Emotional arousal, mediated by the amygdala, is known to modulate episodic memories stored by the hippocampus, a region involved in pattern separation (the process by which similar representations are independently stored). While emotional modulation and pattern separation have been examined independently, this study attempts to link the two areas of research to propose an alternative account for how emotion modulates episodic memory. We used an emotional discrimination task designed to tax pattern separation of emotional information by concurrently varying emotional valence and similarity of stimuli. To examine emotional modulation of memory at the level of hippocampal subfields, we used high-resolution fMRI (1.5 mm isotropic) of the medial temporal lobe. Consistent with prior reports, we observed engagement of the hippocampal dentate gyrus (DG) and CA3 during accurate discrimination of highly similar items (behavioral correlate of pattern separation). Furthermore, we observed an emotional modulation of this signal (negative > neutral) specific to trials on which participants accurately discriminated similar emotional items. The amygdala was also modulated by emotion, regardless of the accuracy of discrimination. Additionally, we found aberrant amygdala-hippocampal network activity in a sample of adults with depressive symptoms. In this sample, amygdala activation was enhanced and DG/CA3 activation was diminished during emotional discrimination compared to those without depressive symptoms. Depressive symptom severity was also negatively correlated with DG/CA3 activity. This study suggests a novel mechanistic account for how emotional information is processed by hippocampal subfields as well as how this network may be altered in mood disorders.

We report on the results of a low-intensity behavioral intervention to reduce second hand smoke (SHS) exposure of children with asthma from low income minority households in Los Angeles, California. In this study, 242 child/adult dyads were randomized to a behavioral intervention (video, workbook, minimal counseling) or control condition (brochure). Main outcome measures included child's urine cotinine and parental reports of child's hours of SHS exposure and number of household cigarettes smoked. Implementation of household bans was also considered. No differences in outcomes were detected between intervention and control groups at follow-up. Limitations included high attrition and low rates of collection of objective measures (few children with urine cotinine samples). There continues to be a need for effective culturally and linguistically appropriate strategies that support reduction of household SHS exposure among children with asthma in low income, minority households.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.