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.

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.

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.

A Case of Capgras Syndrome With Frontotemporal Dementia.

Capgras syndrome (CS), also called imposter syndrome, is a rare psychiatric condition that is characterized by the delusion that a family relative or close friend has been replaced by an identical imposter. Here, we describe a 69-year-old man with CS who presented to the Kemal Arikan Psychiatry Clinic with an ongoing belief that his wife had been replaced by an identical imposter. MRI showed selective anterior left temporal lobe atrophy. Quantitative EEG showed bilateral frontal and temporal slowing. Neuropsychological profiling identified a broad range of deficits in the areas of naming, executive function, and long-term memory. On the basis of these findings, we diagnosed frontotemporal dementia. This case demonstrates that CS can clinically accompany frontotemporal dementia.

The relationship between responsiveness to social and monetary rewards and ADHD symptoms.

Alterations in reward processing are frequently reported in attention deficit hyperactivity disorder (ADHD). One important factor affecting reward processing is the quality of reward as social and monetary rewards are processed by different neural networks. However, the effect of reward type on reward processing in ADHD has not been extensively studied. Hence, in the current study, an exploratory research was conducted to investigate the effect of reward type (i.e., social or monetary) on different phases of reward processing. 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) healthy individuals (age range: 19-27 years) participated in the study. ADHD symptoms were assessed by using ADHD self-report scale (ASRS). Our results revealed a consistent negative correlation between the hyperactivity subscale of ASRS and almost all social-feedback related ERPs (P2, P3, and FRN). ERP amplitudes after social feedbacks were less positive for P2 and P3 and more negative for FRN for individuals with greater hyperactivity levels. Our findings suggest that hyporesponsiveness to social feedbacks may be associated with hyperactivity. However, the results have to be confirmed with clinical populations.

Brainstem reflexes are hyperactive in patients with drug-induced akathisia.

Akathisia is a sensori-motor phenomenon which is generally encountered as an adverse effect of antidopaminergic medications suggesting involvement of dopaminergic pathways. We recently showed nociceptive flexor reflex was altered in akathisia as compared to restless legs syndrome and therefore, these findings may indicate co-involvement of pathways other than dopaminergic ones. To examine functional status of different pathways, we investigated auditory startle reflex (ASR), startle response to somatosensory input (SSS), and trigemino-cervical reflex (TCR) in a group of patients with akathisia. Consecutive seven patients with drug-induced akathisia and age- and gender-matched healthy subjects were prospectively included in the study. The diagnosis was made by appropriate clinical criteria. Brainstem reflexes, ASR, SSS, and TCR were examined in all participants. The probability, onset latency, amplitude, and duration were measured and compared between groups. The probability and amplitudes of ASRs were significantly increased and durations of ASRs and TCRs were prolonged in the patient group. Latencies of all responses as well as patterns of startle responses were similar between groups. The results reveal hyperactivity of the ASR and TCR in drug-induced akathisia. Hyperactive ASRs and TCRs also confirm suprasegmental hypodopaminergic state in akathisia. Although we keep in mind the confounding effects due to concurrent antidopaminergic treatments and the small sample group, we speculate that hyperactive ASRs and TCRs might be related to deficient control by forebrain and limbic-mainly amygdala-network in patients with drug-induced akathisia.

Current use of imaging and electromagnetic source localization procedures in epilepsy surgery centers across Europe.

OBJECTIVE: In 2014 the European Union-funded E-PILEPSY project was launched to improve awareness of, and accessibility to, epilepsy surgery across Europe. We aimed to investigate the current use of neuroimaging, electromagnetic source localization, and imaging postprocessing procedures in participating centers. METHODS: A survey on the clinical use of imaging, electromagnetic source localization, and postprocessing methods in epilepsy surgery candidates was distributed among the 25 centers of the consortium. A descriptive analysis was performed, and results were compared to existing guidelines and recommendations. RESULTS: Response rate was 96%. Standard epilepsy magnetic resonance imaging (MRI) protocols are acquired at 3 Tesla by 15 centers and at 1.5 Tesla by 9 centers. Three centers perform 3T MRI only if indicated. Twenty-six different MRI sequences were reported. Six centers follow all guideline-recommended MRI sequences with the proposed slice orientation and slice thickness or voxel size. Additional sequences are used by 22 centers. MRI postprocessing methods are used in 16 centers. Interictal positron emission tomography (PET) is available in 22 centers; all using 18F-fluorodeoxyglucose (FDG). Seventeen centers perform PET postprocessing. Single-photon emission computed tomography (SPECT) is used by 19 centers, of which 15 perform postprocessing. Four centers perform neither PET nor SPECT in children. Seven centers apply magnetoencephalography (MEG) source localization, and nine apply electroencephalography (EEG) source localization. Fourteen combinations of inverse methods and volume conduction models are used. SIGNIFICANCE: We report a large variation in the presurgical diagnostic workup among epilepsy surgery centers across Europe. This diversity underscores the need for high-quality systematic reviews, evidence-based recommendations, and harmonization of available diagnostic presurgical methods.

Thalamic and Cerebellar Gray Matter Volume Reduction in Synthetic Cannabinoids Users.

BACKGROUND: Synthetic cannabinoids are compounds that bind cannabinoid receptors with a high potency and have been used widely in Europe by young people. However, little is known about the pharmacology and morphological effects of this group of substances in the brain. This study is aimed at investigating the morphological differences among synthetic cannabinoids users and healthy controls. METHODS: Voxel-based morphometry was used to investigate the differences in brain tissue composition in 20 patients with synthetic cannabinoids use and 20 healthy controls. All participants were male. RESULTS: Compared to healthy controls, voxel of interest analyses showed that regional grey matter volume in both left and right thalamus and left cerebellum was significantly reduced in synthetic cannabinoids users (p < 0.05). No correlation has been found between the age of first cannabis use, duration of use, frequency of use and grey matter volume. DISCUSSION: These preliminary results suggest an evidence of some structural differences in the brain of synthetic cannabinoids users, and point the need for further investigation of morphological effects of synthetic cannabinoids in the brain.

Neural correlates of approach-avoidance behavior in healthy subjects: Effects of low-frequency repetitive transcranial magnetic stimulation (rTMS) over the right dorsolateral prefrontal cortex.

The dorsolateral prefrontal cortex (dlPFC) is implicated in top-down regulation of emotion, but the detailed network mechanisms require further elucidation. To investigate network-level functions of the dlPFC in emotion regulation, this study measured changes in task-based activation, resting-state and task-based functional connectivity (FC) patterns following suppression of dlPFC excitability by 1-Hz repetitive transcranial magnetic stimulation (rTMS). In a sham-controlled within-subject design, 1-Hz active or sham rTMS was applied to the right dlPFC of 19 healthy volunteers during two separate counterbalanced sessions. Following active and sham rTMS, functional magnetic resonance imaging (fMRI) was conducted in the resting state (rs-fMRI) and during approach-avoidance task responses to pictures with positive and negative emotional content (task-based fMRI). Activation and generalized psychophysiological interaction analyses were performed on task-based fMRI, and seed-based FC analysis was applied to rs-fMRI data. Task-based fMRI revealed greater and more lateralized activation in the right hemisphere during negative picture responses compared to positive picture responses. After active rTMS, greater activation was observed in the left middle prefrontal cortex compared to sham rTMS. Further, rTMS reduced response times and error rates in approach to positive pictures compared to negative pictures. Significant FC changes due to rTMS were observed predominantly in the frontoparietal network (FPN) and visual network (VN) during the task, and in the default mode network (DMN) and VN at rest. Suppression of right dlPFC activity by 1-Hz rTMS alters large-scale neural networks and modulates emotion, supporting potential applications for the treatment of mood disorders.

Task-based modulation of functional connectivity of dorsal attention network in adult-ADHD.

Recent studies have prompted a shift in the understanding of attention deficit hyperactivity disorder (ADHD) from models positing dysfunction of individual brain areas to those that assume alterations in large-scale brain networks. Despite this shift, the underlying neural mechanism of ADHD in the adult population remains uncertain. With functional magnetic resonance imaging (fMRI), this study examined brain connectivity of dorsal and ventral attention networks. Adults with and without ADHD completed a Go/No-Go task inside the scanner and the functional connectivity of attention networks was analysed. The generalized psychophysiological interaction analysis indicated differences involving the dorsal attention network. For the ADHD group, an interaction effect revealed altered dorsal attention-default mode network connectivity modulation, particularly between the right frontal eye field and posterior cingulate gyrus. We conclude that dorsal attention network dysfunction may be involved in sustained attention deficits in adult-ADHD. This study sheds light into network-level alterations contributing to the understanding of adult-ADHD, which may be a potential avenue for future research and clinical interventions.

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.

Deep Learning-Based Artificial Intelligence Can Differentiate Treatment-Resistant and Responsive Depression Cases with High Accuracy.

Background: Although there are many treatment options available for depression, a large portion of patients with depression are diagnosed with treatment-resistant depression (TRD), which is characterized by an inadequate response to antidepressant treatment. Identifying the TRD population is crucial in terms of saving time and resources in depression treatment. Recently several studies employed various methods on EEG datasets for automatic depression detection or treatment outcome prediction. However, no previous study has used the deep learning (DL) approach and EEG signals for detecting treatment resistance. Method: 77 patients with TRD, 43 patients with non-TRD, and 40 healthy controls were compared using GoogleNet convolutional neural network and DL on EEG data. Additionally, Class Activation Maps (CAMs) acquired from the TRD and non-TRD groups were used to obtain distinctive regions for classification. Results: GoogleNet classified the healthy controls and non-TRD group with 88.43%, the healthy controls and TRD subjects with 89.73%, and the TRD and non-TRD group with 90.05% accuracy. The external validation accuracy for the TRD-non-TRD classification was 73.33%. Finally, the CAM analysis revealed that the TRD group contained dominant features in class detection of deep learning architecture in almost all electrodes. Limitations: Our study is limited by the moderate sample size of clinical groups and the retrospective nature of the study. Conclusion: These findings suggest that EEG-based deep learning can be used to classify treatment resistance in depression and may in the future prove to be a useful tool in psychiatry practice to identify patients who need more vigorous intervention.

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.

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.

Treatment with group psychotherapy for chronic psychogenic nonepileptic seizures.

Although there have been a number of psychotherapy trials for chronic psychogenic nonepileptic seizures, evidence-based treatment options are limited. We developed an eclectic group psychotherapy which combines psychoeducation and behavioral and psychoanalytic techniques. Nine patients completed 12 weeks of psychotherapy. Patients were interviewed with SCID-I. They also filled in the following measures at the beginning and end of the therapy: Beck Depression Inventory, Dissociative Experiences Scale, Spielberger State-Trait Anxiety Scale, SF-36 Life Quality Scale, and Toronto Alexithymia Scale. Seizure frequency was assessed before and after the therapy and on follow-up visits at the fourth, sixth, ninth, and twelfth months. After one year of follow-up, the decrease in seizure frequency was highly significant (p<0.001). In addition, we observed significant improvements in the mental health subscale of the SF-36 (p=0.03) and the state (p=0.006) and trait (p=0.02) subscales of the Spielberger State-Trait Anxiety Scale at the end of the therapy. These results suggest that group psychotherapy might be a treatment option for chronic psychogenic nonepileptic seizures.

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 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.

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 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.