was evaluated in 38 subjects with major depressive disorder (MDD) and 33 healthy subjects. All measures were repeated in a subgroup (n = 22) of depressed patients after successful selective serotonin reuptake inhibitor (SSRI) treatment. RESULTS Hippocampal volume was not significantly different between patients with untreated MMD and healthy subjects, after controlling for whole brain volume, age and gender. However, depressed subjects had significantly greater deficits in delayed memory and percent retention on the verbal portion of the Wechsler Memory Scale-Revised (WMS-R) compared with healthy subjects, without significant differences in visual memory, attention, vigilance, or distractibility. Baseline plasma or urinary free cortisol (UFC) was not related to either hippocampal volume or memory deficits. Successful treatment with antidepressants did not change hippocampal volume but did result in a significant improvement in memory function and a reduction in UFC excretion. CONCLUSIONS Medication-free nonelderly depressed outpatients without alcohol dependence or adverse experiences in childhood had normal hippocampal volume. Focal declarative memory deficits in depression supported localized hippocampal dysfunction in depressed patients. Treatment with antidepressants significantly improved memory and depression but did not alter hippocampal volume, suggesting that antidepressants may improve hippocampal function in the absence of detectable structural changes.

Model-based deformable surface finding for medical images

Describes a new global shape parameterization for smoothly deformable three-dimensional (3-D) objects, such as those found in biomedical images, whose diversity and irregularity make them difficult to represent in terms of fixed features or parts. This representation is used for geometric surface matching to 3-D medical image data, such as from magnetic resonance imaging (MRI). The parameterization decomposes the surface into sinusoidal basis functions. Four types of surfaces are modeled: tori, open surfaces, closed surfaces and tubes. This parameterization allows a wide variety of smooth surfaces to be described with a small number of parameters. Extrinsic model-based information is incorporated by introducing prior probabilities on the parameters. Surface finding is formulated as an optimization problem. Results of the method applied to synthetic images and 3-D medical images of the heart and brain are presented.

Physical model-based non-rigid registration incorporating statistical shape information

This paper describes two new atlas-based methods of 2D single modality non-rigid registration using the combined power of physical and statistical shape models. The transformations are constrained to be consistent with the physical properties of deformable elastic solids in the first method and those of viscous fluids in the second, to maintain smoothness and continuity. A Bayesian formulation, based on each physical model, an intensity similarity measure, and statistical shape information embedded in corresponding boundary points, is employed to derive more accurate and robust approaches to non-rigid registration. A dense set of forces arises from the intensity similarity measure to accommodate complex anatomical details. A sparse set of forces constrains consistency with statistical shape models derived from a training set. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approaches. It is shown that statistical boundary shape information significantly augments and improves physical model-based non-rigid registration and the two methods we present each have advantages under different conditions.

Neighbor-constrained segmentation with level set based 3-D deformable models

A novel method for the segmentation of multiple objects from three-dimensional (3-D) medical images using interobject constraints is presented. Our method is motivated by the observation that neighboring structures have consistent locations and shapes that provide configurations and context that aid in segmentation. We define a maximum a posteriori (MAP) estimation framework using the constraining information provided by neighboring objects to segment several objects simultaneously. We introduce a representation for the joint density function of the neighbor objects, and define joint probability distributions over the variations of the neighboring shape and position relationships of a set of training images. In order to estimate the MAP shapes of the objects, we formulate the model in terms of level set functions, and compute the associated Euler-Lagrange equations. The contours evolve both according to the neighbor prior information and the image gray level information. This method is useful in situations where there is limited interobject information as opposed to robust global atlases. In addition, we compare our level set representation of the object shape to the point distribution model. Results and validation from experiments on synthetic data and medical imagery in two-dimensional and 3-D are demonstrated.

SPECT [I-123]iomazenil measurement of the benzodiazepine receptor in panic disorder

BACKGROUND Alterations in benzodiazepine receptor function have long been hypothesized to play a role in anxiety. Animal models of anxiety involving exposure to chronic stress have shown a specific decrease in benzodiazepine receptor binding in frontal cortex and hippocampus. The purpose of this study was to examine benzodiazepine receptor binding patients with panic disorder and comparison subjects. METHODS A quantitative measure related to benzodiazepine receptor binding (Distribution Volume (DV)) was obtained with single photon emission computed tomography (SPECT) imaging of [123I]iomazenil and measurement of radioligand concentration in plasma in patients with panic disorder and healthy controls. DV image data were analyzed using statistical parametric mapping (spm96). RESULTS A decrease in measures of benzodiazepine receptor binding (DV) was found in left hippocampus and precuneus in panic disorder patients relative to controls. Panic disorder patients who had a panic attack compared to patients who did not have a panic attack at the time of the scan had a decrease in benzodiazepine receptor binding in prefrontal cortex. CONCLUSIONS Findings of a decrease in left hippocampal and precuneus benzodiazepine receptor binding may be related to alterations in benzodiazepine receptor binding, or other factors including changes in GABAergic transmission or possible endogenous benzodiazepine compounds. Benzodiazepine receptor function in prefrontal cortex appears to be involved in changes in state-related panic anxiety.

Shape-based 3D surface correspondence using geodesics and local geometry

This paper describes a new method for determining correspondence between points on pairs of surfaces based on shape using a combination of geodesic distance and surface curvature. An initial sparse set of corresponding points is generated using a shape-based matching procedure. Geodesic interpolation is employed in order to capture the complex surface. In addition, surface correspondence and triangulation are computed simultaneously in a hierarchical way. Results applied to human cerebral cortical surfaces are shown to evaluate the approach.