Brain states as wave-like motifs.

There is ample evidence of wave-like activity in the brain at multiple scales and levels. This emerging literature supports the broader adoption of a wave perspective of brain activity. Specifically, a brain state can be described as a set of recurring, sequential patterns of propagating brain activity, namely a wave. We examine a collective body of experimental work investigating wave-like properties. Based on these works, we consider brain states as waves using a scale-agnostic framework across time and space. Emphasis is placed on the sequentiality and periodicity associated with brain activity. We conclude by discussing the implications, prospects, and experimental opportunities of this framework.

Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available.

Open-source, publicly available neuroimaging datasets - whether from large-scale data collection efforts or pooled from multiple smaller studies - offer unprecedented sample sizes and promote generalization efforts. Releasing data can democratize science, increase the replicability of findings, and lead to discoveries. Partly due to patient privacy, computational, and data storage concerns, researchers typically release preprocessed data with the voxelwise time series parcellated into a map of predefined regions, known as an atlas. However, releasing preprocessed data also limits the choices available to the end-user. This is especially true for connectomics, as connectomes created from different atlases are not directly comparable. Since there exist several atlases with no gold standards, it is unrealistic to have processed, open-source data available from all atlases. Together, these limitations directly inhibit the potential benefits of open-source neuroimaging data. To address these limitations, we introduce Cross Atlas Remapping via Optimal Transport (CAROT) to find a mapping between two atlases. This approach allows data processed from one atlas to be directly transformed into a connectome based on another atlas without the need for raw data access. To validate CAROT, we compare reconstructed connectomes against their original counterparts (i.e., connectomes generated directly from an atlas), demonstrate the utility of transformed connectomes in downstream analyses, and show how a connectome-based predictive model can generalize to publicly available data that was processed with different atlases. Overall, CAROT can reconstruct connectomes from an extensive set of atlases - without needing the raw data - allowing already processed connectomes to be easily reused in a wide range of analyses while eliminating redundant processing efforts. We share this tool as both source code and as a stand-alone web application (http://carotproject.com/).

Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.

Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models have been developed in samples of children school-aged or older. Nevertheless, despite growing evidence that altered brain maturation during the fetal, infant, and toddler (FIT) period modulates risk for poor mental health outcomes in childhood, these models are rarely implemented in FIT samples. Applications of predictive modeling in children of these ages provide an opportunity to develop powerful tools for improved characterization of the neural mechanisms underlying development. To facilitate the broader use of predictive models in FIT neuroimaging, we present a brief primer and systematic review on the methods used in current predictive modeling FIT studies. Reflecting on current practices in more than 100 studies conducted over the past decade, we provide an overview of topics, modalities, and methods commonly used in the field and under-researched areas. We then outline ethical and future considerations for neuroimaging researchers interested in predicting health outcomes in early life, including researchers who may be relatively new to either advanced machine learning methods or using FIT data. Altogether, the last decade of FIT research in machine learning has provided a foundation for accelerating the prediction of early-life trajectories across the full spectrum of illness and health.

Gaining Wings to FLY: Using Drosophila Oogenesis as an Entry Point for Citizen Scientists in Laboratory Research.

Citizen science is a productive approach to include non-scientists in research efforts that impact particular issues or communities. In most cases, scientists at advanced career stages design high-quality, exciting projects that enable citizen contribution, a crowdsourcing process that drives discovery forward and engages communities. The challenges of having citizens design their own research with no or limited training and providing access to laboratory tools, reagents, and supplies have limited citizen science efforts. This leaves the incredible life experiences and immersion of citizens in communities that experience health disparities out of the research equation, thus hampering efforts to address community health needs with a full picture of the challenges that must be addressed. Here, we present a robust and reproducible approach that engages participants from Grade 5 through adult in research focused on defining how diet impacts disease signaling. We leverage the powerful genetics, cell biology, and biochemistry of Drosophila oogenesis to define how nutrients impact phenotypes associated with genetic mutants that are implicated in cancer and diabetes. Participants lead the project design and execution, flipping the top-down hierarchy of the prevailing scientific culture to co-create research projects and infuse the research with cultural and community relevance.

Evidence-Based Intervention (EBI) Mapping: a systematic approach to understanding the components and logic of EBIs.

BACKGROUND: Despite the development of numerous evidence-based interventions (EBIs), many go unused in practice. Hesitations to use existing EBIs may be due to a lack of understanding about EBI components and what it would take to adapt it or implement it as designed. To improve the use of EBIs, program planners need to understand their goals, core components, and mechanisms of action. This paper presents EBI Mapping, a systematic approach based on Intervention Mapping, that can be used to understand and clearly describe EBIs, and help planners put them into practice. METHODS: We describe EBI Mapping tasks and provide an example of the process. EBI Mapping uses principles from Intervention Mapping, a systematic framework for planning multilevel health promotion interventions. EBI Mapping applies the Intervention Mapping steps retrospectively to help planners understand an existing EBI (rather than plan a new one). We explain each EBI Mapping task and demonstrate the process using the VERB Summer Scorecard (VSS), a multi-level community-based intervention to improve youth physical activity. RESULTS: EBI Mapping tasks are: 1) document EBI materials and activities, and their audiences, 2) identify the EBI goals, content, and mechanisms of action, 3) identify the theoretical change methods and practical applications of those methods, 4) describe design features and delivery channels, and 5) describe the implementers and their tasks, implementation strategies, and needed resources. By applying the EBI Mapping tasks, we created a logic model for the VSS intervention. The VSS logic model specifies the links between behavior change methods, practical applications, and determinants for both the at-risk population and environmental change agents. The logic model also links the respective determinants to the desired outcomes including the health behavior and environmental conditions to improve the health outcome in the at-risk population. CONCLUSIONS: EBI Mapping helps program planners understand the components and logic of an EBI. This information is important for selecting, adapting, and scaling-up EBIs. Accelerating and improving the use of existing EBIs can reduce the research-to-practice gap and improve population health.

Are Training and Experience Adapting Evidence-Based Interventions Associated With Self-Efficacy and Attitudes? A Cross-Sectional Survey of Students and Practitioners With Varying Levels of Adaptation Experience.

Increasing use of evidence-based interventions (EBIs) in local settings will help reduce the research-practice gap and improve health equity. Because adaptation to new settings and populations is essential to effective EBI use, frameworks to guide practice are receiving more attention; most, however, only provide broad guidelines without instructions for making adaptations in practice. Therefore, practitioners may need additional training or technical assistance (TA) to implement and adapt EBIs. This study explores whether practitioners' and students' general EBI training or TA and level of adaptation experience are associated with self-efficacy in adapting EBIs and with attitudes toward EBI use. We analyzed baseline survey data of participants in an evaluation of IM-Adapt Online, a newly developed decision support tool. We asked about previous training on EBIs, general and specific adaptation behaviors, and attitudes toward EBIs and found an association between previous training or TA in using EBIs with higher self-efficacy for using and adapting EBIs. Respondents with prior EBI training were significantly more likely to have higher self-efficacy in EBI behaviors across subdomains and in total than those without training. Respondents reported lowest self-efficacy for planning adaptations (M = 3.35) and assessing fit of EBIs to their local context (M = 3.41). This study suggests the importance of EBI adaptation training and TA to increase adoption and adaptation of EBIs, subsequently. More adaptation-specific training is warranted to assist students, practitioners, and researchers undertaking the adaptation process and implement EBIs. Future training on EBI adaptation can help practitioners tailor EBIs to meet the specific needs of their populations.

Applying evidence-based intervention (EBI) mapping to identify the components and logic of colorectal cancer screening interventions.

Implementation of evidence-based interventions (EBIs) can help to increase colorectal cancer screening (CRCS). Potential users of CRCS EBIs are often unclear about the specific features, logic, and core elements of existing EBIs, making it challenging to use or adapt them. We used EBI Mapping, a systematic process developed from Intervention Mapping that identifies an EBI's components and logic, to characterize existing CRCS EBIs from the National Cancer Institute's Evidence-Based Cancer Control Programs website. The resulting information can facilitate intervention adoption, adaptation, and/or implementation. Two trained coders independently coded intervention materials to describe intervention components and logic (n = 20). We display CRCS EBI components (potential mechanism of change) using evidence tables and heat maps. All EBIs addressed completion of at least one CRCS behavior (stool-based test, n = 9; stool-based test or another CRCS test, n = 8; colonoscopy, n = 3; colonoscopy or sigmoidoscopy, n = 1). The psychosocial determinants most frequently addressed by these interventions were knowledge (n = 19), attitudes (n = 17), risk perception/perceived susceptibility (n = 16), skills (n = 15), and overcoming barriers (n = 15). Multi-level EBIs (n = 9) attempted to change an average of 2.1 ± 1.1 conditions in the patients' environment (e.g., accessibility of CRCS); only four EBIs used environmental change agents (e.g., providers, nurses). From the heat maps of EBIs, we describe common theoretical change methods' (e.g., facilitation) used for addressing determinants (e.g., overcoming barriers). EBI Mapping can help users identify important components of a CRCS EBI's logic; these proposed mechanisms of action can inform adoption, adaptation, and implementation in new settings, and facilitate scale up of EBIs.

CTP synthase polymerization in germline cells of the developing Drosophila egg supports egg production.

Polymerization of metabolic enzymes into micron-scale assemblies is an emerging mechanism for regulating their activity. CTP synthase (CTPS) is an essential enzyme in the biosynthesis of the nucleotide CTP and undergoes regulated and reversible assembly into large filamentous structures in organisms from bacteria to humans. The purpose of these assemblies is unclear. A major challenge to addressing this question has been the inability to abolish assembly without eliminating CTPS protein. Here we demonstrate that a recently reported point mutant in CTPS, Histidine 355A (H355A), prevents CTPS filament assembly in vivo and dominantly inhibits the assembly of endogenous wild-type CTPS in the Drosophila ovary. Expressing this mutant in ovarian germline cells, we show that disruption of CTPS assembly in early stage egg chambers reduces egg production. This effect is exacerbated in flies fed the glutamine antagonist 6-diazo-5-oxo-L-norleucine, which inhibits de novo CTP synthesis. These findings introduce a general approach to blocking the assembly of polymerizing enzymes without eliminating their catalytic activity and demonstrate a role for CTPS assembly in supporting egg production, particularly under conditions of limited glutamine metabolism.This article has an associated First Person interview with the first author of the paper.

Using an Implementation Research Framework to Identify Potential Facilitators and Barriers of an Intervention to Increase HPV Vaccine Uptake.

BACKGROUND: Although the incidence of cervical cancer has been decreasing in the United States over the last decade, Hispanic and African American women have substantially higher rates than Caucasian women. The human papillomavirus (HPV) is a necessary, although insufficient, cause of cervical cancer. In the United States in 2013, only 37.6% of girls 13 to 17 years of age received the recommended 3 doses of a vaccine that is almost 100% efficacious for preventing infection with viruses that are responsible for 70% of cervical cancers. Implementation research has been underutilized in interventions for increasing vaccine uptake. The Consolidated Framework for Implementation Research (CFIR), an approach for designing effective implementation strategies, integrates 5 domains that may include barriers and facilitators of HPV vaccination. These include the innovative practice (Intervention), communities where youth and parents live (Outer Setting), agencies offering vaccination (Inner Setting), health care staff (Providers), and planned execution and evaluation of intervention delivery (Implementation Process). METHODS: Secondary qualitative analysis of transcripts of interviews with 30 community health care providers was conducted using the CFIR to code potential barriers and facilitators of HPV vaccination implementation. RESULTS: All CFIR domains except Implementation Process were well represented in providers' statements about challenges and supports for HPV vaccination. CONCLUSION: A comprehensive implementation framework for promoting HPV vaccination may increase vaccination rates in ethnically diverse communities. This study suggests that the CFIR can be used to guide clinicians in planning implementation of new approaches to increasing HPV vaccine uptake in their settings. Further research is needed to determine whether identifying implementation barriers and facilitators in all 5 CFIR domains as part of developing an intervention contributes to improved HPV vaccination rates.

Time from Screening Mammography to Biopsy and from Biopsy to Breast Cancer Treatment among Black and White, Women Medicare Beneficiaries Not Participating in a Health Maintenance Organization.

PURPOSE: There is a breast cancer mortality gap adversely affecting Black women in the United States. This study assessed the relationship between number of days between abnormal mammogram, biopsy, and treatment among Medicare (Part B) beneficiaries ages 65 to 74 and 75 to 84 years, accounting for race and comorbidity. METHODS: A cohort of non-Hispanic Black and non-Hispanic White women residing in the continental United States and receiving no services from a health maintenance organization was randomly selected from the Center for Medicare and Medicaid Services denominator file. The cohort was followed from 2005 to 2008 using Center for Medicare and Medicaid Services claims data. The sample included 4,476 women (weighted n = 70,731) with a diagnosis of breast cancer. Cox proportional hazard modeling was used to identify predictors of waiting times. FINDINGS: Black women had a mean of 16.7 more days between biopsy and treatment (p < .001) and 15.7 more days from mammogram to treatment (p = .003) than White women. Median duration from abnormal mammogram to treatment exceeded National Quality Measures for Breast Centers medians regardless of race, age, or number of comorbidities (overall 43 days vs. the National Quality Measures for Breast Centers value of 28 days). CONCLUSIONS: Medical care delays may contribute, in part, to the widening breast cancer mortality gap between Black women and White women. Further study, with additional clinical and social information, is needed to broaden scientific understanding of racial determinants and assess the clinical significance of mammogram to treatment times among Medicare beneficiaries.