Mary E. Spilker

Quantitative Magnetic Resonance Imaging Analysis of Neovasculature Volume in Carotid Atherosclerotic Plaque

Background—Neovasculature within atherosclerotic plaques is believed to be associated with infiltration of inflammatory cells and plaque destabilization. The aim of the present investigation was to determine whether the amount of neovasculature present in advanced carotid plaques can be noninvasively measured by dynamic, contrast-enhanced MRI. Methods and Results—A total of 20 consecutive patients scheduled for carotid endarterectomy were recruited to participate in an MRI study. Images were obtained at 15-second intervals, and a gadolinium contrast agent was injected coincident with the second of 10 images in the sequence. The resulting image intensity within the plaque was tracked over time, and a kinetic model was used to estimate the fractional blood volume. For validation, matched sections from subsequent endarterectomy were stained with ULEX and CD-31 antibody to highlight microvessels. Finally, all microvessels within the matched sections were identified, and their total area was computed as a fraction of the plaque area. Results were obtained from 16 participants, which showed fractional blood volumes ranging from 2% to 41%. These levels were significantly higher than the histological measurements of fractional vascular area. Nevertheless, the 2 measurements were highly correlated, with a correlation coefficient of 0.80 (P <0.001). Conclusions—Dynamic contrast-enhanced MRI provides an indication of the extent of neovasculature within carotid atherosclerotic plaque. MRI therefore provides a means for prospectively studying the link between neovasculature and plaque vulnerability.

Automated identification of axonal growth cones in time-lapse image sequences.

The isolation and purification of axon guidance molecules has enabled in vitro studies of the effects of axon guidance molecule gradients on numerous neuronal cell types. In a typical experiment, cultured neurons are exposed to a chemotactic gradient and their growth is recorded by manual identification of the axon tip position from two or more micrographs. Detailed and statistically valid quantification of axon growth requires evaluation of a large number of neurons at closely spaced time points (e.g. using a time-lapse microscopy setup). However, manual tracing becomes increasingly impractical for recording axon growth as the number of time points and/or neurons increases. We present a software tool that automatically identifies and records the axon tip position in each phase-contrast image of a time-lapse series with minimal user involvement. The software outputs several quantitative measures of axon growth, and allows users to develop custom measurements. For, example analysis of growth velocity for a dissociated E13 mouse cortical neuron revealed frequent extension and retraction events with an average growth velocity of 0.05 +/- 0.14 microm/min. Comparison of software-identified axon tip positions with manually identified axon tip positions shows that the softwares performance is indistinguishable from that of skilled human users.

An evaluation of extended vs weighted least squares for parameter estimation in physiological modeling.

Weighted least squares (WLS) is the technique of choice for parameter estimation from noisy data in physiological modeling. WLS can be derived from maximum likelihood theory, provided that the measurement error variance is known and independent of the model parameters and the weights are calculated as the inverse of the measurement error variance. However, using measured values in lieu of predicted values to quantify the measurement error variance is approximately valid only when the noise in the data is relatively low. This practice may thus introduce sampling variation in the resulting estimates, as weights can be seriously mis-specified. To avoid this, extended least squares (ELS) has been used, especially in pharmacokinetics. ELS uses an augmented objective function where the measurement error variance depends explicitly on the model parameters. Although it is more complex, ELS accounts for the Gaussian maximum likelihood statistical model of the data better than WLS, yet its usage is not as widespread. The use of ELS in high data noise situations will result in more accurate parameter estimates than WLS (when the underlying model is correct). To support this claim, we have undertaken a simulation study using four different models with varying amounts of noise in the data and further assuming that the measurement error standard deviation is proportional to the model prediction. We also motivate this in terms of maximum likelihood and comment on the practical consequences of using WLS and ELS as well as give practical guidelines for choosing one method over the other.

Mixture model approach to tumor classification based on pharmacokinetic measures of tumor permeability

To categorize the disease severity of mammary tumors in an animal model through the application of a novel tumor permeability mixture model within a hierarchical modeling framework.

Mathematical modeling of receptor occupancy data

In drug development, in vivo assessment of target engagement provides confidence when testing the drugs mechanism of action and improves the likelihood of clinical success. For biologics, receptor occupancy (RO) determined from circulating cells can provide evidence of target engagement. Integrating this information with mathematical modeling can further enhance the understanding of drug‐target interactions and the biological factors that are critical to the successful modulation of the target and ultimately the disease state.