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Perturbations in the prefrontal cortex (PFC), hippocampus, and amygdala are implicated in the development of anxiety disorders. However, most structural neuroimaging studies of patients with anxiety disorders utilize adult samples, and the few studies in youths examine small samples, primarily with volume-based measures. This study tested the hypothesis that cortical thickness of PFC regions and gray matter volume of the hippocampus and amygdala differ between pediatric anxiety disorder patients and healthy volunteers (HVs). High-resolution 3-Tesla T1-weighted MRI scans were acquired in 151 youths (75 anxious, 76 HV; ages 8-18). Analyses tested associations of brain structure with anxiety diagnosis and severity across both groups, as well as response to cognitive-behavioral therapy in a subset of 53 patients. Cortical thickness was evaluated both within an a priori PFC mask (small-volume corrected) and using an exploratory whole-brain-corrected (p<0.05) approach. Anxious relative to healthy youths exhibited thicker cortex in the left ventromedial PFC (vmPFC) and left precentral gyrus. Both anxiety diagnosis and symptom severity were associated with smaller right hippocampal volume. In patients, thinner cortex in parietal and occipital cortical regions was associated with worse treatment response. Pediatric anxiety was associated with structural differences in vmPFC and hippocampus, regions implicated in emotional processing and in developmental models of anxiety pathophysiology. Parietal and occipital cortical thickness were related to anxiety treatment response but not baseline anxiety.

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Enhancing screening practices and developing scalable diagnostic tools are imperative in response to the increasing prevalence of youth mental health challenges. Structured lay psychiatric interviews have emerged as one such promising tool. However, there remains limited research evaluating structured psychiatric interviews, specifically their characterization of internalizing disorders in treatment-seeking youth. This study evaluates the relationship between the Development and Well-Being Assessment (DAWBA), a structured psychiatric interview, and established measures of pediatric anxiety and depression, including the Screen for Child Anxiety Related Disorders (SCARED), the Pediatric Anxiety Rating Scale (PARS), and the Mood and Feelings Questionnaire (MFQ). The study comprised two independent clinical samples of treatment-seeking youth: sample one included 55 youth with anxiety and 29 healthy volunteers (HV), while sample two included 127 youth with Major Depressive Disorder and 73 HVs. We examined the association between the DAWBA band scores, indicating predicted risk for diagnosis, the SCARED and PARS (sample one), and the MFQ (sample two). An exploratory analysis was conducted in a subset of participants to test whether DAWBA band scores predicted the change in anxiety symptoms (SCARED, PARS) across a 12-week course of cognitive behavioral therapy. The results revealed that the DAWBA significantly predicted the SCARED, PARS and MFQ measures at baseline; however, it did not predict changes in anxiety symptoms across treatment. These findings suggest that the DAWBA may be a helpful screening tool for indexing anxiety and depression in treatment-seeking youth but is not especially predictive of longitudinal trajectories in symptomatology across psychotherapy.

Because pediatric anxiety disorders precede the onset of many other problems, successful prediction of response to the first-line treatment, cognitive-behavioral therapy (CBT), could have a major impact. This study evaluates whether structural and resting-state functional magnetic resonance imaging can predict post-CBT anxiety symptoms. Two datasets were studied: (A) one consisted of  = 54 subjects with an anxiety diagnosis, who received 12 weeks of CBT, and (B) one consisted of  = 15 subjects treated for 8 weeks. Connectome predictive modeling (CPM) was used to predict treatment response, as assessed with the PARS. The main analysis included network edges positively correlated with treatment outcome and age, sex, and baseline anxiety severity as predictors. Results from alternative models and analyses are also presented. Model assessments utilized 1000 bootstraps, resulting in a 95% CI for , , and mean absolute error (MAE). The main model showed a MAE of approximately 3.5 (95% CI: [3.1-3.8]) points, an of 0.08 [-0.14-0.26], and an of 0.38 [0.24-0.511]. When testing this model in the left-out sample (B), the results were similar, with an MAE of 3.4 [2.8-4.7], -0.65 [-2.29-0.16], and of 0.4 [0.24-0.54]. The anatomical metrics showed a similar pattern, where models rendered overall low . The analysis showed that models based on earlier promising results failed to predict clinical outcomes. Despite the small sample size, this study does not support the extensive use of CPM to predict outcomes in pediatric anxiety.

Five months ago, when the members of ALA’s Privacy Subcommittee met to decide on this year’s [2018] “Choose Privacy Week” (CPW) theme, it’s a fair bet to say that only a tiny percentage of the general public had ever heard of Cambridge Analytica, Aleksandr Kogan, the SCL Group, or of a fairly obscure app called “thisisyourdigitallife.”And yet, there were warnings about Cambridge Analytica’s program as early as December 2015, when the London Guardian first reported on this data-collection program and its integration with Facebook as part of Ted Cruz’s 2016 bid for the US presidency. Michael Zimmer, a University of Wisconsin-Milwaukee associate professor and a member of ALA’s Privacy Subcommittee, was quoted by the Guardian about why the use of such data was highly problematic. “It’s one thing for a marketer to try to predict if people like Coke or Pepsi,” said Zimmer, “but it’s another thing for them to predict things that are much more central to our identity, and what’s more personal in how I interact with the world in terms of social and cultural issues?”In the wake of Mark Zuckerberg’s Congressional testimony last week [in April 2018] and the related explosion of public interest in how online personal data is collected, stored, shared, used, and sometimes misused, this year’s CPW theme—“Big Data is Watching You”—could not be more perfectly timed.