Individual differences in working memory predict the efficacy of experimenter-manipulated gestures in first-grade children.

Why is instructional gesture ineffective in some contexts? And what is it about learners that predicts whether they will learn from gestures? This between-subjects linear measurement training study compares gesture instruction to two controls-operant action and transient action-in a diverse sample of first-grade students (N = 174, Mage = 7.01 years; Nfemale = 84; Nmale = 90, 10% Latinx-identified; 70% White; 6% Black; 6% Asian; 18% multiple racial categories, Mincome = $59,750, SDincome ≈ $25,000; data collected 03/16-03/19). Results show that instructor-manipulated gestures may be less effective than demonstrative actions in part because they are iterative and do not leave a lasting trace. Verbal working memory, but not spatial, positively predicted an ability to learn from gesture and transient action in children with the lowest context knowledge.

Pleiotropic Meta-Analysis of Cognition, Education, and Schizophrenia Differentiates Roles of Early Neurodevelopmental and Adult Synaptic Pathways.

Susceptibility to schizophrenia is inversely correlated with general cognitive ability at both the phenotypic and the genetic level. Paradoxically, a modest but consistent positive genetic correlation has been reported between schizophrenia and educational attainment, despite the strong positive genetic correlation between cognitive ability and educational attainment. Here we leverage published genome-wide association studies (GWASs) in cognitive ability, education, and schizophrenia to parse biological mechanisms underlying these results. Association analysis based on subsets (ASSET), a pleiotropic meta-analytic technique, allowed jointly associated loci to be identified and characterized. Specifically, we identified subsets of variants associated in the expected ("concordant") direction across all three phenotypes (i.e., greater risk for schizophrenia, lower cognitive ability, and lower educational attainment); these were contrasted with variants that demonstrated the counterintuitive ("discordant") relationship between education and schizophrenia (i.e., greater risk for schizophrenia and higher educational attainment). ASSET analysis revealed 235 independent loci associated with cognitive ability, education, and/or schizophrenia at p < 5 × 10-8. Pleiotropic analysis successfully identified more than 100 loci that were not significant in the input GWASs. Many of these have been validated by larger, more recent single-phenotype GWASs. Leveraging the joint genetic correlations of cognitive ability, education, and schizophrenia, we were able to dissociate two distinct biological mechanisms-early neurodevelopmental pathways that characterize concordant allelic variation and adulthood synaptic pruning pathways-that were linked to the paradoxical positive genetic association between education and schizophrenia. Furthermore, genetic correlation analyses revealed that these mechanisms contribute not only to the etiopathogenesis of schizophrenia but also to the broader biological dimensions implicated in both general health outcomes and psychiatric illness.

Anterior default mode network and posterior insular connectivity is predictive of depressive symptom reduction following serial ketamine infusion.

BACKGROUND: Ketamine is a rapidly-acting antidepressant treatment with robust response rates. Previous studies have reported that serial ketamine therapy modulates resting state functional connectivity in several large-scale networks, though it remains unknown whether variations in brain structure, function, and connectivity impact subsequent treatment success. We used a data-driven approach to determine whether pretreatment multimodal neuroimaging measures predict changes along symptom dimensions of depression following serial ketamine infusion. METHODS: Patients with depression (n = 60) received structural, resting state functional, and diffusion MRI scans before treatment. Depressive symptoms were assessed using the 17-item Hamilton Depression Rating Scale (HDRS-17), the Inventory of Depressive Symptomatology (IDS-C), and the Rumination Response Scale (RRS) before and 24 h after patients received four (0.5 mg/kg) infusions of racemic ketamine over 2 weeks. Nineteen unaffected controls were assessed at similar timepoints. Random forest regression models predicted symptom changes using pretreatment multimodal neuroimaging and demographic measures. RESULTS: Two HDRS-17 subscales, the HDRS-6 and core mood and anhedonia (CMA) symptoms, and the RRS: reflection (RRSR) scale were predicted significantly with 19, 27, and 1% variance explained, respectively. Increased right medial prefrontal cortex/anterior cingulate and posterior insula (PoI) and lower kurtosis of the superior longitudinal fasciculus predicted reduced HDRS-6 and CMA symptoms following treatment. RRSR change was predicted by global connectivity of the left posterior cingulate, left insula, and right superior parietal lobule. CONCLUSIONS: Our findings support that connectivity of the anterior default mode network and PoI may serve as potential biomarkers of antidepressant outcomes for core depressive symptoms.

Modulation of the functional connectome in major depressive disorder by ketamine therapy.

BACKGROUND: Subanesthetic ketamine infusion therapy can produce fast-acting antidepressant effects in patients with major depression. How single and repeated ketamine treatment modulates the whole-brain functional connectome to affect clinical outcomes remains uncharacterized. METHODS: Data-driven whole brain functional connectivity (FC) analysis was used to identify the functional connections modified by ketamine treatment in patients with major depressive disorder (MDD). MDD patients (N = 61, mean age = 38, 19 women) completed baseline resting-state (RS) functional magnetic resonance imaging and depression symptom scales. Of these patients, n = 48 and n = 51, completed the same assessments 24 h after receiving one and four 0.5 mg/kg intravenous ketamine infusions. Healthy controls (HC) (n = 40, 24 women) completed baseline assessments with no intervention. Analysis of RS FC addressed effects of diagnosis, time, and remitter status. RESULTS: Significant differences (p < 0.05, corrected) in RS FC were observed between HC and MDD at baseline in the somatomotor network and between association and default mode networks. These disruptions in FC in MDD patients trended toward control patterns with ketamine treatment. Furthermore, following serial ketamine infusions, significant decreases in FC were observed between the cerebellum and salience network (SN) (p < 0.05, corrected). Patient remitters showed increased FC between the cerebellum and the striatum prior to treatment that decreased following treatment, whereas non-remitters showed the opposite pattern. CONCLUSION: Results support that ketamine treatment leads to neurofunctional plasticity between distinct neural networks that are shown as disrupted in MDD patients. Cortico-striatal-cerebellar loops that encompass the SN could be a potential biomarker for ketamine treatment.

Enhancing Racial/Ethnic Equity in College Student Mental Health Through Innovative Screening and Treatment.

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.

Individual differences in gesture interpretation predict children's propensity to pick a gesturer as a good informant.

To learn from others, children rely on cues (e.g., familiarity, confidence) to infer who around them will provide useful information. We extended this research to ask whether children will use an informant's inclination to gesture as a marker of whether or not the informant is a good person to learn from. Children (N = 459, ages 4-12 years) watched short videos in which actresses made statements accompanied by meaningful iconic gestures, beat gestures (which act as prosodic markers with speech), or no gestures. After each trial, children were asked "Who do you think would be a good teacher?" (good teacher [experimental] condition) or "Who do you think would be a good friend?" (good friend [control] condition). Results show that children do believe that someone who produces iconic gesture would make a good teacher compared with someone who does not, but this is only later in childhood and only if children have the propensity to see gesture as meaningful. The same effects were not found in the good friend condition, indicating that children's responses are not just about liking an adult who gestures more. These findings have implications for how children attend to and learn from instructional gesture.

Modulation of amygdala reactivity following rapidly acting interventions for major depression.

Electroconvulsive therapy (ECT) and ketamine treatment both induce rapidly acting antidepressant effects in patients with major depressive disorder unresponsive to standard treatments, yet their specific impact on emotion processing is unknown. Here, we examined the neural underpinnings of emotion processing within and across patients (N = 44) receiving either ECT (N = 17, mean age: 36.8, 11.0 SD) or repeated subanesthetic (0.5 mg/kg) intravenous ketamine therapy (N = 27, mean age: 37.3, 10.8 SD) using a naturalistic study design. MRI and clinical data were collected before (TP1) and after treatment (TP2); healthy controls (N = 31, mean age: 34.5, 13.5 SD) completed one MRI session (TP1). An fMRI face-matching task probed negative- and positive-valence systems. Whole-brain analysis, comparing neurofunctional changes within and across treatment groups, targeted brain regions involved in emotional facial processing, and included regions-of-interest analysis of amygdala responsivity. Main findings revealed a decrease in amygdalar reactivity after both ECT and ketamine for positive and negative emotional face processing (p < .05 family wise-error (FWE) corrected). Subthreshold changes were observed between treatments within the dorsolateral prefrontal cortex and insula (p < .005, uncorrected). BOLD change for positive faces in the inferior parietal cortex significantly correlated with overall symptom improvement, and BOLD change in frontal regions correlated with anxiety for negative faces, and anhedonia for positive faces (p < .05 FWE corrected). Both serial ketamine and ECT treatment modulate amygdala response, while more subtle treatment-specific changes occur in the larger functional network. Findings point to both common and differential mechanistic upstream systems-level effects relating to fast-acting antidepressant response, and symptoms of anxiety and anhedonia, for the processing of emotionally valenced stimuli.

Inflammation and depression treatment response to electroconvulsive therapy: Sex-specific role of interleukin-8.

Females suffer from depression at twice the rate of males and have differential neural and emotional responses to inflammation. However, sex-specific evaluation of relationships between inflammation and response to depression treatments are lacking. Some data suggest that interleukin(IL)-8 predicts treatment response to antidepressants and has a relationship with depressive symptom severity. This study examines whether IL-8 predicts treatment response to electroconvulsive therapy (ECT), and whether there are sex specific effects. In 40 depressed patients (22 female), plasma levels of IL-8, as well as other markers of inflammation including IL-6, IL-10, tumor necrosis factor (TNF)-α, and C-reactive protein (CRP) were obtained prior to administration of ECT and after completion of the index treatment series. Depression treatment response was defined as ≥ 50% reduction in Hamilton Depression Rating Scale (HAM-D) Score. Baseline levels of IL-8 differed by responder status, depending on sex (group × sex interaction: ß = -0.571, p = 0.04), with female responders having lower levels of IL-8 at baseline as compared to female non-responders [t(20) = 2.37, p = 0.03]. Further, IL-8 levels from baseline to end of treatment differed by responder status, depending on sex (group × sex × time interaction: [F(1,36) = 9.48, p = 0.004]), and change in IL-8 from baseline to end of treatment was negatively correlated with percentage change in HAM-D score in females (ß = -0.458, p = 0.03), but not in males (ß = 0.315, p = 0.20). Other inflammatory markers did not differ in relation to responder status and sex. Further evaluation of sex differences in the relationship between IL-8, depression, and treatment response, across disparate treatment modalities, may inform mechanisms of response and aid in development of personalized medicine strategies.

Gesture helps learners learn, but not merely by guiding their visual attention.

Teaching a new concept through gestures-hand movements that accompany speech-facilitates learning above-and-beyond instruction through speech alone (e.g., Singer & Goldin-Meadow, ). However, the mechanisms underlying this phenomenon are still under investigation. Here, we use eye tracking to explore one often proposed mechanism-gesture's ability to direct visual attention. Behaviorally, we replicate previous findings: Children perform significantly better on a posttest after learning through Speech+Gesture instruction than through Speech Alone instruction. Using eye tracking measures, we show that children who watch a math lesson with gesture do allocate their visual attention differently from children who watch a math lesson without gesture-they look more to the problem being explained, less to the instructor, and are more likely to synchronize their visual attention with information presented in the instructor's speech (i.e., follow along with speech) than children who watch the no-gesture lesson. The striking finding is that, even though these looking patterns positively predict learning outcomes, the patterns do not mediate the effects of training condition (Speech Alone vs. Speech+Gesture) on posttest success. We find instead a complex relation between gesture and visual attention in which gesture moderates the impact of visual looking patterns on learning-following along with speech predicts learning for children in the Speech+Gesture condition, but not for children in the Speech Alone condition. Gesture's beneficial effects on learning thus come not merely from its ability to guide visual attention, but also from its ability to synchronize with speech and affect what learners glean from that speech.

Multi-Trait Analysis of GWAS and Biological Insights Into Cognition: A Response to Hill (2018).

Hill (Twin Research and Human Genetics, Vol. 21, 2018, 84-88) presented a critique of our recently published paper in Cell Reports entitled 'Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets' (Lam et al., Cell Reports, Vol. 21, 2017, 2597-2613). Specifically, Hill offered several interrelated comments suggesting potential problems with our use of a new analytic method called Multi-Trait Analysis of GWAS (MTAG) (Turley et al., Nature Genetics, Vol. 50, 2018, 229-237). In this brief article, we respond to each of these concerns. Using empirical data, we conclude that our MTAG results do not suffer from 'inflation in the FDR [false discovery rate]', as suggested by Hill (Twin Research and Human Genetics, Vol. 21, 2018, 84-88), and are not 'more relevant to the genetic contributions to education than they are to the genetic contributions to intelligence'.

Predicting risky choices from brain activity patterns.

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.

Better together: Simultaneous presentation of speech and gesture in math instruction supports generalization and retention.

When teachers gesture during instruction, children retain and generalize what they are taught (Goldin-Meadow, 2014). But why does gesture have such a powerful effect on learning? Previous research shows that children learn most from a math lesson when teachers present one problem-solving strategy in speech while simultaneously presenting a different, but complementary, strategy in gesture (Singer & Goldin-Meadow, 2005). One possibility is that gesture is powerful in this context because it presents information simultaneously with speech. Alternatively, gesture may be effective simply because it involves the body, in which case the timing of information presented in speech and gesture may be less important for learning. Here we find evidence for the importance of simultaneity: 3rd grade children retain and generalize what they learn from a math lesson better when given instruction containing simultaneous speech and gesture than when given instruction containing sequential speech and gesture. Interpreting these results in the context of theories of multimodal learning, we find that gesture capitalizes on its synchrony with speech to promote learning that lasts and can be generalized.

The effects of word-learning biases on children's concept of angle.

Despite evidence that young children are sensitive to differences in angle measure, older students frequently struggle to grasp this important mathematical concept. When making judgments about the size of angles, children often rely on erroneous dimensions such as the length of the angles' sides. The present study tested the possibility that this misconception stems from the whole-object word-learning bias by providing a subset of children with a separate label to refer to the whole angle figure. Thirty preschoolers (M = 4.86 years, SD = .53) were tested with a pretest-training-posttest design. At pretest, children showed evidence of the whole-object misconception. After training, children who were given a novel-word label for the whole object improved significantly more than those trained on the meaning of "angle" alone.

Decomposing decision components in the stop-signal task: a model-based approach to individual differences in inhibitory control.

The stop-signal task, in which participants must inhibit prepotent responses, has been used to identify neural systems that vary with individual differences in inhibitory control. To explore how these differences relate to other aspects of decision making, a drift-diffusion model of simple decisions was fitted to stop-signal task data from go trials to extract measures of caution, motor execution time, and stimulus processing speed for each of 123 participants. These values were used to probe fMRI data to explore individual differences in neural activation. Faster processing of the go stimulus correlated with greater activation in the right frontal pole for both go and stop trials. On stop trials, stimulus processing speed also correlated with regions implicated in inhibitory control, including the right inferior frontal gyrus, medial frontal gyrus, and BG. Individual differences in motor execution time correlated with activation of the right parietal cortex. These findings suggest a robust relationship between the speed of stimulus processing and inhibitory processing at the neural level. This model-based approach provides novel insight into the interrelationships among decision components involved in inhibitory control and raises interesting questions about strategic adjustments in performance and inhibitory deficits associated with psychopathology.

From action to abstraction: using the hands to learn math.

Previous research has shown that children benefit from gesturing during math instruction. We asked whether gesturing promotes learning because it is itself a physical action, or because it uses physical action to represent abstract ideas. To address this question, we taught third-grade children a strategy for solving mathematical-equivalence problems that was instantiated in one of three ways: (a) in a physical action children performed on objects, (b) in a concrete gesture miming that action, or (c) in an abstract gesture. All three types of hand movements helped children learn how to solve the problems on which they were trained. However, only gesture led to success on problems that required generalizing the knowledge gained. The results provide the first evidence that gesture promotes transfer of knowledge better than direct action on objects and suggest that the beneficial effects gesture has on learning may reside in the features that differentiate it from action.

Exploring Individual Differences: A Case for Measuring Children's Spontaneous Gesture Production as a Predictor of Learning From Gesture Instruction.

Decades of research have established that learners benefit when instruction includes hand gestures. This benefit is seen when learners watch an instructor gesture, as well as when they are taught or encouraged to gesture themselves. However, there is substantial individual variability with respect to this phenomenon-not all individuals benefit equally from gesture instruction. In the current paper, we explore the sources of this variability. First, we review the existing research on individual differences that do or do not predict learning from gesture instruction, including differences that are either context-dependent (linked to the particular task at hand) or context-independent (linked to the learner across multiple tasks). Next, we focus on one understudied measure of individual difference: the learner's own spontaneous gesture rate. We present data showing rates of "non-gesturers" across a number of studies and we provide theoretical motivation for why this is a fruitful area for future research. We end by suggesting ways in which research on individual differences will help gesture researchers to further refine existing theories and develop specific predictions about targeted gesture intervention for all kinds of learners.

Learners' Spontaneous Gesture Before a Math Lesson Predicts the Efficacy of Seeing Versus Doing Gesture During the Lesson.

Gestures-hand movements that accompany speech and express ideas-can help children learn how to solve problems, flexibly generalize learning to novel problem-solving contexts, and retain what they have learned. But does it matter who is doing the gesturing? We know that producing gesture leads to better comprehension of a message than watching someone else produce gesture. But we do not know how producing versus observing gesture impacts deeper learning outcomes such as generalization and retention across time. Moreover, not all children benefit equally from gesture instruction, suggesting that there are individual differences that may play a role in who learns from gesture. Here, we consider two factors that might impact whether gesture leads to learning, generalization, and retention after mathematical instruction: (1) whether children see gesture or do gesture and (2) whether a child spontaneously gestures before instruction when explaining their problem-solving reasoning. For children who spontaneously gestured before instruction, both doing and seeing gesture led to better generalization and retention of the knowledge gained than a comparison manipulative action. For children who did not spontaneously gesture before instruction, doing gesture was less effective than the comparison action for learning, generalization, and retention. Importantly, this learning deficit was specific to gesture, as these children did benefit from doing the comparison manipulative action. Our findings are the first evidence that a child's use of a particular representational format for communication (gesture) directly predicts that child's propensity to learn from using the same representational format.

Personalized mood prediction from patterns of behavior collected with smartphones.

Over the last ten years, there has been considerable progress in using digital behavioral phenotypes, captured passively and continuously from smartphones and wearable devices, to infer depressive mood. However, most digital phenotype studies suffer from poor replicability, often fail to detect clinically relevant events, and use measures of depression that are not validated or suitable for collecting large and longitudinal data. Here, we report high-quality longitudinal validated assessments of depressive mood from computerized adaptive testing paired with continuous digital assessments of behavior from smartphone sensors for up to 40 weeks on 183 individuals experiencing mild to severe symptoms of depression. We apply a combination of cubic spline interpolation and idiographic models to generate individualized predictions of future mood from the digital behavioral phenotypes, achieving high prediction accuracy of depression severity up to three weeks in advance (R2 ≥ 80%) and a 65.7% reduction in the prediction error over a baseline model which predicts future mood based on past depression severity alone. Finally, our study verified the feasibility of obtaining high-quality longitudinal assessments of mood from a clinical population and predicting symptom severity weeks in advance using passively collected digital behavioral data. Our results indicate the possibility of expanding the repertoire of patient-specific behavioral measures to enable future psychiatric research.

Phenotype mining in CNV carriers from a population cohort.

Phenotype mining is a novel approach for elucidating the genetic basis of complex phenotypic variation. It involves a search of rich phenotype databases for measures correlated with genetic variation, as identified in genome-wide genotyping or sequencing studies. An initial implementation of phenotype mining in a prospective unselected population cohort, the Northern Finland 1966 Birth Cohort (NFBC1966), identifies neurodevelopment-related traits-intellectual deficits, poor school performance and hearing abnormalities-which are more frequent among individuals with large (>500 kb) deletions than among other cohort members. Observation of extensive shared single nucleotide polymorphism haplotypes around deletions suggests an opportunity to expand phenotype mining from cohort samples to the populations from which they derive.