Joint sparse canonical correlation analysis for detecting differential imaging genetics modules.

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.

Graph Metrics of Structural Brain Networks in Individuals with Schizophrenia and Healthy Controls: Group Differences, Relationships with Intelligence, and Genetics.

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.

Quetiapine's effect on the SCL-90-R domains in patients with borderline personality disorder.

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.

Open-label treatment with olanzapine for patients with borderline personality disorder.

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.

Efficacy of extended-release divalproex combined with "condensed" dialectical behavior therapy for individuals with borderline personality disorder.

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.

Cigarette smoking and white matter microstructure in schizophrenia.

The majority of patients with schizophrenia smoke cigarettes. Both nicotine use and schizophrenia have been associated with alterations in brain white matter microstructure as measured by diffusion tensor imaging (DTI). The purpose of this study was to examine fractional anisotropy (FA) in smoking and non-smoking patients with schizophrenia and in healthy volunteers. A total of 43 patients (28 smoking and 15 non-smoking) with schizophrenia and 40 healthy, non-smoking participants underwent DTI. Mean FA was calculated in four global regions of interest (ROIs) (whole brain, cerebellum, brainstem, and total cortical) as well as in four regional ROIs (frontal, temporal, parietal and occipital lobes). The non-smoking patient group had a significantly higher intellectual quotient (IQ) compared with the patients who smoked, and our results varied according to whether IQ was included as a covariate. Without IQ correction, significant between-group effects for FA were found in four ROIs: total brain, total cortical, frontal lobe and the occipital lobe. In all cases the FA was lower among the smoking patient group, and highest in the control group. Smoking patients differed significantly from non-smoking patients in the frontal lobe ROI. However, these differences were no longer significant after IQ correction. FA differences between non-smoking patients and controls were not significant. Among smoking and non-smoking patients with schizophrenia but not healthy controls, FA was correlated with IQ. In conclusion, group effects of smoking on FA in schizophrenia might be mediated by IQ. Further, low FA in specific brain areas may be a neural marker for complex pathophysiology and risk for diverse problems such as schizophrenia, low IQ, and nicotine addiction.

Heritability of multivariate gray matter measures in schizophrenia.

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.

WITHDRAWN: Erratum to "Does function follow form?: Methods to fuse structural and functional brain images show decreased linkage in schizophrenia" [NeuroImage 49 (2010) 2626-2637].

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.

Does function follow form?: methods to fuse structural and functional brain images show decreased linkage in schizophrenia.

When both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) data are collected they are typically analyzed separately and the joint information is not examined. Techniques that examine joint information can help to find hidden traits in complex disorders such as schizophrenia. The brain is vastly interconnected, and local brain morphology may influence functional activity at distant regions. In this paper we introduce three methods to identify inter-correlations among sMRI and fMRI voxels within the whole brain. We apply these methods to examine sMRI gray matter data and fMRI data derived from an auditory sensorimotor task from a large study of schizophrenia. In Method 1 the sMRI-fMRI cross-correlation matrix is reduced to a histogram and results show that healthy controls (HC) have stronger correlations than do patients with schizophrenia (SZ). In Method 2 the spatial information of sMRI-fMRI correlations is retained. Structural regions in the cerebellum and frontal regions show more positive and more negative correlations, respectively, with functional regions in HC than in SZ. In Method 3 significant sMRI-fMRI inter-regional links are detected, with regions in the cerebellum showing more significant positive correlations with functional regions in HC relative to SZ. Results from all three methods indicate that the linkage between gray matter and functional activation is stronger in HC than SZ. The methods introduced can be easily extended to comprehensively correlate large data sets.

What we know: findings that every theory of schizophrenia should explain.

The article summarizes the process used to distill schizophrenia science into 22 facts. These facts consist of 6 basic facts, 3 etiological facts, 6 pharmacological and treatment facts, 5 pathology facts, and 2 behavioral facts that were critically reviewed by the scholarly community through a special initiative in cooperation with the Schizophrenia Research Forum. A subset of 10 of these facts was selected to form a common set of findings to be explained from the different theoretical perspectives included in this special section of Schizophrenia Bulletin. The rationale for this exercise is to distinguish more precisely the areas of agreement and disagreement between theories of schizophrenia and to highlight where more thought and data can make the greatest impact for understanding this disease.

Voxel-based morphometric multisite collaborative study on schizophrenia.

Regional gray matter (GM) abnormalities are well known to exist in patients with chronic schizophrenia. Voxel-based morphometry (VBM) has been previously used on structural magnetic resonance images (MRI) data to characterize these abnormalities. Two multisite schizophrenia studies, the Functional Biomedical Informatics Research Network and the Mind Clinical Imaging Consortium, which include 9 data collection sites, are evaluating the efficacy of pooling structural imaging data across imaging centers. Such a pooling of data could yield the increased statistical power needed to elucidate effects that may not be seen with smaller samples. VBM analyses were performed to evaluate the consistency of patient versus control gray matter concentration (GMC) differences across the study sites, as well as the effects of combining multisite data. Integration of data from both studies yielded a large sample of 503 subjects, including 266 controls and 237 patients diagnosed with schizophrenia, schizoaffective or schizophreniform disorder. The data were analyzed using the combined sample, as well as analyzing each of the 2 multisite studies separately. A consistent pattern of reduced relative GMC in schizophrenia patients compared with controls was found across all study sites. Imaging center-specific effects were evaluated using a region of interest analysis. Overall, the findings support the use of VBM in combined multisite studies. This analysis of schizophrenics and controls from around the United States provides continued supporting evidence for GM deficits in the temporal lobes, anterior cingulate, and frontal regions in patients with schizophrenia spectrum disorders.

Olanzapine for the treatment of borderline personality disorder: variable dose 12-week randomised double-blind placebo-controlled study.

BACKGROUND: Despite the prevalence and clinical significance of borderline personality disorder, its treatment remains understudied. AIMS: To evaluate treatment with variably dosed olanzapine in individuals with borderline personality disorder. METHOD: In this 12-week randomised, double-blind trial, individuals received olanzapine (2.5-20 mg/day; n=155) or placebo (n=159) (trial registry: NCT00091650). The primary efficacy measure was baseline to end-point change on the Zanarini Rating Scale for Borderline Personality Disorder (ZAN-BPD) using last-observation-carried-forward methodology. RESULTS: Both olanzapine and placebo groups showed significant improvements but did not differ in magnitude at end-point (-6.56 v. -6.25, P=0.661). Response rates (50% reduction in ZAN-BPD) were 64.7% with olanzapine and 53.5% with placebo (P=0.062); however, time to response was significantly shorter for olanzapine (P=0.022). Weight gain was significantly greater (2.86 v. -0.35 kg, P<0.001), with higher incidence of treatment-emergent abnormal high levels of prolactin for the olanzapine group. CONCLUSIONS: Individuals treated with olanzapine and placebo showed significant but not statistically different improvements on overall symptoms of borderline personality disorder. The types of adverse events observed with olanzapine treatment appeared similar to those observed previously in adult populations.

Quetiapine in patients with borderline personality disorder: an open-label trial.

BACKGROUND: Quetiapine was assessed in patients diagnosed with borderline personality disorder (BPD) to examine its potential effect on symptoms and explore a tolerated dosing pattern. METHOD: An open label case series with objective rating measures was undertaken. The study was of 8 weeks duration. RESULTS: Sixteen research subjects received quetiapine and completed at least one rating. Nine subjects completed the entire 8 week trial. In the LOCF and completer analyses, significant improvement was noted on GAF, SCL-90, BIS, and SIB. Specifically for the LOCF analysis, GAF increased from 57.7 to 64.6 (p = 0.001), SCL-90 decreased from 120.1 to 78.4 (p = 0.004), BIS improved from 73.4 to 59.9 (p = 0.021), and the SIB started at 267.8 and ended at 202.3 (p < 0.001). The average dose of quetiapine for LOCF analysis was 223.4 mg/d and was 286.1 mg/d for the completers. Common side-effects were similar to those seen in schizophrenic patients--sedation and increased appetite. CONCLUSIONS: Significant reductions in symptoms assessed by objective rating scales were observed in this pilot study of quetiapine administered to subjects with BPD. The dosing strategy in the study was well tolerated.

Disruption of hippocampal connectivity in children and adolescents with schizophrenia--a voxel-based diffusion tensor imaging study.

INTRODUCTION: One hypothesis that unifies the diversity of symptoms associated with schizophrenia involves the disruption of connectivity between brain regions. As white matter provides rapid and efficient communication between brain regions, this study was initiated to assess the early disruption of white matter pathways in children and adolescent with schizophrenia. MATERIALS AND METHODS: Diffusion tensor images were acquired on 14 children and adolescents with schizophrenia, one subject with schizoaffective disorder, and 15 age and gender matched controls. The DTI images were acquired in twelve directions on a 3 T Siemens Trio scanner. The images were transformed into fractional anisotropy and mean diffusivity maps and a group analysis was performed using SPM2. RESULTS: Children and adolescent patients with schizophrenia demonstrated a significant decrease in FA and associated increase in AD in the left posterior hippocampus (p<0.001, Bonferroni corrected on the cluster-level). These diffusion differences were not statistically significant when IQ was used as a covariate in the analysis. DISCUSSION: These findings suggest hippocampal white matter abnormalities that present early in the development of schizophrenia. The lack of significant differences when IQ is used as a covariate suggests that this hippocampal region is associated with cognitive changes associated with schizophrenia.

Decreased memory loss associated with right unilateral ultra-brief pulse wave ECT.

The purpose of this brief article is to share with our colleagues in the psychiatric community and other physicians information about the efficacy of an emerging new method of electroconvulsive therapy (ECT) that shows advantages over existing treatments for depression. Patients treated with the method, ultra-brief pulse wave ECT, have less memory loss and confusion than those treated with longer-duration ECT.

Assessment of Proteomic Measures Across Serious Psychiatric Illness.

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.

Classification of adolescent psychotic disorders using linear discriminant analysis.

BACKGROUND: The differential diagnosis between schizophrenia and bipolar disorder during adolescence presents a major clinical problem. Can these two diagnoses be differentiated objectively early in the courses of illness? METHODS: We used linear discrimination analysis (LDA) to classify 28 adolescent subjects into one of three diagnostic categories (healthy, N=8; schizophrenia, N=10; bipolar, N=10) using subsets from a pool of 45 variables as potential predictors (22 neuropsychological test scores and 23 quantitative structural brain measurements). The predictor variables were adjusted for age, gender, race, and psychotropic medication. All possible subsets composed of k=2-12 variables, from the set of 45 variables available, were evaluated using the robust leaving-one-subject-out method. RESULTS: The highest correct classification (96%) of the 3 diagnostic categories was yielded by 9 sets of k=12 predictors, comprising both neuropsychological and brain structural measures. Although each one of these sets misclassified one case, each set correctly classified (100%) at least one group, such that a fully correct diagnosis could be reached by a tree-type decision procedure. CONCLUSIONS: We conclude that LDA with 12 predictor variables can provide correct and robust classification of subjects into the three diagnostic categories above. This robust classification relies upon both neuropsychological and brain structural information. Our results demonstrate that, despite overlapping clinical symptoms, schizophrenia and bipolar disorder can be differentiated early in the course of disease. This finding has two important implications. Firstly, schizophrenia and bipolar disorder are different illnesses. If schizophrenia and bipolar are dissimilar clinical manifestations of the same disease, we would not be able to use non-clinical information to classify ('diagnose') schizophrenia and bipolar disorder. Secondly, if this study's findings are replicated, brain structure (MRI) and brain function (neuropsychological) used together may be useful in the diagnosis of new patients.

Assessing cognitive impairment in patients with schizophrenia.

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.

Behavioral and emerging pharmacologic treatment options for cognitive impairment in schizophrenia.

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.

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI.

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.