A graph-based integration of multimodal brain imaging data for the detection of early mild cognitive impairment (E-MCI)

Alzheimer's disease (AD) is the most common cause of dementia in older adults. By the time an individual has been diagnosed with AD, it may be too late for potential disease modifying therapy to strongly influence outcome. Therefore, it is critical to develop better diagnostic tools that can recognize AD at early symptomatic and especially pre-symptomatic stages. Mild cognitive impairment (MCI), introduced to describe a prodromal stage of AD, is presently classified into early and late stages (E-MCI, L-MCI) based on severity. Using a graph-based semi-supervised learning (SSL) method to integrate multimodal brain imaging data and select valid imaging-based predictors for optimizing prediction accuracy, we developed a model to differentiate E-MCI from healthy controls (HC) for early detection of AD. Multimodal brain imaging scans (MRI and PET) of 174 E-MCI and 98 HC participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were used in this analysis. Mean targeted region-of-interest (ROI) values extracted from structural MRI (voxel-based morphometry (VBM) and FreeSurfer V5) and PET (FDG and Florbetapir) scans were used as features. Our results show that the graph-based SSL classifiers outperformed support vector machines for this task and the best performance was obtained with 66.8% cross-validated AUC (area under the ROC curve) when FDG and FreeSurfer datasets were integrated. Valid imaging-based phenotypes selected from our approach included ROI values extracted from temporal lobe, hippocampus, and amygdala. Employing a graph-based SSL approach with multimodal brain imaging data appears to have substantial potential for detecting E-MCI for early detection of prodromal AD warranting further investigation

Customer emotions in service failure and recovery encounters

Emotions play a significant role in the workplace, and considerable attention has been given to the study of employee emotions. Customers also play a central function in organizations, but much less is known about customer emotions. This chapter reviews the growing literature on customer emotions in employee–customer interfaces with a focus on service failure and recovery encounters, where emotions are heightened. It highlights emerging themes and key findings, addresses the measurement, modeling, and management of customer emotions, and identifies future research streams. Attention is given to emotional contagion, relationships between affective and cognitive processes, customer anger, customer rage, and individual differences

Changes in Serum Luteinizing Hormone Following Intraventricular and Intravenous Injections of Luteinizing Hormone-Releasing Hormone in the Rat

A comparison was made of the effects of administering luteinizing hormone-releasing hormone (LH-RH) by the intravenous (i.v.) vs. intraventricular routes to ovariectomized estrogenprimed rats. LH-RH was effective by either route in causing an increase in serum LH as measured by radioimmunoassay, but i.v. injection induced the greater LH discharge. The time courses of the LH responses were identical by the two approaches. A cross-transfusion technique failed to demonstrate the uptake of intraventricularly injected LH-RH into the general circulation. These results support the feasibility of direct uptake and transport of LH-RH from the ventricle to the hypophyseal portal system. However, the direct pathway appears at best to be of questionable physiological significance, since intraventricularly administered LH-RH was less potent in causing LH release than i.v. LH-RH, even though the latter was diluted by the systemic circulation