Country-level gender inequality is associated with structural differences in the brains of women and men.

Gender inequality across the world has been associated with a higher risk to mental health problems and lower academic achievement in women compared to men. We also know that the brain is shaped by nurturing and adverse socio-environmental experiences. Therefore, unequal exposure to harsher conditions for women compared to men in gender-unequal countries might be reflected in differences in their brain structure, and this could be the neural mechanism partly explaining women's worse outcomes in gender-unequal countries. We examined this through a random-effects meta-analysis on cortical thickness and surface area differences between adult healthy men and women, including a meta-regression in which country-level gender inequality acted as an explanatory variable for the observed differences. A total of 139 samples from 29 different countries, totaling 7,876 MRI scans, were included. Thickness of the right hemisphere, and particularly the right caudal anterior cingulate, right medial orbitofrontal, and left lateral occipital cortex, presented no differences or even thicker regional cortices in women compared to men in gender-equal countries, reversing to thinner cortices in countries with greater gender inequality. These results point to the potentially hazardous effect of gender inequality on women's brains and provide initial evidence for neuroscience-informed policies for gender equality.

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Mega-analysis of association between obesity and cortical morphology in bipolar disorders: ENIGMA study in 2832 participants.

BACKGROUND: Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact. METHODS: We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations. RESULTS: BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI. CONCLUSIONS: We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.

In vivo hippocampal subfield volumes in bipolar disorder-A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group.

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.

Effects of socioeconomic status in cognition of people with schizophrenia: results from a Latin American collaboration network with 1175 subjects.

BACKGROUND: Cognition heavily relies on social determinants and genetic background. Latin America comprises approximately 8% of the global population and faces unique challenges, many derived from specific demographic and socioeconomic variables, such as violence and inequality. While such factors have been described to influence mental health outcomes, no large-scale studies with Latin American population have been carried out. Therefore, we aim to describe the cognitive performance of a representative sample of Latin American individuals with schizophrenia and its relationship to clinical factors. Additionally, we aim to investigate how socioeconomic status (SES) relates to cognitive performance in patients and controls. METHODS: We included 1175 participants from five Latin American countries (Argentina, Brazil, Chile, Colombia, and Mexico): 864 individuals with schizophrenia and 311 unaffected subjects. All participants were part of projects that included cognitive evaluation with MATRICS Consensus Cognitive Battery and clinical assessments. RESULTS: Patients showed worse cognitive performance than controls across all domains. Age and diagnosis were independent predictors, indicating similar trajectories of cognitive aging for both patients and controls. The SES factors of education, parental education, and income were more related to cognition in patients than in controls. Cognition was also influenced by symptomatology. CONCLUSIONS: Patients did not show evidence of accelerated cognitive aging; however, they were most impacted by a lower SES suggestive of deprived environment than controls. These findings highlight the vulnerability of cognitive capacity in individuals with psychosis in face of demographic and socioeconomic factors in low- and middle-income countries.

Association between body mass index and subcortical brain volumes in bipolar disorders-ENIGMA study in 2735 individuals.

Individuals with bipolar disorders (BD) frequently suffer from obesity, which is often associated with neurostructural alterations. Yet, the effects of obesity on brain structure in BD are under-researched. We obtained MRI-derived brain subcortical volumes and body mass index (BMI) from 1134 BD and 1601 control individuals from 17 independent research sites within the ENIGMA-BD Working Group. We jointly modeled the effects of BD and BMI on subcortical volumes using mixed-effects modeling and tested for mediation of group differences by obesity using nonparametric bootstrapping. All models controlled for age, sex, hemisphere, total intracranial volume, and data collection site. Relative to controls, individuals with BD had significantly higher BMI, larger lateral ventricular volume, and smaller volumes of amygdala, hippocampus, pallidum, caudate, and thalamus. BMI was positively associated with ventricular and amygdala and negatively with pallidal volumes. When analyzed jointly, both BD and BMI remained associated with volumes of lateral ventricles  and amygdala. Adjusting for BMI decreased the BD vs control differences in ventricular volume. Specifically, 18.41% of the association between BD and ventricular volume was mediated by BMI (Z = 2.73, p = 0.006). BMI was associated with similar regional brain volumes as BD, including lateral ventricles, amygdala, and pallidum. Higher BMI may in part account for larger ventricles, one of the most replicated findings in BD. Comorbidity with obesity could explain why neurostructural alterations are more pronounced in some individuals with BD. Future prospective brain imaging studies should investigate whether obesity could be a modifiable risk factor for neuroprogression.

Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness-ENIGMA study in 2436 individuals.

AIMS: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry. METHODS: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles. RESULTS: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex. CONCLUSIONS: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD.

Structural brain abnormalities in schizophrenia in adverse environments: examining the effect of poverty and violence in six Latin American cities.

BACKGROUND: Social and environmental factors such as poverty or violence modulate the risk and course of schizophrenia. However, how they affect the brain in patients with psychosis remains unclear. AIMS: We studied how environmental factors are related to brain structure in patients with schizophrenia and controls in Latin America, where these factors are large and unequally distributed. METHOD: This is a multicentre study of magnetic resonance imaging in patients with schizophrenia and controls from six Latin American cities. Total and voxel-level grey matter volumes, and their relationship with neighbourhood characteristics such as average income and homicide rates, were analysed with a general linear model. RESULTS: A total of 334 patients with schizophrenia and 262 controls were included. Income was differentially related to total grey matter volume in both groups (P = 0.006). Controls showed a positive correlation between total grey matter volume and income (R = 0.14, P = 0.02). Surprisingly, this relationship was not present in patients with schizophrenia (R = -0.076, P = 0.17). Voxel-level analysis confirmed that this interaction was widespread across the cortex. After adjusting for global brain changes, income was positively related to prefrontal cortex volumes only in controls. Conversely, the hippocampus in patients with schizophrenia, but not in controls, was relatively larger in affluent environments. There was no significant correlation between environmental violence and brain structure. CONCLUSIONS: Our results highlight the interplay between environment, particularly poverty, and individual characteristics in psychosis. This is particularly important for harsh environments such as low- and middle-income countries, where potentially less brain vulnerability (less grey matter loss) is sufficient to become unwell in adverse (poor) environments.

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

Increased hippocampal, thalamus and amygdala volume in long-term lithium-treated bipolar I disorder patients compared with unmedicated patients and healthy subjects.

OBJECTIVE: Magnetic resonance imaging (MRI) studies in bipolar I disorder (BD-I) suggest that lithium is associated with increased volumes of cortico-limbic structures. However, more rigorous control of confounding factors is needed to obtain further support for this hypothesis. The aim of the present study was to assess differences in brain volumes among long-term lithium-treated BD-I patients, unmedicated BD-I patients, and healthy controls. METHODS: This was a cross-sectional study with 32 euthymic BD-I patients (16 on lithium monotherapy for a mean of 180 months, and 16 receiving no medication for at least the 2 months prior to the study) and 20 healthy controls. Patients were euthymic (Hamilton Depression Rating Scale [HDRS] <6 and Young Mania Rating Scale [YMRS] <7) and had not taken psychotropic medications other than lithium for at least 6 months. Brain images were acquired on a 1.5 Tesla MRI (Phillips, Amsterdam, The Netherlands) and segmented to generate volumetric measures of cortical and subcortical brain areas, ventricles and global brain. RESULTS: Significant differences were found in the volumes of the left amygdala (P=.0003), right amygdala (P=.030), left hippocampus (P=.022), left thalamus (P=.022), and right thalamus (P=.019) in long-term lithium-treated BD-I patients, compared to unmedicated patients and controls, after multivariable adjustment. No differences were observed in global brain volume or in ventricular size among the three groups. Likewise, there was no correlation between serum lithium levels and the increase in size in the described brain areas. CONCLUSIONS: The structural differences found among the three groups, and specifically those between long-term lithium-treated and unmedicated BD-I patients, indicate increased limbic structure volumes in lithium-treated patients.

Geospatial investigations in Colombia reveal variations in the distribution of mood and psychotic disorders.

BACKGROUND: Geographical variations in mood and psychotic disorders have been found in upper-income countries. We looked for geographic variation in these disorders in Colombia, a middle-income country. We analyzed electronic health records from the Clínica San Juan de Dios Manizales (CSJDM), which provides comprehensive mental healthcare for the one million inhabitants of Caldas. METHODS: We constructed a friction surface map of Caldas and used it to calculate the travel-time to the CSJDM for 16,295 patients who had received an initial diagnosis of mood or psychotic disorder. Using a zero-inflated negative binomial regression model, we determined the relationship between travel-time and incidence, stratified by disease severity. We employed spatial scan statistics to look for patient clusters. RESULTS: We show that travel-times (for driving) to the CSJDM are less than 1 h for ~50% of the population and more than 4 h for ~10%. We find a distance-decay relationship for outpatients, but not for inpatients: for every hour increase in travel-time, the number of expected outpatient cases decreases by 20% (RR = 0.80, 95% confidence interval [0.71, 0.89], p = 5.67E-05). We find nine clusters/hotspots of inpatients. CONCLUSIONS: Our results reveal inequities in access to healthcare: many individuals requiring only outpatient treatment may live too far from the CSJDM to access healthcare. Targeting of resources to comprehensively identify severely ill individuals living in the observed hotspots could further address treatment inequities and enable investigations to determine factors generating these hotspots.

The frequencies of mental disorders vary by geographic region. Investigating such variations may lead to more equitable access to mental healthcare and to scientific discoveries that reveal specific localized factors that contribute to the causes of mental illness. This study examined the frequency of three disorders with a major impact on public health ­ schizophrenia, bipolar disorder, and major depressive disorder ­ by analyzing electronic health records from a hospital providing comprehensive mental health care for a large region in Colombia. We show that individuals receiving outpatient care mainly live relatively near the facility. Those receiving inpatient care live throughout the region, but cluster in a few scattered locations. Future research could lead to strategies for more equitable provision of mental healthcare in Colombia and identify environmental or genetic factors that affect the likelihood that someone will develop one of these disorders.

Ancestry component as a major predictor of lithium response in the treatment of bipolar disorder.

BACKGROUND: Bipolar Disorder (BD) represents the seventh major cause of disability life-years-adjusted. Lithium remains as a first-line treatment, but clinical improvement occurs only in 30 % of treated patients. Studies suggest that genetics plays a major role in shaping the individual response of BD patients to lithium. METHODS: We used machine-learning techniques (Advance Recursive Partitioned Analysis, ARPA) to build a personalized prediction framework of BD lithium response using biological, clinical, and demographical data. Using the Alda scale, we classified 172 BD I-II patients as responders or non-responders to lithium treatment. ARPA methods were used to build individual prediction frameworks and to define variable importance. Two predictive models were evaluated: 1) demographic and clinical data, and 2) demographic, clinical and ancestry data. Model performance was assessed using Receiver Operating Characteristic (ROC) curves. RESULTS: The predictive model including ancestry yield the best performance (sensibility = 84.6 %, specificity = 93.8 % and AUC = 89.2 %) compared to the model without ancestry (sensibility = 50 %, Specificity = 94.5 %, and AUC = 72.2 %). This ancestry component best predicted lithium individual response. Clinical variables such as disease duration, the number of depressive episodes, the total number of affective episodes, and the number of manic episodes were also important predictors. CONCLUSION: Ancestry component is a major predictor and significantly improves the definition of individual Lithium response in BD patients. We provide classification trees with potential bench application in the clinical setting. While this prediction framework might be applied in specific populations, the used methodology might be of general use in precision and translational medicine.

Predicting diagnostic conversion from major depressive disorder to bipolar disorder: an EHR based study from Colombia.

Bipolar Disorder (BD) is a severe and chronic disorder characterized by recurrent episodes of depression, mania, and/or hypomania. Most BD patients initially present with depressive symptoms, resulting in a delayed diagnosis of BD and poor clinical outcomes. This study leverages electronic health record (EHR) data from the Clínica San Juan de Dios Manizales in Colombia to identify features predictive of the transition from Major Depressive Disorder (MDD) to BD. Analyzing EHR data from 13,607 patients diagnosed with MDD over 15 years, we identified 1,610 cases of conversion to BD. Using a multivariate Cox regression model, we identified severity of the initial MDD episode, the presence of psychosis and hospitalization at first episode, family history of mood or psychotic disorders, female gender to be predictive of the conversion to BD. Additionally, we observed associations with medication classes (prescriptions of mood stabilizers, antipsychotics, and antidepressants) and clinical features (delusions, suicide attempt, suicidal ideation, use of marijuana and alcohol use/abuse) derived from natural language processing (NLP) of clinical notes. Together, these risk factors predicted BD conversion within five years of the initial MDD diagnosis, with a recall of 72% and a precision of 38%. Our study confirms many previously identified risk factors identified through registry-based studies (such as female gender and psychotic depression at the index MDD episode), and identifies novel ones (specifically, suicidal ideation and suicide attempt extracted from clinical notes). These results simultaneously demonstrate the validity of using EHR data for predicting BD conversion as well as underscore its potential for the identification of novel risk factors and improving early diagnosis.

Distinct and shared contributions of diagnosis and symptom domains to cognitive performance in severe mental illness in the Paisa population: a case-control study.

BACKGROUND: Severe mental illness diagnoses have overlapping symptomatology and shared genetic risk, motivating cross-diagnostic investigations of disease-relevant quantitative measures. We analysed relationships between neurocognitive performance, symptom domains, and diagnoses in a large sample of people with severe mental illness not ascertained for a specific diagnosis (cases), and people without mental illness (controls) from a single, homogeneous population. METHODS: In this case-control study, cases with severe mental illness were ascertained through electronic medical records at Clínica San Juan de Dios de Manizales (Manizales, Caldas, Colombia) and the Hospital Universitario San Vicente Fundación (Medellín, Antioquía, Colombia). Participants were assessed for speed and accuracy using the Penn Computerized Neurocognitive Battery (CNB). Cases had structured interview-based diagnoses of schizophrenia, bipolar 1, bipolar 2, or major depressive disorder. Linear mixed models, using CNB tests as repeated measures, modelled neurocognition as a function of diagnosis, sex, and all interactions. Follow-up analyses in cases included symptom factor scores obtained from exploratory factor analysis of symptom data as main effects. FINDINGS: Between Oct 1, 2017, and Nov 1, 2019, 2406 participants (1689 cases [schizophrenia n=160; bipolar 1 disorder n=519; bipolar 2 disorder n=204; and major depressive disorder n=806] and 717 controls; mean age 39 years (SD 14); and 1533 female) were assessed. Participants with bipolar 1 disorder and schizophrenia had similar impairments in accuracy and speed across cognitive domains. Participants with bipolar 2 disorder and major depressive disorder performed similarly to controls, with subtle deficits in executive and social cognition. A three-factor model (psychosis, mania, and depression) best represented symptom data. Controlling for diagnosis, premorbid IQ, and disease severity, high lifetime psychosis scores were associated with reduced accuracy and speed across cognitive domains, whereas high depression scores were associated with increased social cognition accuracy. INTERPRETATION: Cross-diagnostic investigations showed that neurocognitive function in severe mental illness is characterised by two distinct profiles (bipolar 1 disorder and schizophrenia, and bipolar 2 disorder and major depressive disorder), and is associated with specific symptom domains. These results suggest the utility of this design for elucidating severe mental illness causes and trajectories. FUNDING: US National Institute of Mental Health.

Imaging Social and Environmental Factors as Modulators of Brain Dysfunction: Time to Focus on Developing Non-Western Societies.

Social and environmental factors are known risk factors and modulators of mental health disorders. We here conducted a nonsystematic review of the neuroimaging literature studying the effects of poverty, urbanicity, and community violence, highlighting the opportunities of studying non-Western developing societies such as those in Latin America. Social and environmental factors in these communities are widespread and have a large magnitude, as well as an unequal distribution, providing a good opportunity for their characterization. Studying the effect of poverty in these settings could help to explore the brain effect of economic improvements, disentangle the effect of absolute and relative poverty, and characterize the modulating impact of poverty on the underlying biology of mental health disorders. Exploring urbanicity effects in highly unequal cities could help identify the specific factors that modulate this effect as well as examine a possible dose-response effect by studying megacities. Studying brain changes in those living among violence, which is particularly high in places such as Latin America, could help to characterize the interplay between brain predisposition and exposure to violence. Furthermore, exploring the brain in an adverse environment should shed light on the mechanisms underlying resilience. We finally provide examples of two methodological approaches that could contribute to this field, namely a big cohort study in the developing world and a consortium-based meta-analytic approach, and argue about the potential translational value of this research on the development of effective social policies and successful personalized medicine in disadvantaged societies.

10Kin1day: A Bottom-Up Neuroimaging Initiative.

We organized 10Kin1day, a pop-up scientific event with the goal to bring together neuroimaging groups from around the world to jointly analyze 10,000+ existing MRI connectivity datasets during a 3-day workshop. In this report, we describe the motivation and principles of 10Kin1day, together with a public release of 8,000+ MRI connectome maps of the human brain.

Inflammatory Markers in the Staging of Bipolar Disorder: A Systematic Review of the Literature.

BACKGROUND: Previous studies suggest that inflammatory molecules play an important role in the pathophysiology of Bipolar Disorder (BD). The evidence suggests that BD may present a progressive course. Therefore there are theories that postulate the relationship between progression and stages of the disease with distinct peripheral biomarkers. OBJECTIVE: The aim of this study was to carry out a systematic review of the literature of studies about the association between peripheral inflammatory markers and clinical variables related with staging in BD patients. METHODS: We conducted a systematic review using electronic databases: PubMed, SciELO, LiLACS and PsycINFO. Keywords were divided into inflammatory markers and, BD and staging. Studies involving euthymic BD patients, studies evaluating peripheral biomarkers and studies correlating these with clinical variables related to neuroprogression or stage of BD were included. RESULTS: We present and discuss the methods and findings of ten articles. The inflammatory markers were measured with different techniques and show some contradictories results. The TNF superfamily and inflammatory cytokines may have a relationship with the neuroprogression of the disease. CONCLUSIONS: This study suggests that TNF and ILs could play a role in neuroprogression. However, longitudinal studies are needed to clarify the relationship between factors associated with neuroprogression.

Evaluation and Socio-occupational Intervention in Bipolar and Schizophrenic Patients within a Multimodal Intervention Program- PRISMA. / Evaluación e intervención socioocupacional en pacientes con TAB y esquizofrenia, dentro del programa de intervención multimodal PRISMA.

BACKGROUND: Functional improvement in bipolar and schizophrenic patients is one of the main aims of treatment. Nevertheless, there is no evidence about the effect of socio-occupational intervention within a multimodal intervention (MI) programme. OBJECTIVE: To describe the socio-occupational profile and to evaluate the functional effect of a MI in bipolar I and schizophrenic patients. METHODS: A prospective, longitudinal, therapeutic-comparative study was performed including 302 subjects (104 schizophrenic and 198 Bipolar Disorder I [BDI] patients), who were randomised into two groups, multimodal (psychiatry, psychology, medicine, occupational therapy, neuropsychology, and family therapy), or traditional intervention (psychiatry and medicine only). Several scales were applied to assess assertiveness, free time management, social abilities, general anxiety, self-care and performance in home, work and community tasks. RESULTS: After performing the longitudinal analysis, it was shown that the multimodal intervention was more effective than traditional intervention in general anxiety scores (P=.026) and development in home tasks (P=.03) in schizophrenic patients. No statistical differences were found in bipolar patients. The other variables showed improvement, however, their effect was similar in both intervention groups. CONCLUSIONS: Our study identified functional improvement in home tasks in schizophrenic patients after receiving multimodal intervention. Other variables also showed improvement for both interventions groups. Future studies, applying longer rehabilitation programs and other ecological strategies should be performed to identify the most effective interventions.

[Impact of a Multimodal Intervention on the Psychological Profile of Schizophrenic and Bipolar I Patients: A Study of PRISMA Program]. / Efecto de una intervención multimodal en el perfil psicológico de pacientes con Esquizofrenia y TAB tipo I: Estudio del Programa PRISMA.

BACKGROUND: Bipolar Disorder (BD) and schizophrenia are included in the group of severe mental illness and are main causes of disability and morbidity in the local population due to the bio-psycho-social implications in patients. In the last 20 years or so, adjunctive psychological interventions been studied with the purpose of decreasing recurrences, stabilising the course of the disease, and improving the functionality in these patients. OBJECTIVE: To analyse the psychological effect of a multimodal intervention (MI) vs a traditional intervention (TI) program in BD I and schizophrenic patients. METHODS: A prospective, longitudinal, therapeutic-comparative study was conducted with 302 patients (104 schizophrenic and 198 bipolar patients) who were randomly assigned to the MI or TI groups of a multimodal intervention program PRISMA. The MI group received care from psychiatry, general medicine, neuropsychology, family therapy, and occupational therapy. The TI group received care from psychiatry and general medicine. The Hamilton and Young scales, and the Scales for the Assessment of Negative Symptoms (SANS) and Postive Symptoms (SAPS) were used on bipolar and schizophrenic patients, respectively. The scales AQ-12, TEMPS-A, FAST, Zuckerman sensation seeking scale, BIS-11, SAI-E and EEAG were applied to measure the psychological variables. The scales were performed before and after the interventions. The psychotherapy used in this study was cognitive behavioural therapy. RESULTS: There were statistically significant differences in socio-demographic and clinical variables in the schizophrenia and bipolar disorder group. There were no statistically significant differences in the psychological scales after conducting a multivariate analysis between the intervention groups and for both times (initial and final). CONCLUSION: This study did not show any changes in variables of psychological functioning variables between bipolar and schizophrenic groups, who were subjected to TI vs MI (who received cognitive behavioural therapy). Further studies are needed with other psychological interventions or other psychometric scales.