P300 parameters in major depressive disorder: A systematic review and meta-analysis

OBJECTIVES

Event-related potential measures have been extensively studied in mental disorders. Among them, P300 amplitude and latency reflect impaired cognitive abilities in major depressive disorder (MDD). The present systematic review and meta-analysis was conducted to investigate whether patients with MDD differ from healthy controls (HCs) with respect to P300 amplitude and latency.

METHODS

PubMed and Web of Science databases were searched from inception to 15 January 2023 for case-control studies comparing P300 amplitude and latency in patients with MDD and HCs. The primary outcome was the standard mean difference. A total of 13 articles on P300 amplitude and latency were included in the meta-analysis.

RESULTS

Random effect models indicated that MDD patients had decreased P300 amplitude, but similar latency compared to healthy controls. According to regression analysis, the effect size increased with the severity of depression and decreased with the proportion of women in the MDD samples. Funnel plot asymmetry was not significant for publication bias.

CONCLUSIONS

Decreased P300 amplitude may be a candidate diagnostic biomarker for MDD. However, prospective studies testing P300 amplitude as a monitoring biomarker for MDD are needed.

qEEG in the Diagnosis and Prognosis of a Case with Delusional Infestation

Stroke can have neuropsychological consequences, such as poststroke psychosis. One aspect of poststroke psychosis is delusional infestation (DI), also known as delusional parasitosis. Patients with DI have fixed sensations that they get infested by lice. Some explanations of DI indicate that striatal and fronto-cortical structural and functional abnormalities are related to DI symptomatology. In this case report, we present a patient with DI due to right intracarotid artery occlusion, which is detected radiologically. Before treatment with escitalopram and risperidone, the case had a frontal slowing in quantitative electroencephalography activity, which was normalized after 2 weeks of treatment. At the end of treatment, psychotic symptoms and OCD symptoms evaluated by the Yale-Brown Obsessive Compulsive Scale, the Scale for the Assessment of Positive Symptoms, were remarkably reduced. These results revealed the role of brain imaging studies in the diagnosis and prognosis of DI.

Predictive Value of qEEG in Manic Switch of Depressed Patients

Backgrounds: More than half of the patients with bipolar disorder (BD) had depressive episodes at the onset of BD. Despite some suggested clinical predictors, there are no certain criteria for predicting which unipolar depression patient switch to manic episodes during the treatment course. Electrophysiological markers can address this issue. Methods: Pretreatment quantitative electroencephalography (qEEG) records of patients diagnosed with major depressive disorder (MDD) or BD at the first visit were included in the study. Patients with MDD were also grouped with manic switch (MS) or MDD based on the diagnosis of later visits. The qEEG spectral power was analyzed across 3 groups, that is, MS, MDD, and BD. Results: Compared to patients whose diagnosis did not change, patients with MS had accelerated high-frequency activities predominantly in the left hemisphere (central-parietal-occipital regions). In contrast, they showed increased slow wave activity predominantly in the right hemisphere (parietal-occipital regions). Conclusion: It can be concluded that searching for electrophysiological markers, which have distinct advantages of repeatability, noninvasiveness, and cost-effectiveness, can facilitate the prediction of the MS.

Statistical Comparison Between Interview Questions and Rating Scales in Psychiatry

Background

Psychiatric evaluations consist of both qualitative questions and quantitative assessments, sometimes questioning the same issue. The present study attempts to investigate the statistical equivalency of several close-ended questions of a procedural psychiatric examination and rating scales addressing a similar problem.

Methods

The current retrospective analysis included 314 patients who made their first visit to a private psychiatry clinic. Subjects underwent a routine psychiatric examination, including close-ended questions and related clinical scales. Questions included sleep and sexual problems, problems in marriage, parent relationship problems, and childhood abuse. The related psychiatric scales were Jenkins Sleep Scale, Arizona Sexual Experience Scale, Dyadic Adjustment Scale, and Childhood Trauma Questionnaire, respectively. First, receiver operating curve analysis was conducted for each yes/no question and clinical scale. Then, area under curve sensitivity and specificity values were calculated. Multinomial logistic regression analysis was also performed to observe paired predictor variables.

Results

Among clinical questionnaires, the receiver operating curve model provided good area under curve values as prediction criteria for Dyadic Adjustment Scale (0.78; P < .001), Childhood Trauma Questionnaire (0.74; P < .001), Childhood Trauma Questionnaire-physical abuse (0.826; P < .001), Childhood Trauma Questionnaire-sexual abuse (0.828; P < .001), Arizona Sexual Experience Scale (0.796; P < .001), and Jenkins Sleep Scale (0.920; P < .001). Multinomial logistic regression models also revealed good correct classification values for Dyadic Adjustment Scale-Childhood Trauma Questionnaire (61%), Childhood Trauma Questionnaire-Physical abuse-Childhood Trauma Questionnaire-Sexual abuse (87.6%), and Arizona Sexual Experience Scale-Jenkins Sleep Scale (67%).

Conclusion

When the symptoms are investigated in general terms, the present study reveals that an experienced clinician could rely on clinical questions as much as the quantitative scales in both clinical and research domains.

When to stop medication in unipolar depression: A systematic review and a meta-analysis of randomized controlled trials

BACKGROUND

Currently, there is no clear answer to the question of how long antidepressants should be continued or when they can be safely discontinued.

METHODS

Pubmed/Medline was systematically searched from inception to Feb 20, 2021. Double-blind, randomized placebo-controlled trials (RCTs) with maintenance phase were selected to examine the relationship between relapse rate and treatment duration. Among 5351 screened records, 37 RCTs meeting inclusion criteria were selected. Odds ratios were calculated from relapse rates for each study and pooled in random-effect models. Possible predictors of effect sizes, i.e., open-label treatment duration, double-blind phase duration, age, medication type, history of recurrence, were analyzed by meta-regression.

RESULTS

The random-effects model showed the superiority of active medication over placebo for relapse during the follow-up phase (OR = 0.37; 95 % CI, 0.32-0.42). The meta-regression did not show a relationship between treatment duration and the effect sizes. Other clinical variables were not related with effect sizes. Subgroup analysis revealed that, for atypical ADs the effect size increased as the treatment duration increased. Further analysis showed that the relapse rate in the placebo group decreased as function of time, which reduced the absolute benefit of continued treatment.

CONCLUSION

The results may indicate that long term use of antidepressants may not be justified, and this strategy may expose the patients to more adverse effects.

Quantitative Electroencephalography Findings in Patients With Diabetes Mellitus

Objective. Diabetes mellitus (DM) causes structural central nervous system (CNS) impairment, and this situation can be detected by quantitative electroencephalography (QEEG) findings before cognitive impairment is clinically observed. The main aim of this study is to uncover the effect of DM on brain function. Since QEEG reflects the CNS functioning, particularly in cognitive aspects, we expected electrophysiological clues to be found for prevention and follow-up in DM-related cognitive decline. Since a majority of the psychiatric population have cognitive dysfunction, we have given particular attention to those people. It was stated that a decrease was observed in the posterior cortical alpha power due to the hippocampal atrophy by several previous studies and we hypothesize that decreased alpha power will be observed also in DM. Methods. This study included 2094 psychiatric patients, 207 of whom were diagnosed with DM and 1887 of whom were not diagnosed with DM, and QEEG recordings were performed. Eyes-closed electroencephalography data were segmented into consecutive 2 s epochs. Fourier analysis was performed by averaging across 2 s epochs without artifacts. The absolute alpha power in the occipital regions (O1 and O2) of patients with and without DM was compared. Results. In the DM group, a decrease in the absolute alpha, alpha 1, and alpha 2 power in O1 and O2 was observed in comparison with the control group. It was determined that the type of psychiatric diagnosis did not affect QEEG findings. Conclusion. The decrease in absolute alpha power observed in patients diagnosed with DM may be related to the CNS impairment in DM. QEEG findings in DM can be useful while monitoring the CNS impairment, diagnosing DM-related dementia, in the follow-up of the cognitive process, constructing the protocols for electrophysiological interventions like neurofeedback and transcranial magnetic stimulation and monitoring the response to treatment.

Gamma oscillations predict treatment response to aripiprazole in bipolar disorder

OBJECTIVE

Treatment of Bipolar Disorder (BD) is a challenging issue. Aripiprazole monotherapy is a recommended option for the treatment of mania in BD. The electrophysiological markers of treatment response to aripiprazole could be potentially identified by quantitative Electroencephalography (qEEG).

METHODS

Twenty-four patients with BD were analysed retrospectively. Based on the percentage reduction in Young Mania Rating Scale, they were classified as responders (N = 14) and non-responders (N = 10) to aripiprazole monotherapy. Their resting-state qEEG recordings were examined. Spectral power across all frequency bands were calculated. Absolute powers for all frequency bands were compared between these groups.

RESULTS

Independent sample Mann-Whitney U test revealed that patients who did not respond to aripiprazole had greater gamma power than aripiprazole treatment responders.

CONCLUSIONS

Based on the present findings, it can be proposed that excess in gamma power could be the electrophysiological biomarkers of unresponsiveness to aripiprazole treatment in BD.

Use of EEG for Predicting Treatment Response to Transcranial Magnetic Stimulation in Obsessive Compulsive Disorder

Aim. In this study we assessed the predictive power of quantitative EEG (qEEG) for the treatment response to right frontal transcranial magnetic stimulation (TMS) in obsessive compulsive disorder (OCD) using a machine learning approach. Method. The study included 50 OCD patients (35 responsive to TMS, 15 nonresponsive) who were treated with right frontal low frequency stimulation and identified retrospectively from Uskudar Unversity, NPIstanbul Brain Hospital outpatient clinic. All patients were diagnosed with OCD according to the DSM-IV-TR and DSM-5 criteria. We first extracted pretreatment band powers for patients. To explore the prediction accuracy of pretreatment EEG, we employed machine learning methods using an artificial neural network model. Results. Among 4 EEG bands, theta power successfully discriminated responsive from nonresponsive patients. Responsive patients had more theta powers for all electrodes as compared to nonresponsive patients. Discussion. qEEG could be helpful before deciding about treatment strategy in OCD. The limitations of our study are moderate sample size and limited number of nonresponsive patients and that treatment response was defined by clinicians and not by using a formal symptom measurement scale. Future studies with larger samples and prospective design would show the role of qEEG in predicting TMS response better.

Multimodal Neuroimaging: Basic Concepts and Classification of Neuropsychiatric Diseases

Neuroimaging techniques are widely used in neuroscience to visualize neural activity, to improve our understanding of brain mechanisms, and to identify biomarkers-especially for psychiatric diseases; however, each neuroimaging technique has several limitations. These limitations led to the development of multimodal neuroimaging (MN), which combines data obtained from multiple neuroimaging techniques, such as electroencephalography, functional magnetic resonance imaging, and yields more detailed information about brain dynamics. There are several types of MN, including visual inspection, data integration, and data fusion. This literature review aimed to provide a brief summary and basic information about MN techniques (data fusion approaches in particular) and classification approaches. Data fusion approaches are generally categorized as asymmetric and symmetric. The present review focused exclusively on studies based on symmetric data fusion methods (data-driven methods), such as independent component analysis and principal component analysis. Machine learning techniques have recently been introduced for use in identifying diseases and biomarkers of disease. The machine learning technique most widely used by neuroscientists is classification-especially support vector machine classification. Several studies differentiated patients with psychiatric diseases and healthy controls with using combined datasets. The common conclusion among these studies is that the prediction of diseases increases when combining data via MN techniques; however, there remain a few challenges associated with MN, such as sample size. Perhaps in the future N-way fusion can be used to combine multiple neuroimaging techniques or nonimaging predictors (eg, cognitive ability) to overcome the limitations of MN.

Cognitive Psychophysiological Substrates of Affective Temperaments

Affective temperaments are the subclinical manifestations or phenotypes of mood states and hypothetically represent one healthy end of the mood disorder spectrum. However, there is a scarcity of studies investigating the neurobiological basis of affective temperaments. One fundamental aspect of temperament is the behavioral reactivity to environmental stimuli, which can be effectively evaluated by use of cognitive event-related potentials (ERPs) reflecting the diversity of information processing. The aim of the present study is to explore the associations between P300 and the affective temperamental traits in healthy individuals. We recorded the P300 ERP waves using an auditory oddball paradigm in 50 medical student volunteers (23 females, 27 males). Participants' affective temperaments were evaluated using the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-auto questionnaire version (TEMPS-A). In bivariate analyses, depressive temperament score was significantly correlated with P300 latency ( r_s = 0.37, P < .01). In a multiple linear regression analysis, P300 latency showed a significant positive correlation with scores of depressive temperament (β = 0.40, P < .01) and a significant negative one with scores of cyclothymic temperament (β = -0.29, P = .03). Affective temperament scores were not associated with P300 amplitude and reaction times. These results indicate that affective temperaments are related to information processing in the brain. Depressive temperament may be characterized by decreased physiological arousal and slower information processing, while the opposite was observed for cyclothymic temperament.