Differences Between MR Brain Region Segmentation Methods: Impact on Single-Subject Analysis.

For the segmentation of magnetic resonance brain images into anatomical regions, numerous fully automated methods have been proposed and compared to reference segmentations obtained manually. However, systematic differences might exist between the resulting segmentations, depending on the segmentation method and underlying brain atlas. This potentially results in sensitivity differences to disease and can further complicate the comparison of individual patients to normative data. In this study, we aim to answer two research questions: 1) to what extent are methods interchangeable, as long as the same method is being used for computing normative volume distributions and patient-specific volumes? and 2) can different methods be used for computing normative volume distributions and assessing patient-specific volumes? To answer these questions, we compared volumes of six brain regions calculated by five state-of-the-art segmentation methods: Erasmus MC (EMC), FreeSurfer (FS), geodesic information flows (GIF), multi-atlas label propagation with expectation-maximization (MALP-EM), and model-based brain segmentation (MBS). We applied the methods on 988 non-demented (ND) subjects and computed the correlation (PCC-v) and absolute agreement (ICC-v) on the volumes. For most regions, the PCC-v was good ( > 0.75 ), indicating that volume differences between methods in ND subjects are mainly due to systematic differences. The ICC-v was generally lower, especially for the smaller regions, indicating that it is essential that the same method is used to generate normative and patient data. To evaluate the impact on single-subject analysis, we also applied the methods to 42 patients with Alzheimer's disease (AD). In the case where the normative distributions and the patient-specific volumes were calculated by the same method, the patient's distance to the normative distribution was assessed with the z-score. We determined the diagnostic value of this z-score, which showed to be consistent across methods. The absolute agreement on the AD patients' z-scores was high for regions of thalamus and putamen. This is encouraging as it indicates that the studied methods are interchangeable for these regions. For regions such as the hippocampus, amygdala, caudate nucleus and accumbens, and globus pallidus, not all method combinations showed a high ICC-z. Whether two methods are indeed interchangeable should be confirmed for the specific application and dataset of interest.

Instantiated mixed effects modeling of Alzheimer's disease markers.

The assessment and prediction of a subject's current and future risk of developing neurodegenerative diseases like Alzheimer's disease are of great interest in both the design of clinical trials as well as in clinical decision making. Exploring the longitudinal trajectory of markers related to neurodegeneration is an important task when selecting subjects for treatment in trials and the clinic, in the evaluation of early disease indicators and the monitoring of disease progression. Given that there is substantial intersubject variability, models that attempt to describe marker trajectories for a whole population will likely lack specificity for the representation of individual patients. Therefore, we argue here that individualized models provide a more accurate alternative that can be used for tasks such as population stratification and a subject-specific prognosis. In the work presented here, mixed effects modeling is used to derive global and individual marker trajectories for a training population. Test subject (new patient) specific models are then instantiated using a stratified "marker signature" that defines a subpopulation of similar cases within the training database. From this subpopulation, personalized models of the expected trajectory of several markers are subsequently estimated for unseen patients. These patient specific models of markers are shown to provide better predictions of time-to-conversion to Alzheimer's disease than population based models.

Automatic quantification of normal cortical folding patterns from fetal brain MRI.

We automatically quantify patterns of normal cortical folding in the developing fetus from in utero MR images (N=80) over a wide gestational age (GA) range (21.7 to 38.9weeks). This work on data from healthy subjects represents a first step towards characterising abnormal folding that may be related to pathology, facilitating earlier diagnosis and intervention. The cortical boundary was delineated by automatically segmenting the brain MR image into a number of key structures. This utilised a spatio-temporal atlas as tissue priors in an expectation-maximization approach with second order Markov random field (MRF) regularization to improve the accuracy of the cortical boundary estimate. An implicit high resolution surface was then used to compute cortical folding measures. We validated the automated segmentations with manual delineations and the average surface discrepancy was of the order of 1mm. Eight curvature-based folding measures were computed for each fetal cortex and used to give summary shape descriptors. These were strongly correlated with GA (R(2)=0.99) confirming the close link between neurological development and cortical convolution. This allowed an age-dependent non-linear model to be accurately fitted to the folding measures. The model supports visual observations that, after a slow initial start, cortical folding increases rapidly between 25 and 30weeks and subsequently slows near birth. The model allows the accurate prediction of fetal age from an observed folding measure with a smaller error where growth is fastest. We also analysed regional patterns in folding by parcellating each fetal cortex using a nine-region anatomical atlas and found that Gompertz models fitted the change in lobar regions. Regional differences in growth rate were detected, with the parietal and posterior temporal lobes exhibiting the fastest growth, while the cingulate, frontal and medial temporal lobes developed more slowly.

A primate model for the study of the interaction of 111In-labeled baboon platelets with Dacron arterial prostheses.

This paper presents early experience with a primate model for the noninvasive study of the interaction of circulating platelets with healing arterial prostheses. These experiments demonstrate that baboon platelets can be isolated and labeled with 111Indium with high efficiency using a sterile technique. Platelets subjected to this process have a linear life span similar to that of 51Chromium-labeled baboon platelets. The high energy gamma emission of 111Indium oxine allows for external scanning using a standard gamma camera. The small quantity of 111Indium-labeled platelets in the region of the graft can be discriminated from the surrounding blood vessel and quantitated by gamma camera imaging and computer analysis. There was a significant increase in the platelet deposition on prosthetic surfaces observed 5--48 hours after graft implantation and injection of 111Indium-labeled autologous platelets.

Results of carotid endarterectomy for vertebrobasilar insufficiency: an evaluation over ten years.

A review was performed of 114 patients with symptoms of vertebrobasilar insufficiency (VBI) alone, or in combination with carotid territory transient ischemic attacks or carotid territory completed stroke (cCS) with follow-up extending to ten years. The most frequent symptoms of VBI were visual changes (50%), dizziness (31%), and syncope (30%). Patients with symptoms of VBI and arteriographic evidence of intracranial disease, regardless of stump pressure, are at high risk for cerebral ischemia during endarterectomy. At late follow-up, ranging from one to ten years, 63% of the patients were alive; 88% were asymptomatic. Causes of death were mainly cardiac (44%) and stroke (36%), but patients with symptoms of VBI and cCS died earlier and from a second cerebrovascular accident. When a correct preoperative diagnosis was established, carotid endarterectomy produced relief of symptoms in 90% of the patients.