Brain charts for the human lifespan
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
- Teacher spread
- 0.271 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Abstract Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight 1 . Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories 2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones 3 , showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Nature
- Topic
- Health, Environment, Cognitive Aging
- Field
- Environmental Science
- Canadian institutions
- McGill University Health CentreUniversité de MontréalWestern UniversityHospital for Sick ChildrenUniversity of TorontoMontreal Neurological Institute and HospitalDouglas Mental Health University InstituteMcGill UniversityCentre Hospitalier Universitaire Sainte-JustineQueen's UniversityHolland Bloorview Kids Rehabilitation Hospital
- Funders
- Biotechnology and Biological Sciences Research CouncilNational Institute of Mental HealthMedical Research CouncilNational Institute for Health and Care ResearchNational Institute on AgingUK Research and InnovationEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentMQ: Transforming Mental HealthNational Institute of Biomedical Imaging and BioengineeringWellcome Trust
- Keywords
- BiologyNeuroscienceComputational biologyComputer science
- Has abstract in OpenAlex
- yes