Elvan Ceyhan

Morphometry of superior temporal gyrus and planum temporale in schizophrenia and psychotic bipolar disorder

Structural abnormalities in temporal lobe, including the superior temporal gyrus (STG) and planum temporale (PT), have been reported in schizophrenia (SCZ) and bipolar disorder (BPD) patients. While most MRI studies have suggested gray matter volume and surface area reduction in temporal lobe regions, few have explored changes in laminar thickness in PT and STG in SCZ and BPD. ROI subvolumes of the STG from 94 subjects were used to yield gray matter volume, gray/white surface area and laminar thickness for STG and PT cortical regions. Morphometric analysis suggests that there may be gender and laterality effects on the size and shape of the PT in BPD (n=36) and SCZ (n=31) with reduced laterality in PT in subjects with SCZ but not in BPD. In addition, PT surface area was seen to be larger in males, and asymmetry in PT surface area was larger in BPD. Subjects with SCZ had reduced thickness and smaller asymmetry in PT volume. Thus, the PT probably plays a more sensitive role than the STG in structural abnormalities seen in SCZ.

Statistical modeling of single target cell encapsulation.

High throughput drop-on-demand systems for separation and encapsulation of individual target cells from heterogeneous mixtures of multiple cell types is an emerging method in biotechnology that has broad applications in tissue engineering and regenerative medicine, genomics, and cryobiology. However, cell encapsulation in droplets is a random process that is hard to control. Statistical models can provide an understanding of the underlying processes and estimation of the relevant parameters, and enable reliable and repeatable control over the encapsulation of cells in droplets during the isolation process with high confidence level. We have modeled and experimentally verified a microdroplet-based cell encapsulation process for various combinations of cell loading and target cell concentrations. Here, we explain theoretically and validate experimentally a model to isolate and pattern single target cells from heterogeneous mixtures without using complex peripheral systems.

Reduced anterior cingulate gray matter volume and thickness in subjects with deficit schizophrenia

BACKGROUND Patients with deficit schizophrenia (D-SZ) differ from patients with the non-deficit form of schizophrenia (ND-SZ) in several aspects such as risk factors, neurobiological correlates, treatment response and clinical outcome. It has been debated if brain morphology could differentiate D-SZ from ND-SZ. Anterior cingulate gyrus (ACG) region regulates cognitive and emotional processing and past studies reported structural changes in this region in patients with SZ. METHODS 1.5-T 3D MRI scans were obtained from 18 D-SZ patients, 30 ND-SZ patients and 82 healthy controls (HCs). We used FreeSurfer-initalized labeled cortical distance mapping (FSLCDM) to measure ACG gray matter volume, cortical thickness, and area of the gray/white interface. Furthermore, cortical thickness was compared among the 3 groups using the pooled labeled cortical distance mapping (LCDM) method. RESULTS The ACG cortex of the D-SZ group was thinner than the ND-SZ group. Pooled LCDM demonstrated that the ACG cortex was bilaterally thinner in both the ND-SZ group and the D-SZ group compared with the control group. The right ACG gray matter volume was significantly reduced in D-SZ patients as compared with healthy controls (p=0.005 CONCLUSION: Our data suggest that qualitative, categorical differences in neuroanatomy may distinguish between deficit and non-deficit subtypes of schizophrenia.

On the use of nearest neighbor contingency tables for testing spatial segregation

For two or more classes (or types) of points, nearest neighbor contingency tables (NNCTs) are constructed using nearest neighbor (NN) frequencies and are used in testing spatial segregation of the classes. Pielou’s test of independence, Dixon’s cell-specific, class-specific, and overall tests are the tests based on NNCTs (i.e., they are NNCT-tests). These tests are designed and intended for use under the null pattern of random labeling (RL) of completely mapped data. However, it has been shown that Pielou’s test is not appropriate for testing segregation against the RL pattern while Dixon’s tests are. In this article, we compare Pielou’s and Dixon’s NNCT-tests; introduce the one-sided versions of Pielou’s test; extend the use of NNCT-tests for testing complete spatial randomness (CSR) of points from two or more classes (which is called CSR independence, henceforth). We assess the finite sample performance of the tests by an extensive Monte Carlo simulation study and demonstrate that Dixon’s tests are also appropriate for testing CSR independence; but Pielou’s test and the corresponding one-sided versions are liberal for testing CSR independence or RL. Furthermore, we show that Pielou’s tests are only appropriate when the NNCT is based on a random sample of (base, NN) pairs. We also prove the consistency of the tests under their appropriate null hypotheses. Moreover, we investigate the edge (or boundary) effects on the NNCT-tests and compare the buffer zone and toroidal edge correction methods for these tests. We illustrate the tests on a real life and an artificial data set.

Overall and pairwise segregation tests based on nearest neighbor contingency tables

Multivariate interaction between two or more classes (or species) has important consequences in many fields and may cause multivariate clustering patterns such as spatial segregation or association. The spatial segregation occurs when members of a class tend to be found near members of the same class (i.e., near conspecifics) while spatial association occurs when members of a class tend to be found near members of the other class or classes. These patterns can be studied using a nearest neighbor contingency table (NNCT). The null hypothesis is randomness in the nearest neighbor (NN) structure, which may result from-among other patterns-random labeling (RL) or complete spatial randomness (CSR) of points from two or more classes (which is called the CSR independence, henceforth). New versions of overall and cell-specific tests based on NNCTs (i.e., NNCT-tests) are introduced and compared with Dixons overall and cell-specific tests and various other spatial clustering methods. Overall segregation tests are used to detect any deviation from the null case, while the cell-specific tests are post hoc pairwise spatial interaction tests that are applied when the overall test yields a significant result. The distributional properties of these tests are analyzed and finite sample performance of the tests are assessed by an extensive Monte Carlo simulation study. Furthermore, it is shown that the new NNCT-tests have better performance in terms of Type I error and power estimates. The methods are also applied on two real life data sets for illustrative purposes.

HYPOTONIC SWELLING OF SALICYLATE-TREATED COCHLEAR OUTER HAIR CELLS

The outer hair cell (OHC) is a hydrostat with a low hydraulic conductivity of Pf=3x10(-4) cm/s across the plasma membrane (PM) and subsurface cisterna that make up the OHCs lateral wall. The SSC is structurally and functionally a transport barrier in normal cells that is known to be disrupted by salicylate. The effect of sodium salicylate on Pf is determined from osmotic experiments in which isolated, control and salicylate-treated OHCs were exposed to hypotonic solutions in a constant flow chamber. The value of Pf=3.5+/-0.5x10(-4) cm/s (mean+/-s.e.m., n=34) for salicylate-treated OHCs was not significantly different from Pf=2.4+/-0.3x10(-4) cm/s (mean+/-s.e.m., n=31) for untreated OHCs (p=.3302). Thus Pf is determined by the PM and is unaffected by salicylate treatment. The ratio of longitudinal strain to radial strain epsilonz/epsilonc=-0.76 for salicylate-treated OHCs was significantly smaller (p=.0143) from -0.72 for untreated OHCs, and is also independent of the magnitude of the applied osmotic challenge. Salicylate-treated OHCs took longer to attain a steady-state volume which is larger than that for untreated OHCs and increased in volume by 8-15% prior to hypotonic perfusion unlike sodium alpha-ketoglutarate-treated OHCs. It is suggested that depolymerization of cytoskeletal proteins and/or glycogen may be responsible for the large volume increase in salicylate-treated OHCs as well as the different responses to different modes of application of the hypotonic solution.

Relative density of the random r-factor proximity catch digraph for testing spatial patterns of segregation and association

Statistical pattern classification methods based on data-random graphs were introduced recently. In this approach, a random directed graph is constructed from the data using the relative positions of the data points from various classes. Different random graphs result from different definitions of the proximity region associated with each data point and different graph statistics can be employed for data reduction. The approach used in this article is based on a parameterized family of proximity maps determining an associated family of data-random digraphs. The relative arc density of the digraph is used as the summary statistic, providing an alternative to the domination number employed previously. An important advantage of the relative arc density is that, properly re-scaled, it is a U-statistic, facilitating analytic study of its asymptotic distribution using standard U-statistic central limit theory. The approach is illustrated with an application to the testing of spatial patterns of segregation and association. Knowledge of the asymptotic distribution allows evaluation of the Pitman and Hodges-Lehmann asymptotic efficacies, and selection of the proximity map parameter to optimize efficiency. Furthermore the approach presented here also has the advantage of validity for data in any dimension.

Prediction and control of number of cells in microdroplets by stochastic modeling

Manipulation and encapsulation of cells in microdroplets has found many applications in various fields such as clinical diagnostics, pharmaceutical research, and regenerative medicine. The control over the number of cells in individual droplets is important especially for microfluidic and bioprinting applications. There is a growing need for modeling approaches that enable control over a number of cells within individual droplets. In this study, we developed statistical models based on negative binomial regression to determine the dependence of number of cells per droplet on three main factors: cell concentration in the ejection fluid, droplet size, and cell size. These models were based on experimental data obtained by using a microdroplet generator, where the presented statistical models estimated the number of cells encapsulated in droplets. We also propose a stochastic model for the total volume of cells per droplet. The statistical and stochastic models introduced in this study are adaptable to various cell types and cell encapsulation technologies such as microfluidic and acoustic methods that require reliable control over number of cells per droplet provided that settings and interaction of the variables is similar.

A Comparison of Analysis of Covariate-Adjusted Residuals and Analysis of Covariance

Various methods to control the influence of a covariate on a response variable are compared. These methods are ANOVA with or without homogeneity of variances (HOV) of errors and Kruskal–Wallis (K–W) tests on (covariate-adjusted) residuals and analysis of covariance (ANCOVA). Covariate-adjusted residuals are obtained from the overall regression line fit to the entire data set ignoring the treatment levels or factors. It is demonstrated that the methods on covariate-adjusted residuals are only appropriate when the regression lines are parallel and covariate means are equal for all treatments. Empirical size and power performance of the methods are compared by extensive Monte Carlo simulations. We manipulated the conditions such as assumptions of normality and HOV, sample size, and clustering of the covariates. The parametric methods on residuals and ANCOVA exhibited similar size and power when error terms have symmetric distributions with variances having the same functional form for each treatment, and covariates have uniform distributions within the same interval for each treatment. In such cases, parametric tests have higher power compared to the K–W test on residuals. When error terms have asymmetric distributions or have variances that are heterogeneous with different functional forms for each treatment, the tests are liberal with K–W test having higher power than others. The methods on covariate-adjusted residuals are severely affected by the clustering of the covariates relative to the treatment factors when covariate means are very different for treatments. For data clusters, ANCOVA method exhibits the appropriate level. However, such a clustering might suggest dependence between the covariates and the treatment factors, so makes ANCOVA less reliable as well.

Spatial Clustering Tests Based on the Domination Number of a New Random Digraph Family

We use the domination number of a parametrized random digraph family called proportional-edge proximity catch digraphs (PCDs) for testing multivariate spatial point patterns. This digraph family is based on relative positions of data points from various classes. We extend the results on the distribution of the domination number of proportional-edge PCDs, and use the domination number as a statistic for testing segregation and association against complete spatial randomness. We demonstrate that the domination number of the PCD has binomial distribution when size of one class is fixed while the size of the other (whose points constitute the vertices of the digraph) tends to infinity and has asymptotic normality when sizes of both classes tend to infinity. We evaluate the finite sample performance of the test by Monte Carlo simulations and prove the consistency of the test under the alternatives. We find the optimal parameters for testing each of the segregation and association alternatives. Furthermore, the methodology discussed in this article is valid for data in higher dimensions also.