In multisite neuroimaging studies there is often unwanted technical variation across scanners and sites. These “scanner effects” can hinder detection of biological features of interest, produce inconsistent results, and lead to spurious associations. We propose mica (multisite image harmonization by cumulative distribution function alignment), a tool to harmonize images taken on different scanners by identifying and removing within-subject scanner effects. Our goals in the present study were to (1) establish a method that removes scanner effects by leveraging multiple scans collected on the same subject, and, building on this, (2) develop a technique to quantify scanner effects in large multisite studies so these can be reduced as a preprocessing step. We illustrate scanner effects in a brain MRI study in which the same subject was measured twice on seven scanners, and assess our method’s performance in a second study in which ten subjects were scanned on two machines. We found that unharmonized images were highly variable across site and scanner type, and our method effectively removed this variability by aligning intensity distributions. We further studied the ability to predict image harmonization results for a scan taken on an existing subject at a new site using cross-validation.

Background: Culturally relevant interventions are needed to help American Indian and Alaska Native (AI/AN) teenagers and young adults navigate common risky situations involving family and friends, including drug and alcohol misuse, dating violence, and suicidality. Leveraging We R Native, a multimedia health resource for Native teenagers and young adults, staff of the Northwest Portland Area Indian Health Board designed the BRAVE intervention for Native youth. The program is delivered via SMS text messaging and includes role model videos, mental wellness strategies, links to culturally relevant resources, and social support from family and friends. Objective: We aim to conduct a randomized controlled trial of the BRAVE intervention among AI/AN teenagers and young adults (aged 15-24 years) to assess its impact on their physical, mental, and spiritual health; their resilience and self-esteem; and their coping and help-seeking skills. Methods: From October to December 2019, we recruited 2334 AI/AN teenagers and young adults nationwide via social media channels and SMS text messages and enrolled 1044 participants. AI/AN teenagers and young adults enrolled in the study received either BRAVE SMS text messages, designed to improve mental health, help-seeking skills, and cultural resilience, or 8 weeks of science, technology, engineering, and math (STEM) SMS text messages, designed to elevate and reaffirm Native voices in STEM and medicine and then received the BRAVE SMS text messages. The impacts of the BRAVE intervention were tested using linear mixed-effect models and linear regressions. Results: A total of 833 AI/AN teenagers and young adults were included in the analysis. Individuals in the BRAVE and STEM arms showed significant positive trends over the course of the study for all outcomes, except cultural identity and help-seeking behavior. Mean scores were significantly different for health (P<.001), resilience (P<.001), negative coping (P=.03), positive coping (P<.001), self-efficacy (P=.02), and self-esteem (P<.001). Changes in help-seeking self-efficacy were significant in those exhibiting risky behaviors at baseline to exit (P=.01). Those who reported positive coping scores at baseline also reported better health on average; however, no difference was found in risky drug and alcohol use (P<.001). The number of participants who used SMS text messages to help themselves increased from 69.1% (427/618) at 3 months to 76% (381/501; P<.001) at 8 months. Similarly, the number of participants who used SMS text messages to help friends or family members increased from 22.4% (138/616) at 3 months to 54.6% (272/498) at 8 months. Conclusions: This is the first national randomized controlled trial of a mobile health intervention among AI/AN teenagers and young adults to test the efficacy of a mental wellness intervention in relation to STEM career messages. This study provides new insights for supporting the next generation of AI/AN changemakers. Trial Registration: ClinicalTrials.gov NCT04979481; https://clinicaltrials.gov/ct2/show/NCT04979481

Ovarian cancer is the deadliest gynecologic malignancy. Multi-omics techniques have provided a platform for improved predictive modeling of therapy response and patient outcomes. While high-grade serous carcinoma (HGSOC) tumors are immunogenic and numerous studies have defined positive correlation to immune cell infiltration, immunotherapies in clinical trials have exhibited low efficacy rates. There is a significant need to better comprehend the role and composition of immune cells in mediating ovarian cancer therapeutic response and progression. We performed multiplex IHC with an HGSOC tissue microarray (n = 127) to characterize the immune cell composition within tumors. After analyzing the composition and spatial context of T cells (CD4/CD8), macrophages (CD68), and B cells (CD19) within the tumor, we found that increased B-cell and CD4 T-cell presence correlated with overall survival. More importantly, we observed that the proximity between tumor-associated macrophages and B cells or CD4 T cells significantly correlated with overall survival. Implications: The results highlight the antitumor role of B cells and CD4 T cells, and that the spatial interactions between immune cell types are a novel predictor of therapeutic response and patient outcomes.

The ability of individuals to engage in physical activity is a critical component of overall health and quality of life. However, there is a natural decline in physical activity associated with the aging process. Establishing normative trends of physical activity in aging populations is essential to developing public health guidelines and informing clinical perspectives regarding individuals’ levels of physical activity. Beyond overall quantity of physical activity, patterns regarding the timing of activity provide additional insights into latent health status. Wearable accelerometers, paired with statistical methods from functional data analysis, provide the means to estimate diurnal patterns in physical activity. To date, these methods have been only applied to study aging trends in populations based in the United States. Here, we apply curve registration and functional regression to 24 h activity profiles for 88,793 men (N = 39,255) and women (N = 49,538) ages 42–78 from the UK Biobank accelerometer study to understand how physical activity patterns vary across ages and by gender. Our analysis finds that daily patterns in both the volume of physical activity and probability of being active change with age, and that there are marked gender differences in these trends. This work represents the largest-ever population analyzed using tools of this kind, and suggest that aging trends in physical activity are reproducible in different populations across countries.