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.

The Deep Learning Method Differentiates Patients with Bipolar Disorder from Controls with High Accuracy Using EEG Data

Background: Bipolar disorder (BD) is a mental disorder characterized by depressive and manic or hypomanic episodes. The complexity in the diagnosis of Bipolar disorder (BD) due to its overlapping symptoms with other mood disorders prompted researchers and clinicians to seek new and advanced techniques for the precise detection of Bipolar disorder (BD). One of these methods is the use of advanced machine learning algorithms such as deep learning (DL). However, no study of BD has previously adopted DL techniques using EEG signals. Method: EEG signals of 169 BD patients and 45 controls were cleaned from the artifacts and processed using two different DL methods: a one-dimensional convolutional neural network (1D-CNN) combined with the long-short term memory (LSTM) and a two-dimensional convolutional neural network (2D-CNN). Additionally, Class Activation Maps (CAMs) acquired from the bipolar and control groups were used to obtain distinctive regions to specify a particular class in an image. Results: Group identifications were confirmed with 95.91% overall accuracy through the 2D-CNN method, demonstrating very high sensitivity and lower specificity. Also, the overall accuracy obtained from the 1D-CNN + LSTM method was 93%. We also found that F4, C3, F7, and F8 electrode activities produce predominant features to detect the bipolar group. Conclusion: To our knowledge, this study used EEG-based DL analysis for the first time in BD. Our results suggest that the raw EEG-based DL algorithm can successfully differentiate individuals with BD from controls. Class Activation Map (CAM) analysis suggests that prefrontal changes are predominant in EEG data of patients with BD.

REM parameters in drug-free major depressive disorder: A systematic review and meta-analysis

Previous studies revealed that rapid eye movement (REM) parameters, such as REM latency (RL) and REM density (RD) could be used as electrophysiological markers of depression. Yet these finding should be re-tested in a comorbid-free and drug-free sample. The present systematic review and meta-analysis was conducted to investigate whether drug-free and comorbid-free patients with unipolar depression differentiate from controls with respect to the RL and RD. The PubMed and Web of Science databases were screened from inception to 23 January 2023 for case-control studies comparing RL and RD of patients with unipolar depression and controls. The primary outcome was the standard mean difference. The data were fitted with a random-effects model. Meta-regressions were conducted to investigate patient characteristics and effect size. Publication bias assessment was checked by Egger's Regression and funnel plot asymmetry. Among 43 articles accepted as eligible, 46 RL and 22 RD measurements were included in the meta-analysis. The results indicated shortened RL and increased RD in the patient group than controls. Neither Egger's regression nor funnel plot asymmetry were significant for publication bias. In conclusion, our results tested within drug-free and comorbid-free samples are in line with the literature.

Application of Hybrid DeepLearning Architectures for Identification of Individuals with Obsessive Compulsive Disorder Based on EEG Data

Objective: Obsessive-compulsive disorder (OCD) is a highly common psychiatric disorder. The symptoms of this condition overlap and co-occur with those of other psychiatric illnesses, making diagnosis difficult. The availability of biomarkers could be useful for aiding in diagnosis, although prior neuroimaging studies were unable to provide such biomarkers. Method: In this study, patients with OCD were classified from healthy controls using 2 different hybrid deep learning models: one-dimensional convolutional neural networks (1DCNN) together with long-short term memory (LSTM) and gradient recurrent units (GRU), respectively. Results: Both models exhibited exceptional classification accuracies in cross-validation and external validation phases. The mean classification accuracies in the cross-validation stage were 90.88% and 85.91% for the 1DCNN-LSTM and 1DCNN-GRU models, respectively. The inferior frontal, temporal, and occipital electrodes were predominant in providing discriminative features. Conclusion: Our findings underscore the potential of hybrid deep learning architectures utilizing EEG data to effectively differentiate patients with OCD from healthy controls. This promising approach holds implications for advancing clinical decision-making by offering valuable insights into diagnostic markers for OCD.

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.

The association between vitamin B12 and plasma homocysteine levels with episodic memory and the volume of memory related brain structures in middle-aged individuals: a retrospective correlational study

In previous studies, decreased vitamin B12 and increased plasma homocysteine levels were reported as risk factors for dementia. The aim of this study was to clarify this relationship in earlier ages. Twenty-one healthy middle-aged adults (9 females, 12 males) with a mean age of 46.21 ± 7.99 were retrospectively included in the study. A voxel-based morphometry analysis was performed to measure brain volume. Plasma homocysteine, vitamin B12 levels, verbal and non-verbal memory test performances were recorded. Correlation analyses showed that increased plasma homocysteine was associated with lower memory score. Decreased vitamin B12 level was found to be associated with smaller brain volume in temporal regions. These results suggest that vitamin B12 and plasma homocysteine levels are associated with brain and cognition as early as middle adulthood. Future studies are needed to clarify whether they might be utilized as early hematological biomarkers to predict cognitive decline and neural loss.

Perceived Stress During the COVID-19 Pandemic Mediates the Association Between Self-quarantine Factors and Psychological Characteristics and Elevated Maladaptive Daydreaming

Since the emergence of the COVID-19 pandemic, almost all countries have employed varying degrees of lockdown measures to limit the spread of the infection. Previous studies showed that individuals with maladaptive daydreaming (MD) are affected negatively by the lockdown. In this study, we explored a set of lockdown measures (e.g., self-quarantine) and personal factors (e.g., education, history of depression, and personality traits) that might potentially exacerbate MD experienced during the lockdown period. We also examined whether perceived stress acted as a mediator in the relationship between these factors and MD. During the first lockdown from April to June, we analyzed data provided by 1083 individuals from the USA, the UK, Italy, and Turkey. A path analysis revealed that perceived stress mediated the effects on MD of self-quarantine, previous episodes of depression, low education level, and introversion and emotional instability. Our study suggests a conceptual framework for the factors that intensify maladaptive daydreaming under the threats of the pandemic and forced home confinement, offering implications for interventions with vulnerable populations.

The cognitive dynamics of small-sooner over large-later preferences during temporal discounting task through event-related oscillations (EROs)

Event-related oscillations (ERO) may provide a useful tool for the identification of cognitive processes during economic decisions. In the present study, we investigate peak-to-peak amplitude of task event-related oscillations of healthy subjects during delay discounting task. The study included forty-seven consecutive volunteers with mean 22 age- and matched education and socioeconomic condition. We used two temporal discounting (TD) tasks: the first was used to find individual indifference points for a set of delays and in the second, we recorded EEG as the participants made now vs delay decisions for the indifferent options. The EEG activity were recorded from 24 electrodes placed on the head surface according to the international 10-20 system. EEG activity for each choice (now and future) was averaged separately. The ERO responses were calculated for delta, theta, alpha and beta bands by the peak-to-peak measures. After Bonferroni correction, we found a significant effect of the decision process on the left frontal theta, left centroparietal delta, and frontoparietal beta oscillations. These were significantly greater during future decisions compared to now condition. These results indicate that a widespread frontoparietal network is implicated during delay discounting.

Quantitative EEG Findings in Patients With Psychogenic Nonepileptic Seizures

Objective. Psychogenic nonepileptic seizures (PNES), is one of the clinical manifestations of conversion disorder that epileptiform discharges do not accompany. Factors capable of increasing susceptibility to these seizures have not been adequately investigated yet. This study aims to investigate the quantitative electroencephalography (QEEG) findings for PNES by evaluating the resting EEG spectral power changes during the periods between seizures. Methods. Thirty-nine patients (29 females, 10 males) diagnosed with PNES (group 1) and 47 patients (23 females, 24 males) without any psychiatric diagnosis (group 2) were included in the study. The patients underwent a psychiatric examination at their first visit, were diagnosed and their EEGs were recorded. Using fast Fourier transformation (FFT), spectral power analysis was calculated for delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (15-30 Hz), high-beta (25-30 Hz), gamma-1 (31-40 Hz), gamma-2 (41-50 Hz), and gamma (30-80 Hz) frequency bands. Results. Six separate EEG band power, namely (C3-high beta, C3-gamma, C3-gamma-1, C3-gamma-2, P3-gamma, P3 gamma-1), were found to be higher in the patients diagnosed with PNES than in the control group. Conclusion. Our findings show that PNES correlate with high-frequency oscillations on central motor and somatosensory cortices.

Dissociative Aggression Triggered by Headache

Headache is generally perceived as a negative symptom focused on oneself. However, there are reports suggesting that patients suffering from pain, especially headache, can be aggressive. The precise nature of the link between headache and aggression is not known. Here, we describe a homicidal attack, triggered by headache, in a middle-aged man. The patient's background and the characteristics of the attack suggested a dissociative behavior. The case shows that headache may be a trigger for homicidal behavior. Case-control studies are needed to determine the prevalence of aggressive tendencies in patients with headache.

Different cultures, similar daydream addiction? An examination of the cross-cultural measurement equivalence of the Maladaptive Daydreaming Scale

BACKGROUND AND AIMS

Maladaptive Daydreaming (MD) is a proposed mental disorder, in which absorption in rich, narrative fantasy becomes addictive and compulsive, resulting in emotional, social, vocational, or academic dysfunction. Most studies on MD were carried out on aggregated international samples, using translated versions of the Maladaptive Daydreaming Scale (MDS-16). However, it is unknown whether the properties of MD are affected by culture. Thus, we investigated the cross-cultural measurement invariance of the MDS-16.

METHODS

We recruited both individuals self-identified as suffering from MD and non-clinical community participants from four countries: the USA, Italy, Turkey, and the UK (N = 1,081).

RESULTS

Configural invariance was shown, suggesting that the hypothesized four-factor structure of the MDS-16 (including Yearning, Impairment, Kinesthesia, and Music) holds across cultures. Metric invariance was shown for Impairment, Kinesthesia, and Music, but not for Yearning, suggesting that the psychological meaning of the latter factor may be understood differently across cultures. Scalar invariance was not found, as MD levels were higher in the USA and UK, probably due to the over-representation of English-speaking members of MD communities, who volunteered for the study.

DISCUSSION AND CONCLUSIONS

We conclude that the urge to be absorbed in daydreaming and the fantasies' comforting and addictive properties may have different meanings across countries, but the interference of MD to one's daily life and its obstruction of long-term goals may be the central defining factor of MD.

Prediction of treatment resistance in obsessive compulsive disorder patients based on EEG complexity as a biomarker

OBJECTIVE

This study aimed to identify an Electroencephalography (EEG) complexity biomarker that could predict treatment resistance in Obsessive compulsive disorder (OCD) patients. Additionally, the statistical differences between EEG complexity values in treatment-resistant and treatment-responsive patients were determined. Moreover, the existence of correlations between EEG complexity and Yale-Brown Obsessive Compulsive Scale (YBOCS) score were evaluated.

METHODS

EEG data for 29 treatment-resistant and 28 treatment-responsive OCD patients were retrospectively evaluated. Approximate entropy (ApEn) method was used to extract the EEG complexity from both whole EEG data and filtered EEG data, according to 4 common frequency bands, namely delta, theta, alpha, and beta. The random forests method was used to classify ApEn complexity.

RESULTS

ApEn complexity extracted from beta band EEG segments discriminated treatment-responsive and treatment-resistant OCD patients with an accuracy of 89.66% (sensitivity: 89.44%; specificity: 90.64%). Beta band EEG complexity was lower in the treatment-resistant patients and the severity of OCD, as measured by YBOCS score, was inversely correlated with complexity values.

CONCLUSIONS

The results indicate that, EEG complexity could be considered a biomarker for predicting treatment response in OCD patients.

SIGNIFICANCE

The prediction of treatment response in OCD patients might help clinicians devise and administer individualized treatment plans.

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.

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.

Seasonal mood changes in patients with obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is frequently associated with mood disorders. However, to date, the co-occurrence of OCD with seasonal affective disorder (SAD) has not been investigated. We have aimed to estimate the prevalence of seasonal mood changes in patients with OCD and explore the contribution of seasonality in mood to the severity of OCD. The Seasonal Pattern Assessment Questionnaire (SPAQ), the Yale-Brown Obsession and Compulsion Scale (Y-BOCS), the Hamilton Depression Rating Scale-17 Items (HDRS-17), and the Beck Anxiety Inventory (BAI) were administered to patients with OCD (n=104) and controls (n=125). The degree of seasonality was measured by the Global Seasonality Score (GSS) calculated from the SPAQ. SAD and subsyndromal seasonal affective disorder (S-SAD) were significantly more prevalent in patients with OCD (53%, n=55) than controls (25%, n=31). When patients were assessed in the season in which SAD occurs, depression and compulsions (but not obsessions, OCD or anxiety) were more severe than those assessed in a season during which SAD does not occur. SAD frequently co-occurs with OCD and, given this co-occurrence, depression symptoms in some patients with OCD might be expected to vary on a seasonal basis.

The Relationship Between Responsiveness to Social and Monetary Rewards and ADHD Symptoms

Introduction

Alterations in reward processing are frequently reported in ADHD. One important factor that affects reward processing is the quality of reward, as social and monetary, rewards are processed by different neural networks. However, effect of reward type on reward processing in ADHD was not extensively studied.

Aims

We aimed to explore the effect of reward type (i.e., social or monetary) on different phases of reward processing and also to test the hypothesis that ADHD symptoms may be associated with a problem in processing of social rewards.

Methods

We recorded event-related potentials (ERPs) during a spatial attention paradigm in which cues heralded availability and type of the upcoming reward and feedbacks informed about the reward earned. Thirty-nine (19 males and 20 females) healthy individuals (age range: 19–27) participated in the study. ADHD symptoms were measured using ADHD self-report scale (ASRS).

Results

The feedback related potentials, namely feedback related negativity (FRN), P200 and P300 amplitudes, were larger for social rewards compared to monetary rewards (Fig. 1). There was a consistent negative correlation between the hyperactivity subscale of ASRS and almost all feedback related ERPs. ERP amplitudes after social rewards were smaller for individuals with more hyperactivity.

Conclusions

Our findings suggest that hypo responsiveness to social rewards may be associated with hyperactivity. However, the results have to be confirmed with clinical populations.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

Connectivity differences between bipolar disorder, unipolar depression and schizophrenia

Introduction

Diffusion tensor imaging (DTI) is used frequently to explore white matter tract morphology and connectivity in psychiatric disorders. Connectivity alterations were previously reported for bipolar disorder, unipolar depression and schizophrenia. However, there is limited data on how these disorders differ from one another in terms of connectivity.

Aims

In this study, we aimed to explore connectivity differences between these disorders.

Methods

We analyzed DTI data of 37 patients with schizophrenia, 41 patients with bipolar disorder and 46 patients with unipolar depression. Group analyses were performed for schizophrenia versus bipolar and bipolar versus unipolar contrasts with using age as a covariate.

Results

Threshold corrected results showed that connectivity at internal capsule and corpus callosum were most distinctive between groups. For corpus callosum (splenium), unipolar group showed the highest connectivity and schizophrenia group showed the lowest connectivity (Fig. 1). For internal capsule, schizophrenia group had the highest connectivity and unipolar group had the lowest connectivity (Fig. 2). Bipolar group had intermediate values for both tracts.

Conclusions

These results indicate that connectivity analysis may be helpful for differentiating psychiatric disorders.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

The Differential Effect of Event Rate on Pupil Dilation Patterns Suggests Effort Dysregulation Problems in ADHD

Introduction

The state regulation model postulates that ADHD performance difficulties result from failures to regulate activation states in response to changing environmental conditions – producing poor performance under sub-optimal conditions. Behavioral and electrophysiological studies involving the manipulation of event rate (ER) lend support to this idea.

Aim

In this preliminary study, we extended this investigation by comparing pupil dilation, an established marker of cognitive effort allocation, in individuals with ADHD, and controls, in response to varying ERs on a simple cognitive task.

Methods

Nineteen children with ADHD (age range: 8–14 years) and 21 controls (age range: 10–16 years) completed a target detection task under three different ERs (1300, 4000, and 8000 msec). Pupil dilation was monitored using an eye-tracker.

Results

Our results show that for controls, pupil dilation to targets varied as a function of ER according to a “U” function – with fast and slow ERs inducing greater phasic dilation than the moderate ER. However, for children with ADHD the relationship was linear with dilation increasing as ER decreased.

Conclusions

The results provide the first pupillary evidence suggestive of effort allocation dysregulation in ADHD especially under fast event rate conditions. Future studies should explore interventions to overcome effort allocation problems.

Disclosure of interest

The authors have not supplied their declaration of competing interest.