Bipolar Disorder Group Research Articles

Association between olfactory function and metabolic syndrome in bipolar disorder patients: a cross-sectional study

BackgroundOlfactory function is closely related to mood and the endocrine system. However, the role of olfactory function in bipolar disorder combined with metabolic syndrome remains unclear. The purpose of this study was to explore the associations among olfactory function, tumor necrosis factor alpha (TNF-α), and metabolic syndrome and its components in patients with bipolar disorder.MethodsNinety-six bipolar disorder patients were divided into two groups with and without metabolic syndrome. We also included 46 healthy controls. Olfactory function was assessed using the Sniffin’ Sticks test. Blood samples were collected to measure metabolic indicators and serum TNF-α levels.ResultsSignificant differences in olfactory function were found among the three groups. Compared with the healthy controls, the bipolar disorder without metabolic syndrome group showed poorer olfactory identification ability (P < 0.001) and the bipolar disorder with metabolic syndrome group showed impaired olfactory sensitivity (P = 0.003) and olfactory identification (P < 0.001). Moreover, the bipolar disorder with metabolic syndrome group had poorer olfactory identification ability than the bipolar disorder without metabolic syndrome group (P = 0.015). Both bipolar disorder groups showed lower TNF-α levels than healthy controls. However, there was no significant difference between the two patient groups. Correlation analysis showed that, in the bipolar disorder with metabolic syndrome group, olfactory identification was negatively correlated with systolic blood pressure (r = − 0.424, P = 0.031), and serum TNF-α level was negatively correlated with body mass index (BMI; r = − 0.398, P = 0.049), triglyceride (r = − 0.503, P = 0.010), total cholesterol (r = − 0.491, P = 0.013), low-density lipoprotein-cholesterol (r = − 0.491, P = 0.013), and high-density lipoprotein-cholesterol (r = − 0.454, P = 0.023).ConclusionsThe olfactory identification ability of patients with bipolar disorder is worse than that of healthy controls, and the occurrence of metabolic syndrome will further aggravate the olfactory identification impairment of those patients. Furthermore, there may be a stronger link between serum TNF-α level and multiple metabolic indicators in bipolar disorder patients with metabolic syndrome than in bipolar disorder patients without metabolic syndrome.

Cortical folding in distinguishing first-episode bipolar and unipolar depression

BackgroundsClinical studies to date have yet to establish distinct boundaries between depression in bipolar disorder (BD) and unipolar depression (UD), leading to misdiagnoses and even exacerbation of the conditions. This study aimed to explore the distinctions in the local gyrification index (LGI) between BD and UD, and to evaluate its potential diagnostic value as a biomarker. MethodsLGI values across 68 cortical regions were measured from 42 patients with BD, 45 patients with UD, and 45 healthy controls (HCs) based on the Desikan–Killiany atlas. General linear model was performed to compare LGI values among the three groups. XGBoost classifier was implemented to develop a binary classification model for distinguishing BD from UD. Additionally, the correlation between clinical characteristics and LGI values was investigated separately within the BD and UD groups. ResultsCompared to HCs, individuals with BD and UD exhibited significantly reduced LGI values in various cortical regions. Nine LGI regions in the BD group displayed reduced values compared to the UD group, except for a singular increase in the left frontal pole (ηp2 = 0.173; P = 0.006). No significant association was found between LGI values and clinical characteristics within the patient groups. The XGBoost classifier achieved a distinction accuracy of 73.7 % between BD and UD, with the left frontal pole making the most significant contribution to the model. ConclusionsThe findings suggest that LGI could be a relatively stable neuroimaging biomarker for distinguishing between BD and UD.

Relationship between subjective cognitive functioning and fluid and crystallized cognitive abilities in bipolar disorder.

People with bipolar disorder (BD) often show inaccurate subjective ratings of their objective cognitive function. However, it is unclear what information individuals use to formulate their subjective ratings. This study evaluated whether people with BD are likely using information about their crystallized cognitive abilities (which involve an accumulated store of verbal knowledge and skills and are typically preserved in BD) or their fluid cognitive abilities (which involve the capacity for new learning and information processing in novel situations and are typically impaired in BD) to formulate their subjective cognitive ratings. Eighty participants diagnosed with BD and 55 control volunteers were administered cognitive tests assessing crystallized and fluid cognitive abilities. Subjective cognitive functioning was assessed with the Cognitive Failures Questionnaire (CFQ), daily functioning was rated using the Multidimensional Scale of Independent Functioning (MSIF) and the Global Assessment of Functioning Scale (GAF), and quality of life was assessed with the Quality of Life in Bipolar Disorder scale (QoL.BD). The BD group exhibited considerably elevated subjective cognitive complaints relative to controls. Among participants with BD, CFQ scores were associated with fluid cognitive abilities including measures of memory and executive function, but not to crystallized abilities. After controlling for objective cognition and depression, higher cognitive complaints predicted poorer psychosocial outcomes. Cognitive self-reports in BD may represent a metacognitive difficulty whereby cognitive self-appraisals are distorted by a person's focus on their cognitive weaknesses rather than strengths. Moreover, negative cognitive self-assessments are associated with poorer daily functioning and diminished quality of life.

Copy number variations in RNF216 and postsynaptic membrane-associated genes are associated with bipolar disorder: a case-control study in the Japanese population.

Bipolar disorder (BD) is a common psychiatric disorder characterized by alterations between manic/hypomanic and depressive states. Rare pathogenic copy number variations (CNVs) that overlap with exons of synaptic genes have been associated with BD. However, no study has comprehensively explored CNVs in synaptic genes associated with BD. Here, we evaluated the relationship between BD and rare CNVs that overlap with synaptic genes, not limited to exons, in the Japanese population. Using array comparative genome hybridization, we detected CNVs in 1839 patients with BD and 2760 controls. We used the Synaptic Gene Ontology database to identify rare CNVs that overlap with synaptic genes. Using gene-based analysis, we compared their frequencies between the BD and control groups. We also searched for synaptic gene sets related to BD. The significance level was set to a false discovery rate of 10%. The RNF216 gene was significantly associated with BD (odds ratio, 4.51 [95% confidence interval, 1.66-14.89], false discovery rate < 10%). The BD-associated CNV that corresponded with RNF216 also partially overlapped with the minimal critical region of the 7p22.1 microduplication syndrome. The integral component of the postsynaptic membrane (Gene Ontology:0099055) was significantly associated with BD. The CNV overlapping with the intron region of GRM5 in this gene set showed a nominal significant association between cases and controls (P < 0.05). We provide evidence that CNVs in RNF216 and postsynaptic membrane-related genes confer a risk of BD, contributing to a better understanding of the pathogenesis of BD.

Association of plasma arachidonic acid levels with a bipolar disorder and the effects of a FADS gene variant

Recent genome-wide association studies (GWASs) have identified fatty acid desaturase (FADS) genes, which code key enzymes involved in polyunsaturated fatty acid (PUFA) desaturation as susceptibility genes for bipolar disorder (BD). Several quantitative changes in PUFAs suggest their involvement in BD pathogenesis. Therefore, this study aimed to clarify the relationship between BD and PUFAs by conducting lipidomics covariating with the FADS gene variant (rs174550), which is associated with PUFA levels and BD susceptibility. The concentrations of 23 fatty acids were measured using plasma samples from the BD group (n = 535) and the control group (n = 107). Differences in each PUFA concentration ratio were compared between the two groups. Also, differences in each PUFA concentration ratio were compared for each genotype in rs174550. Our results showed that the BD group had significantly lower concentrations of linoleic acid (LA) (β = −0.36, p = 0.023) and arachidonic acid (AA) (β = −0.18, p = 0.013) than the control group. Concerning the effect of FADS on the PUFA concentration ratio, carriers of C-allele at rs174550 had significantly decreased γ-linolenic acid and AA concentration ratios. A previous GWAS reported that the presence of a C-allele at rs174550 increased the BD risk. This direction is consistent with the lipidomic results of the present study. In conclusion, both the FADS and BD were considered to regulate the AA concentration. Thus, as the FADS gene variant is crucial for conducting lipidomics of BD we believe that the allele frequency of FADS must be analyzed.

Highfield imaging of the subgenual anterior cingulate cortex in uni- and bipolar depression.

The subgenual Anterior Cingulate Cortex (sgACC), as a part of the Anterior Cingulate Cortex and the limbic system plays a crucial role in mood regulation. Previous structural and functional brain imaging studies of the sgACC have revealed alterations of Gray Matter (GM) volumes and Blood Oxygenation Level Dependent signals (BOLD) in patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD), suggesting potential biomarker traits for affective disorders. In this study we investigated the gray matter volume of the sgACC in 3 different patient groups: 40 MDD patients, of which 20 were medicated (MDDm) and 20 were unmedicated (MDDu), and 21 medicated BD patients, and compared them with 23 healthy volunteers. We examined GM volume alteration using high-resolution 7T Magnetic Resonance Imaging (MRI) which produced quantitative maps of the spin-lattice relaxation time (T1). T1 maps provide high contrast between gray and white matter, and at 7 Tesla voxels with submillimeter resolution can be acquired in a reasonable scan time. We developed a semi-automatic segmentation protocol based on refined landmarks derived from previous volumetric studies using quantitative T1 maps as raw input data for automatic tissue segmentation of GM, WM and CSF (cerebrospinal fluid) tissue. The sgACC ROI was then superimposed on these tissue probability maps and traced manually by two independent raters (F.B., M.M.) following our semi-automatic segmentation protocol. Interrater reliability was calculated on a subset of 10 brain scans for each hemisphere, showing an Intra-Class Correlation coefficient (ICC) r = 0.96 for left sgACC and r = 0.84 for right sgACC respectively. In summary, we have developed a reproducible and reliable semi-automatic segmentation protocol to measure gray matter volume in the sgACC. Based on previous findings from meta-analyses on morphometric studies of the sgACC, we hypothesized that patients with MDD would have lower gray matter sgACC volumes compared to healthy subjects. Post-hoc analysis revealed smaller subgenual volumes for the left hemisphere in both the medicated (MDDm) and non-medicated (MDDu) group versus healthy controls (p = .001, p = .008) respectively. For the right hemisphere, the (MDDu) and BD group exhibited significantly lower subgenual volumes than healthy controls (p < .001, p = .004) respectively. To our knowledge, this is the first morphometric MRI study using T1 maps obtained in high-resolution 7 Tesla MRI to compare MDD and BD patients with healthy controls.

The Relationship Between Brain-Derived Neurotrophic Factor and Serotonin in Major Depressive and Bipolar Disorders: A Cross-Sectional Analysis.

Background Mood disorders like major depressive disorder (MDD) and bipolar disorder (BD) involve complex interactions between brain-derived neurotrophic factor (BDNF) and serotonin. While extensive research has explored these factors individually, their combined effects and interactions in these disorders are less understood. This study uniquely addresses this gap by examining how BDNF and serotonin interact and relate to mood disorder severity, providing new insights into their joint role in MDD and BD. Objectives The objective of this study was to examine the correlation between serum BDNF and plasma serotonin levels and to assess how these correlations relate to the severity of symptoms and overall disease severity in MDD and BD. Methodology This cross-sectional study, conducted at the Khyber Medical University, Peshawar, from January to September 2023, examined the correlation between BDNF and serotonin in individuals with MDD and BD. Participants (n = 63) aged 18-65 were recruited based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria, excluding those with neurological disorders, substance abuse, or severe medical illness. A control group of 21 healthy individuals was matched by age and gender. Data collection involved demographic details, clinical history, and comorbid diagnoses assessed using the Mini International Neuropsychiatric Interview (MINI). Mood disorder severity was measured using the Hamilton Depression Rating Scale (HAM-D) for MDD and the Young Mania Rating Scale (YMRS) for BD, along with additional assessments (Beck Depression Inventory, Global Assessment of Functioning). Serum BDNF and serotonin levels were analyzed using enzyme-linked immunosorbent assay (ELISA) kits. Statistical analyses included t-tests, Mann-Whitney U tests, Pearson correlations, and subgroup analyses to assess relationships between biomarkers, mood disorder severity, and influencing factors. Results BDNF levels were found to be 20.1 ± 5.3 ng/mL in MDD, 18.5 ± 4.7 ng/mL in BD, and 25.9 ± 6.2 ng/mL in controls. Serotonin levels were 45.8 ± 12.6 ng/mL in MDD, 43.2 ± 11.4 ng/mL in BD, and 52.1 ± 14.3 ng/mL in controls. In the MDD group, significant negative correlations were observed between BDNF levels and mood disorder severity (r = -0.32, p = 0.045), whereas serotonin levels did not show significant correlations (r = -0.21, p = 0.23). In the BD group, BDNF levels also showed a significant negative correlation with manic symptoms (r = -0.28, p = 0.048), but serotonin levels showed no significant correlation. Subgroup analyses revealed that participants under 40 years had higher BDNF levels (22.8 ± 5.6 ng/mL) compared to those aged 40 and above (19.7 ± 4.3 ng/mL). Females showed higher BDNF levels (24.5 ± 6.3 ng/mL) than males (19.3 ± 3.8 ng/mL). Participants not on medication had higher BDNF levels (23.6 ± 5.1 ng/mL) compared to those on medication (17.9 ± 4.2 ng/mL). Those without comorbidities also had higher BDNF levels (23.8 ± 5.9 ng/mL) than those with comorbidities (18.2 ± 4.5 ng/mL), while serotonin levels varied similarly across these subgroups. Conclusion Lower BDNF levels are associated with mood disorders and symptom severity, indicating their potential as a biomarker.

Neurocognitive correlates of polygenic risk for bipolar disorder among youth with and without bipolar disorder

IntroductionThere is well-established evidence of reduced neurocognitive performance in adults and youth with bipolar disorder (BD). However, little is known about the polygenic underpinnings of neurocognition in individuals with BD, particularly in youth. The current study aimed to examine the association between polygenic risk score for BD (BD-PRS) and neurocognition among youth with BD and healthy controls (HC). Methods129 youth of European ancestry (72 BD, 57 HC), ages 13–20 years, were included. Six neurocognitive tasks within the Cambridge Neuropsychological Test Automated Battery were assessed. General linear models were used to examine the association between BD-PRS and neurocognitive composite scores, controlling for age, sex, IQ, and two genetic principal components. ResultsIn the overall sample, higher BD-PRS was associated with significantly poorer affective processing (β = −0.25, p = 0.01), decision-making (β = −0.23, p = 0.02), and sustained attention (β = −0.28, p = 0.002). Secondary analyses revealed that higher BD-PRS was associated with significantly poorer sustained attention within the BD group (β = −0.27, p = 0.04), and with significantly poorer affective processing within the HC group (β = −0.29, p = 0.04). LimitationsCross-sectional design. Modest sample size may have reduced power to detect smaller effect sizes. ConclusionThe current study found that higher BD-PRS generated based on adult GWAS was associated with poorer neurocognitive performance in youth with BD and HC. Future longitudinal studies incorporating repeated neurocognitive assessments would further inform whether the associations of BD-PRS with neurocognition vary from youth to adulthood, and whether BD-PRS is associated with differential neurodevelopmental trajectories in individuals with and without BD.

Dynamic brain entropy predicts risky decision-making across transdiagnostic dimensions of psychopathology

ObjectivesMaladaptive risky decision-making is a common pathological behavior among patients with various psychiatric disorders. Brain entropy, which measures the complexity of brain time series signals, provides a novel approach to assessing brain health. Despite its potential, the dynamics of brain entropy have seldom been explored. This study aimed to construct a dynamic model of brain entropy and examine its predictive value for risky decision-making in patients with mental disorders, utilizing resting-state functional magnetic resonance imaging (rs-fMRI). MethodsThis study analyzed the rs-fMRI data from a total of 198 subjects, including 48 patients with bipolar disorder (BD), 47 patients with schizophrenia (SZ), 40 patients with adult attention deficit hyperactivity disorder (ADHD), as well as 63 healthy controls (HC). Time series signals were extracted from 264 brain regions based on rs-fMRI. The traditional static entropy and dynamic entropy (coefficient of variation, CV; rate of change, Rate) were constructed, respectively. Support vector regression was employed to predict risky decision-making utilizing leave-one-out cross-validation within each group. ResultsOur findings showed that CV achieved the best performances in HC and BD groups (r = −0.58, MAE = 6.43, R2 = 0.32; r = −0.78, MAE = 12.10, R2 = 0.61), while the Rate achieved the best in SZ and ADHD groups (r = −0.69, MAE = 10.20, R2 = 0.47; r = −0.78, MAE = 7.63, R2 = 0.60). For the dynamic entropy, the feature selection threshold rather than the time window length and overlapping ratio influenced predictive performance. ConclusionsThese results suggest that dynamic brain entropy could be a more effective predictor of risky decision-making than traditional static brain entropy. Our findings offer a novel perspective on exploring brain signal complexity and can serve as a reference for interventions targeting risky decision-making behaviors, particularly in individuals with psychiatric diagnoses.

Impaired facial emotion recognition in individuals with bipolar disorder

BackgroundIndividuals with bipolar disorder (BD) often struggle with emotional regulation and social interactions, partly due to difficulties in accurately recognizing facial emotions. MethodsFrom September 2021 to February 2023, 69 BD individuals-comprising 23 with bipolar manic/hypomanic episode (BME), 23 with bipolar depressive episode (BDE), 23 with bipolar euthymic (EUT)-and 23 healthy controls (HCs) were enrolled. Diagnosis adhered to DSM-IV criteria using M.I.N.I 5.0, alongside assessments via Hamilton Depression Scale 17 and Young Manic Rating Scale. Recognition tasks involving 84 facial expression images across six categories. The Wilcoxon rank-sum test compares two groups, while the Kruskal-Wallis test compares multiple groups with subsequent adjusted pairwise comparisons. ResultsThe overall correct recognition rate of facial expressions in the BD group (79 %) was significantly lower than that of the HC group (83 %) (P=0.004). Primary differences were noted in neutral (93 % vs. 100 %, P=0.012) and fear (79 % vs. 86 %, P=0.023) expressions. Within the BD group, correct recognition rates were 71 % for BME, 80 % for BDE, and 80 % for EUT, all lower than in the HC group. Significant differences in correct recognition rates of neutral, fear, and joy expressions were observed among the four groups (P<0.05), with the BME group exhibiting the lowest rate. Misidentification of facial expressions was more frequent in the BD group compared to the HC group, particularly among negative expressions. ConclusionPatients with BD demonstrate lower correct recognition and higher misidentification rates of facial expressions, with those experiencing manic episodes showing impaired recognition of neutral, joy, and fear expressions.

Divergent pattern of functional connectivity within the dorsal attention network differentiates schizophrenia and bipolar disorder patients.

Schizophrenia (SZ) and bipolar disorder (BD) share common clinical features, symptoms, and neurocognitive deficits, which results in common misdiagnosis. Recently, it has been suggested that alterations within brain networks associated with perceptual organization yield potential to distinguish SZ and BD individuals. The aim of our study was to evaluate whether functional connectivity (FC) of the dorsal attention network (DAN) may differentiate both conditions. The study involved 90 participants: 30 remitted SZ patients, 30 euthymic BD patients, and 30 healthy controls (HC). Resting state functional magnetic resonance imaging was used to compare the groups in terms of the FC within the core nodes of the DAN involving frontal eye fields (FEF) and intraparietal sulcus (IPS). BD patients presented weaker inter-hemispheric FC between right and left FEF than HC. While SZ did not differ from HC in terms of inter-FEF connectivity, they presented increased inter- and intra-hemispheric FC between FEF and IPS. When compared with BD, SZ patients showed increased FC between right FEF and other nodes of the network (bilateral IPS and left FEF). We have shown that altered resting state FC within DAN differentiates BD, SZ, and HC groups. Divergent pattern of FC within DAN, consisting of hypoconnectivity in BD and hyperconnectivity in SZ, might yield a candidate biomarker for differential diagnosis between both conditions. More highly powered studies are needed to confirm these possibilities.

A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework.

The heterogeneity of psychiatric disorders makes researching disorder-specific neurobiological markers an ill-posed problem. Here, we face the need for disease stratification models by presenting a generalizable multivariate normative modelling framework for characterizing brain morphology, applied to bipolar disorder (BD). We employed deep autoencoders in an anomaly detection framework, combined with a confounder removal step integrating training and external validation. The model was trained with healthy control (HC) data from the human connectome project and applied to multi-site external data of HC and BD individuals. We found that brain deviating scores were greater, more heterogeneous, and with increased extreme values in the BD group, with volumes prominently from the basal ganglia, hippocampus and adjacent regions emerging as significantly deviating. Similarly, individual brain deviating maps based on modified z scores expressed higher abnormalities occurrences, but their overall spatial overlap was lower compared to HCs. Our generalizable framework enabled the identification of subject- and group-level brain normative-deviating patterns, a step forward towards the development of more effective and personalized clinical decision support systems and patient stratification in psychiatry.

Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech.

Psychosis poses substantial social and healthcare burdens. The analysis of speech is a promising approach for the diagnosis and monitoring of psychosis, capturing symptoms like thought disorder and flattened affect. Recent advancements in Natural Language Processing (NLP) methodologies enable the automated extraction of informative speech features, which has been leveraged for early psychosis detection and assessment of symptomology. However, critical gaps persist, including the absence of standardized sample collection protocols, small sample sizes, and a lack of multi-illness classification, limiting clinical applicability. Our study aimed to (1) identify an optimal assessment approach for the online and remote collection of speech, in the context of assessing the psychosis spectrum and evaluate whether a fully automated, speech-based machine learning (ML) pipeline can discriminate among different conditions on the schizophrenia-bipolar spectrum (SSD-BD-SPE), help-seeking comparison subjects (MDD), and healthy controls (HC) at varying layers of analysis and diagnostic complexity. We adopted online data collection methods to collect 20 minutes of speech and demographic information from individuals. Participants were categorized as "healthy" help-seekers (HC), having a schizophrenia-spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), or being on the psychosis spectrum with sub-clinical psychotic experiences (SPE). SPE status was determined based on self-reported clinical diagnosis and responses to the PHQ-8 and PQ-16 screening questionnaires, while other diagnoses were determined based on self-report from participants. Linguistic and paralinguistic features were extracted and ensemble learning algorithms (e.g., XGBoost) were used to train models. A 70%-30% train-test split and 30-fold cross-validation was used to validate the model performance. The final analysis sample included 1140 individuals and 22,650 minutes of speech. Using 5-minutes of speech, our model could discriminate between HC and those with a serious mental illness (SSD or BD) with 86% accuracy (AUC = 0.91, Recall = 0.7, Precision = 0.98). Furthermore, our model could discern among HC, SPE, BD and SSD groups with 86% accuracy (F1 macro = 0.855, Recall Macro = 0.86, Precision Macro = 0.86). Finally, in a 5-class discrimination task including individuals with MDD, our model had 76% accuracy (F1 macro = 0.757, Recall Macro = 0.758, Precision Macro = 0.766). Our ML pipeline demonstrated disorder-specific learning, achieving excellent or good accuracy across several classification tasks. We demonstrated that the screening of mental disorders is possible via a fully automated, remote speech assessment pipeline. We tested our model on relatively high number conditions (5 classes) in the literature and in a stratified sample of psychosis spectrum, including HC, SPE, SSD and BD (4 classes). We tested our model on a large sample (N = 1150) and demonstrated best-in-class accuracy with remotely collected speech data in the psychosis spectrum, however, further clinical validation is needed to test the reliability of model performance.

Correlates of cannabis use and cannabis use disorder among adolescents with major depressive disorder and bipolar disorder in the National Comorbidity Survey-Adolescent Supplement (NCS-A)

BackgroundDespite evidence regarding prevalence and correlates of cannabis use (CU) and cannabis use disorder (CUD) in major depressive disorder (MDD) and bipolar disorder (BD) in adults, little is known about this topic among adolescents. MethodsData is from the 2001–2004 National Comorbidity Survey–Adolescent Supplement, an in-person, cross-sectional epidemiologic survey of mental disorders. Participants included adolescents, ages 13–18 years, with MDD (n = 354 with CU, n = 70 with CUD, n = 688 with no CU), BD (n = 79 with CU, n = 32 with CUD, n = 184 with no CU), or adolescents without mood disorders (n = 1413 with CU, n = 333 with CUD, n = 6970 with no CU). Analyses examined prevalence and correlates of CU and CUD within MDD and BD groups. ResultsCU was most prevalent in adolescents with MDD followed by adolescents with BD then controls. CUD was most prevalent in adolescents with BD followed by adolescents with MDD then controls. In covariate-adjusted ordinal logistic regression models, within MDD and BD, CU and CUD groups had significantly higher odds of lifetime suicidal ideation/attempts, as well as other significant indicators of clinical severity. LimitationsBased on changes in cannabis acceptance, potency, and availability in the two decades since this study was conducted, present findings may underestimate adverse cannabis associations. ConclusionCU and CUD are both associated with adverse clinical characteristics in a community-based sample of adolescents with MDD and BD. Evidence that risks of cannabis use extend across the spectrum of use is important for adolescents with MDD and BD, in whom cannabis-related consequences tend to be more severe.

Hyperactivity and altered functional connectivity of the ventral striatum in schizophrenia compared with bipolar disorder: A resting state fMRI study

BackgroundSchizophrenia patients frequently present with structural and functional abnormalities of the ventral striatum (VS). Methodswe examined basal activation state and functional connectivity (FC) in four subregions of the bilateral ventral striatum: left inferior ventral striatum (VSi_L), left superior ventral striatum(VSs_L), right inferior ventral striatum(VSi_R), and right superior ventral striatum(VSs_R). Resting-state functional magnetic resonance images were obtained from 62 schizophrenia patients (SCH), 57 bipolar disorder (BD) patients, and 26 healthy controls (HCs). ResultsThe schizophrenia group exhibited greater fALFF in bilateral VS subregions compared to BD and HC groups as well as greater FC between the bilateral VSi and multiple brain regions, including the thalamus, putamen, posterior cingulate gyrus (PCC), frontal cortex and caudate. Moreover, the fALFF values of the bilateral ventral striatum were positively correlated with the severity of positive symptoms. We also found the functional connectivity between the bilateral inferior ventral striatum and some brain regions aforementioned were positively correlated with the severity of negative symptoms. ConclusionThese findings confirm a crucial contribution of ventral striatum dysfunction, especially of the bilateral VSi in schizophrenia. Functionally dissociated regions of the ventral striatum are differentially disturbed in schizophrenia.

Is there a relationship between resting state connectivity within large-scale functional networks and implicit motor learning impairments in schizophrenia and bipolar disorder?

The aim of this exploratory study is to evaluate whether implicit motor learning impairments observed in schizophrenia (SZ) and bipolar disorder (BD) are associated with the resting state functional connectivity (rs-FC) within large-scale functional networks. The study involved 30 BD patients, 30 SZ patients and 30 healthy controls (HC). Implicit motor learning was evaluated with the use of serial reaction time task (SRTT). Prior to the training patients underwent resting state functional magnetic resonance imaging (rsfMRI) examination. We have measured rs-FC within salience network (SAN), default mode network (DMN), frontoparietal network (FPN), sensorimotor network (SMN), limbic network (LN) and visual network (VIN) and their associations with implicit motor learning indices. rs-FC within SAN, DMN, FPN, SMN, LN and VIN reveal no significant association with implicit motor learning indices. BD, SZ and HC groups did not differ in terms of rs-FC within abovementioned networks. We have shown that in the studied groups SRTT performance could not be predicted by rs-FC within the major large-scale functional networks, i.e., SMN, FPN, VIN, LN, SAN and DMN. The observation of the independence of implicit motor learning from the initial activity of these systems is important for proper understanding of neuronal underpinnings of this process and planning further neuroimaging research on this topic.

The mediating role of family functioning between childhood trauma and depression severity in major depressive disorder and bipolar disorder

BackgroundChildhood trauma (CT) and family functioning exert significant influences on the course and long-term outcome of major depressive disorder (MDD) and bipolar disorder (BD) patients. Hence, we examined the intricate relationship between CT, family function, and the severity of depressive episodes in MDD and BD patients. Methods562 patients with depressive episodes (336 MDD and 226 BD) and 204 healthy controls (HCs) were included in this retrospective study. The 17-item Hamilton Depression Rating Scale (HAMD-17), Childhood Trauma Questionnaire (CTQ), and Family Adaptability and Cohesion Evaluation Scale (FACES II-CV) were assessed. Pearson correlation analysis and mediation analysis were performed. ResultsCT had both a direct and indirect impact on depression severity in MDD and BD groups. In MDD, family adaptability mediated the impact of all CT subtypes on depression severity (Effect = 0.113, [0.030, 0.208]). In BD, family cohesion played a mediating role between emotional neglect (EN) and HAMD-17 scores (Effect = 0.169, [0.008, 0.344]). Notable differences were observed in onset age, illness duration, episode frequency, family history, and CT subtypes between MDD and BD (P < 0.05). LimitationsThis study has several limitations including recall bias, lack of objective family functioning measures, small sample size, and cross-sectional design. ConclusionsFamily functioning mediated the impact of CT on depressive symptoms severity in MDD and BD patients. MDD patients with a history of CT exhibited reduced family adaptability, while BD patients with a history of EN had weaker familial emotional bonds. Our findings highlighted the importance of family-focused preventive interventions in mitigating the long-term effects of CT.

Characterization and assessment of executive functions through a virtual cooking task in euthymic patients with bipolar disorder

Bipolar disorder (BD)1 implies impairments in executive functions during euthymia that interfere in psychosocial functioning. Virtual reality assessments may confer advantages respect to traditional assessments in terms of efficiency and ecological validity. The aim of this study was to validate a novel Virtual Cooking Task (VCT) for executive functions assessment in euthymic patients with BD. This is a cross-sectional study in which a group of BD patients (n = 42) and healthy controls (n = 42) were assessed with the VCT and a battery of computerized standard tasks (CST). Additionally, the influence on psychosocial functioning of both forms of assessment, measured with the FAST, was investigated to check ecological validity. In BD group significant impairments in interference, working memory and sustained attention were found in CST and VCT respect to controls. However, deficits in planning and problem-solving were also revealed with the VCT. With respect to psychosocial functioning, only VCT variables were able to predict FAST scores at the assessment time. The VCT showed a greater sensitivity than CST to assess executive functions and real-life functioning in BD. This provides evidence about the opportunity to design novel cognitive assessments for diagnostic and therapeutic purposes in BD.

Hypomania-Checklist-33: risk stratification and factor structure in a mixed psychiatric adolescent sample

BackgroundThe 33-item Hypomania Checklist (HCL-33) has been shown to distinguish between adolescent bipolar disorder (BD) and unipolar depression. To investigate the utility of the HCL-33 as a screening tool in routine diagnostics, the frequency and psychopathological characteristics of detected individuals in a mixed psychiatric sample necessitate more examination.MethodsThe HCL-33, Children’s Depression Inventory, Beck’s Anxiety Inventory, and Strengths and Difficulties Questionnaire were completed by 285 children and adolescents (12–18 years) in a mixed psychiatric sample. Applying the proposed HCL-33 cut-off score of ≥ 18, individuals with depressive symptoms were divided into at-risk or not at-risk for BD groups. The factorial structure, sum and factor score correlations with psychopathology, and impact on daily functioning were assessed.Results20.6% of the sample met at-risk criteria for BD. These individuals (n = 55) were older, more anxious, and showed more conduct problems vs the not at-risk group (n = 107). A two- and a three-factor model were pursued with the same Factor 1 (“active-elated”). Factor 2 (“risk-taking/irritable”) was separated into 2a (“irritable-erratic”) and 2b (“outgoing-disinhibited”) in the three-factor model. Whereas higher Factor 2 and 2a scores correlated with a broad range of more severe symptomatology (i.e., depression, anxiety, hyperactivity), higher Factor 1 and 2b scores correlated with more emotional and conduct problems, respectively. 51.7% of the sample reported a negative impact from hypomanic symptoms on daily functioning.LimitationsCross-sectional design and data collection in a single mental health service.ConclusionsThe HCL-33 may be a useful tool to improve diagnostics, especially in adolescents with depressive symptoms additionally presenting with anxious symptoms and conduct problems.

'The Relationship Between Childhood Traumas and Social Cognition Through Theory of Mind and Alexithymia in Bipolar Disorder'.

This study aimed to investigate the potential differences in childhood trauma (CT), theory of mind (ToM), a significant component of social cognition, and alexithymia in bipolar disorder (BD) patients and healthy controls. The study included 50 BD patients who met the study criteria and were under follow-up at our clinic along with 50 healthy controls. The two groups were matched for age, gender, and educational status. A sociodemographic questionnaire, Childhood Trauma Questionnaire (CTQ), Toronto Alexithymia Scale (TAS), Dokuz Eylul Theory of Mind Index (DEZIKO), Hamilton Depression Rating Scale (HDRS), and Young Mania Rating Scale (YMRS) were applied to all participants. The CTQ-Total, TAS total, and DEZIKO total scores were significantly higher in the BD group compared to the healthy group (p < .001). A significant positive correlation was identified between the TAS total score and CTQ physical neglect (r = 0.472, p = .001), CTQ emotional neglect (r = 0.449, p = .001) and CTQ total scores (r = 0.5, p < .001) in the BD group. A statistically significant negative correlation was identified between the DEZIKO faux pas score and the CTQ physical neglect score (r = -0.437, p = .002). BD patients had more adverse childhood experiences, lower ToM abilities despite being in remission, and more pronounced alexithymic personality features compared to healthy controls. We also identified a relationship between ToM, alexithymia, and adverse childhood experiences in BD.