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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.

Inflammation plays a role in depression pathophysiology and treatment response, with effects varying by sex and therapeutic modality. Lower levels of interleukin(IL)-8 predict depression response to antidepressant medication and to electroconvulsive therapy (ECT), although ECT effects are specific to females. Whether IL-8 predicts depression response to ketamine and in a sex-specific manner is not known. Here, depressed patients (n = 46; female, n = 17) received open label infusion of ketamine (0.5 mg/kg over 40 min; NCT02165449). Plasma levels of IL-8 were evaluated at baseline and post-treatment. Baseline levels of IL-8 had a trending association with response to ketamine, depending upon sex (responder status × sex interaction: p = 0.096), in which lower baseline levels of IL-8 in females (p = 0.095) but not males (p = 0.96) trended with treatment response. Change in levels of IL-8 from baseline to post-treatment differed significantly by responder status (defined as ≥50% reduction in Hamilton Depression Rating Scale [HAM-D] Score), depending upon sex (responder status × sex × time interaction: F(1,42)=6.68, p = 0.01). In addition, change in IL-8 interacted with sex to predict change in HAM-D score (β = -0.63, p = 0.003); increasing IL-8 was associated with decreasing HAM-D score in females (p = 0.08) whereas the inverse was found in males (p = 0.02). Other inflammatory markers (IL-6, IL-10, tumor necrosis factor-α, C-reactive protein) were explored with no significant relationships identified. Given these preliminary findings, further evaluation of sex differences in the relationship between IL-8 and treatment response is warranted to elucidate mechanisms of response and aid in the development of personalized approaches to depression treatment.

Producing gesture can be a powerful tool for facilitating learning. This effect has been replicated across a variety of academic domains, including algebra, chemistry, geometry, and word learning. Yet the mechanisms underlying the effect are poorly understood. Here we address this gap using functional magnetic resonance imaging (fMRI). We examine the neural correlates underlying how children solve mathematical equivalence problems learned with the help of either a speech + gesture strategy, or a speech-alone strategy. Children who learned through a speech + gesture were more likely to recruit motor regions when subsequently solving problems during a scan than children who learned through speech alone. This suggests that gesture promotes learning, at least in part, because it is a type of action. In an exploratory analysis, we also found that children who learned through speech + gesture showed subthreshold activation in regions outside the typical action-learning network, corroborating behavioral findings suggesting that the mechanisms supporting learning through gesture and action are not identical. This study is one of the first to explore the neural mechanisms of learning through gesture.

Gestures are hand movements that are produced simultaneously with spoken language and can supplement it by representing semantic information, emphasizing important points, or showing spatial locations and relations. Gestures’ specific features make them a promising tool to improve spatial thinking. Yet, there is recent work showing that not all learners benefit equally from gesture instruction and that this may be driven, in part, by children’s difficulty understanding what an instructor’s gesture is intended to represent. The current study directly compares instruction with gestures to instruction with plastic unit chips (Action) in a linear measurement learning paradigm aimed at teaching children the concept of spatial units. Some children performed only one type of movement, and some children performed both: Action-then-Gesture [AG] or Gesture-then-Action [GA]. Children learned most from the Gesture-then-Action [GA] and Action only [A] training conditions. After controlling for initial differences in learning, the gesture-then-action condition outperformed all three other training conditions on a transfer task. While gesture is cognitively challenging for some learners, that challenge may be desirable—immediately following gesture with a concrete representation to clarify that gesture’s meaning is an especially effective way to unlock the power of this spatial tool and lead to deep, generalizable learning.

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.

Subanesthetic ketamine is found to induce fast-acting and pronounced antidepressant effects, even in treatment resistant depression (TRD). However, it remains unclear how ketamine modulates neural function at the brain systems-level to regulate emotion and behavior. Here, we examined treatment-related changes in the inhibitory control network after single and repeated ketamine therapy in TRD. Forty-seven TRD patients (mean age = 38, 19 women) and 32 healthy controls (mean age = 35, 18 women) performed a functional magnetic resonance imaging (fMRI) response inhibition task at baseline, and 37 patients completed the fMRI task and symptom scales again 24 h after receiving both one and four 0.5 mg/kg intravenous ketamine infusions. Analyses of fMRI data addressed effects of diagnosis, time, and differences between treatment remitters and non-remitters. Significant decreases in brain activation were observed in the inhibitory control network, including in prefrontal and parietal regions, and visual cortex following serial ketamine treatment, p  < 0.05 corrected. Remitters were distinguished from non-remitters by having lower functional activation in the supplementary motor area (SMA) prior to treatment, which normalized towards controls following serial ketamine treatment. Results suggest that ketamine treatment leads to neurofunctional plasticity in executive control networks including the SMA during a response-inhibitory task. SMA changes relate to reductions in depressive symptoms, suggesting modulation of this network play an important role in therapeutic response. In addition, early changes in the SMA network during response inhibition appear predictive of overall treatment outcome, and may serve as a biomarker of treatment response.

The effect of life stress on depression is moderated by a repeat length variation in the transcriptional control region of the serotonin transporter gene, which renders carriers of the short variant vulnerable for depression. We investigated the underlying neural mechanisms of these epigenetic processes in individuals with no history of psychopathology by using multimodal magnetic resonance-based imaging (functional, perfusion, and structural), genotyping, and self-reported life stress and rumination. Based on functional MRI and perfusion data, we found support for a model by which life stress interacts with the effect of serotonin transporter genotype on amygdala and hippocampal resting activation, two regions involved in depression and stress. Life stress also differentially affected, as a function of serotonin transporter genotype, functional connectivity of the amygdala and hippocampus with a wide network of other regions, as well as gray matter structural features, and affected individuals' level of rumination. These interactions may constitute a neural mechanism for epigenetic vulnerability toward, or protection against, depression.

The global burden of anxiety and depression has created an urgent need for scalable approaches to increase access to evidence-based mental health care. The Screening and Treatment for Anxiety and Depression (STAND) system of care was developed to meet this need through the use of internet-connected devices for assessment and provision of treatment. STAND triages to level of care (monitoring only, digital therapy with coaches, digital therapy assisted by clinicians in training, and clinical care) and then continuously monitors symptoms to adapt level of care. Triaging and adaptation are based on symptom severity and suicide risk scores obtained from computerized adaptive testing administered remotely. This article discusses how the STAND system of care improves upon current clinical paradigms, and presents preliminary data on feasibility, acceptability, and effectiveness of STAND in a sample of US-based university students. US-based university students were recruited and enrolled in an open trial of the STAND system of care. Participants were triaged based on initial symptom severity derived from a computerized adaptive test and monitored over 40 weeks on anxiety, depression, and suicide risk to inform treatment adaptation and evaluate preliminary effectiveness. Nearly 5000 students were screened and 516 received care. Depression and anxiety severity scores improved across all tiers (P<.001 in all cases). Suicide risk severity improved in the highest tier (ie, clinical care; P<.001). Acceptability and feasibility were demonstrated. STAND is a feasible and acceptable model of care that can reach large numbers of individuals. STAND showed preliminary effectiveness on all primary outcome measures. Current directions to improve STAND are described.

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 ( R 2 ≥ 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.

Children have persistent difficulty with foundational measurement concepts, which may be linked to the instruction they receive. Here, we focus on testing various ways to support their understanding that rulers comprise spatial interval units. We examined whether evidence-based learning tools—disconfirming evidence and/or structural alignment—enhance their understanding of ruler units. Disconfirming evidence, in this context, involves having children count the spatial interval units under an object that is not aligned with the origin of a ruler. Structural alignment, in this context, involves highlighting what a ruler unit is by overlaying plastic unit chips on top of ruler units when an object is aligned with the origin of a ruler. In three experiments employing a pre-test/training/post-test design, a total of 120 second graders were randomly assigned to one of six training conditions (two training conditions per experiment). The training conditions included different evidence-based learning principles or “business-as-usual” instruction (control), with equal allocation to each (N = 20 for each condition). In each experiment, children who did not perform above chance level on the pre-test were selected to continue with training, which resulted in a total of 88 students for the analysis of improvement. The children showed significant improvement in training conditions that included disconfirming evidence, but not in the structural alignment or control conditions. However, an exploratory analysis suggests that improvement occurred more rapidly and was retained better when structural alignment was combined with disconfirming evidence compared to disconfirming evidence alone.