Cognitive inhibition is key to cognitive control in healthy and psychiatric conditions. Bipolar Disorder (BD) individuals display a range of inhibitory deficits and high levels of impulsivity across all stages of the disease, including euthymia.

We tested how the inhibition of heuristics in favor of analytical strategies influences the elaboration of sentences with logical quantifiers by means of a sentence-picture matching task in which the processing of quantified sentences containing the logical universal and particular quantifiers was required. Behavioral and brain oscillatory responses were assessed employing EEG recordings.

In Experiment 1, in a group of healthy volunteers, we demonstrated how the presence of a universal quantifier generates an inhibition, characterized by a high cognitive load, which is resolved at the expense of a poorer behavioral performance compared to a lower cognitive load and neutral control task. In Experiment 2, comparing healthy adults and BD patients, EEG time-frequency analysis showed a different modulation of the theta frequency band localized centrally in the medial frontal areas and representative of the different degrees of cognitive control between groups.

Electrophysiological description should be interpreted with caution in light of the high signal-to-noise ratio determined by the complexity of the task.

Even in euthymia, BD limited availability of resources for cognitive inhibition impacts the functionality of a fronto-parietal cortical network, responsible for cognitive control, and orchestrated by the activity of frontal areas synchronized in theta and beta frequency.

Major Depressive Disorder (MDD) and Bipolar Disorder (BD) exhibit overlapping depressive symptoms, complicating their differentiation in clinical practice. Traditional neuroimaging studies have focused on specific regions of interest, but few have employed whole-brain analyses like regional homogeneity (ReHo). This study aims to differentiate MDD from BD by identifying key brain regions with abnormal ReHo and using advanced machine learning techniques to improve diagnostic accuracy.

A total of 63 BD patients, 65 MDD patients, and 70 healthy controls were recruited from the Shanghai Mental Health Center. Resting-state functional MRI (rs-fMRI) was used to analyze ReHo across the brain. We applied Support Vector Machine (SVM) and SVM-Recursive Feature Elimination (SVM-RFE), a robust machine learning model known for its high precision in feature selection and classification, to identify critical brain regions that could serve as biomarkers for distinguishing BD from MDD. SVM-RFE allows for the recursive removal of non-informative features, enhancing the model's ability to accurately classify patients. Correlations between ReHo values and clinical scores were also evaluated.

ReHo analysis revealed significant differences in several brain regions. The study results revealed that, compared to healthy controls, both BD and MDD patients exhibited reduced ReHo in the superior parietal gyrus. Additionally, MDD patients showed decreased ReHo values in the Right Lenticular nucleus, putamen (PUT.R), Right Angular gyrus (ANG.R), and Left Superior occipital gyrus (SOG.L). Compared to the MDD group, BD patients exhibited increased ReHo values in the Left Inferior occipital gyrus (IOG.L). In BD patients only, the reduction in ReHo values in the right superior parietal gyrus and the right angular gyrus was positively correlated with Hamilton Depression Scale (HAMD) scores. SVM-RFE identified the IOG.L, SOG.L, and PUT.R as the most critical features, achieving an area under the curve (AUC) of 0.872, with high sensitivity and specificity in distinguishing BD from MDD.

This study demonstrates that BD and MDD patients exhibit distinct patterns of regional brain activity, particularly in the occipital and parietal regions. The combination of ReHo analysis and SVM-RFE provides a powerful approach for identifying potential biomarkers, with the left inferior occipital gyrus, left superior occipital gyrus, and right putamen emerging as key differentiating regions. These findings offer valuable insights for improving the diagnostic accuracy between BD and MDD, contributing to more targeted treatment strategies.

Pediatric mood disorders, including Major Depressive Disorder (MDD) and Bipolar Disorder (BD), exhibit overlapping symptomatology and complex neurodevelopmental trajectories, necessitating a comprehensive investigation of their neuroanatomical underpinnings. This study aimed to characterize structural brain differences in children with MDD and euthymic BD using high-resolution structural magnetic resonance imaging (MRI). A total of 51 children (aged 10-14 years) were categorized into MDD, euthymic BD, and typically developing (TD) controls. Utilizing advanced surface-based morphometry, we examined four cortical features: fractal dimension, gyrification, sulcal depth, and cortical thickness, to delineate disorder-specific and shared neuroanatomical alterations. Additionally, we explored the interaction between white matter volumetrics and these surface-based metrics to assess its modulatory role in structural brain differences. Our results revealed significant cortical alterations, with distinct and overlapping patterns in both MDD and BD. The findings demonstrated disruptions in cortical complexity, folding patterns, and sulcal morphology, particularly in regions implicated in emotion regulation and cognitive processing. These structural variations provide critical insights into the neurodevelopmental alterations associated with pediatric mood disorders. By integrating multiple morphometric parameters, this study offers a comprehensive framework for understanding the neuroanatomical changes in MDD and BD, contributing to more precise diagnostic biomarkers. The results underscore the importance of incorporating surface-based morphometry and white matter interactions in future research to refine early diagnosis and targeted interventions for mood disorders in children.

Research has reported group-level differences in cortical grey/white matter contrast (GWC) in individuals with psychotic disorders. However, no studies to date have explored GWC in individuals at elevated risk for psychosis. In this study, we examined brain microstructure differences between young adults with psychotic-like experiences or a high genetic risk for psychosis and unaffected individuals. Moreover, we investigated the association between GWC and the number of and experiences of psychosis-like symptoms. The sample was obtained from the Avon Longitudinal Study of Parents and Children (ALSPAC): the psychotic experiences study, consisting of young adults with psychotic-like symptoms (n = 119) and unaffected individuals (n = 117), and the schizophrenia recall-by-genotype study, consisting of individuals with a high genetic risk for psychosis (n = 95) and those with low genetic risk for psychosis (n = 95). Statistical analyses were performed using FSL's Permutation Analysis of Linear Models (PALM), controlling for age and sex. The results showed no statistically significant differences in GWC between any of the groups and no significant associations between GWC and the number and experiences of psychosis-like symptoms. In conclusion, the results indicate there are no differences in GWC in individuals with high, low or no risk for psychosis.

Ontologies are structured frameworks for representing knowledge by systematically defining concepts, categories, and their relationships. While widely adopted in biomedicine, ontologies remain largely absent in mental health research and clinical care, where the field continues to rely heavily on existing classification systems (DSM). Although useful for clinical communication and administrative purposes, they lack the semantic structure, computational, and reasoning properties needed to integrate diverse data sources or support artificial intelligence (AI)-enabled analysis. This reliance on classification systems limits efforts to analyze and interpret complex, heterogeneous psychiatric data. In mood disorders, particularly bipolar disorder, the lack of formalized semantic models contributes to diagnostic inconsistencies, fragmented data structures, and barriers to precision medicine. Ontologies, by contrast, provide a standardized, machine-readable foundation for linking multimodal data sources, such as electronic health records (EHRs), genetic and neuroimaging data, and social determinants of health, while enabling secure, de-identified computation. This review surveys the current landscape of mental health ontologies and highlights the Human Phenotype Ontology (HPO) as a promising framework for bridging psychiatric and medical phenotypes. We describe ongoing efforts to enhance HPO through curated psychiatric terms, refined definitions, and structured mappings of observed phenomena. The Global Bipolar Cohort (GBC), an international collaboration, exemplifies this approach through the development of a consensus-driven ontology tailored to bipolar disorder. By supporting semantic interoperability, reproducible research, and individualized care, ontology-based approaches provide essential infrastructure for overcoming the limitations of classification systems and advancing data-driven precision psychiatry.

Precision psychiatry aims to improve routine clinical practice by integrating biological, clinical, and environmental data. Many studies have been performed in different areas of research on major depressive disorder, bipolar disorder, and schizophrenia. Neuroimaging and electroencephalography findings have identified potential circuit-level abnormalities predictive of treatment response. Protein biomarkers, including IL-2, S100B, and NfL, and the kynurenine pathway illustrate the role of immune and metabolic dysregulation. Circadian rhythm disturbances and the gut microbiome have also emerged as critical transdiagnostic contributors to psychiatric symptomatology and outcomes. Moreover, advances in genomic research and polygenic scores support the perspective of personalized risk stratification and medication selection. While challenges remain, such as data replication issues, prediction model accuracy, and scalability, the progress so far achieved underscores the potential of precision psychiatry in improving diagnostic accuracy and treatment effectiveness.

Objective: Patients with bipolar disorder (BD) may exhibit common and significant changes in brain activity across different networks. Our aim was to investigate the changes in functional connectivity (FC) within different brain networks in BD, as well as their neuroimaging homogeneity, heterogeneity, and genetic variation. Methods: In this study, we analyzed the seed points and whole-brain FC of the sensorimotor network (SMN) and visual network (VN) in 83 healthy controls (HCs) and 77 BD patients, along with their genetic neuroimaging associations. Results: The results showed that, compared to HCs, BD patients exhibited abnormal FC in the SMN and VN brain regions. However, after three months of treatment, there were no significant differences in SMN and VN FC in the brain regions of the patients compared to pre-treatment levels. Enrichment analysis indicated that genes associated with changes in FC were shared among different SMN seed points, but no shared genes were found among VN seed points. Conclusions: In conclusion, changes in SMN FC may serve as a potential neuroimaging marker in BD patients. Our genetic neuroimaging association analysis may help to comprehensively understand the molecular mechanisms underlying FC changes in BD patients.

Bipolar disorder (BD) is a severe psychiatric condition whose pathophysiology is complex and multifactorial. Genetic, environmental and social risk factors play a role in its development as well as in its progressive course. Research is currently focusing on the identification of the biological basis underlying these processes in order to suggest novel biomarkers capable to predict BD etiopathogenesis and staging. Staging has been recognized as of great value for the treatment and management of many illnesses and might also be suitable for mental health issues, particularly in disorders like BD, which progress from an initial mild phase to a more severe and thus difficult-to-treat situation. Thus, it would be of great help the characterization of to suggest better treatment requirements and improve prognosis across the different stages of the illness. Here, we summarize current research on the biological hypotheses of BD and the biomarkers associated with its progression, reviewing clinical studies available in the literature.

Neurodevelopmental mechanisms are increasingly implicated in bipolar disorder (BD), highlighting the importance of their study in young persons. Neuroimaging studies have demonstrated a central role for frontotemporal corticolimbic brain systems that subserve processing and regulation of emotions, and processing of reward in adults with BD. As adolescence and young adulthood (AYA) is a time when fully syndromal BD often emerges, and when these brain systems undergo dynamic maturational changes, the AYA epoch is implicated as a critical period in the neurodevelopment of BD. Functional magnetic resonance imaging (fMRI) studies can be especially informative in identifying the functional neuroanatomy in adolescents and young adults with BD (BDAYA) and at high risk for BD (HR-BDAYA) that is related to acute mood states and trait vulnerability to the disorder. The identification of early emerging brain differences, trait- and state-based, can contribute to the elucidation of the developmental neuropathophysiology of BD, and to the generation of treatment and prevention targets. In this critical review, fMRI studies of BDAYA and HR-BDAYA are discussed, and a preliminary neurodevelopmental model is presented based on a convergence of literature that suggests early emerging dysfunction in subcortical (e.g., amygdalar, striatal, thalamic) and caudal and ventral cortical regions, especially ventral prefrontal cortex (vPFC) and insula, and connections among them, persisting as trait-related features. More rostral and dorsal cortical alterations, and bilaterality progress later, with lateralization, and direction of functional imaging findings differing by mood state. Altered functioning of these brain regions, and regions they are strongly connected to, are implicated in the range of symptoms seen in BD, such as the insula in interoception, precentral gyrus in motor changes, and prefrontal cortex in cognition. Current limitations, and outlook on the future use of neuroimaging evidence to inform interventions and prevent the onset of mood episodes in BDAYA, are outlined.

Parkinsonism is a frequently encountered symptom in individuals with bipolar disorder (BD). It can be drug-induced, co-occurring with Parkinson's disease (PD), or a genuine motor abnormality of BD itself. This study aims to address the primary pathophysiology of parkinsonism in BD.

Sixteen patients with BD and parkinsonism were recruited from consecutive patients who were referred to a neurology clinic at a tertiary psychiatric centre. The patients underwent clinical assessments, dopamine transporter single-photon computed tomography (DAT-SPECT), cardiac 123I-metaiodo-benzylguanidine (MIBG) scintigraphy, and morphometric magnetic resonance imaging (MRI). The positivity or negativity of Lewy body disease (LBD) biomarkers was determined based on the visual assessment of DAT-SPECT and heart-to-mediastinum ratio on cardiac MIBG scintigraphy. Four out of the 16 participants received 300-600 mg of levodopa.

Thirteen patients were diagnosed with BD type 1, and 12 had experienced >5 previous mood episodes. Parkinsonism developed more than 10 years after the onset of BD and after the age of 50 years in all patients. Four cases were positive for LBD biomarkers. Six patients with negative LBD biomarkers showed reduced striatal uptake with z-scores below -2.0. MRI morphometry revealed varying degrees of brain atrophy in most patients. Three of the four patients did not respond to 600 mg of levodopa.

The results of this study indicate that the majority of parkinsonism observed in BD is not a consequence of PD/LBD. Instead, it may represent a genuine motor abnormality of BD in late life.

Bipolar disorder is a leading contributor to the global burden of disease1. Despite high heritability (60-80%), the majority of the underlying genetic determinants remain unknown2. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.8 million controls), combining clinical, community and self-reported samples. We identified 298 genome-wide significant loci in the multi-ancestry meta-analysis, a fourfold increase over previous findings3, and identified an ancestry-specific association in the East Asian cohort. Integrating results from fine-mapping and other variant-to-gene mapping approaches identified 36 credible genes in the aetiology of bipolar disorder. Genes prioritized through fine-mapping were enriched for ultra-rare damaging missense and protein-truncating variations in cases with bipolar disorder4, highlighting convergence of common and rare variant signals. We report differences in the genetic architecture of bipolar disorder depending on the source of patient ascertainment and on bipolar disorder subtype (type I or type II). Several analyses implicate specific cell types in the pathophysiology of bipolar disorder, including GABAergic interneurons and medium spiny neurons. Together, these analyses provide additional insights into the genetic architecture and biological underpinnings of bipolar disorder.

Background: Bipolar disorder often begins in adolescence or early adulthood, characterized by recurrent manic episodes that can lead to neurodegenerative brain changes and functional decline. While several oral second-generation antipsychotics are Food and Drug Administration (FDA)-approved for mania, adherence to maintenance treatment is frequently poor due to factors such as anosognosia, cognitive dysfunction, impulsivity, side effects aversion, and substance use. Long-acting injectable (LAI) antipsychotics, approved for adults with bipolar mania or schizoaffective disorder (bipolar type), offer a potential solution for adolescents with similar conditions. This study reports on the efficacy of LAI antipsychotics in managing bipolar mania in adolescents, tracking outcomes over up to a year with baseline and follow-up Young Mania Rating Scale (YMRS) assessments. Methods: The study included 116 adolescents with a mean age of 16.17 years (66% male, 48% white, 23% black). Of these, 73% were diagnosed with bipolar mania and 22% with schizoaffective disorder, bipolar type. The mean illness duration was 1.9 years, with a baseline YMRS score of 33.8 and a body mass index (BMI) of 23.4 kg/m². LAI antipsychotics administered included aripiprazole, paliperidone, and risperidone, given at intervals of 1, 2, or 3 months. Results: YMRS scores showed substantial improvement, declining to 21.7 at 1 month, 12.3 at 2 months, 4.9 at 6 months, and 3.0 at 1 year. Common side effects were increased appetite and weight gain (mean BMI rose to 26.3 kg/m²). There were no dropouts, although 12% of participants switched formulations due to side effects. Notably, 86.2% of adolescents improved sufficiently to return to school or work. While 28.4% experienced depressive episodes, there were no suicide attempts or deaths during the 4- to 14-month follow-up. Discussion: This study demonstrates that LAI antipsychotics can effectively stabilize adolescents with bipolar mania or schizoaffective disorder, bipolar type, showing a marked decline in YMRS scores and high rates of remission and functional recovery. Despite the lack of FDA approval for LAI antipsychotics in those younger than 18, our results from off-label use suggest significant efficacy and tolerability. Further FDA clinical trials are needed to explore LAI antipsychotic formulations in adolescents to address the needs of this high-risk, nonadherent population.

Bipolar disorder (BD) and schizophrenia (SCZ) are uniquely human disorders with a complex pathophysiology that involves adverse neuropathological events in brain development. High disease polygenicity and limited access to live human brain tissue make these disorders exceedingly challenging to study mechanistically. Cellular cultures and brain organoids generated from human-derived pluripotent stem cells preserve the genetic background of the donor cells and recapitulate some of the defining characteristics of human brain architecture and early spatiotemporal development. These model systems have already proven successful in deciphering some of the neuropathological perturbations in BD and SCZ, and methodological advancements, such as the functional integration of 2 or more region-specific organoids and organoid transplantation in animals, promise to deliver increasingly refined insights. Here, we review a selection of recent discoveries achieved by stem cell-based models, with a particular focus on patterns of cellular and molecular convergence and divergence between BD and SCZ. First, we provide a brief overview of the evidence from glial and neuronal cell cultures and brain organoids, centering our discussion on several key functional domains, including neuroinflammation, neuronal excitability, and mitochondrial function. Then, we review recent findings demonstrating the power of integrating stem cell-based systems with gene editing technologies to elucidate the functional consequences of risk variants identified through genetic association studies. We end with a discussion of current challenges and some promising avenues for future research.

Neuroimaging is a powerful noninvasive method for studying brain alterations in bipolar disorder (BD). To date, most neuroimaging studies of BD have included smaller cross-sectional samples reporting case versus control comparisons, revealing small to moderate effect sizes. In this narrative review, we discuss the current state of structural neuroimaging studies using magnetic resonance imaging, which inform our understanding of altered brain trajectories in BD across the lifespan. Alternative methodologies such as those that model patient deviations from age-specific norms are discussed, which may help derive new markers of BD pathophysiology. We discuss evidence from neuroimaging genetics and transcriptomics studies, which attempt to bridge the gap between macroscale brain variations and underlying microscale neurodevelopmental mechanisms. We conclude with a look toward the future and how ambitious investments in longitudinal, deeply phenotyped, population-based cohorts can improve modeling of complex clinical factors and provide more clinically actionable brain markers for BD.

Background: Pathophysiological models of pediatric bipolar disorder (PBD) are lacking. Multimodal approaches may provide a comprehensive description of the complex relationship between the brain and behavior. Aim: To assess behavioral, neuropsychological, neurophysiological, and neuroanatomical alterations in youth with PBD. Methods: Subjects with PBD (n = 23) and healthy controls (HCs, n = 23) underwent (a) clinical assessments encompassing the severity of psychiatric symptoms, (b) neuropsychological evaluation, (c) analyses of event-related potentials (related to the passive viewing of fearful, neutral, and happy faces during electroencephalography recording, and (d) cortical thickness and deep gray matter volume measurement using magnetic resonance imaging. Canonical correlation analyses were used to assess the relationships between these dimensions. Results: Youth with PBD had higher levels of anxiety (p < 0.001) and borderline personality features (p < 0.001), greater commission errors for negative stimuli (p = 0.003), delayed deliberation time (p < 0.001), and smaller risk adjustment scores (p = 0.002) than HCs. Furthermore, they showed cortical thinning in the frontal, parietal, and occipital areas (all p < 0.001) and greater P300 for happy faces (p = 0.29). In youth with PBD, cortical thickening and P300 amplitude positively correlated with more commission errors for negative stimuli, longer deliberation times, reduced risk adjustment, higher levels of panic and separation anxiety, and greater levels of negative relationships, whereas they negatively correlated with levels of depression (overall loadings > or <0.3). Limitations: Small sample size, cross-sectional design, and limited variables investigated. Conclusions: This preliminary work showed that multimodal assessment might be a viable tool for providing a pathophysiological model that unifies brain and behavioral alterations in youth with PBD.

Diagnosing bipolar disorder poses a challenge in clinical practice and demands a substantial time investment. With the growing utilization of artificial intelligence in mental health, researchers are endeavoring to create AI-based diagnostic models. In this context, some researchers have sought to develop machine learning models for bipolar disorder diagnosis. Nevertheless, the accuracy of these diagnoses remains a subject of controversy. Consequently, we conducted this systematic review to comprehensively assess the diagnostic value of machine learning in the context of bipolar disorder.

We searched PubMed, Embase, Cochrane, and Web of Science, with the search ending on April 1, 2023. QUADAS-2 was applied to assess the quality of the literature included. In addition, we employed a bivariate mixed-effects model for the meta-analysis.

18 studies were included, covering 3152 participants, including 1858 cases of bipolar disorder. 28 machine learning models were encompassed. Sensitivity and specificity in discriminating between bipolar disorder and normal individuals were 0.88 (9.5% CI: 0.74~0.95) and 0.89 (95% CI: 0.73~0.96) respectively, and the SROC curve was 0.94(95% CI: 0.92~0.96). The sensitivity and specificity for distinguishing between bipolar disorder and depression were 0.84 (95%CI: 0.80~0.87) and 0.82 (95%CI: 0.75~0.88) respectively. The SROC curve was 0.89 (95%CI: 0.86~0.91).

Machine learning methods can be employed for discriminating and diagnosing bipolar disorder. However, in current research, they are predominantly utilized for binary classification tasks, limiting their progress in clinical practice. Therefore, in future studies, we anticipate the development of more multi-class classification tasks to enhance the clinical applicability of these methods.

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023427290, identifier CRD42023427290.

Recent studies indicate extensive shared white matter (WM) abnormalities between bipolar disorder (BD) and schizophrenia (SZ). However, the heterogeneity of WM in BD in terms of the presence of psychosis remains a critical issue for exploring the boundaries between BD and SZ. Previous studies comparing WM microstructures in psychotic and nonpsychotic BDs (PBD and NPBD) have resulted in limited findings, probably due to subtle changes, emphasizing the need for further investigation.

Diffusion tensor imaging measures were obtained from 8 individuals with PBD, 8 with NPBD, and 22 healthy controls (HC), matched for age, gender, handedness, and educational years. Group comparisons were conducted using tract-based spatial statistics (TBSS). The most significant voxels showing differences between PBD and HC in the TBSS analyses were defined as a TBSS-ROI and subsequently analyzed.

Increased radial diffusivity (RD) in PBD compared to NPBD (p < 0.006; d = 1.706) was observed in TBSS-ROI, distributed in the confined regions of some WM tracts, including the body of the corpus callosum (bCC), the left genu of the CC (gCC), and the anterior and superior corona radiata (ACR and SCR). Additionally, NPBD exhibited significant age-associated RD increases (R2 = 0.822, p < 0.001), whereas the greater RD observed in PBD compared to NPBD remained consistent across middle age.

Preliminary findings from this small sample suggest severe frontal WM disconnection in the anterior interhemispheric communication, left fronto-limbic circuits, and cortico-striatal-thalamic loop in PBD compared to NPBD. While these results require replication and validation in larger and controlled samples, they provide insights into the pathophysiology of PBD, which is diagnostically located at the boundary between BD and SZ.

Patients with early psychosis (EP) (within 3 years after psychosis onset) show significant variability, which makes predicting outcomes challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, which limits the development of early interventions.

A data-driven approach, partial least squares correlation, was used across 2 independent datasets to examine multivariate relationships between white matter properties and symptomatology and to identify stable and generalizable signatures in EP. The primary cohort included patients with EP from the Human Connectome Project for Early Psychosis (n = 124). The replication cohort included patients with EP from the Feinstein Institute for Medical Research (n = 78) as part of the MEND (Multimodal Evaluation of Neural Disorders) Project. Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders.

In both cohorts, a significant latent component corresponded to a symptom profile that combined negative symptoms, primarily diminished expression, with specific somatic symptoms. Both latent components captured comprehensive features of white matter disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the partial least squares model trained on the primary cohort accurately predicted microstructural features and symptoms in the replication cohort. Findings were not driven by diagnosis, medication, or substance use.

This data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural white matter alterations in EP across diagnoses and datasets, showing strong covariance of these alterations with a unique profile of negative and somatic symptoms. These findings suggest the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.

Effective connectivity (EC) analysis provides valuable insights into the directionality of neural interactions, which are crucial for understanding the mechanisms underlying cognitive and emotional regulation in depressive and anxiety disorders. In this study, we examined EC within key neural networks during working memory (WM) and emotional regulation (ER) tasks in young adults, both healthy individuals and those seeking help from mental health professionals for emotional distress.

Dynamic causal modeling was used to analyze EC in 2 independent samples (n = 97 and n = 94). Participants performed an emotional n-back task to assess EC across the central executive network (CEN), default mode network (DMN), salience network (SN), and face processing network. Group-level parametric empirical Bayes analyses were conducted to examine EC patterns, with subanalyses comparing individuals with and without depression and anxiety.

Consistent patterns of positive (posterior probability > .95) DMN→CEN and DMN→SN EC were observed in both samples, predominantly in low and high WM conditions without ER. However, individuals without depressive or anxiety disorders exhibited a significantly greater number of preserved connections that were replicated across both samples.

This study highlights the different patterns of DMN→CEN EC in conditions with high and low WM loads with and without ER, suggesting that in higher WM loads with ER, the integration of the DMN with the CEN is reduced to facilitate successful cognitive task performance. The findings also suggest that DMN→CEN and DMN→SN EC are significantly reduced in depressive and anxiety disorders, highlighting this pattern of reduced EC as a potential neural marker of these disorders.

Psychotic disorders are characterized by white matter (WM) abnormalities; however, their relationship with the various aspects of illness presentation remains unclear. Sleep disturbances are common in psychosis, and emerging evidence suggests that sleep plays a critical role in WM physiology. Therefore, it is plausible that sleep disturbances are associated with impaired WM integrity in these disorders. To test this hypothesis, we examined the association of self-reported sleep disturbances with WM transverse (T2) relaxation times in a cross-diagnostic sample of patients with psychosis.

A total of 28 patients with psychosis (11 schizophrenia spectrum disorders and 17 bipolar disorder with psychotic features) were included. Metabolite (N-acetyl aspartate, choline, and creatine) and water T2 relaxation times were measured in the anterior corona radiata at 4T. Sleep was evaluated using the Pittsburgh Sleep Quality Index (PSQI).

PSQI total score showed a moderate to strong positive correlation with water T2 (r = 0.64, p< 0.001). Linear regressions showed that this association was independent of the overall severity of depressive, manic, or psychotic symptoms. In our exploratory analysis, sleep disturbance was correlated with free water percentage, suggesting that increased extracellular water may be a mechanism underlying the association of disturbed sleep and prolonged water T2 relaxation.

Our results highlight the connection between poor sleep and WM abnormalities in psychotic disorders. Future research using objective sleep measures and neuroimaging techniques suitable to probe free water is needed to further our insight into this relationship.

The St. Göran Bipolar Project (SBP) is a longitudinal outpatient study investigation aimed at identifying predictive factors associated with long-term outcomes in individuals with bipolar disorder. These outcomes include cognitive function, relapse rate, treatment responses, and functional outcomes. The study employs a multifaceted approach, integrating brain imaging, biochemical analyses of cerebrospinal fluid and blood, and genetics. This paper provides an overview of the research methods used in the SBP, along with a summary of the main findings to date.

SBP is a collaborative effort between academia and healthcare, enrolling study participants from bipolar outpatient clinics in Stockholm (SBP-S) and Gothenburg (SBP-G), Sweden. Healthy controls were recruited through Statistics Sweden. Data and samples were collected using structured interviews, self-rated questionnaires, blood and cerebrospinal fluid samples, magnetic resonance imaging, and neuropsychological tests. Follow-up visits are conducted 7 and 14 years after baseline.

The SBP has generated numerous original findings and has contributed to advancing knowledge on cognitive function, personality, cerebrospinal and blood biomarkers, neuroimaging, and genetics. Further, as data collection nears completion, new research questions can be addressed. The study's strengths include detailed, multimodal information from each study visit and a long follow-up period. The naturalistic setting ensures that findings are relevant to real-world scenarios. However, variability in data completeness can introduce selection bias. Additionally, the control population, while randomly selected, may not be fully representative due to the voluntary nature of participation. Future projects will focus on longitudinal analyses and novel methods to exploit the study's multifaceted approach.

The St. Göran Bipolar Project (SBP) is a longitudinal outpatient study investigation aimed at identifying predictive factors associated with long-term outcomes in individuals with bipolar disorder. These outcomes include cognitive function, relapse rate, treatment responses, and functional outcomes. The study employs a multifaceted approach, integrating brain imaging, biochemical analyses of cerebrospinal fluid and blood, and genetics. This paper provides an overview of the research methods used in the SBP, along with a summary of the main findings to date.

SBP is a collaborative effort between academia and healthcare, enrolling study participants from bipolar outpatient clinics in Stockholm (SBP-S) and Gothenburg (SBP-G), Sweden. Healthy controls were recruited through Statistics Sweden. Data and samples were collected using structured interviews, self-rated questionnaires, blood and cerebrospinal fluid samples, magnetic resonance imaging, and neuropsychological tests. Follow-up visits are conducted 7 and 14 years after baseline.

The SBP has generated numerous original findings and has contributed to advancing knowledge on cognitive function, personality, cerebrospinal and blood biomarkers, neuroimaging, and genetics. Further, as data collection nears completion, new research questions can be addressed. The study's strengths include detailed, multimodal information from each study visit and a long follow-up period. The naturalistic setting ensures that findings are relevant to real-world scenarios. However, variability in data completeness can introduce selection bias. Additionally, the control population, while randomly selected, may not be fully representative due to the voluntary nature of participation. Future projects will focus on longitudinal analyses and novel methods to exploit the study's multifaceted approach.