A comparison of personality disorder characteristics of patients with nonepileptic psychogenic pseudosizures with those of patients with epilepsy

Comprehensive Epilepsy Center, Department of Neurology, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Epilepsy & Behavior (Impact Factor: 2.26). 02/2009; 14(3):481-3. DOI: 10.1016/j.yebeh.2008.12.012
Source: PubMed


We sought to determine the type of personality disorder cluster associated with patients with nonepileptic psychogenic seizures (NES) compared with that of patients with epileptic seizures (ES). Consecutive adult patients admitted for video/EEG monitoring found to have NES were compared with a simultaneously admitted patient with confirmed epilepsy. Personality was assessed using the Structured Clinical Interview for DSM-IV-TR Axis II Personality Disorders. Personality disorders were then divided into personality clusters described in the DSM-IV-TR: A = paranoid, schizotypal, schizoid; B = borderline, histrionic, antisocial, narcissistic; or C = avoidant, dependent, obsessive-compulsive. Thirteen of 16 patients with NES and 12 of 16 patients with ES met criteria for personality disorders. Patients with NES were more likely to meet criteria for a personality disorder in Cluster A or B, compared with patients with ES, who were more likely to have Cluster C personality disorders (chi(2) test, P=0.007). We propose that the personality traits of patients with NES contribute to the development of nonepileptic psychogenic seizures. However, the large proportion of patients with ES with Cluster C personality disorders was unexpected, and further, for the patients with epilepsy, the direction of the association of their personality traits with the development of epilepsy is unknown.

19 Reads
  • Source
    • "The prevalence of borderline personality disorder has also shown to be significantly higher in patients with PNES than in either those with epilepsy or healthy controls [11] [12]. Some common traits observed in both PNES and borderline personality disorder subjects include higher prevalence rates of sexual trauma, PTSD, dissociative disorders, somatoform disorders, depressive disorders, and suicide attempts [11] [12] [13] [14] [15]. Several behavioral and emotional traits of borderline personality disorder have been observed in patients with PNES. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Psychogenic nonepileptic seizures (PNES) often mimic epileptic seizures and occur in both people with and without epilepsy. Pathophysiology of conversion disorders such as PNES remains unclear though significant psychological, psychiatric and environmental factors have been correlated with a diagnosis of PNES. Many clinical signs that have been considered typical for PNES can also be found in frontal epileptic seizures. Given the resemblance of seizures and affective changes from Orbitofrontal cortical dysfunction to PNES like events and correlation of psychological and environmental stress to conversion disorders such as PNES, we propose a two-factor model for the pathogenesis of PNES. We hypothesize that patients with PNES could have a higher likelihood of having both Orbitofrontal cortical dysfunction and a history of psychological stressors rather than a higher likelihood of having either one or the other. We further explore the implications of this two-factor model, including possible therapies. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Full-text · Article · Jan 2015 · Medical Hypotheses
  • [Show abstract] [Hide abstract]
    ABSTRACT: The problem of inferring a finite binary sequence w* ∈ {-1, 1}n is considered. It is supposed that at epochs t = 1,2,..., the learner is provided with random half-space data in the form of finite binary sequences u(t) ∈ {-1, 1}n which have positive inner-product with w*. The goal of the learner is to determine the underlying sequence w* in an efficient, on-line fashion from the data {u(t), t ≥ 1}. In this context, it is shown that the randomized, on-line directed drift algorithm produces a sequence of hypotheses {w(t) ∈ {-1, 1}n, t ≥ 1} which converges to w* in finite time with probability 1. It is shown that while the algorithm has a minimal space complexity of 2n bits of scratch memory, it has exponential time complexity with an expected mistake bound of order Ω(e0.139n). Batch incarnations of the algorithm are introduced which allow for massive improvements in running time with a relatively small cost in space (batch size). In particular, using a batch of script O sign(n log n) examples at each update epoch reduces the expected mistake bound of the (batch) algorithm to script O sign(n) (in an asynchronous bit update mode) and script O sign(1) (in a synchronous bit update mode). The problem considered here is related to binary integer programming and to learning in a mathematical model of a neuron. © 1999 John Wiley & Sons, Inc. Random Struct. Alg., 14, 345-381 (1999).
    No preview · Conference Paper · Dec 1995
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Intelligent control and estimation of power electronic systems by fuzzy logic and neural network techniques show tremendous promise for the future. The paper discusses primarily the work done in this area in the University of Tennessee Power Electronics Laboratory. Altogether seven projects are discussed These are: (1) fuzzy controlled DC motor drive, (2) induction motor drive with fuzzy efficiency optimizer, (3) fuzzy logic based control of wind generation system, (4) AC machine temperature and winding resistance estimation by fuzzy logic, (3) neural network based feedback signal estimation of AC drive, (6) neuro-fuzzy efficiency optimization of induction motor drive, and (7) waveform estimation by neural network
    Full-text · Conference Paper · Jun 1997
Show more