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.

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.

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.

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.

Real-world efficacy of deep TMS for obsessive-compulsive disorder: Post-marketing data collected from twenty-two clinical sites.

BACKGROUND: Deep transcranial magnetic stimulation (dTMS) with the H7-coil was FDA cleared for obsessive-compulsive disorder (OCD) in August 2018 based on multicenter sham-controlled studies. Here we look at the efficacy of dTMS for OCD in real world practices. METHODS: All dTMS clinics were asked to supply their data on treatment details and outcome measures. The primary outcome measure was response, defined by at least a 30% reduction in the Yale Brown Obsessive Compulsive Scale (YBOCS) score from baseline to endpoint. Secondary outcome measures included first response, defined as the first time the YBOCS score has met response criteria, and at least one-month sustained response. Analyses included response rate at the endpoint (after 29 dTMS sessions), number of sessions and days required to reach first response and sustained response. RESULTS: Twenty-two clinical sites with H7-coils provided data on details of treatment and outcome (YBOCS) measures from a total of 219 patients. One-hundred-sixty-seven patients who had at least one post-baseline YBOCS measure were included in the main analyses. Overall first and sustained response rates were 72.6% and 52.4%, respectively. The response rate was 57.9% in patients who had YBOCS scores after 29 dTMS sessions. First response was achieved in average after 18.5 sessions (SD = 9.4) or 31.6 days (SD = 25.2). Onset of sustained one-month response was achieved in average after 20 sessions (SD = 9.8) or 32.1 days (SD = 20.5). Average YBOCS scores demonstrated continuous reduction with increasing numbers of dTMS sessions. CONCLUSIONS: In real-world clinical practice, the majority of OCD patients benefitted from dTMS, and the onset of improvement usually occurs within 20 sessions. Extending the treatment course beyond 29 sessions results in continued reduction of OCD symptoms, raising the prospect of value for extended treatment protocols in non-responders.

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.

Borderline Personality and Bipolar Disorders Cannot Be Differentiated Electrophysiologically.

Objectives. Certain studies have claimed that borderline personality disorder (BPD) could be evaluated as a subtype of bipolar disorder (BD), whereas others have argued that BPD should be regarded as an independent disorder because of its distinct clinical features. The aim of this study was to investigate if there was a difference between these 2 disorders biologically based on EEG recordings. Methods. A total of 111 subjects (11 healthy, 25 BPD, 75 BD) who had resting EEG recordings were included. The EEGs were analyzed to compute absolute power values. Results. One-way analysis of variance results revealed statistically significant differences among the 3 groups on 55 out of 229 EEG variables. However, post hoc analysis indicated that all of the significant changes were between healthy and patient groups and no significant differences were found between 2 clinical groups. Conclusion. The findings suggested that these 2 clinical entities are biologically similar; however, further research should be performed to explain the basis clinical differences between the 2 disorders.

High-Gamma: A biological marker for suicide attempt in patients with depression.

BACKGROUND: Quantitative EEG (qEEG) analysis can be used to evaluate brain correlates of human psychology in all aspects. As the gamma oscillations of qEEG rhythms are related to depression, and particularly to treatment resistance, they may also be related to suicidality. AIM: The present study aimed to identify the neural correlates of suicidal ideation and suicide attempt in depression using qEEG, based on the hypothesis that gamma rhythm in patients with depression would be higher in patients with suicidal ideation and suicide attempt. METHOD: qEEG were recorded in 533 participants (276 female). Groups were divided into the following: Non-suicidal (n = 218), Suicide Ideation (n = 211), Suicide Attempt (n = 74), and control (n = 30). RESULTS: High-gamma power at the F4, Fz, C4, Cz, O2, F8, T5 and T6 regions was significantly higher in the Suicide Ideation than the other groups. CONCLUSION: If confirmed by further studies, high-gamma rhythm has the potential to be used as a biomarker for screening suicidality.

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.

High Frequencies in QEEG Are Related to the Level of Insight in Patients With Schizophrenia.

Lack of insight is a neurocognitive problem commonly encountered in patients with psychotic disorders that negatively affects treatment compliance and prognosis. Measurement of insight is based on self-report scales, which are limited due to subjectivity. This study aimed to determine the correlation between resting state beta and gamma power in 23 patients with schizophrenia and insight. It was observed that as beta and gamma power measured via qualitative electroencephalography (qEEG) increased the level of insight decreased. Negative correlation was found in F3, C3, Cz for gamma activity and in F3 and C3 for beta activity. This finding indicates that resting state qEEG could be used to evaluate the level of insight in patients with schizophrenia.

EEG gamma synchronization is associated with response to paroxetine treatment.

BACKGROUND: Resistance to medication is a significant problem in psychiatric practice, and effective methods for predicting response are needed to optimize treatment efficacy and limit morbidity. Gamma oscillations are considered as an index of the brain's general cognitive activity; however, the role of gamma oscillations in disease has not been studied sufficiently. AIM: This study aimed to determine if gamma power during rest can be used to predict response to anti-depressant medication treatment. METHOD: Hamilton Depression Rating Scale (HDRS) score and resting state gamma power was measured in 18 medication-free patients during an episode of major depression. After 6 weeks of paroxetine monotherapy HDRS was administered again. RESULTS: Baseline gamma power at frontal, central and temporal electrodes before treatment was significantly related to post-treatment change in HDRS scores. CONCLUSION: The results indicate that gamma oscillations could be considered a marker of response to paroxetine treatment in patients with major depression.

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 ( rs = 0.37, P < .01). In a multiple linear regression analysis, P300 latency showed a significant positive correlation with scores of depressive temperament (ß = 0.40, P < .01) and a significant negative one with scores of cyclothymic temperament (ß = -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.

Clinical and cognitive insight in patients with acute-phase psychosis: Association with treatment and neuropsychological functioning.

BACKGROUND: The severity of psychopathology cannot fully explain deficits in the multi-dimensional construct of insight. AIMS: The aim of this study was to evaluate the correlates and associations of clinical and cognitive insight in patients in an acute phase of psychosis and to analyse the impact of acute treatment on these variables. METHODS: This study examined 47 inpatients who were recently hospitalized with acute exacerbation of schizophrenia. All subjects were assessed at both admission and discharge with the Positive and Negative Syndrome Scale (PANSS), Schedule for the Assessment of Insight-Expanded Version (SAI-E), Beck Cognitive Insight Scale (BCIS), and a neurocognition battery. RESULTS: Patients with schizophrenia gained clinical insight after treatment. Cognitive insight did not change significantly after treatment. Insight showed significant negative correlations with positive symptoms and general psychopathology, but not with negative symptoms. Clinical insight was not associated with neuropsychological functioning in this cohort. CONCLUSION: Gaining clinical insight in the acute phase of illness was associated with the remission of positive symptoms, but not with neuropsychological functioning. Some significant correlations between clinical and cognitive insights were detected, which suggests that cognitive insight contributes to clinical insight but is not treatment-dependent. Long-term treatment may be required to understand the contribution of insight to the outcome of patients with schizophrenia.