Matching Items (3)
Description
The use of bias indicators in psychological measurement has been contentious, with some researchers questioning whether they actually suppress or moderate the ability of substantive psychological indictors to discriminate (McGrath, Mitchell, Kim, & Hough, 2010). Bias indicators on the MMPI-2-RF (F-r, Fs, FBS-r, K-r, and L-r) were tested for suppression or moderation of the ability of the RC1 and NUC scales to discriminate between Epileptic Seizures (ES) and Non-epileptic Seizures (NES, a conversion disorder that is often misdiagnosed as ES). RC1 and NUC had previously been found to be the best scales on the MMPI-2-RF to differentiate between ES and NES, with optimal cut scores occurring at a cut score of 65 for RC1 (classification rate of 68%) and 85 for NUC (classification rate of 64%; Locke et al., 2010). The MMPI-2-RF was completed by 429 inpatients on the Epilepsy Monitoring Unit (EMU) at the Scottsdale Mayo Clinic Hospital, all of whom had confirmed diagnoses of ES or NES. Moderated logistic regression was used to test for moderation and logistic regression was used to test for suppression. Classification rates of RC1 and NUC were calculated at different bias level indicators to evaluate clinical utility for diagnosticians. No moderation was found. Suppression was found for F-r, Fs, K-r, and L-r with RC1, and for all variables with NUC. For F-r and Fs, the optimal RC1 and NUC cut scores increased at higher levels of bias, but tended to decrease at higher levels of K-r, L-r, and FBS-r. K-r provided the greatest suppression for RC1, as well as the greatest increases in classification rates at optimal cut scores, given different levels of bias. It was concluded that, consistent with expectations, taking account of bias indicator suppression on the MMPI-2-RF can improve discrimination of ES and NES. At higher levels of negative impression management, higher cut scores on substantive scales are needed to attain optimal discrimination, whereas at higher levels of positive impression management and FBS-r, lower cut scores are needed. Using these new cut scores resulted in modest improvements in accuracy in discrimination. These findings are consistent with prior research in showing the efficacy of bias indicators, and extend the findings to a psycho-medical context.
ContributorsWershba, Rebecca E (Author) / Lanyon, Richard I (Thesis advisor) / Barrera, Manuel (Committee member) / Karoly, Paul (Committee member) / Millsap, Roger E (Committee member) / Arizona State University (Publisher)
Created2013
Description
Psychogenic non-epileptic seizures (PNES), is a conversion disorder thought to be linked to unresolved emotional distress. While some studies suggest that PNES patients do not attribute their somatic symptoms to severe psychological experiences (Stone, Binzer, & Sharpe, 2004; LaFrance & Barry, 2005), it is unclear what PNES patients do think causes their seizures, and the psychological consequences of those attributions. The aim of the present study was to investigate PNES patients' attributions for their seizures, and to determine how these attributions relate to stress and emotion regulation. It was hypothesized that participants who attribute their seizures to something (i.e., have an explanation for their seizures) will have lower perceived stress and less difficulty with emotion regulation than those who are unsure of the cause of their seizures. Twenty-four PNES participants completed a questionnaire assessing seizure diagnosis, characteristics of seizure impact, perceived stress, psychological symptoms, emotion regulation, attributions for seizures, and coping resources. Contrary to the hypothesis, having an explanation for seizures, rather than being “unsure” of seizure cause, was related to greater perceived stress. While it would seem that attributing unpredictable seizure events to a cause would lower perceived stress and emotion regulation difficulty, this study indicates that an attribution to an unknown cause may be more beneficial for the individual.
ContributorsBarker, Mallory (Author) / Roberts, Nicole A. (Thesis advisor) / Miller, Paul A. (Committee member) / Burleson, Mary H (Committee member) / Arizona State University (Publisher)
Created2012
Description
From time immemorial, epilepsy has persisted to be one of the greatest impediments to human life for those stricken by it. As the fourth most common neurological disorder, epilepsy causes paroxysmal electrical discharges in the brain that manifest as seizures. Seizures have the effect of debilitating patients on a physical and psychological level. Although not lethal by themselves, they can bring about total disruption in consciousness which can, in hazardous conditions, lead to fatality. Roughly 1\% of the world population suffer from epilepsy and another 30 to 50 new cases per 100,000 increase the number of affected annually. Controlling seizures in epileptic patients has therefore become a great medical and, in recent years, engineering challenge.
In this study, the conditions of human seizures are recreated in an animal model of temporal lobe epilepsy. The rodents used in this study are chemically induced to become chronically epileptic. Their Electroencephalogram (EEG) data is then recorded and analyzed to detect and predict seizures; with the ultimate goal being the control and complete suppression of seizures.
Two methods, the maximum Lyapunov exponent and the Generalized Partial Directed Coherence (GPDC), are applied on EEG data to extract meaningful information. Their effectiveness have been reported in the literature for the purpose of prediction of seizures and seizure focus localization. This study integrates these measures, through some modifications, to robustly detect seizures and separately find precursors to them and in consequence provide stimulation to the epileptic brain of rats in order to suppress seizures. Additionally open-loop stimulation with biphasic currents of various pairs of sites in differing lengths of time have helped us create control efficacy maps. While GPDC tells us about the possible location of the focus, control efficacy maps tells us how effective stimulating a certain pair of sites will be.
The results from computations performed on the data are presented and the feasibility of the control problem is discussed. The results show a new reliable means of seizure detection even in the presence of artifacts in the data. The seizure precursors provide a means of prediction, in the order of tens of minutes, prior to seizures. Closed loop stimulation experiments based on these precursors and control efficacy maps on the epileptic animals show a maximum reduction of seizure frequency by 24.26\% in one animal and reduction of length of seizures by 51.77\% in another. Thus, through this study it was shown that the implementation of the methods can ameliorate seizures in an epileptic patient. It is expected that the new knowledge and experimental techniques will provide a guide for future research in an effort to ultimately eliminate seizures in epileptic patients.
In this study, the conditions of human seizures are recreated in an animal model of temporal lobe epilepsy. The rodents used in this study are chemically induced to become chronically epileptic. Their Electroencephalogram (EEG) data is then recorded and analyzed to detect and predict seizures; with the ultimate goal being the control and complete suppression of seizures.
Two methods, the maximum Lyapunov exponent and the Generalized Partial Directed Coherence (GPDC), are applied on EEG data to extract meaningful information. Their effectiveness have been reported in the literature for the purpose of prediction of seizures and seizure focus localization. This study integrates these measures, through some modifications, to robustly detect seizures and separately find precursors to them and in consequence provide stimulation to the epileptic brain of rats in order to suppress seizures. Additionally open-loop stimulation with biphasic currents of various pairs of sites in differing lengths of time have helped us create control efficacy maps. While GPDC tells us about the possible location of the focus, control efficacy maps tells us how effective stimulating a certain pair of sites will be.
The results from computations performed on the data are presented and the feasibility of the control problem is discussed. The results show a new reliable means of seizure detection even in the presence of artifacts in the data. The seizure precursors provide a means of prediction, in the order of tens of minutes, prior to seizures. Closed loop stimulation experiments based on these precursors and control efficacy maps on the epileptic animals show a maximum reduction of seizure frequency by 24.26\% in one animal and reduction of length of seizures by 51.77\% in another. Thus, through this study it was shown that the implementation of the methods can ameliorate seizures in an epileptic patient. It is expected that the new knowledge and experimental techniques will provide a guide for future research in an effort to ultimately eliminate seizures in epileptic patients.
ContributorsShafique, Md Ashfaque Bin (Author) / Tsakalis, Konstantinos (Thesis advisor) / Rodriguez, Armando (Committee member) / Muthuswamy, Jitendran (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2016