Hebbian analysis of the transformation of medial entorhinal grid-cell inputs to hippocampal place fields.

The discovery of grid cells in the medial entorhinal cortex (MEC) permits the characterization of hippocampal computation in much greater detail than previously possible. The present study addresses how an integrate-and-fire unit driven by grid-cell spike trains may transform the multipeaked, spatial firing pattern of grid cells into the single-peaked activity that is typical of hippocampal place cells. Previous studies have shown that in the absence of network interactions, this transformation can succeed only if the place cell receives inputs from grids with overlapping vertices at the location of the place cell's firing field. In our simulations, the selection of these inputs was accomplished by fast Hebbian plasticity alone. The resulting nonlinear process was acutely sensitive to small input variations. Simulations differing only in the exact spike timing of grid cells produced different field locations for the same place cells. Place fields became concentrated in areas that correlated with the initial trajectory of the animal; the introduction of feedback inhibitory cells reduced this bias. These results suggest distinct roles for plasticity of the perforant path synapses and for competition via feedback inhibition in the formation of place fields in a novel environment. Furthermore, they imply that variability in MEC spiking patterns or in the rat's trajectory is sufficient for generating a distinct population code in a novel environment and suggest that recalling this code in a familiar environment involves additional inputs and/or a different mode of operation of the network.

Molecular analysis of the human $\gamma$-chain T cell receptor genes

In our studies we have focused on the issue of variability and diversity of the $\gamma$ (or $\delta)$ chain T cell receptor (TCR) genes by studying cDNA transcripts in peripheral blood mononuclear cells or $\gamma\delta$ TCR+ T cell clones. The significance of these studies lies in the better understanding of the molecular biology of the $\gamma\delta$ T cell receptor as well as in answering the question whether certain molecular forms predominate in $\gamma\delta$ T cells exhibiting specific immunologic functions. We establish that certain $\gamma$-chain TCR genes exhibit particular patterns of rearrangements in cDNA transcripts in normal individuals. V$\gamma$I subgroup were shown to preferentially rearrange to J$\gamma$2C$\gamma$2 gene segments. These preferential VJC rearrangements, may have implications regarding the potential for diversity and polymorphism of the $\gamma$-chain TCR gene. In addition, the preferential association of V$\gamma$I genes with J$\gamma$2C$\gamma$2, which encode a non-disulfide-linked $\gamma\delta$ TCR, suggests that $\gamma$ chains utilizing V$\gamma$I are predominantly expressed as non-disulfide-linked $\gamma\delta$ TCR heterodimers. The implications of this type of expression remain to be determined. We identified two alternative splicing events of the $\gamma$-chain TCR genes occurring in high frequency in all the normal individuals examined. These events may suggest additional mechanisms of regulation and control as well as diversification of $\gamma\delta$ TCR gene expression. The question whether particular forms of $\gamma$ or $\delta$-chain TCR genes are involved in HLA Class I recognition by specific $\gamma\delta$ cytotoxic T cell clones was addressed. Our results indicated that the T cell clones expressed identical $\gamma$ but distinct $\delta$-chains suggesting that the specificity for recognition of HLA-A2 or HLA-A3 may be conferred by the $\delta$-chain TCR. The issue of the degree of diversity and polymorphism of the $\delta$-chain TCR genes in a patient with a primary immunodeficiency (Omenn's syndrome) was addressed. A limited pattern of rearrangements in peripheral blood transcripts was found, suggesting that a limited $\gamma\delta$ TCR repertoire may be expressed in this particular primary immunodeficiency syndrome. Overall, our findings suggest that $\delta$-chain TCR genes exhibit the potential for significant diversity and that there are certain preferential patterns of expression that may be associated with particular immunologic functions. ^

Bayesian generalized linear models for meta-analysis of diagnostic tests

With the recognition of the importance of evidence-based medicine, there is an emerging need for methods to systematically synthesize available data. Specifically, methods to provide accurate estimates of test characteristics for diagnostic tests are needed to help physicians make better clinical decisions. To provide more flexible approaches for meta-analysis of diagnostic tests, we developed three Bayesian generalized linear models. Two of these models, a bivariate normal and a binomial model, analyzed pairs of sensitivity and specificity values while incorporating the correlation between these two outcome variables. Noninformative independent uniform priors were used for the variance of sensitivity, specificity and correlation. We also applied an inverse Wishart prior to check the sensitivity of the results. The third model was a multinomial model where the test results were modeled as multinomial random variables. All three models can include specific imaging techniques as covariates in order to compare performance. Vague normal priors were assigned to the coefficients of the covariates. The computations were carried out using the 'Bayesian inference using Gibbs sampling' implementation of Markov chain Monte Carlo techniques. We investigated the properties of the three proposed models through extensive simulation studies. We also applied these models to a previously published meta-analysis dataset on cervical cancer as well as to an unpublished melanoma dataset. In general, our findings show that the point estimates of sensitivity and specificity were consistent among Bayesian and frequentist bivariate normal and binomial models. However, in the simulation studies, the estimates of the correlation coefficient from Bayesian bivariate models are not as good as those obtained from frequentist estimation regardless of which prior distribution was used for the covariance matrix. The Bayesian multinomial model consistently underestimated the sensitivity and specificity regardless of the sample size and correlation coefficient. In conclusion, the Bayesian bivariate binomial model provides the most flexible framework for future applications because of its following strengths: (1) it facilitates direct comparison between different tests; (2) it captures the variability in both sensitivity and specificity simultaneously as well as the intercorrelation between the two; and (3) it can be directly applied to sparse data without ad hoc correction. ^

Improved Techniques for Acquisition and Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Detecting Vascular Permeability in the Central Nervous System

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive technique for quantitative assessment of the integrity of blood-brain barrier and blood-spinal cord barrier (BSCB) in the presence of central nervous system pathologies. However, the results of DCE-MRI show substantial variability. The high variability can be caused by a number of factors including inaccurate T1 estimation, insufficient temporal resolution and poor contrast-to-noise ratio. My thesis work is to develop improved methods to reduce the variability of DCE-MRI results. To obtain fast and accurate T1 map, the Look-Locker acquisition technique was implemented with a novel and truly centric k-space segmentation scheme. In addition, an original multi-step curve fitting procedure was developed to increase the accuracy of T1 estimation. A view sharing acquisition method was implemented to increase temporal resolution, and a novel normalization method was introduced to reduce image artifacts. Finally, a new clustering algorithm was developed to reduce apparent noise in the DCE-MRI data. The performance of these proposed methods was verified by simulations and phantom studies. As part of this work, the proposed techniques were applied to an in vivo DCE-MRI study of experimental spinal cord injury (SCI). These methods have shown robust results and allow quantitative assessment of regions with very low vascular permeability. In conclusion, applications of the improved DCE-MRI acquisition and analysis methods developed in this thesis work can improve the accuracy of the DCE-MRI results.