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The diagnoses of serious psychiatric illnesses, such as schizophrenia, schizoaffective disorder, and bipolar disorder, rely on the subjective recall and interpretation of often overlapping symptoms, and are not based on the objective pathophysiology of the illnesses. The subjectivity of symptom reporting and interpretation contributes to the delay of accurate diagnoses and limits effective treatment of these illnesses. Proteomics, the study of the types and quantities of proteins an organism produces, may offer an objective biological approach to psychiatric diagnosis. For this pilot study, we used the Myriad RBM Discovery Map 250+ platform to quantify 205 serum proteins in subjects with schizophrenia (n=26), schizoaffective disorder (n=20), bipolar disorder (n=16), and healthy controls with no psychiatric illness (n=23). Fifty-seven analytes that differed significantly between groups were used for multivariate modeling with linear discriminant analysis (LDA). Diagnoses generated from these models were compared to SCID-generated clinical diagnoses to determine whether the proteomic markers: 1) distinguished the three disorders from controls, and 2) distinguished between the three disorders. We found that a series of binary classification models including 8-12 analytes produced separation between all subjects and controls, and between each diagnostic group and controls. There was a high degree of accuracy in the separations, with training areas-under-the-curve (AUC) of 0.94-1.0, and cross-validation AUC of 0.94-0.95. Models with 7-14 analytes produced separation between the diagnostic groups, though less robustly, with training AUC of 0.72-1.0 and validation AUC of 0.69-0.89. While based on a small sample size, not adjusted for medication state, these preliminary results support the potential of proteomics as a diagnostic aid in psychiatry. The separation of schizophrenia, schizoaffective disorder, and bipolar disorder suggests that further work in this area is warranted.

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MOTIVATION: Imaging genetics combines brain imaging and genetic information to identify the relationships between genetic variants and brain activities. When the data samples belong to different classes (e.g. disease status), the relationships may exhibit class-specific patterns that can be used to facilitate the understanding of a disease. Conventional approaches often perform separate analysis on each class and report the differences, but ignore important shared patterns. RESULTS: In this paper, we develop a multivariate method to analyze the differential dependency across multiple classes. We propose a joint sparse canonical correlation analysis method, which uses a generalized fused lasso penalty to jointly estimate multiple pairs of canonical vectors with both shared and class-specific patterns. Using a data fusion approach, the method is able to detect differentially correlated modules effectively and efficiently. The results from simulation studies demonstrate its higher accuracy in discovering both common and differential canonical correlations compared to conventional sparse CCA. Using a schizophrenia dataset with 92 cases and 116 controls including a single nucleotide polymorphism (SNP) array and functional magnetic resonance imaging data, the proposed method reveals a set of distinct SNP-voxel interaction modules for the schizophrenia patients, which are verified to be both statistically and biologically significant. AVAILABILITY AND IMPLEMENTATION: The Matlab code is available at https://sites.google.com/site/jianfang86/JSCCA CONTACT: wyp@tulane.eduSupplementary information: Supplementary data are available at Bioinformatics online.

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Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impaired sense of self, and self-harm behaviors. Neuroimaging research on BPD has revealed structural and functional abnormalities in specific brain regions and connections. However, little is known about the topological organizations of brain networks in BPD. We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 patients with BPD and 10 healthy controls, and constructed frequency-specific functional brain networks by correlating wavelet-filtered fMRI signals from 82 cortical and subcortical regions. We employed graph-theory based complex network analysis to investigate the topological properties of the brain networks, and employed network-based statistic to identify functional dysconnections in patients. In the 0.03-0.06 Hz frequency band, compared to controls, patients with BPD showed significantly larger measures of global network topology, including the size of largest connected graph component, clustering coefficient, small-worldness, and local efficiency, indicating increased local cliquishness of the functional brain network. Compared to controls, patients showed lower nodal centrality at several hub nodes but greater centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in 0.03-0.06 Hz frequency band was identified that showed significantly lower connectivity in patients. The links in the subnetwork were mainly long-distance connections between regions located at different lobes; and the mean connectivity of this subnetwork was negatively correlated with the increased global topology measures. Lastly, the key network measures showed high correlations with several clinical symptom scores, and classified BPD patients against healthy controls with high accuracy based on linear discriminant analysis. The abnormal topological properties and connectivity found in this study may add new knowledge to the current understanding of functional brain networks in BPD. However, due to limitation of small sample sizes, the results of the current study should be viewed as exploratory and need to be validated on large samples in future works.

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Schizophrenia has existed as a distinct disorder for nearly a century, and, ever since this disorder was first described and studied, cognitive impairment has been recognized as a prominent feature. However, the positive symptoms of schizophrenia moved to the forefront of clinical and research attention in the latter half of the twentieth century. With the new movement toward functional recovery in schizophrenia, cognitive dysfunction has become an important treatment target. This shift in focus has been prompted by our evolving knowledge of brain changes associated with schizophrenia and by the mounting body of evidence indicating that cognition is closely related to functional outcome. Cognitive assessments can enhance the evaluation and treatment of all patients with schizophrenia, and clinicians may select from a variety of valid and reliable scales and assessment measures.

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In recent years, the goal of treatment for individuals with schizophrenia has shifted from symptom control to functional recovery. For recovery to occur, the substantial cognitive impairments associated with this disorder must be addressed. Advances in neuroscience have paved the way for the development of more effective behavioral and pharmacologic treatments. Behavioral interventions such as cognitive training are tapping into the innate plasticity and adaptive qualities of the brain. Emerging pharmacologic treatments are targeting new neurotransmitters and systems, such as the glutamatergic system and the nicotinic-cholinergic system, which are involved in the cognitive and sensory deficits that lead to impairment. The best chances for recovery will most likely occur by combining behavioral and pharmacologic interventions.

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OBJECTIVES: One of the most prominent features of schizophrenia is relatively lower general cognitive ability (GCA). An emerging approach to understanding the roots of variation in GCA relies on network properties of the brain. In this multi-center study, we determined global characteristics of brain networks using graph theory and related these to GCA in healthy controls and individuals with schizophrenia. METHODS: Participants (N=116 controls, 80 patients with schizophrenia) were recruited from four sites. GCA was represented by the first principal component of a large battery of neurocognitive tests. Graph metrics were derived from diffusion-weighted imaging. RESULTS: The global metrics of longer characteristic path length and reduced overall connectivity predicted lower GCA across groups, and group differences were noted for both variables. Measures of clustering, efficiency, and modularity did not differ across groups or predict GCA. Follow-up analyses investigated three topological types of connectivity--connections among high degree "rich club" nodes, "feeder" connections to these rich club nodes, and "local" connections not involving the rich club. Rich club and local connectivity predicted performance across groups. In a subsample (N=101 controls, 56 patients), a genetic measure reflecting mutation load, based on rare copy number deletions, was associated with longer characteristic path length. CONCLUSIONS: Results highlight the importance of characteristic path lengths and rich club connectivity for GCA and provide no evidence for group differences in the relationships between graph metrics and GCA.

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BACKGROUND: This study aimed to examine the impact of quetiapine on the symptom and distress domains measured by the Symptom Checklist-90-Revised (SCL-90-R) in patients with borderline personality disorder (BPD). METHODS: Ninety-five participants meeting DSM-IV diagnostic criteria for BPD were randomly assigned to low-dosage (quetiapine, 150 mg/d; n = 33), moderate-dosage (quetiapine, 300 mg/d; n = 33), or placebo (n = 29). SCL-90-R was administered weekly over the course of an 8-week double-blind treatment phase. We used a mixed-effects model to analyze subscale scores of the SCL-90-R. RESULTS: Results showed that both dosages of quetiapine were effective in reducing levels of overall psychological distress, interpersonal sensitivity, depression, and hostility compared with those who received placebo. CONCLUSIONS: SCL-90-R can be a useful tool that would allow clinicians to collect information in addition to the DSM symptoms to better understand the diagnostic heterogeneity found in patients diagnosed with BPD.

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This study aims to extend previous research by considering gender differences in borderline personality (BPD) using both dimensional self-reported and clinical measures of symptomatology. Drawing from a cross-cultural, clinical trial sample, the authors compared female and male BPD subjects (N = 770; 211 male) between the ages of 18 and 65 using diagnostic and self-report data. The authors found that women with BPD have greater hostility and relationship disruption than men. Gender differences in eating disorders, particularly bulimia, are more divergent than in the general population. Generally, gender differences in BPD in this sample are consistent with known general population differences. Women show greater overall symptomatology, including depressive, anxious, and somatic symptoms. Men have higher rates of antisocial personality disorder and a trend toward higher rates of narcissistic personality disorder. However, several gender differences consistently found in the general population are not present in this BPD sample. There are no differences in aggression, suicidality, substance abuse, panic disorder, or obsessive-compulsive disorder. Gender differences in major depression and posttraumatic stress disorder are attenuated. These findings support the conclusion that BPD may diminish normal gender differences.

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OBJECTIVE: The authors compared the efficacy and tolerability of low and moderate dosages of extended-release quetiapine in adults with borderline personality disorder. METHOD: Ninety-five participants with DSM-IV borderline personality disorder were randomly assigned to receive 150 mg/day of quetiapine (the low-dosage group; N=33), 300 mg/day of quetiapine (the moderate-dosage group; N=33), or placebo (N=29). Total score over time on the clinician-rated Zanarini Rating Scale for Borderline Personality Disorder ("Zanarini scale") was analyzed in a mixed-effects model accounting for informative dropout. RESULTS: Participants in the low-dosage quetiapine group had significant improvement on the Zanarini scale compared with those in the placebo group. Time to response (defined as a reduction of 50% or more on the Zanarini scale total score) was significantly shorter for both the low-dosage quetiapine group (hazard ratio=2.54, p=0.007) and the moderate-dosage quetiapine group (hazard ratio=2.37, p=0.011) than for the placebo group. Among participants who completed the study, 82% in the low-dosage quetiapine group were rated as "responders," compared with 74% in the moderate-dosage group and 48% in the placebo group. Treatment-emergent adverse events included sedation, change in appetite, and dry mouth. The overall completion rate for the 8-week double-blind treatment phase was 67% (67% for the low-dosage quetiapine group, 58% for the moderate-dosage quetiapine group, and 79% for the placebo group). Participants who experienced sedation were more likely to drop out. CONCLUSIONS: Participants treated with 150 mg/day of quetiapine had a significant reduction in the severity of borderline personality disorder symptoms compared with those who received placebo. Adverse events were more likely in participants taking 300 mg/day of quetiapine.

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BACKGROUND: Cognitive deficits are prominent in schizophrenia and represent promising endophenotypes for genetic research. METHODS: The current study investigated the importance of two conceptually distinct genetic aggregates, one based on copy number variations (uncommon deletion burden), and one based on single nucleotide polymorphisms identified in recent risk studies (genetic risk score). The impact of these genetic factors, and their interaction, was examined on cognitive endophenotypes defined by principal component analysis (PCA) in a multi-center sample of 50 patients with schizophrenia and 86 controls. PCA was used to identify three different types of executive function (EF: planning, fluency, and inhibition), and in separate analyses, a measure general cognitive ability (GCA). RESULTS: Cognitive deficits were prominent among individuals with schizophrenia, but no group differences were evident for either genetic factor. Among patients the deletion burden measures predicted cognitive deficits across the three EF components and GCA. Further, an interaction was noted between the two genetic factors for both EF and GCA and the observed patterns of interaction suggested antagonistic epistasis. In general, the set of genetic interactions examined predicted a substantial portion of variance in these cognitive endophenotypes. LIMITATIONS: Though adequately powered, our sample size is small for a genetic study. CONCLUSIONS: These results draw attention to genetic interactions and the possibility that genetic influences on cognition differ in patients and controls.

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Borderline personality disorder (BPD) is a serious mental illness that can cause significant suffering and carries a risk of suicide. Assigning an accurate diagnosis is critical to guide treatment. Currently, the diagnosis of BPD is made exclusively through the use of clinical assessment; no objective test is available to assist with its diagnosis. Thus, it is highly desirable to explore quantitative biomarkers to better characterize this illness. In this study, we extract spectral power features from the power spectral density and cross spectral density of resting-state fMRI data, covering 20 brain regions and 5 frequency bands. Machine learning approaches are employed to select the most discriminating features to identify BPD. Following a leave-one-out cross validation procedure, the proposed approach achieves 93.55% accuracy (100% specificity and 90.48% sensitivity) in classifying 21 BPD patients from 10 healthy controls based on the top ranked features. The most discriminating features are selected from the 0.1~0.15 Hz frequency band, and are located at the left medial orbitofrontal cortex, the left thalamus, and the right rostral anterior cingulate cortex. The high classification accuracy indicates the discriminating power of the spectral power features in BPD identification. The proposed machine learning approach may be used as an objective test to assist clinical diagnosis of BPD.

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Expertly collected, well-curated data sets consisting of comprehensive clinical characterization and raw structural, functional and diffusion-weighted DICOM images in schizophrenia patients and sex and age-matched controls are now accessible to the scientific community through an on-line data repository (coins.mrn.org). The Mental Illness and Neuroscience Discovery Institute, now the Mind Research Network (MRN, http://www.mrn.org/ ), comprised of investigators at the University of New Mexico, the University of Minnesota, Massachusetts General Hospital, and the University of Iowa, conducted a cross-sectional study to identify quantitative neuroimaging biomarkers of schizophrenia. Data acquisition across multiple sites permitted the integration and cross-validation of clinical, cognitive, morphometric, and functional neuroimaging results gathered from unique samples of schizophrenia patients and controls using a common protocol across sites. Particular effort was made to recruit patients early in the course of their illness, at the onset of their symptoms. There is a relatively even sampling of illness duration in chronic patients. This data repository will be useful to 1) scientists who can study schizophrenia by further analysis of this cohort and/or by pooling with other data; 2) computer scientists and software algorithm developers for testing and validating novel registration, segmentation, and other analysis software; and 3) educators in the fields of neuroimaging, medical image analysis and medical imaging informatics who need exemplar data sets for courses and workshops. Sharing provides the opportunity for independent replication of already published results from this data set and novel exploration. This manuscript describes the inclusion/exclusion criteria, imaging parameters and other information that will assist those wishing to use this data repository.

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There is considerable evidence implicating brain white matter (WM) abnormalities in the pathophysiology of schizophrenia; however, the spatial localization of WM abnormalities reported in the existing studies is heterogeneous. Thus, the goal of this study was to quantify the spatial characteristics of WM abnormalities in schizophrenia. One hundred and fourteen patients with schizophrenia and 138 matched controls participated in this multisite study involving the Universities of Iowa, Minnesota, and New Mexico, and the Massachusetts General Hospital. We measured fractional anisotropy (FA) in brain WM regions extracted using 3 different image-processing algorithms: regions of interest, tract-based spatial statistics, and the pothole approach. We found that FA was significantly lower in patients using each of the 3 image-processing algorithms. The region-of-interest approach showed multiple regions with lower FA in patients with schizophrenia, with overlap at all 4 sites in the corpus callosum and posterior thalamic radiation. The tract-based spatial statistic approach showed (1) global differences in 3 of the 4 cohorts and (2) lower frontal FA at the Iowa site. Finally, the pothole approach showed a significantly greater number of WM potholes in patients compared to controls at each of the 4 sites. In conclusion, the spatial characteristics of WM abnormalities in schizophrenia reflect a combination of a global low-level decrease in FA, suggesting a diffuse process, coupled with widely dispersed focal reductions in FA that vary spatially among individuals (ie, potholes).

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BACKGROUND: General cognitive ability is usually lower in individuals with schizophrenia, partly due to genetic influences. However, the specific genetic features related to general cognitive ability are poorly understood. Individual variation in a specific type of mutation, uncommon genetic deletions, has recently been linked with both general cognitive ability and risk for schizophrenia. METHODS: We derived measures of the aggregate number of "uncommon" deletions (i.e., those occurring in 3% or less of our combined samples) and the total number of base pairs affected by these deletions in individuals with schizophrenia (n = 79) and healthy control subjects (n = 110) and related each measure to the first principal component of a large battery of cognitive tests, a common technique for characterizing general cognitive ability. These two measures of mutation load were also evaluated for relationships with total brain gray matter, white matter, and lateral ventricle volume. RESULTS: The groups did not differ on genetic variables. Multivariate general linear models revealed a group (control subjects vs. patients) × uncommon deletion number interaction, such that the latter variable was associated with lower general cognitive ability and larger ventricles in patients but not control subjects. CONCLUSIONS: These data suggest that aggregate uncommon deletion burden moderates central features of the schizophrenia phenotype.

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BACKGROUND: Borderline personality disorder (BPD) is a significant psychiatric illness for which medication treatments are still being explored. The goal of this study was to assess divalproex extended release (ER) vs placebo for patients receiving dialectal behavior therapy (DBT). METHODS: Patients with BPD received 4 weeks of "condensed DBT." Those with Symptom Checklist-90 (SCL-90) scores >150 after this treatment were then randomly and blindly assigned to placebo or divalproex ER for 12 weeks. Repeated measures analysis of variance utilizing last observation carried forward was used to assess the results. RESULTS: Seventeen participants completed the full assessment. Two patients had a significant decrease in SCL-90 in the first 4 weeks, leaving 15 patients for the medication phase of the trial. There were no significant differences between the participants assigned to divalproex ER compared with placebo. However, there was a significant improvement in both groups from baseline to endpoint (P = .001). CONCLUSIONS: The response of 2 of 17 participants in the first 4 weeks prior to medication may point to a practice strategy in approaching outpatients with BPD. Although the patients had a decrease in symptoms during the study, there was no advantage observed for divalproex ER and DBT over placebo and DBT.

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Structural brain measures are employed as endophenotypes in the search for schizophrenia susceptibility genes. We analyzed two independent structural imaging datasets with voxel-based morphometry and with source-based morphometry, a multivariate, independent components analysis, to determine the stability and heritability of regional gray matter concentration abnormalities in schizophrenia. The samples comprised 209 and 102 patients with schizophrenia and 208 and 96 healthy volunteers, respectively. The second sample additionally included non-ill siblings of participants with and without schizophrenia. A standard voxel-based analysis showed reproducible regional gray matter deficits in the affected participants compared with unrelated, unaffected controls in both datasets: patients showed significant gray matter concentration deficits in cortical frontal, temporal, and insular lobes. Source-based morphometry (SBM) was applied to the gray matter images of the entire sample to determine the effects of diagnosis on networks of covarying structures. The SBM analysis extracted 24 significant sets of covarying regions (components). Four of these components showed significantly lower gray matter concentrations in patients (p < .05). We determined the familiality of the observed SBM components based on 66 sibling pairs (25 discordant for schizophrenia). Two components, one including the medial frontal, insular, inferior frontal, and temporal lobes, and the other including the posterior occipital lobe, showed significant familiality (p < .05). We conclude that structural brain deficits in schizophrenia are replicable, and that SBM can extract unique familial and likely heritable components. SBM provides a useful data reduction technique that can provide measures that may serve as endophenotypes for schizophrenia.

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This report presents efficacy and safety outcomes for patients with borderline personality disorder (BPD) treated with olanzapine for up to 24 weeks. In 2 concurrent studies, patients received open-label olanzapine for 12 weeks after 12 weeks of double-blind olanzapine or placebo. Open-label dosing started at 2.5 or 5 mg/d and could be increased up to 20 mg/d (study 1) or 15 mg/d (study 2). The primary efficacy measure was open-label baseline-to-endpoint change in Zanarini Rating Scale for BPD (ZAN-BPD) total score. Of 472 patients who completed the double-blind acute phase, 444 entered and 320 (72.1%) completed 12 weeks of open-label extension treatment. Mean ZAN-BPD total scores at the start of the acute phase were approximately 17, indicating moderate symptom severity. Mean ZAN-BPD total scores ranged from 7.8 to 10.5 at the start of the open-label treatment and decreased to 5.7 to 6.5, indicating mild symptom severity, by the end of the open-label treatment. Patients taking placebo during the acute phase showed increases in weight, prolactin level, and other laboratory values during open-label olanzapine treatment similar in magnitude to increases seen in olanzapine-treated patients during the acute phase. Patients proceeding from olanzapine during the acute phase to open-label olanzapine showed smaller changes in weight and laboratory values. In conclusion, these results suggest that continued therapy with olanzapine may sustain and build upon improvements seen with acute olanzapine treatment of patients with BPD. However, no medication is currently approved for treatment of BPD, and physicians should carefully weigh potential benefits and risks of antipsychotic treatment in this population.

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