Mining the Mind Research Network: A Novel Framework for Exploring Large Scale, Heterogeneous Translational Neuroscience Research Data Sources

A neuroinformatics (NI) system is critical to brain imaging research in order to shorten the time between study conception and results. Such a NI system is required to scale well when large numbers of subjects are studied. Further, when multiple sites participate in research projects organizational issues become increasingly difficult. Optimized NI applications mitigate these problems. Additionally, NI software enables coordination across multiple studies, leveraging advantages potentially leading to exponential research discoveries. The web-based, Mind Research Network (MRN), database system has been designed and improved through our experience with 200 research studies and 250 researchers from seven different institutions. The MRN tools permit the collection, management, reporting and efficient use of large scale, heterogeneous data sources, e.g., multiple institutions, multiple principal investigators, multiple research programs and studies, and multimodal acquisitions. We have collected and analyzed data sets on thousands of research participants and have set up a framework to automatically analyze the data, thereby making efficient, practical data mining of this vast resource possible. This paper presents a comprehensive framework for capturing and analyzing heterogeneous neuroscience research data sources that has been fully optimized for end-users to perform novel data mining

Intergenerational programming during the pandemic: Transformation during (constantly) changing times

Intergenerational programs have long been employed to reduce ageism and optimize youth and older adult development. Most involve in-person meetings, which COVID-19 arrested. Needs for safety and social contact were amplified during COVID-19, leading to modified programming that engaged generations remotely rather than eliminating it. Our collective case study incorporates four intergenerational programs in five US states prior to and during COVID-19. Each aims to reduce ageism, incorporating nutrition education, technology skills, or photography programming. Authors present case goals, participants, implementation methods, including responses to COVID-19, outcomes, and lessons learned. Technology afforded opportunities for intergenerational connections; non-technological methods also were employed. Across cases, programmatic foci were maintained through adaptive programming. Community partners’ awareness of immediate needs facilitated responsive programming with universities, who leveraged unique resources. While new methods and partnerships will continue post-pandemic, authors concurred that virtual contact cannot fully substitute for in-person relationship-building. Remote programming maintained ties between groups ready to resume shared in-person programming as soon as possible; they now have tested means for responding to routine or novel cancellations of in-person programming. Able to implement in-person and remote intergenerational programming, communities can fight ageism and pursue diverse goals regardless of health, transportation, weather, or other restrictions

A Baseline for the Multivariate Comparison of Resting-State Networks

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease

Integrative radiogenomic profiling of squamous cell lung cancer

Radiation therapy is one of the mainstays of anti-cancer treatment, but the relationship between the radiosensitivity of cancer cells and their genomic characteristics is not well defined. Here we report the development of a high-throughput platform for measuring radiation survival in vitro and its validation by comparison to conventional clonogenic radiation survival analysis. We combined results from this high-throughput assay with genomic parameters in cell lines from squamous cell lung carcinoma, which is standardly treated by radiation therapy, to identify parameters that predict radiation sensitivity. We confirmed that activation of NFE2L2, a frequent event in lung squamous cancers, correlates with radiation resistance. NFE2L2 knockdown conferred both growth arrest and radiation sensitivity in a cell line with NFE2L2 mutation but not in a wild type cell line. An expression-based, in silico screen nominated inhibitors of PI3K as NFE2L2 antagonists. We showed that the selective PI3K inhibitor, NVP-BKM120, both decreased NRF2 protein levels and sensitized NFE2L2 or KEAP1 mutant cells to radiation. To assess determinants of radiation sensitivity further, we combined results from this high-throughput assay with single-sample gene set enrichment analysis (ssGSEA) of gene expression data. The resulting analysis identified pathways implicated in cell survival, genotoxic stress, detoxification, and innate and adaptive immunity as key correlates of radiation sensitivity. The integrative, high-throughput approach shown here for large-scale profiling of radiation survival and genomic features of solid-tumor derived cell lines should facilitate tumor radiogenomics and the discovery of radiation sensitizers and protective agents

Advanced life support 2015 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendations

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Part 4: Advanced Life Support: 2015 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations

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