The main objective of "Lifebrain" is to identify the determinants of brain, cognitive and mental (BCM) health at different stages of life. By integrating, harmonising and enriching major European neuroimaging studies across the life span, we will merge fine-grained BCM health measures of more than 5000 individuals. Longitudinal brain imaging, genetic and health data are available for a major part, as well as cognitive and mental health measures for the broader cohorts, exceeding 27,000 examinations in total. By linking these data to other databases and biobanks, including birth registries, national and regional archives, and by enriching them with a new online data collection and novel measures, we will address the risk factors and protective factors of BCM health. We will identify pathways through which risk and protective factors work and their moderators. Exploiting existing European infrastructures and initiatives, we hope to make major conceptual, methodological and analytical contributions towards large integrative cohorts and their efficient exploitation. We will thus provide novel information on BCM health maintenance, as well as the onset and course of BCM disorders. This will lay a foundation for earlier diagnosis of brain disorders, aberrant development and decline of BCM health, and translate into future preventive and therapeutic strategies. Aiming to improve clinical practice and public health we will work with stakeholders and health authorities, and thus provide the evidence base for prevention and intervention.
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http://dx.doi.org/10.1016/j.eurpsy.2017.12.006 | DOI Listing |
PLOS Glob Public Health
January 2025
Texas Children's Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston, Texas, United States of America.
NPJ Digit Med
January 2025
Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, USA.
This article critiques the shift towards personalized AI in healthcare and other high-stakes domains, cautioning that without careful deliberation, customized AI systems can compromise the diversity and reach of human knowledge by restricting exposure to critical information that may conflict with users' preferences and biases. Customized AI should be applied with caution and intention where access to a wide and diverse range of perspectives is essential for impartial, informed decision making.
View Article and Find Full Text PDFVaccine
January 2025
Division of Microbiology and Infectious Diseases, National Institutes of Health, Rockville, MD, United States.
Acad Psychiatry
January 2025
University of Texas Southwestern Medical Center, Dallas, TX, USA.
Nat Commun
January 2025
Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
Computational methods for estimating missense variant impact suffer from inconsistent performance across genes, which poses a major challenge for their reliable use in clinical practice. While ensemble scores leverage multiple prediction methods to enhance consistency, the overrepresentation of certain genes in the training data can bias their outcomes. To address this critical limitation, we propose a gene-specific ensemble framework trained on reference computational annotations rather than on clinical or experimental data.
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