Changes of P300 elicited during a Working Memory test in Individuals with depersonalization-derealization experiences

Symptoms of depersonalisation/derealisation disorder as measured by brain electrical activity: A systematic review

Depersonalisation/derealisation disorder (DPD) refers to frequent and persistent detachment from bodily self and disengagement from the outside world. As a dissociative disorder, DPD affects 1-2 % of the population, but takes 7-12 years on average to be accurately diagnosed. In this systematic review, we comprehensively describe research targeting the neural correlates of core DPD symptoms, covering publications between 1992 and 2020 that have used electrophysiological techniques. The aim was to investigate the diagnostic potential of these relatively inexpensive and convenient neuroimaging tools. We review the EEG power spectrum, components of the event-related potential (ERP), as well as vestibular and heartbeat evoked potentials as likely electrophysiological biomarkers to study DPD symptoms. We argue that acute anxiety- or trauma-related impairments in the integration of interoceptive and exteroceptive signals play a key role in the formation of DPD symptoms, and that future research needs analysis methods that can take this integration into account. We suggest tools for prospective studies of electrophysiological DPD biomarkers, which are urgently needed to fully develop their diagnostic potential.

The Effect of Apolipoprotein E ε4 (APOE ε4) on Visuospatial Working Memory in Healthy Elderly and Amnestic Mild Cognitive Impairment Patients: An Event-Related Potentials Study

Background: Apolipoprotein E (APOE) ε4 is the only established risk gene for late-onset, sporadic Alzheimer’s disease (AD). Previous studies have provided inconsistent evidence for the effect of APOE ε4 status on the visuospatial working memory (VSWM).

Objective: The aim was to investigate the effect of APOE ε4 on VSWM with an event-related potential (ERP) study in healthy controls (HC) and amnestic mild cognitive impairment (aMCI) patients.

Methods: The study recorded 39 aMCI patients (27 APOE ε4 non-carriers and 12 APOE ε4 carriers) and their 43 matched controls (25 APOE ε4 non-carriers and 18 APOE ε4 carriers) with an 64-channel electroencephalogram. Participants performed an N-back task, a VSWM paradigm that manipulated the number of items to be stored in memory.

Results: The present study detected reduced accuracy and delayed mean correct response time (RT) in aMCI patients compared to HC. P300, a positive component that peaks between 300 and 500 ms, was elicited by the VSWM task. In addition, aMCI patients showed decreased P300 amplitude at the central–parietal (CP1, CPz, and CP2) and parietal (P1, Pz, and P2) electrodes in 0- and 1-back task compared to HC. In both HC and aMCI patients, APOE ε4 carriers showed reduced P300 amplitude with respect to non-carriers, whereas no significant differences in accuracy or RT were detected between APOE ε4 carriers and non-carriers. Additionally, standardized low-resolution brain electromagnetic tomography analysis (s-LORETA) showed enhanced brain activation in the right parahippocampal gyrus (PHG) during P300 time range in APOE ε4 carriers with respect to non-carriers in aMCI patients.

Conclusion: It demonstrated that P300 amplitude could predict VSWM deficits in aMCI patients and contribute to early detection of VSWM deficits in APOE ε4 carriers.

Identifying Differences in the P600 Component of ERP-Signals between OCD Patients and Controls Employing a PNN-based Majority Vote Classification Scheme

In the present study an attempt was made to focus in the differences between Obsessive-Compulsive Disorder (OCD) patients and healthy controls, as reflected by the P600 component of event-related potential (ERP) signals, to locate brain areas that may be related to Working Memory (WM) deficits. Neuropsychological research has yielded contradicting results regarding WM in OCD. Eighteen patients with OCD symptomatology and 20 normal controls (age and sex matched) were subjected to a computerized version of the digit span Wechsler test. EEG activity was recorded from 15 scalp electrodes (leads). A dedicated computer software was developed to read the ERP signals and to calculate features related to the ERP P600 component (500-800 ms). Nineteen features were generated, from each ERP-signal and each lead, and were employed in the design of the Probabilistic Neural Network (PNN) classifier. Highest single-lead precision (86.8%) was found at the Fp2 and C6 leads. When the output from all single-lead PNN classifiers fed a Majority Vote Engine (MVE), the system classified correctly all subjects, providing a powerful classification scheme. Findings indicated that OCD patients differed from normal controls at the prefrontal and temporo-central brain regions.

Independent component analysis applied to the P600 component of event-related potentials

The analysis of the P600 component of Event-related Potentials (ERPs) has attracted attention due to its relation to covert cognitive mechanisms, in connection to memory processes. The component may often be low-amplitude, compared to other components such as the P300. Independent component analysis (ICA) techniques have been successfully applied in ERP processing, in the framework of blind source separation (BSS) for unmixing recorded potentials into a sum of temporally independent and spatially fixed components. In the present work ICA was used for reconstructing averaged ERPs in the time window of the P600 component, selecting a subset of independent components' projections to the original electrode recording positions. The selection is based on two empirical criteria, selecting the projection that reconstructs a P600 nearest temporally to the original P600, or selecting the projection combination - up to a preselected maximum number of combined projections providing maximum reconstructed P600 amplitude. The techniques are tested on ERPs recorded from healthy subjects and psychiatric patients, notably improving the differentiation of the two groups, based on either the amplitude or the latency of the reconstructed P600 component, in comparison to results achieved using the original ERPs.

Narcissistic regulation of the self and interpersonal problems in depersonalized patients

BACKGROUND

Psychoanalytical theories coincide in understanding depersonalization (DP) as a disorder of narcissistic self-regulation. DP is described as an ego defense against overwhelming shame resulting in a splitting of an observing ego detached from the experiencing self. In contrast to a behavioral-cognitive theory on DP, which suggests that the catastrophic appraisal of normal transient DP maintains the disorder, psychodynamic approaches stress that DP is an important defensive function for the individual. We examine this psychodynamic aspect more closely as it relates to narcissistic self-regulation and interpersonal behavior in depersonalized patients.

SAMPLING AND METHODS

Thirty-five patients with pathological DP are compared with 28 patient controls concerning their narcissistic self-regulation and interpersonal behavior. For the assessment, we used the German Narcissism Inventory and the Inventory of Interpersonal Problems. The two groups were controlled for sociodemographic data, comorbidity with a personality disorder, and the General Severity Index of the Symptom Check List-90-R.

RESULTS

Bonferroni-corrected group comparison showed that the depersonalized patients are characterized by perceiving themselves as helpless, hopeless, socially isolated and worthless, perceiving others as bad and disappointing, and that they avoid interpersonal relations and reality significantly more than other patients with equal symptom severity.

CONCLUSIONS

Treatment approaches on DP should take the issue of low self-esteem, pervasive shame and the related defensive social avoidance into account. Further empirical research on psychodynamic concepts of DP is warranted also for the sake of linking modern neurobiological findings with clinical experience.

[Depersonalization--current data]

OBJECTIVE

Depersonalization is a fascinating clinical phenomenon referring to a self-consciousness disorder, characterized by emotional detachment from one's own feelings, thoughts, or actions. This article intends to summarize the current literature in this area.

METHOD

Using the Medline data base, we reviewed literature addressing the clinical, etiology, nosology, physiopathology, and treatment of depersonalization.

CONCLUSIONS

Derealization means that perception of the world and of external reality are altered. These 2 phenomena are often associated. They are not specific to any psychiatric entity and are reported in many different psychiatric syndromes. Many factors, including use of different substances, are involved in their onset. The physiopathology is still little known. However, some conceptual models suggest partial amygdala inhibition combined with activation of other amygdaloid structures. A serotoninergic functioning impairment is indicated in different pharmacologic studies. Different psychotropic drugs, especially serotoninergic antidepressants, have been proposed for pharmacotherapy; however, there are no conclusive randomized studies, and the contribution of psychotherapy in treating these patients is still questioned.

Design and implementation of an SVM-based computer classification system for discriminating depressive patients from healthy controls using the P600 component of ERP signals

A computer-based classification system has been designed capable of distinguishing patients with depression from normal controls by event-related potential (ERP) signals using the P600 component. Clinical material comprised 25 patients with depression and an equal number of gender and aged-matched healthy controls. All subjects were evaluated by a computerized version of the digit span Wechsler test. EEG activity was recorded and digitized from 15 scalp electrodes (leads). Seventeen features related to the shape of the waveform were generated and were employed in the design of an optimum support vector machine (SVM) classifier at each lead. The outcomes of those SVM classifiers were selected by a majority-vote engine (MVE), which assigned each subject to either the normal or depressive classes. MVE classification accuracy was 94% when using all leads and 92% or 82% when using only the right or left scalp leads, respectively. These findings support the hypothesis that depression is associated with dysfunction of right hemisphere mechanisms mediating the processing of information that assigns a specific response to a specific stimulus, as those mechanisms are reflected by the P600 component of ERPs. Our method may aid the further understanding of the neurophysiology underlying depression, due to its potentiality to integrate theories of depression and psychophysiology.