2017 — 2018 |
Byrne, Michelle Mills, Kathryn Allen, Nicholas (co-PI) [⬀] Pfeifer, Jennifer (co-PI) [⬀] Sabb, Fred |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Modeling Developmental Change: Practical Integration of Advanced Neuroimaging and Statistical Methods @ University of Oregon Eugene
Neuroimaging techniques, including structural and functional magnetic resonance imaging (MRI), have allowed researchers to investigate the neural bases of developmental changes in cognition. In recent years, it has become more common for researchers to obtain multiple measures of the same individual across development. These longitudinal MRI datasets require special consideration for processing and analysis, yet the field as a whole has not standardized best practices for these datasets, which could be one reason why it is difficult to replicate results across laboratories and research studies. A two-day workshop will be open to 60 developmental neuroimaging researchers and will teach best practices for processing, analyzing, modeling, and interpreting longitudinal neuroimaging data. This will help researchers conduct robust and consistent research on how the brain and cognition change across development. Importantly, this workshop will fund at least nine students or trainees who are planning to, or are directly working with, longitudinal neuroimaging data, providing strong practical skills for emerging research scientists in the field of developmental cognitive neuroscience.
It is both timely and vital to hold a workshop for researchers in the field of developmental cognitive neuroscience to examine differences in longitudinal modeling, statistical processing and analysis, and interpretation. Recent work has uncovered how methodological differences may be adversely affecting replicability in the field of neuroimaging, and there is an increasing drive to validate the processing and statistical techniques that are employed in neuroimaging research. There has also been increasing support for standardization of techniques and reporting criteria, such as the recent Brain Imaging Data Structure (BIDS) protocol for organizing and describing MRI datasets. The overarching aim of this workshop is to teach best practice guidelines for processing, analyzing, modeling, and interpreting longitudinal structural and functional neuroimaging data, which will inform our knowledge of how the brain and cognition change across development. It will address additional statistical concerns specific to longitudinal neuroimaging that also need validation, and consider standardization of techniques and reporting criteria that will improve the comparability of findings. The main outcome of this workshop will be that researchers leave with answers to questions about processing longitudinal functional and structural MRI data and the correct tools to move forward with research in developmental cognitive neuroscience. This type of work has the potential to answer fundamental questions about neural plasticity and sensitive periods of cognitive development through observing neural changes during learning. It is also inherently related to the fields of developmental affective, social, and clinical neuroscience, and therefore has the potential to translate directly to the prevention and treatment of emerging psychopathology. This workshop will push the field of developmental cognitive neuroscience forward to develop robust and precise models that have strong translational applications for public policy.
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0.966 |
2019 |
Mills, Kathryn L |
R25Activity Code Description: For support to develop and/or implement a program as it relates to a category in one or more of the areas of education, information, training, technical assistance, coordination, or evaluation. |
Abcd Workshop On Brain Development in Relation to Mental Health
Project Summary/Abstract Abstract: This project will fund a five-day immersive workshop that will prepare junior and senior researchers alike to utilize the ABCD data for investigations of brain development in relation to mental health. The workshop will focus on training researchers on reproducible neuroimaging methods, predictive modeling approaches, and clinical mental health outcomes, with equal emphasis on theory and application. The workshop will be composed of instructional, tutorial, roundtable, break-out session, and open hacking components. Workshop attendees will immediately apply what they learn during the workshop by competing in a hackathon challenge to develop classification tools for mental health related outcomes based on brain imaging data, with attention to relevant social and environmental factors. The main outcome of this workshop will be that researchers leave with answers to questions about analyzing ABCD data with newly developed tools, with a deeper understanding of the theory behind these tools as well as analytic considerations for highly dimensional data. This will include training on methodological considerations regarding clinical outcomes and fostering collaboration between computational and clinical researchers across career stages. Another principal aim of this workshop is to contribute to national efforts to diversify the pool of highly trained biomedical researchers by recruiting a diverse group of attendees by eliminating participation barriers associated with social inequality, including financial burden, parenting, or disability, and by creating a safe and welcoming environment during the workshop. The immediate goal of this workshop is for attendees to acquire the following competencies: a) knowledge of analytical and statistical considerations specific to large, open datasets; b) how to conduct reproducible research, with emphasis on neuroimaging; c) knowledge of theory behind predictive modeling and ability to apply newly developed tools; d) design and execute investigations with clinically meaningful outcomes; e) identify social and environmental factors that impact the development of psychopathology; and f) communicate with researchers from diverse backgrounds and build interdisciplinary teams. The dissemination goals of the proposed workshop include publishing papers, presenting at conferences, and making any code or analytic techniques developed during the workshop available in online repositories for the broader research community. A long- term goal of this workshop is to form sustainable interdisciplinary networks between researchers from development, clinical, and computational backgrounds as these areas of expertise are all needed to advance our understanding of mental health.
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0.946 |
2020 |
Mills, Kathryn L |
R25Activity Code Description: For support to develop and/or implement a program as it relates to a category in one or more of the areas of education, information, training, technical assistance, coordination, or evaluation. |
Modeling Developmental Change in the Abcd Study: Longitudinal Analyses For Clinical Outcomes
Project Summary/Abstract Abstract: This project will fund a five-day immersive workshop that will prepare junior and senior researchers alike to utilize the ABCD data for longitudinal investigations of brain development in relation to mental health. The workshop will focus on training researchers on reproducible neuroimaging methods, longitudinal modeling approaches, and clinical mental health outcomes, with equal emphasis on theory and application. The workshop will be composed of instructional, tutorial, roundtable, break-out session, and open hacking components. Workshop attendees will immediately apply what they learn during the workshop through group projects (?open hacking?) focused on applying longitudinal approaches to relate brain development to mental health measures in the ABCD dataset. The main outcome of this workshop will be that researchers leave with answers to questions about analyzing the longitudinal ABCD data, with a deeper understanding of the theory behind modeling approaches as well as analytic considerations for highly dimensional data. This will include training on robust methodology appropriate for clinical outcomes, as well as fostering collaboration between computational and clinical researchers across career stages. Another principal aim of this workshop is to contribute to national efforts to diversify the pool of highly trained biomedical researchers by recruiting a diverse group of attendees by eliminating participation barriers associated with social inequality, including financial burden, parenting, or disability, and by creating a safe and welcoming environment during the workshop. The immediate goal of this workshop is for attendees to acquire the following competencies: a) knowledge of analytical and statistical considerations specific to large-scale, open, longitudinal datasets; b) how to conduct reproducible research, with emphasis on secondary analysis of neuroimaging data; c) knowledge of theory behind longitudinal modeling and ability to apply these models using open-source software; d) design and execute investigations with clinically meaningful outcomes; e) identify social and environmental factors that impact the development of psychopathology; and f) communicate with researchers from diverse backgrounds and build interdisciplinary teams. The dissemination goals of the proposed workshop include forming working groups to publish papers and present at conferences, as well as making all code or analytic techniques developed during the workshop available in online repositories for the broader research community. A long-term goal of this workshop is to form sustainable interdisciplinary networks between researchers from development, clinical, and computational backgrounds as these areas of expertise are all needed to advance our understanding of the development of mental health.
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0.946 |