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.

Neurocognition across bipolar disorder phases compared to healthy subjects.

INTRODUCTION: Bipolar disorder (BD) is associated with cognitive abnormalities that may persist during euthymia and are linked to poor occupational performance. The cognitive differences between phases of BD are not well known. Therefore, a cross-sectional study with a relatively large population was conducted to evaluate the differences among BD phases in a wide range of neurocognitive parameters. METHODS: Neuropsychological profile of 169 patients with a diagnosis of BD in manic, depressive, mixed, and euthymic phases between the ages of 18 and 70 years were compared to 45 healthy individuals' between ages of 24 and 69 years. The working memory (digit-span backward test), face recognition, executive functions (verbal fluency and Stroop test), face recognition, and visual and verbal memory (immediate and delayed recall) were evaluated. For BD subgroup analyses, we used the Kruskal-Wallis (KW) test. Then, for the comparison of BD versus healthy individuals, we used the Mann-Whitney U (MWU) test. RESULTS: Analyses based on non-parametric tests showed impairments in BD for all tests. There were no significant differences between phases. CONCLUSION: Cognitive performance in patients with BD appears to be mostly unrelated to the phase of the disorder, implying that cognitive dysfunction in BD is present even during remission.

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.

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.

Combined use of gray matter volume and neuropsychological test performance for classification of individuals with bipolar I disorder via artificial neural network method.

Diagnosis of patients with bipolar disorder may be challenging and delayed in clinical practice. Neuropsychological impairments and brain abnormalities are commonly reported in bipolar disorder (BD); therefore, they can serve as potential biomarkers of the disorder. Rather than relying on these predictors separately, using both structural and neuropsychiatric indicators together could be more informative and increase the accuracy of the automatic disorder classification. Yet, to our information, no Artificial Intelligence (AI) study has used multimodal data using both neuropsychiatric tests and structural brain changes to classify BD. In this study, we first investigated differences in gray matter volumes between patients with bipolar I disorder (n = 37) and healthy controls (n = 27). The results of the verbal and non-verbal memory tests were then compared between the two groups. Finally, we used the artificial neural network (ANN) method to model all the aforementioned values for group classification. Our voxel-based morphometry results demonstrated differences in the left anterior parietal lobule and bilateral insula gray matter volumes, suggesting a reduction of these brain structures in BD. We also observed a decrease in both verbal and non-verbal memory scores of individuals with BD (p < 0.001). The ANN model of neuropsychiatric test scores combined with gray matter volumes has classified the bipolar group with 89.5% accuracy. Our results demonstrate that when bilateral insula volumes are used together with neuropsychological test results the patients with bipolar I disorder and controls could be differentiated with very high accuracy. The findings imply that multimodal data should be used in AI studies as it better represents the multi-componential nature of the condition, thus increasing its diagnosability.

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.

A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data.

Automatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Magnetic Resonance Imaging (fMRI) through Deep Learning (DL) is becoming a quite useful methodology due to the curse of-dimensionality problem of the data is solved. Also, this method proposes an invasive and robust solution to the variances in data acquisition and class distribution imbalances. In this paper, a transfer learning approach, specifically ResNet-50 type pre-trained 2D-Convolutional Neural Network (CNN) was used to automatically classify ADHD and healthy children. The results demonstrated that ResNet-50 architecture with 10-k cross-validation (CV) achieves an overall classification accuracy of 93.45%. The interpretation of the results was done via the Class Activation Map (CAM) analysis which showed that children with ADHD differed from controls in a wide range of brain areas including frontal, parietal and temporal lobes.

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.

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.

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.

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.

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.

Working memory and emotional interpretation bias in a sample of Syrian refugee adolescents.

The number of adolescent refugees around the world has been continuously increasing over the past few years trying to escape war and terror, among other things. Such experience not only increases the risk for mental health problems including anxiety, depression, and post-traumatic stress disorder (PTSD), but also may have implications for socio-cognitive development. This study tested cognitive-affective processing in refugee adolescents who had escaped armed conflict in Syria and now resided in Istanbul, Turkey. Adolescents were split into a high trauma (n = 31, 12 girls, mean age = 11.70 years, SD = 1.15 years) and low trauma (n = 27, 14 girls, mean age = 11.07 years, SD = 1.39 years) symptom group using median split, and performed a working memory task with emotional distraction to assess cognitive control and a surprise faces task to assess emotional interpretation bias. The results indicated that high (vs. low) trauma symptom youth were ~ 20% worse correctly remembering the spatial location of a cue, although both groups performed at very low levels. However, this finding was not modulated by emotion. In addition, although all youths also had a ~ 20% bias toward interpreting ambiguous (surprise) faces as more negative, the high (vs. low) symptom youth were faster when allocating such a face to the positive (vs. negative) emotion category. The findings suggest the impact of war-related trauma on cognitive-affective processes essential to healthy development.

Heightened Levels of Maladaptive Daydreaming Are Associated With COVID-19 Lockdown, Pre-existing Psychiatric Diagnoses, and Intensified Psychological Dysfunctions: A Multi-country Study.

The COVID-19 pandemic has been spreading globally since December 2019, bringing with it anxieties, mortal risk, and agonizing psychological suffering. This study aimed to explore the relationship between maladaptive daydreaming (MD)-an addictive mental behavior to vivid fantasy associated with distress and functional impairment-and forced COVID-19 pandemic-related self-isolation and quarantine. Previous literature indicated that individuals employ MD for the regulation of distress and boredom, wish fulfillment, and entertainment experiences. The literature on the impact of the COVID-19 pandemic on mental health identifies a flareup in psychological difficulties in the general population. In this study we explored the associations between the pandemic threat and mental health indices among individuals with MD. We surveyed 1,565 adults from over 70 countries who responded to calls for participants posted in online MD communities and other general social media sites. Probable MD was determined based on an empirically derived cut-off score on a pertinent measure. After controlling for sociodemographic variables, a series of MANCOVAs, followed by post-hoc ANCOVAs, revealed that individuals with probable MD who were observing lockdown restrictions reported having spent more time in fantasy, experienced more intense and vivid daydreaming, and had a stronger urge to daydream than other participants. Similar statistical procedures indicated that, individuals with probable MD who reported pre-existing anxiety and depression disorders described a greater urge to daydream due to the pandemic and greater difficulty to control this addictive behavior. Compared to individuals with likely normal daydreaming, individuals with suspected MD reported more pandemic-attributed deterioration on a wide array of psychological distress indices. Our data show that the current worldwide pandemic threat is connected with an elevated intensity of this addictive form of mental activity, and that MD is associated with the exacerbation of psychological distress and dysfunction rather than with beneficial regulation of the experienced stressor.

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.

Resting-state network dysconnectivity in ADHD: A system-neuroscience-based meta-analysis.

OBJECTIVES: Neuroimaging studies report altered resting-state functional connectivity in attention deficit/hyperactivity disorder (ADHD) across multiple brain systems. However, there is inconsistency among individual studies. METHODS: We meta-analyzed seed-based resting state studies of ADHD connectivity within and between four established resting state brain networks (default mode, cognitive control, salience, affective/motivational) using Multilevel Kernel Density Analysis method. RESULTS: Twenty studies with 944 ADHD patients and 1121 controls were included in the analysis. Compared to controls, ADHD was associated with disrupted within-default mode network (DMN) connectivity - reduced in the core (i.e. posterior cingulate cortex seed) but elevated in the dorsal medial prefrontal cortex sub-system (i.e. temporal pole-inferior frontal gyrus). Connectivity was elevated between nodes in the cognitive control system. When the analysis was restricted to children and adolescents, additional reduced connectivity was detected between DMN and cognitive control and affective/motivational and salience networks. CONCLUSIONS: Our data are consistent with the hypothesis that paediatric ADHD is a DMN-dysconnectivity disorder with reduced connectivity both within the core DMN sub-system and between that system and a broad set of nodes in systems involved in cognition and motivation.

Entropy: A Promising EEG Biomarker Dichotomizing Subjects With Opioid Use Disorder and Healthy Controls.

Electroencephalography (EEG) signals are known to be nonstationary and often multicomponential signals containing information about the condition of the brain. Since the EEG signal has complex, nonlinear, nonstationary, and highly random behaviour, numerous linear feature extraction methods related to the short-time windowing technique do not satisfy higher classification accuracy. Since biosignals are highly subjective, the symptoms may appear at random in the time scale and very small variations in EEG signals may depict a definite type of brain abnormality it is valuable and vital to extract and analyze the EEG signal parameters using computers. The challenge is to design and develop signal processing algorithms that extract this subtle information and use it for diagnosis, monitoring, and treatment of subjects suffering from psychiatric disorders. For this purpose, finite impulse response-based filtering process was employed rather than traditional time and frequency domain methods. Finite impulse response subbands were analyzed further to obtain feature vectors of different entropy markers and these features were fed into a classifier namely multilayer perceptron. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies. The results also indicate that entropy estimators can distinguish normal and opioid use disorder subjects. EEG data and theta frequency band have distinctive capability for almost all types of entropies while nonextensive Tsallis entropy outperforms compared with other types of entropies.

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.