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Emerging evidence suggests that obesity impacts brain physiology at multiple levels. Here we aimed to clarify the relationship between obesity and brain structure using structural MRI (n = 6420) and genetic data (n = 3907) from the ENIGMA Major Depressive Disorder (MDD) working group. Obesity (BMI > 30) was significantly associated with cortical and subcortical abnormalities in both mass-univariate and multivariate pattern recognition analyses independent of MDD diagnosis. The most pronounced effects were found for associations between obesity and lower temporo-frontal cortical thickness (maximum Cohen´s d (left fusiform gyrus) = −0.33). The observed regional distribution and effect size of cortical thickness reductions in obesity revealed considerable similarities with corresponding patterns of lower cortical thickness in previously published studies of neuropsychiatric disorders. A higher polygenic risk score for obesity significantly correlated with lower occipital surface area. In addition, a significant age-by-obesity interaction on cortical thickness emerged driven by lower thickness in older participants. Our findings suggest a neurobiological interaction between obesity and brain structure under physiological and pathological brain conditions.

The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5–90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology.

Emerging evidence suggests that obesity impacts brain physiology at multiple levels. Here we aimed to clarify the relationship between obesity and brain structure using structural MRI (n = 6420) and genetic data (n = 3907) from the ENIGMA Major Depressive Disorder (MDD) working group. Obesity (BMI > 30) was significantly associated with cortical and subcortical abnormalities in both mass-univariate and multivariate pattern recognition analyses independent of MDD diagnosis. The most pronounced effects were found for associations between obesity and lower temporo-frontal cortical thickness (maximum Cohen´s d (left fusiform gyrus) = −0.33). The observed regional distribution and effect size of cortical thickness reductions in obesity revealed considerable similarities with corresponding patterns of lower cortical thickness in previously published studies of neuropsychiatric disorders. A higher polygenic risk score for obesity significantly correlated with lower occipital surface area. In addition, a significant age-by-obesity interaction on cortical thickness emerged driven by lower thickness in older participants. Our findings suggest a neurobiological interaction between obesity and brain structure under physiological and pathological brain conditions.

BACKGROUND AND AIMS: Cognitive impairments are one of the primary hallmarks symptoms of bipolar disorder (BD). Whether these deficits are markers of vulnerability or symptoms of the disease is still an open debate. This study used a component-wise gradient (CGB) machine learning algorithm to identify cognitive measures that could accurately differentiate pediatric BD, unaffected offspring of BD parents, and healthy volunteers. METHODS: 59 healthy controls (HC; 11.19±3.15 yo; 30 girls), 119 children and adolescents with BD (13.31±3.02 yo, 52 girls) and 49 unaffected offspring of BD parents (UO; 9.36±3.18 yo; 22 girls) completed the CANTAB cognitive battery. RESULTS: After algorithm training, CGB achieved accuracy of 71.4% and an AUROC of 0.797 in classifying individuals as either BD or non-BD on a dataset held out for validation for testing. The strongest cognitive predictors of BD were measures of affective processing and sustained attention. Measures of cognition did not differentiate between unaffected offspring and HC. CONCLUSIONS: Our findings suggest that alterations in affective processing and sustained attention are markers of BD in pediatric populations. Longitudinal studies should determine whether UO with a cognitive profile similar to that of HC in late childhood or early adolescence are at less or equal risk for mood disorders. Future studies should include relevant cognitive measures for BD such as verbal memory and individuals’ genetic risk scores.

Bipolar disorder (BD) is a common disorder with high reoccurrence rate in general population. It is critical to have objective biomarkers to identify BD patients at an individual level. Neurocognitive signatures including affective Go/No-go task and Cambridge Gambling task showed the potential to distinguish BD patients from health controls as well as identify individual siblings of BD patients. Moreover, these neurocognitive signatures showed the ability to be replicated at two independent cohorts which indicates the possibility for generalization. Future studies will examine the possibility of combining neurocognitive data with other biological data to develop more accurate signatures. Electronic supplementary material The online version of this article (doi:10.1186/s40345-017-0101-9) contains supplementary material, which is available to authorized users.

Bipolar disorder (BD) is a common disorder with high reoccurrence rate in general population. It is critical to have objective biomarkers to identify BD patients at an individual level. Neurocognitive signatures including affective Go/No-go task and Cambridge Gambling task showed the potential to distinguish BD patients from health controls as well as identify individual siblings of BD patients. Moreover, these neurocognitive signatures showed the ability to be replicated at two independent cohorts which indicates the possibility for generalization. Future studies will examine the possibility of combining neurocognitive data with other biological data to develop more accurate signatures.

Cortical gyrification of the brain represents the folding characteristic of the cerebral cortex. How the brain cortical gyrification changes from childhood to old age in healthy human subjects is still unclear. Additionally, studies have shown regional gyrification alterations in patients with major psychiatric disorders, such as major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ). However, whether the lifespan trajectory of gyrification over the brain is altered in patients diagnosed with major psychiatric disorders is still unknown. In this study, we investigated the trajectories of gyrification in three independent cohorts based on structural brain images of 881 subjects from age 4 to 83. We discovered that the trajectory of gyrification during normal development and aging was not linear and could be modeled with a logarithmic function. We also found that the gyrification trajectories of patients with MDD, BD and SCZ were deviated from the healthy one during adulthood, indicating altered aging in the brain of these patients.

Neuroanatomical abnormalities in Bipolar disorder (BD) have previously been reported. However, the utility of these abnormalities in distinguishing individual BD patients from Healthy controls and stratify patients based on overall illness burden has not been investigated in a large cohort.In this study, we examined whether structural neuroimaging scans coupled with a machine learning algorithm are able to distinguish individual BD patients from Healthy controls in a large cohort of 256 subjects. Additionally, we investigated the relationship between machine learning predicted probability scores and subjects' clinical characteristics such as illness duration and clinical stages. Neuroimaging scans were acquired from 128 BD patients and 128 Healthy controls. Gray and white matter density maps were obtained and used to 'train' a relevance vector machine (RVM) learning algorithm which was used to distinguish individual patients from Healthy controls.The RVM algorithm distinguished patients from Healthy controls with 70.3 % accuracy (74.2 % specificity, 66.4 % sensitivity, chi-square p0.005) using white matter density data and 64.9 % accuracy (71.1 % specificity, 58.6 % sensitivity, chi-square p0.005) with gray matter density. Multiple brain regions - largely covering the fronto - limbic system were identified as 'most relevant' in distinguishing both groups. Patients identified by the algorithm with high certainty (a high probability score) - belonged to a subgroup with more than ten total lifetime manic episodes including hospitalizations (late stage).These results indicate the presence of widespread structural brain abnormalities in BD which are associated with higher illness burden - which points to neuroprogression.

Bipolar disorder (BD) is considered one of the most disabling mental conditions, with high rates of morbidity, disability, and premature death from suicide. Although BD is often misdiagnosed as major depressive disorder, some attention has recently been drawn to the possibility that BD could be overdiagnosed in some settings. The present paper focuses on a critical analysis of the overdiagnosis issue among bipolar patients. It includes a review of the available literature findings, followed by some recommendations aiming at optimizing the diagnosis of BD and increasing its reliability.

The objective of this study was to examine whether anxiety increases impulsivity among patients with bipolar disorder (BPD) and major depressive disorder (MDD). Subjects comprised 205 BPD (mean age ± SD 36.6 ± 11.5 y; 29.3% males) and 105 with MDD (mean age ± SD 38 ± 13.1 y; 29.5% males) diagnosed using the DSM-IV-SCID. Impulsivity was assessed with the Barratt Impulsivity Scale and anxiety with the Hamilton Anxiety Rating Scale. Comorbid anxiety disorders were present in 58.9% of the BPD and 29.1% of MDD. BPD were significantly more impulsive than MDD (p < 0.001), and both BPD and MDD subjects showed significantly higher impulsivity when anxiety was present either as a comorbidity (p = 0.010) or as a symptom (p = 0.011). Impulsivity rose more rapidly with increasing anxiety symptoms in MDD than in BPD. The presence of anxiety, either as a comorbid disorder or as current anxiety symptoms, is associated with higher impulsivity in subjects with either BPD or MDD.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) NARSAD Krus Endowed Chair in Psychiatry UTHSCSA BACKGROUND: Recent studies have demonstrated high rates of psychopathology in the offspring of parents with bipolar disorder. The aim of this study was to identify psychiatric diagnoses in a sample of children of bipolar parents. METHOD: This case series comprised 35 children and adolescents aged 6 to 17 years, with a mean age of 12.5 + 2.9 years (20 males and 15 females), who had at least one parent with bipolar disorder type I. The subjects were assessed using the Schedule for Affective Disorders and Schizophrenia for School-Age Children - Present and Lifetime version (K-SADS-PL). Family psychiatric history and demographics were also evaluated. RESULTS: Of the offspring studied, 71.4% had a lifetime diagnosis of at least one psychiatric disorder (28.6% with a mood disorder, 40% with a disruptive behavior disorder and 20% with an anxiety disorder). Pure mood disorders (11.4%) occurred less frequently than mood disorders comorbid with attention deficit hyperactivity disorder (17.1%). Psychopathology was commonly reported in second-degree relatives of the offspring of parents with bipolar disorder (71.4%). CONCLUSIONS: Our results support previous findings of an increased risk for developing psychopathology, predominantly mood and disruptive disorders, in the offspring of bipolar individuals. Prospective studies with larger samples are needed to confirm and expand these results. Federal University of São Paulo Department of Psychiatry The University of Texas Health Science Center Departments of Psychiatry and Orthodontics Universidade de São Paulo Faculdade de Medicina Department of Psychiatry University of Texas Health Science Center The University of Texas Health Science Center Department of Psychiatry UNIFESP, Department of Psychiatry NARSAD: MH 69774 NARSAD: RR 20571 NARSAD: MH068280 SciELO