Background

The Consortium for Neuropsychiatric Phenomics (CNP) at UCLA was an investigation into the biological bases of traits such as memory and response inhibition phenotypes-to explore whether they are linked to syndromes including ADHD, Bipolar disorder, and Schizophrenia. An aim of the consortium was in moving from traditional categorical approaches for psychiatric syndromes towards more quantitative approaches based on large-scale analysis of the space of human variation. It represented an application of phenomics-wide-scale, systematic study of phenotypes-to neuropsychiatry research.

Results

This paper reports on a system for exploration of hypotheses in data obtained from the LA2K, LA3C, and LA5C studies in CNP. ViVA is a system for exploratory data analysis using novel mathematical models and methods for visualization of variance. An example of these methods is called VISOVA, a combination of visualization and analysis of variance, with the flavor of exploration associated with ANOVA in biomedical hypothesis generation. It permits visual identification of phenotype profiles-patterns of values across phenotypes-that characterize groups. Visualization enables screening and refinement of hypotheses about variance structure of sets of phenotypes.

Conclusions

The ViVA system was designed for exploration of neuropsychiatric hypotheses by interdisciplinary teams. Automated visualization in ViVA supports 'natural selection' on a pool of hypotheses, and permits deeper understanding of the statistical architecture of the data. Large-scale perspective of this kind could lead to better neuropsychiatric diagnostics.

Changes in gene expression (GE) during antidepressant treatment may increase understanding of the action of antidepressant medications and serve as biomarkers of efficacy. GE changes in peripheral blood are desirable because they can be assessed easily on multiple occasions during treatment. We report here on GE changes in 68 individuals who were treated for 8 weeks with either escitalopram alone, or escitalopram followed by bupropion. GE changes were assessed after 1, 2, and 8 weeks of treatment, with significant changes observed in 156, 121, and 585 peripheral blood gene transcripts, respectively. Thirty-one transcript changes were shared between the 1- and 8-week time points (seven upregulated, 24 downregulated). Differences were detected between the escitalopram- and bupropion-treated subjects, although there was no significant association between GE changes and clinical outcome. A subset of 18 genes overlapped with those previously identified as differentially expressed in subjects with MDD compared with healthy control subjects. There was statistically significant overlap between genes differentially expressed in the current and previous studies, with 10 genes overlapping in at least two previous studies. There was no enrichment for genes overexpressed in nervous system cell types, but there was a trend toward enrichment for genes in the WNT/β-catenin pathway in the anterior thalamus; three genes in this pathway showed differential expression in the present and in three previous studies. Our dataset and other similar studies will provide an important source of information about potential biomarkers of recovery and for potential dysregulation of GE in MDD.

Response inhibition plays a critical role in adaptive functioning and can be assessed with the Stop-signal task, which requires participants to suppress prepotent motor responses. Evidence suggests that this ability to inhibit a prepotent motor response (reflected as Stop-signal reaction time (SSRT)) is a quantitative and heritable measure of interindividual variation in brain function. Although attention has been given to the optimal method of SSRT estimation, and initial evidence exists in support of its reliability, there is still variability in how Stop-signal task data are treated across samples. In order to examine this issue, we pooled data across three separate studies and examined the influence of multiple SSRT calculation methods and outlier calling on reliability (using Intra-class correlation). Our results suggest that an approach which uses the average of all available sessions, all trials of each session, and excludes outliers based on predetermined lenient criteria yields reliable SSRT estimates, while not excluding too many participants. Our findings further support the reliability of SSRT, which is commonly used as an index of inhibitory control, and provide support for its continued use as a neurocognitive phenotype.

Inclusion of gesture – meaningful movements of the hands –during mathematics instruction is beneficial for teachingnaïve learners novel concepts, and it can affect a learner’sallocation of visual attention. Yet, it is unknown how childrenwith pre-existing knowledge of a math concept approachinstruction that includes gesture. Here, we examine howchildren’s prior knowledge and either the presence or absenceof gesture during instruction drive patterns in visual attentionduring a lesson. We find that prior knowledge does determinevisual attention patterns, independent of type of instruction(i.e. with or without gesture). These findings further ourunderstanding of the attentional mechanisms of gesture andhave implications for real-world classrooms, where levels ofprior knowledge are often mixed.

To learn from others, children rely on cues (e.g., familiarity) toinfer who will provide useful information. We extend thisresearch to ask whether children will use an informant’sinclination to gesture as a marker of whether they are a goodperson to learn from. Children (N=459, ages 4-12 years)watched videos in which actresses made statementsaccompanied by meaningful iconic gestures, beat gestures, orno gestures. After each trial, children were asked “Who do youthink would be a good teacher?” (good teacher- experimentalcondition) or “Who do you think would be a good friend?”(good friend-control condition). Results show children dobelieve that someone who produces iconic gesture would makea good teacher over someone who does not, but this is only laterin childhood and only if a child has the propensity to seegesture as meaningful. The same effects were not found in thegood-friend condition.

Teaching a new concept with gestures – hand movements thataccompany speech – facilitates learning above-and-beyondinstruction through speech alone (e.g., Singer & Goldin-Meadow, 2005). However, the mechanisms underlying thisphenomenon are still being explored. Here, we use eyetracking to explore one mechanism – gesture’s ability todirect visual attention. We examine how children allocatetheir visual attention during a mathematical equivalencelesson that either contains gesture or does not. We show thatgesture instruction improves posttest performance, andadditionally that gesture does change how children visuallyattend to instruction: children look more to the problem beingexplained, and less to the instructor. However lookingpatterns alone cannot explain gesture’s effect, as posttestperformance is not predicted by any of our looking-timemeasures. These findings suggest that gesture does guidevisual attention, but that attention alone cannot account for itsfacilitative learning effects.

Changes in gene expression (GE) during antidepressant treatment may increase understanding of the action of antidepressant medications and serve as biomarkers of efficacy. GE changes in peripheral blood are desirable because they can be assessed easily on multiple occasions during treatment. We report here on GE changes in 68 individuals who were treated for 8 weeks with either escitalopram alone, or escitalopram followed by bupropion. GE changes were assessed after 1, 2, and 8 weeks of treatment, with significant changes observed in 156, 121, and 585 peripheral blood gene transcripts, respectively. Thirty-one transcript changes were shared between the 1- and 8-week time points (seven upregulated, 24 downregulated). Differences were detected between the escitalopram- and bupropion-treated subjects, although there was no significant association between GE changes and clinical outcome. A subset of 18 genes overlapped with those previously identified as differentially expressed in subjects with MDD compared with healthy control subjects. There was statistically significant overlap between genes differentially expressed in the current and previous studies, with 10 genes overlapping in at least two previous studies. There was no enrichment for genes overexpressed in nervous system cell types, but there was a trend toward enrichment for genes in the WNT/β-catenin pathway in the anterior thalamus; three genes in this pathway showed differential expression in the present and in three previous studies. Our dataset and other similar studies will provide an important source of information about potential biomarkers of recovery and for potential dysregulation of GE in MDD.

OBJECTIVE: Schizophrenia patients exhibit impaired working and episodic memory, but this may represent generalized impairment across memory modalities or performance deficits restricted to particular memory systems in subgroups of patients. Furthermore, it is unclear whether deficits are unique from those associated with other disorders. METHOD: Healthy controls (n=1101) and patients with schizophrenia (n=58), bipolar disorder (n=49) and attention-deficit-hyperactivity-disorder (n=46) performed 18 tasks addressing primarily verbal and spatial episodic and working memory. Effect sizes for group contrasts were compared across tasks and the consistency of subjects distributional positions across memory domains was measured. RESULTS: Schizophrenia patients performed poorly relative to the other groups on every test. While low to moderate correlation was found between memory domains (r=.320), supporting modularity of these systems, there was limited agreement between measures regarding each individuals task performance (ICC=.292) and in identifying those individuals falling into the lowest quintile (kappa=0.259). A general ability factor accounted for nearly all of the group differences in performance and agreement across measures in classifying low performers. CONCLUSIONS: Pathophysiological processes involved in schizophrenia appear to act primarily on general abilities required in all tasks rather than on specific abilities within different memory domains and modalities. These effects represent a general shift in the overall distribution of general ability (i.e., each case functioning at a lower level than they would have if not for the illness), rather than presence of a generally low-performing subgroup of patients. There is little evidence that memory impairments in schizophrenia are shared with bipolar disorder and ADHD.

Although college campuses are diversifying rapidly, students of color remain an underserved and understudied group. Online screening and subsequent allocation to treatment represents a pathway to enhancing equity in college student mental health. The purpose of the current study was to evaluate racial/ethnic differences in mental health problems and treatment enrollment within the context of a largescale screening and treatment research initiative on a diverse college campus. The sample was comprised of n = 2090 college students who completed an online mental health screening survey and were offered either free online or face-to-face treatment based on symptom severity as a part of a research study. A series of ordinal, binomial and multinomial logistic regression models were specified to examine racial/ethnic differences in mental health problems, prior treatment receipt, and enrollment in online and face-to-face treatment through the campus-wide research initiative. Racial/ethnic differences in depression, anxiety and suicidality endorsed in the screening survey were identified. Students of color were less likely to have received prior mental health treatment compared to non-Hispanic white students, but were equally likely to enroll in and initiate online and face-to-face treatment offered through the current research initiative. Rates of enrollment in online therapy were comparable to prior studies. Online screening and treatment may be an effective avenue to reaching underserved students of color with mental health needs on college campuses. Digital mental health tools hold significant promise for bridging gaps in care, but efforts to improve uptake and engagement are needed.

Previous research has implicated a large network of brain regions in the processing of risk during decision making. However, it has not yet been determined if activity in these regions is predictive of choices on future risky decisions. Here, we examined functional MRI data from a large sample of healthy subjects performing a naturalistic risk-taking task and used a classification analysis approach to predict whether individuals would choose risky or safe options on upcoming trials. We were able to predict choice category successfully in 71.8% of cases. Searchlight analysis revealed a network of brain regions where activity patterns were reliably predictive of subsequent risk-taking behavior, including a number of regions known to play a role in control processes. Searchlights with significant predictive accuracy were primarily located in regions more active when preparing to avoid a risk than when preparing to engage in one, suggesting that risk taking may be due, in part, to a failure of the control systems necessary to initiate a safe choice. Additional analyses revealed that subject choice can be successfully predicted with minimal decrements in accuracy using highly condensed data, suggesting that information relevant for risky choice behavior is encoded in coarse global patterns of activation as well as within highly local activation within searchlights.