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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. [ABSTRACT FROM AUTHOR]

Diagnosis, clinical management and research of psychiatric disorders remain subjective — largely guided by historically developed categories which may not effectively capture underlying pathophysiological mechanisms of dysfunction. Here, we report a novel approach of identifying and validating distinct and biologically meaningful clinical phenotypes of bipolar disorders using both unsupervised and supervised machine learning techniques. First, neurocognitive data were analyzed using an unsupervised machine learning approach and two distinct clinical phenotypes identified namely; phenotype I and phenotype II. Second, diffusion weighted imaging scans were pre-processed using the tract-based spatial statistics (TBSS) method and ‘skeletonized’ white matter fractional anisotropy (FA) and mean diffusivity (MD) maps extracted. The ‘skeletonized’ white matter FA and MD maps were entered into the Elastic Net machine learning algorithm to distinguish individual subjects' phenotypic labels (e.g. phenotype I vs. phenotype II). This calculation was performed to ascertain whether the identified clinical phenotypes were biologically distinct. Original neurocognitive measurements distinguished individual subjects' phenotypic labels with 94% accuracy (sensitivity = 92%, specificity = 97%). TBSS derived FA and MD measurements predicted individual subjects' phenotypic labels with 76% and 65% accuracy respectively. In addition, individual subjects belonging to phenotypes I and II were distinguished from healthy controls with 57% and 92% accuracy respectively. Neurocognitive task variables identified as most relevant in distinguishing phenotypic labels included; Affective Go/No-Go (AGN), Cambridge Gambling Task (CGT) coupled with inferior fronto-occipital fasciculus and callosal white matter pathways. These results suggest that there may exist two biologically distinct clinical phenotypes in bipolar disorders which can be identified from healthy controls with high accuracy and at an individual subject level. We suggest a strong clinical utility of the proposed approach in defining and validating biologically meaningful and less heterogeneous clinical sub-phenotypes of major psychiatric disorders. [ABSTRACT FROM AUTHOR]

Background: Cognitive deficits have been consistently reported in individuals with bipolar disorder (BD). The cognitive profile of siblings of individuals with BD is, however, less clearly established possibly due to the heterogeneity of neuropsychological measures used in previous studies. The aim of this exploratory study was to assess the cognitive function of siblings of individuals with BD and compare it with that of their first-degree relatives suffering with BD, and healthy controls (HC) using the Cambridge Neuropsychological Test Automated Battery (CANTAB) - a comprehensive and validated computerized cognitive battery.Methods: We recruited 23 HC (33.52±10.29 years, 8 males), 27 individuals with BD (34.26±10.19 years, 9 males, 25 BDI, 1BDII and 1 BD-NOS), and 15 of their biologically related siblings (37.47±13.15 years, 4 males). Siblings had no current or lifetime history of mental disorders. Participants performed the CANTAB and completed questionnaires assessing mood and global functioning. Multivariate analyses compared CANTAB measures across the three participant groups.Results: Individuals with BD and their siblings were less accurate in a task of sustained attention (Rapid Visual Processing) when compared to HC. Further, individuals with BD displayed pronounced deficits in affective processing (Affective Go/No-Go) compared to HC. There were no cognitive differences between siblings and individuals with BD. After correcting for current depressive symptoms, these results did not reach statistical significance.Conclusions: Subthreshold depressive symptoms may be associated with reduced sustained attention in healthy siblings of BD patients. This preliminary result needs to be corroborated by large-scale, longitudinal studies assessing the relationship between cognition and mood in vulnerable individuals. [ABSTRACT FROM AUTHOR]

Background Bipolar disorder (BD) is characterized by affective processing bias and variations in personality traits. It is still unknown whether these features are linked to the same structural brain alterations. The aim of this study was to investigate relationships between specific personality traits, white matter (WM) properties, and affective processing in BD and HC. Methods 24 healthy controls (HC) and 38 adults with BDI (HC: 29.47 ± 2.23 years, 15 females; BDI: 32.44 ± 1.84 years, 20 females) completed clinical scales and the Big Five Inventory. They were also administered the Affective Go/No-Go (AGN) and the Rapid Visual Processing (RVP) tasks of the Cambridge Neuropsychological Test Automated Battery. Diffusion Tensor Imaging (DTI) assessed the microstructure of WM tracts. Results In BDI measures of WM properties were reduced across all major brain white matter tracts. As expected, individuals with BDI reported greater neuroticism, lower agreeableness and conscientiousness, and made a greater number of errors in response to affective stimuli in the AGN task compared to HC. High neuroticism scores were associated with faster AGN latency, and overall reduced AGN accuracy in both HC and BDI. Elevated FA values were associated with reduced neuroticism and increased cognitive processing in HC but not in BDI. Conclusions Our findings showed important potential links between personality, affective processing and WM integrity in BD. In the future therapeutic interventions for BD using brain stimulation protocols might benefit from the use of DTI to target pathways underlying abnormal affective processing. [ABSTRACT FROM AUTHOR]

Background: Previous studies have reported that patients with bipolar disorder (BD) present with cognitive impairments during mood episodes as well as euthymic phase. However, it is still unknown whether reported neurocognitive abnormalities can objectively identify individual BD patients from healthy controls (HC).Methods: A total of 21 euthymic BD patients and 21 demographically matched HC were included in the current study. Participants performed the computerized Cambridge Neurocognitive Test Automated Battery (CANTAB) to assess cognitive performance. The least absolute shrinkage selection operator (LASSO) machine learning algorithm was implemented to identify neurocognitive signatures to distinguish individual BD patients from HC.Results: The LASSO machine learning algorithm identified individual BD patients from HC with an accuracy of 71%, area under receiver operating characteristic curve of 0.7143 and significant at p=0.0053. The LASSO algorithm assigned individual subjects with a probability score (0-healthy, 1-patient). Patients with rapid cycling (RC) were assigned increased probability scores as compared to patients without RC. A multivariate pattern of neurocognitive abnormalities comprising of affective Go/No-go and the Cambridge gambling task was relevant in distinguishing individual patients from HC.Limitations: Our study sample was small as we only considered euthymic BD patients and demographically matched HC.Conclusion: Neurocognitive abnormalities can distinguish individual euthymic BD patients from HC with relatively high accuracy. In addition, patients with RC had more cognitive impairments compared to patients without RC. The predictive neurocognitive signature identified in the current study can potentially be used to provide individualized clinical inferences on BD patients. [ABSTRACT FROM AUTHOR]

Previous studies have reported abnormalities of white-matter diffusivity in pediatric bipolar disorder. However, it has not been established whether these abnormalities are able to distinguish individual subjects with pediatric bipolar disorder from healthy controls with a high specificity and sensitivity. Diffusion-weighted imaging scans were acquired from 16 youths diagnosed with DSM-IV bipolar disorder and 16 demographically matched healthy controls. Regional white matter tissue microstructural measurements such as fractional anisotropy, axial diffusivity and radial diffusivity were computed using an atlas-based approach. These measurements were used to ‘train’ a support vector machine (SVM) algorithm to predict new or ‘unseen’ subjects’ diagnostic labels. The SVM algorithm predicted individual subjects with specificity=87.5%, sensitivity=68.75%, accuracy=78.12%, positive predictive value=84.62%, negative predictive value=73.68%, area under receiver operating characteristic curve (AUROC)=0.7812 and chi-square p -value=0.0012. A pattern of reduced regional white matter fractional anisotropy was observed in pediatric bipolar disorder patients. These results suggest that atlas-based diffusion weighted imaging measurements can distinguish individual pediatric bipolar disorder patients from healthy controls. Notably, from a clinical perspective these findings will contribute to the pathophysiological understanding of pediatric bipolar disorder. [ABSTRACT FROM AUTHOR]

Background and Aims: Cognitive impairments are primary hallmarks symptoms of bipolar disorder (BD). Whether these deficits are markers of vulnerability or symptoms of the disease is still unclear. 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 controls. 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: CGB achieved accuracy of 73.2% and an AUROC of 0.785 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 processing speed and affective processing. Measures of cognition did not differentiate between UO and HC. Conclusions: Alterations in processing speed and affective processing are markers of BD in pediatric populations. Longitudinal studies should determine whether UO with a cognitive profile similar to that of HC are at less or equal risk for mood disorders. Future studies should include relevant measures for BD such as verbal memory and genetic risk scores. [ABSTRACT FROM AUTHOR]

Background: Anhedonia - a key symptom of depression - is highly associated with poorer outcomes and suicidal behavior. Alterations in the circuitry of reward-related brain regions have been robustly associated with anhedonia in unipolar depression, but not bipolar disorder (BD). We investigated white matter microstructures associated with anhedonia in participants with BD types I and II and first-degree relatives of patients with BD (BD-siblings).Methods: Eighty participants (BD types I and II: 56 [70%], and BD-siblings: 24 [30%]) underwent diffusion tensor imaging (DTI); Fractional anisotropy (FA) of different tracts were computed. Anhedonia was assessed using item 8, ("inability to feel'') of the MADRS scale. General linear models were used to compare the FA of different tracts in participants with and without anhedonia controlling for several clinical and demographic variables.Results: The mean age of the sample was 37 (± 11) years old, and 68.8% were female. Participants with anhedonia (32.5%) presented lower mean FA in the left uncinate fasciculus (UF) (p = 0.005), right temporal endings of the superior longitudinal fasciculus (SLFT) (p = 0.04), and in the left and right parietal endings of the superior longitudinal fasciculus (SLFP) (p = 0.003, and p = 0.04, respectively). Similar comparisons between participants with or without current depressive episodes and between participants with or without inner tension according to the MADRS did not show significant differences, specificity of the findings for anhedonia.Conclusions: Lower FA in the left UF and SLF are potential neuroimaging markers of anhedonia in individuals with BD and high-risk for BD. [ABSTRACT FROM AUTHOR]

In proton magnetic resonance spectroscopy (¹H MRS) studies, aberrant levels of choline-containing compounds that include glycerophosphocholine plus phosphocholine (GPC+PC), can signify alterations in the metabolism of cellular membrane phospholipids (MPLs) from a healthy baseline. In a recent ¹H MRS study, we reported increased GPC+PC in cortical and subcortical areas of adult patients with bipolar disorder I (BP-I). Post-traumatic stress disorder (PTSD) can worsen the severity of BP-I, but it is unclear whether the effect of a PTSD comorbidity in BP-I is associated with altered MPL metabolism. The purpose of this study was to re-investigate the ¹H MRS data to determine whether the regional extent of elevated GPC+PC was greater in BP-I patients with PTSD (BP-I/wPTSD) compared to BP-I without comorbid PTSD (BP-I/woPTSD) patients and healthy controls. GPC+PC levels from four brain areas [the anterior cingulate cortex (ACC), anterior-dorsal ACC, caudate, and putamen] were measured in 14 BP-I/wPTSD, 36 BP-I/woPTSD, and 44 healthy controls using a multi-voxel 1H MRS approach on a 3 Tesla system with high spatial resolution and absolute quantification. Results show a significant increase in GPC+PC levels from the caudate and putamen of BP-I/wPTSD patients compared to healthy controls (P<0.05) and in the putamen compared to BP-I/woPTSD patients (P<0.05). These findings are consistent with evidence of elevated degradation of MPLs in the neuropil that is more pronounced in BP-I patients with comorbid PTSD. [ABSTRACT FROM AUTHOR]

Young adults experiencing first-episode psychosis have historically been difficult to retain in mental health treatment. Communities across the United States are implementing Coordinated Specialty Care to improve outcomes for individuals experiencing first-episode psychosis. This mixed-methods research study examined the relationship between program services and treatment retention, operationalized as the likelihood of remaining in the program for 9 months or more. In the adjusted analysis, male gender and participation in home-based cognitive behavioral therapy were associated with an increased likelihood of remaining in treatment. The key informant interview findings suggest the shared decision-making process and the breadth, flexibility, and focus on functional recovery of the home-based cognitive behavioral therapy intervention may have positively influenced treatment retention. These findings suggest the use of shared decision-making and improved access to home-based cognitive behavioral therapy for first-episode psychosis patients may improve outcomes for this vulnerable population. [ABSTRACT FROM AUTHOR]

Sodium valproate (VPA) has well-established neuroprotective effects and is recommended as treatment in bipolar disorder patients. The neural effects of VPA in pediatric bipolar disorder (PBD) have yet to be established. This preliminary study explored the effects of VPA on brain structure in PBD. Fourteen PBD patients (10 males; mean = 13.43 ± 3.05 years old) underwent a structural MRI before and after a 6-week VPA treatment period. Bayesian linear mixed modeling explored seven brain region volumes as a function of dichotomous pre/post time. Results showed a decrease in amygdala volume over time. These findings need to be confirmed by large-scale, longitudinal studies. [ABSTRACT FROM AUTHOR]

The hippocampus has been implicated in various mood disorders, with global volume deficits consistently found in patient populations. The hippocampus, however, consists of anatomically distinct subfields, and examination of specific subfield differences may elucidate the possible molecular mechanisms behind psychiatric pathologies. Indeed, adult studies have reported smaller hippocampal subfield volumes in regions within the cornu ammonis (CA1 and CA4), dentate gyrus (DG), and hippocampal tails in both patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD) compared to healthy controls. Subfield differences in pediatric patients with mood disorders, on the other hand, have not been extensively investigated. In the current study, magnetic resonance imaging scans were acquired for 141 children and adolescents between the ages of eight and eighteen (57 with BD, 30 with MDD, and 54 healthy controls). An automated segmentation method was then used to assess differences in hippocampal subfield volumes. Children and adolescents with BD were found to have significantly smaller volumes in the right CA1, CA4, and right subiculum, as well as the bilateral granule cell layer (GCL), molecular layer (ML), and hippocampal tails. The volume of the right subiculum in BD patients was also found to be negatively correlated with illness duration. Overall, the findings from this cross-sectional study provide evidence for specific hippocampal subfield volume differences in children and adolescents with BD compared to healthy controls and suggest progressive reductions with increased illness duration. [ABSTRACT FROM AUTHOR]

Objective: A growing body of evidence has put forward clinical risk factors associated with patients with mood disorders that attempt suicide. However, what is not known is how to integrate clinical variables into a clinically useful tool in order to estimate the probability of an individual patient attempting suicide.Method: A total of 144 patients with mood disorders were included. Clinical variables associated with suicide attempts among patients with mood disorders and demographic variables were used to 'train' a machine learning algorithm. The resulting algorithm was utilized in identifying novel or 'unseen' individual subjects as either suicide attempters or non-attempters. Three machine learning algorithms were implemented and evaluated.Results: All algorithms distinguished individual suicide attempters from non-attempters with prediction accuracy ranging between 65% and 72% (p<0.05). In particular, the relevance vector machine (RVM) algorithm correctly predicted 103 out of 144 subjects translating into 72% accuracy (72.1% sensitivity and 71.3% specificity) and an area under the curve of 0.77 (p<0.0001). The most relevant predictor variables in distinguishing attempters from non-attempters included previous hospitalizations for depression, a history of psychosis, cocaine dependence and post-traumatic stress disorder (PTSD) comorbidity.Conclusion: Risk for suicide attempt among patients with mood disorders can be estimated at an individual subject level by incorporating both demographic and clinical variables. Future studies should examine the performance of this model in other populations and its subsequent utility in facilitating selection of interventions to prevent suicide. [ABSTRACT FROM AUTHOR]