Prediction in dynamic models with time-dependent conditional variances

This paper considers forecasting the conditional mean and variance from a single-equation dynamic model with autocorrelated disturbances following an ARMA process, and innovations with time-dependent conditional heteroskedasticity as represented by a linear GARCH process. Expressions for the minimum MSE predictor and the conditional MSE are presented. We also derive the formula for all the theoretical moments of the prediction error distribution from a general dynamic model with GARCH(1, 1) innovations. These results are then used in the construction of ex ante prediction confidence intervals by means of the Cornish-Fisher asymptotic expansion. An empirical example relating to the uncertainty of the expected depreciation of foreign exchange rates illustrates the usefulness of the results. © 1992.

Multifactorial determinants of the neurocognitive effects of electroconvulsive therapy.

For many patients with neuropsychiatric illnesses, standard psychiatric treatments with mono or combination pharmacotherapy, psychotherapy, and transcranial magnetic stimulation are ineffective. For these patients with treatment-resistant neuropsychiatric illnesses, a main therapeutic option is electroconvulsive therapy (ECT). Decades of research have found ECT to be highly effective; however, it can also result in adverse neurocognitive effects. Specifically, ECT results in disorientation after each session, anterograde amnesia for recently learned information, and retrograde amnesia for previously learned information. Unfortunately, the neurocognitive effects and underlying mechanisms of action of ECT remain poorly understood. The purpose of this paper was to synthesize the multiple moderating and mediating factors that are thought to underlie the neurocognitive effects of ECT into a coherent model. Such factors include demographic and neuropsychological characteristics, neuropsychiatric symptoms, ECT technical parameters, and ECT-associated neurophysiological changes. Future research is warranted to evaluate and test this model, so that these findings may support the development of more refined clinical seizure therapy delivery approaches and efficacious cognitive remediation strategies to improve the use of this important and widely used intervention tool for neuropsychiatric diseases.

Multifactorial determinants of the neurocognitive effects of electroconvulsive therapy

For many patients with neuropsychiatric illnesses, standard psychiatric treatments with mono or combination pharmacotherapy, psychotherapy, and transcranial magnetic stimulation are ineffective. For these patients with treatment-resistant neuropsychiatric illnesses, a main therapeutic option is electroconvulsive therapy (ECT). Decades of research have found ECT to be highly effective; however, it can also result in adverse neurocognitive effects. Specifically, ECT results in disorientation after each session, anterograde amnesia for recently learned information, and retrograde amnesia for previously learned information. Unfortunately, the neurocognitive effects and underlying mechanisms of action of ECT remain poorly understood. The purpose of this paper was to synthesize the multiple moderating and mediating factors that are thought to underlie the neurocognitive effects of ECT into a coherent model. Such factors include demographic and neuropsychological characteristics, neuropsychiatric symptoms, ECT technical parameters, and ECT-associated neurophysiological changes. Future research is warranted to evaluate and test this model, so that these findings may support the development of more refined clinical seizure therapy delivery approaches and efficacious cognitive remediation strategies to improve the use of this important and widely used intervention tool for neuropsychiatric diseases. Copyright © 2014 by Lippincott Williams & Wilkins.

Structured Illumination Microscopy and a Quantitative Image Analysis for the Detection of Positive Margins in a Pre-Clinical Genetically Engineered Mouse Model of Sarcoma.

Intraoperative assessment of surgical margins is critical to ensuring residual tumor does not remain in a patient. Previously, we developed a fluorescence structured illumination microscope (SIM) system with a single-shot field of view (FOV) of 2.1 × 1.6 mm (3.4 mm2) and sub-cellular resolution (4.4 μm). The goal of this study was to test the utility of this technology for the detection of residual disease in a genetically engineered mouse model of sarcoma. Primary soft tissue sarcomas were generated in the hindlimb and after the tumor was surgically removed, the relevant margin was stained with acridine orange (AO), a vital stain that brightly stains cell nuclei and fibrous tissues. The tissues were imaged with the SIM system with the primary goal of visualizing fluorescent features from tumor nuclei. Given the heterogeneity of the background tissue (presence of adipose tissue and muscle), an algorithm known as maximally stable extremal regions (MSER) was optimized and applied to the images to specifically segment nuclear features. A logistic regression model was used to classify a tissue site as positive or negative by calculating area fraction and shape of the segmented features that were present and the resulting receiver operator curve (ROC) was generated by varying the probability threshold. Based on the ROC curves, the model was able to classify tumor and normal tissue with 77% sensitivity and 81% specificity (Youden's index). For an unbiased measure of the model performance, it was applied to a separate validation dataset that resulted in 73% sensitivity and 80% specificity. When this approach was applied to representative whole margins, for a tumor probability threshold of 50%, only 1.2% of all regions from the negative margin exceeded this threshold, while over 14.8% of all regions from the positive margin exceeded this threshold.