About this Research Topic
This Research Topic is the first volume of the 'Community Series in Individualized Assessment of Brain Aging across the Lifespan: Applications in Health and Disease'. The second volume can be found here
A growing body of research is using multivariate analysis of neuroimaging data to model brain aging across the lifespan in healthy and diseased groups. This has a broad range of implementations, including: disentangling age-related from disease-related changes in brain structure, providing reference curves for healthy brain development and aging, predicting personalized brain aging trajectories, early detection of neurodegenerative diseases, tracking disease progression, discovering protective and harmful environmental influences on health, and exploring how cognitive performance or neuropsychiatric symptoms relate to individual patterns of brain aging.
In this Research Topic, we welcome original methodological papers, empirical work, clinical studies and review papers that promote our understanding of: the modeling and prediction of individual brain aging characteristics across the life-course, the effects of various factors (e.g., genetic, environmental, therapeutic) on brain aging trajectories, cognitive or neuropsychiatric links to individual brain aging phenotypes. Among others, we encourage the submission of methodological papers that integrate and/or compare different types of neuroimaging data, automatic image processing pipelines, feature selection techniques, and pattern recognition algorithms. We encourage studies to include validation of the proposed algorithms with additional variables (e.g. cognitive scores, functional variables, disease stages) or implement predictions in samples independent from the training sample. This Research Topic is designed to highlight the benefits and strengths of different brain-aging models with regards to theoretical background, methodological or application-oriented features, and prospective utility in clinical research contexts, particularly for aiding diagnosis.
A growing body of research is using multivariate analysis of neuroimaging data to model brain aging across the lifespan in healthy and diseased groups. This has a broad range of implementations, including: disentangling age-related from disease-related changes in brain structure, providing reference curves for healthy brain development and aging, predicting personalized brain aging trajectories, early detection of neurodegenerative diseases, tracking disease progression, discovering protective and harmful environmental influences on health, and exploring how cognitive performance or neuropsychiatric symptoms relate to individual patterns of brain aging.
In this Research Topic, we welcome original methodological papers, empirical work, clinical studies and review papers that promote our understanding of: the modeling and prediction of individual brain aging characteristics across the life-course, the effects of various factors (e.g., genetic, environmental, therapeutic) on brain aging trajectories, cognitive or neuropsychiatric links to individual brain aging phenotypes. Among others, we encourage the submission of methodological papers that integrate and/or compare different types of neuroimaging data, automatic image processing pipelines, feature selection techniques, and pattern recognition algorithms. We encourage studies to include validation of the proposed algorithms with additional variables (e.g. cognitive scores, functional variables, disease stages) or implement predictions in samples independent from the training sample. This Research Topic is designed to highlight the benefits and strengths of different brain-aging models with regards to theoretical background, methodological or application-oriented features, and prospective utility in clinical research contexts, particularly for aiding diagnosis.
Keywords: Aging, Alzheimer’s dementia, brain, developmental programming, lifespan, maturation, MRI, neuroimaging, neurodegeneration, pattern recognition
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
