An examination of volumetric and cortical thickness correlates of posttraumatic nightmares in male active duty service members.

Posttraumatic nightmares commonly occur after a traumatic experience. Despite significant deleterious effects on well-being and their role in posttraumatic stress disorder, posttraumatic nightmares remain understudied. The neuroanatomical structures of the amygdala, medial prefrontal cortex, hippocampus, and anterior cingulate cortex constitute the AMPHAC model (Levin and Nielsen, 2007), which is implicated in the neurophysiology of disturbing dreams of which posttraumatic nightmares is a part. However, this model has not been investigated using neuroimaging data. The present study sought to determine whether there are structural differences in the AMPHAC regions in relation to the occurrence of posttraumatic nightmares. Data were obtained from treatment-seeking male active duty service members (N = 351). Posttraumatic nightmares were not significantly related to gray matter volume, cortical surface area, or cortical thickness of any the AMPHAC regions when controlling for age and history of mild traumatic brain injury. Although the present analyses do not support an association between structural measures of AMPHAC regions and posttraumatic nightmares, we suggest that functional differences within and/or between these brain regions may be related to the occurrence of posttraumatic nightmares because functional and structural associations are distinct. Future research should examine whether functional differences may be associated with posttraumatic nightmares.

Modeling and reconstruction of mixed functional and molecular patterns.

Functional medical imaging promises powerful tools for the visualization and elucidation of important disease-causing biological processes in living tissue. Recent research aims to dissect the distribution or expression of multiple biomarkers associated with disease progression or response, where the signals often represent a composite of more than one distinct source independent of spatial resolution. Formulating the task as a blind source separation or composite signal factorization problem, we report here a statistically principled method for modeling and reconstruction of mixed functional or molecular patterns. The computational algorithm is based on a latent variable model whose parameters are estimated using clustered component analysis. We demonstrate the principle and performance of the approaches on the breast cancer data sets acquired by dynamic contrast-enhanced magnetic resonance imaging.

Development and validation of a method for using breast core needle biopsies for gene expression microarray analyses.

PURPOSE: Gene expression microarray technologies have the potential to define molecular profiles that may identify specific phenotypes(diagnosis), establish a patient's expected clinical outcome (prognosis), and indicate the likelihood of a beneficial effect of a specific therapy (prediction). We wished to develop optimal tissue acquisition, processing, and analysis procedures for exploring the gene expression profiles of breast core needle biopsies representing cancer and noncancer tissues. EXPERIMENTAL DESIGN: Human breast cancer xenografts were used to evaluate several processing methods for prospectively collecting adequate amounts of high-quality RNA for gene expression microarray studies. Samples were assessed for the preservation of tissue architecture and the quality and quantity of RNA recovered. An optimized protocol was applied to a small study of core needle breast biopsies from patients, in which we compared the molecular profiles from cancer with those from noncancer biopsies. Gene expression data were obtained using Research Genetics, Inc. Named Genes cDNA microarrays. Data were visualized using simple hierarchical clustering and a novel principal component analysis-based multidimensional scaling. Data dimensionality was reduced by simple statistical approaches. Predictive neural networks were built using a multilayer perceptron and evaluated in an independent data set from snap-frozen mastectomy specimens. RESULTS: Processing tissue through RNALater preserves tissue architecture when biopsies are washed for 5 min on ice with ice-cold PBS before histopathological analysis. Cell margins are clear, tissue folding and fragmentation are not observed, and integrity of the cores is maintained, allowing optimal pathological interpretation and preservation of important diagnostic information. Adequate concentrations of high-quality RNA are recovered; 51 of 55 biopsies produced a median of 1.34 microg of total RNA (range, 100 ng to 12.60 microg). Snap-freezing or the use of RNALater does not affect RNA recovery or the molecular profiles obtained from biopsies. The neural network predictors accurately discriminate between predominantly cancer and noncancer breast biopsies. CONCLUSIONS: The approaches generated in these studies provide a simple, safe, and effective method for prospectively acquiring and processing breast core needle biopsies for gene expression studies. Gene expression data from these studies can be used to build accurate predictive models that separate different molecular profiles. The data establish the use and effectiveness of these approaches for future prospective studies.