Relationship between cognitive flexibility and subsequent course of mood symptoms and suicidal ideation in young adults with childhood-onset bipolar disorder.

Neurocognitive deficits, such as cognitive flexibility impairments, are common in bipolar disorder (BD) and predict poor academic, occupational, and functional outcomes. However, the association between neurocognition and illness trajectory is not well understood, especially across developmental transitions. This study examined cognitive flexibility and subsequent mood symptom and suicidal ideation (SI) course in young adults with childhood-onset BD-I (with distinct mood episodes) vs. BD-not otherwise specified (BD-NOS) vs. typically-developing controls (TDCs). Sample included 93 young adults (ages 18-30) with prospectively verified childhood-onset DSM-IV BD-I (n = 34) or BD-NOS (n = 15) and TDCs (n = 44). Participants completed cross-sectional neuropsychological tasks and clinical measures. Then participants with BD completed longitudinal assessments of mood symptoms and SI at 6-month intervals (M = 39.18 ± 16.57 months of follow-up data). Analyses included ANOVAs, independent-samples t tests, chi-square analyses, and multiple linear regressions. Participants with BD-I had significant deficits in cognitive flexibility and executive functioning vs. BD-NOS and TDCs, and impaired spatial working memory vs. TDCs only. Two significant BD subtype-by-cognitive flexibility interactions revealed that cognitive flexibility deficits were associated with subsequent percentage of time depressed and with SI in BD-I but not BD-NOS, regardless of other neurocognitive factors (full-scale IQ, executive functioning, spatial working memory) and clinical factors (current and prior mood and SI symptoms, age of BD onset, global functioning, psychiatric medications, comorbidity). Thus, cognitive flexibility may be an important etiological brain/behavior mechanism, prognostic indicator, and intervention target for childhood-onset BD-I, as this deficit appears to endure into young adulthood and is associated with worse prognosis for subsequent depression and SI.

Facial emotion recognition and mood symptom course in young adults with childhood-onset bipolar disorder.

Facial emotion recognition deficits are common in bipolar disorder (BD) and associated with impairment. However, the relationship between facial emotion recognition and mood course is not well understood. This study examined facial emotion recognition and subsequent mood symptoms in young adults with childhood-onset BD versus typically developing controls (TDCs). The sample included 116 young adults (ages 18-30, 58% male, 78% White) with prospectively verified childhood-onset BD (n = 52) and TDCs (n = 64). At baseline, participants completed a facial emotion recognition task (Diagnostic Analysis of Non-Verbal Accuracy-2) and clinical measures. Then, participants with BD completed mood symptom assessments every 6 months (M = 8.7 ± 5.2 months) over two years. Analyses included independent-samples t tests and mixed-effects regression models. Participants with BD made significantly more recognition errors for child expressions than TDCs. There were no significant between-group differences for recognition errors for adult expressions, or errors for specific child or adult emotional expressions. Participants had moderate baseline mood symptoms. Significant time-by-facial emotion recognition interactions revealed more recognition errors for child emotional expressions predicted lower baseline mania and stable/consistent trajectory; fewer recognition errors for child expressions predicted higher baseline mania and decreasing trajectory. In addition, more recognition errors for adult sad expressions predicted stable/consistent depression trajectory and decreasing mania; fewer recognition errors for adult sad expressions predicted decreasing depression trajectory and stable/consistent mania. Effects remained when controlling for baseline demographics and clinical variables. Facial emotion recognition may be an important brain/behavior mechanism, prognostic indicator, and intervention target for childhood-onset BD, which endures into young adulthood and is associated with mood trajectory.

A rank-based marker selection method for high throughput scRNA-seq data.

BACKGROUND: High throughput microfluidic protocols in single cell RNA sequencing (scRNA-seq) collect mRNA counts from up to one million individual cells in a single experiment; this enables high resolution studies of rare cell types and cell development pathways. Determining small sets of genetic markers that can identify specific cell populations is thus one of the major objectives of computational analysis of mRNA counts data. Many tools have been developed for marker selection on single cell data; most of them, however, are based on complex statistical models and handle the multi-class case in an ad-hoc manner. RESULTS: We introduce RANKCORR, a fast method with strong mathematical underpinnings that performs multi-class marker selection in an informed manner. RANKCORR proceeds by ranking the mRNA counts data before linearly separating the ranked data using a small number of genes. The step of ranking is intuitively natural for scRNA-seq data and provides a non-parametric method for analyzing count data. In addition, we present several performance measures for evaluating the quality of a set of markers when there is no known ground truth. Using these metrics, we compare the performance of RANKCORR to a variety of other marker selection methods on an assortment of experimental and synthetic data sets that range in size from several thousand to one million cells. CONCLUSIONS: According to the metrics introduced in this work, RANKCORR is consistently one of most optimal marker selection methods on scRNA-seq data. Most methods show similar overall performance, however; thus, the speed of the algorithm is the most important consideration for large data sets (and comparing the markers selected by several methods can be fruitful). RANKCORR is fast enough to easily handle the largest data sets and, as such, it is a useful tool to add into computational pipelines when dealing with high throughput scRNA-seq data. RANKCORR software is available for download at https://github.com/ahsv/RankCorr with extensive documentation.

Imaging from the inside out: inverse scattering with photoactivated internal sources.

We propose a method to reconstruct the optical properties of a scattering medium with subwavelength resolution. The method is based on the solution to the inverse scattering problem with internal sources. Applications to photoactivated localization microscopy are described.

Effects of sexual orientation and NSSI severity on suicide risk.

INTRODUCTION: People who identify as sexual minorities are at increased risk for suicide. Non-suicidal self-injury (NSSI) is also a risk factor for suicide and NSSI severity may contribute to development of capability for lethal self-injury. Further research is needed to understand how NSSI severity increases suicide risk, specifically in high-risk populations like sexual minorities. The current study seeks to examine whether sexual minority adults exhibit greater NSSI severity and suicide risk than heterosexuals, and if NSSI severity moderates the relationship between sexual orientation and suicide risk. METHODS: Undergraduate students (N = 1,994) who reported five or more acts of NSSI in their lifetime completed online self-report questionnaires including sexual orientation, NSSI severity, and suicide risk. RESULTS: A factorial ANOVA demonstrated main effects of sexual orientation and NSSI severity on suicide risk. DISCUSSION: The lack of significant interaction effect indicates NSSI severity does not amplify the effect of on sexual orientation on suicide risk; rather, it predicts the same level of increased risk across orientations. Therefore, suicidality related to both sexual orientation and NSSI severity are equally important treatment targets.

Fast joint design method for parallel excitation radiofrequency pulse and gradient waveforms considering off-resonance.

A fast parallel excitation pulse design algorithm to select and to order phase-encoding (PE) locations (also known as "spokes") of an Echo-Volumar excitation k-space trajectory considering B(0) field inhomogeneity is presented. Recently, other groups have conducted research to choose optimal PE locations, but the potential benefit of considering B(0) field inhomogeneity during PE location selection or their ordering has not been fully investigated. This article introduces a novel fast greedy algorithm to determine PE locations and their order that takes into account the off-resonance effects. Computer simulations of the proposed algorithm for B(1) field inhomogeneity correction demonstrate that it not only improves excitation accuracy but also provides an effective ordering of the PE locations.

Maltreatment history and reasons for self-injurious behavior among adolescents engaged in non-suicidal self-injury versus adolescents who attempted suicide.

OBJECTIVE: Self-injurious behavior (SIB) is a significant public health concern in the United States, especially among adolescents with histories of maltreatment. This study compared maltreatment characteristics and reasons for SIB between three homogenous samples of adolescents with either: (1) non-suicidal self-injury (NSSI); (2) suicide attempt/s (SA), and (3) typically developing controls (TDC). METHOD: Participants (N = 124) aged 13-17 years completed questionnaires about their maltreatment and SIB histories. RESULTS: Maltreatment rates were as follows: 90% NSSI group, 76% SA group, and 40% TDC group. Adolescents in the NSSI group reported significantly higher rates of emotional neglect compared to the SA group. Maltreated adolescents in the NSSI and SA groups reported the same top three SIB reasons: (1) get rid of bad feelings, (2) mental state at the time, and (3) problems with family. However, maltreated NSSI participants were significantly more likely to engage in SIB for emotion regulation reasons than maltreated SA participants, who were more likely to engage in SIB for interpersonal reasons. Physical neglect and physical abuse also arose as significant predictors of specific SIB reasons. CONCLUSIONS: Our findings help elucidate the maltreatment profiles and reasons for SIB among adolescents engaged in NSSI or SA. Specific maltreatment experiences may also influence the reasons why adolescents engaged in SIB.

Maladaptive personality traits and the course of suicidal ideation in young adults with bipolar disorder: Cross-sectional and prospective approaches.

BACKGROUND: Childhood-onset bipolar disorder (BD) has considerable morbidity and mortality, including suicide. Many risk factors have been identified for suicidality, but the potential role of personality traits as assessed by a computer-assisted self-report measure remains unclear. AIMS: To address this gap in knowledge, we tested relations between pathological-range personality traits and suicidal ideation among young adults whose childhood-onset BD was prospectively confirmed by enrollment in the Course and Outcome of Bipolar Youth study (COBY) as children (n = 45) and a newly enrolled group of typically developing controls (TDCs; n = 52) both cross-sectionally and longitudinally after 1.5 years of follow up. MATERIALS & METHODS: Personality traits were assessed with the computerized Schedule for Nonadaptive and Adaptive Personality-2 (SNAP-2). RESULTS: Cross-sectionally, we found that participants with BD had elevated Suicide Proneness and Low Self-esteem versus TDCs at baseline. Furthermore, longitudinal analyses in the BD participants for whom we had 1.5 years of prospectively collected illness-course data showed that greater Suicide Proneness and Low Self-esteem prospectively predicted greater levels, shorter time until occurrence, and greater frequency of suicidal ideation during the follow-up. CONCLUSION: Our findings suggest the role of specific personality-related vulnerabilities in the course of BD that, pending replication, could contribute to development of interventions focused on personality traits among individuals with BD.

This Is Your Brain on Irritability: A Clinician's Guide to Understanding How We Know What We Know Now, and What We Need to Know in the Future, About Irritability in Children and Adolescents.

Irritability is a common reason why children and adolescents are brought for psychiatric care. Although research is advancing what is known about the underlying brain and behavior mechanisms of irritability, clinicians often are shut out of that research. This article explains some of these research methods, providing brief summaries of what is known about brain/behavior mechanisms in disorders involving irritability, including bipolar disorder, disruptive mood dysregulation disorder, attention-deficit/hyperactivity disorder, and autism spectrum disorder. Greater access to these methods may help clinicians now and in the future, with such mechanisms translated into improved care, as occurs in the treatment of childhood leukemia.

poolMC: smart pooling of mRNA samples in microarray experiments.

BACKGROUND: Typically, pooling of mRNA samples in microarray experiments implies mixing mRNA from several biological-replicate samples before hybridization onto a microarray chip. Here we describe an alternative smart pooling strategy in which different samples, not necessarily biological replicates, are pooled in an information theoretic efficient way. Further, each sample is tested on multiple chips, but always in pools made up of different samples. The end goal is to exploit the compressibility of microarray data to reduce the number of chips used and increase the robustness to noise in measurements. RESULTS: A theoretical framework to perform smart pooling of mRNA samples in microarray experiments was established and the software implementation of the pooling and decoding algorithms was developed in MATLAB. A proof-of-concept smart pooled experiment was performed using validated biological samples on commercially available gene chips. Differential-expression analysis of the smart pooled data was performed and compared against the unpooled control experiment. CONCLUSIONS: The theoretical developments and experimental demonstration in this paper provide a useful starting point to investigate smart pooling of mRNA samples in microarray experiments. Although the smart pooled experiment did not compare favorably with the control, the experiment highlighted important conditions for the successful implementation of smart pooling - linearity of measurements, sparsity in data, and large experiment size.

Do styles of emotion dysregulation differentiate adolescents engaging in non-suicidal self-injury from those attempting suicide?

Emotion dysregulation is implicated in both suicide attempts (SA) and non-suicidal self-injury (NSSI). However, little is known about how emotion dysregulation may differ between adolescents who have made an SA from those engaged in NSSI. We sought to address this gap by comparing emotion dysregulation profiles across three homogenous groups of adolescents (1) SA-only (2) NSSI-only (3) and typically developing controls (TDCs). Mean comparisons suggest that adolescents with a history of NSSI reported significantly lower distress tolerance and higher emotional reactivity when compared to adolescents who made an SA. After controlling for shared variance across emotion dysregulation measures, parent report of affective lability was the only scale to uniquely distinguish between NSSI and SA groups. Accurately distinguishing emotion dysregulation patterns across self-injurious groups has practical implications towards assessment, treatment, course of illness, and prevention.

White matter correlates of cognitive flexibility in youth with bipolar disorder and typically developing children and adolescents.

Prior studies using behavioral tasks and neuroimaging have shown that children and adolescents with bipolar disorder (BD) have deficits in cognitive flexibility (CF)-defined as adaptation to changing rewards and punishments. However, no study, to our knowledge, has examined the white matter microstructural correlates of CF in youth with BD. To address this gap, we examined the relationship between CF assessed with the Cambridge Neuropsychological Testing Automated Battery (CANTAB)'s Intra-Extra Dimensional Set Shift task (ID/ED) and diffusion tensor imaging analyzed with FSL's preprocessing tools and Tract-Based Spatial Statistics (TBSS). We found a significantly different relationship between microstructural integrity of multiple white matter regions and CF performance in BD (n=28) and age-matched typically developing control (TDC) youths (n=26). Evaluation of the slopes of linear regressions in BD vs. TDC (ID/ED Simple Reversal error rate vs. fractional anisotropy) revealed significantly different slopes across the groups, indicating an aberrant relationship between CF and underlying white matter microstructure in youth with BD. These results underscore the importance of examining specific CF-neuroimaging relationships in BD youth. Future longitudinal studies could seek to define the white matter microstructural trajectories in BD vs. TDC, and relative to CF deficits and BD illness course.

Gradient-based image recovery methods from incomplete Fourier measurements.

A major problem in imaging applications such as magnetic resonance imaging and synthetic aperture radar is the task of trying to reconstruct an image with the smallest possible set of Fourier samples, every single one of which has a potential time and/or power cost. The theory of compressive sensing (CS) points to ways of exploiting inherent sparsity in such images in order to achieve accurate recovery using sub-Nyquist sampling schemes. Traditional CS approaches to this problem consist of solving total-variation (TV) minimization programs with Fourier measurement constraints or other variations thereof. This paper takes a different approach. Since the horizontal and vertical differences of a medical image are each more sparse or compressible than the corresponding TV image, CS methods will be more successful in recovering these differences individually. We develop an algorithm called GradientRec that uses a CS algorithm to recover the horizontal and vertical gradients and then estimates the original image from these gradients. We present two methods of solving the latter inverse problem, i.e., one based on least-square optimization and the other based on a generalized Poisson solver. After a thorough derivation of our complete algorithm, we present the results of various experiments that compare the effectiveness of the proposed method against other leading methods.