Brain charts for the human lifespan
By R. Bethlehem, J. Seidlitz, S. White, Jacob W. Vogel, K. Anderson, C. Adamson, S. Adler, George S. Alexopoulos, E. Anagnostou, A. Areces-Gonzalez and 185 more. Nature, 2021
1,503 citations recorded by the source corpus.
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 weight1. 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 trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, 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. MRI data from more than 100 studies have been aggregated to yield new insights about brain development and ageing, and create an interactive open resource for comparison of brain structures throughout the human lifespan, including those associated with neurological and psychiatric disorders.
Subfields: Cognitive Neuroscience, Health, Toxicology and Mutagenesis, Pediatrics, Perinatology and Child Health
View Paper: https://www.semanticscholar.org/paper/29901962c18bc6bb4a5b86a7fece66c9ddc54a57
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
Citations in the corpus
- The development of aperiodic neural activity in the human brain (2024, unknown): “This adds to the finding that sensorimotor cortical development—as indexed by neural noise—stabilizes during young adulthood, challenging models of early sensorimotor development based on cortical structure 1,99 .”
- Resting-state periodic and aperiodic brain oscillations from birth to preschool years: Aperiodic maturity predicts developmental course (2026, unknown): “Scientists examining the maturation of RS neural activity often study the dominant frequency, referred to in older children and adults as the ‘peak alpha frequency ’ and in infants as the infant ‘alpha ’ rhythm or the ‘infant dominant frequency ’ (Lodder and van Putten, 2011; Marcuse, 2008; Bethlehe”
- Lifespan brain age prediction based on multiple EEG oscillatory features and sparse group lasso (2025, unknown): “The brain aging process is a multidimensional phenomenon that involves the omics change at the molecular level and structural and functional changes at the neuronal level (Rutledge et al., 2022; Bethlehem et al., 2022).”
- Rhythmic visual stimulation as a window into early brain development: A systematic review (2023, background): “3 Over 80% of structural brain development takes place in the first years of life (Bethlehem et al., 2022), a period of vast ontogenetic change and unparalleled importance for the developing”