Charting brain growth and aging at high spatial precision
Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2–100) and used normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision-making.Read more…
Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2–100) and used normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision-making.Read more…
Type | Path | Last modified | Size | Actions |
---|---|---|---|---|
.gitignore | 2 years, 9 months ago | 1.8KiB |
|
|
Dockerfile | 2 years, 9 months ago | 432.0B |
|
|
LICENSE | 2 years, 9 months ago | 34.3KiB |
|
|
README.md | 2 years, 9 months ago | 124.0B |
|
|
docs | 2 years, 9 months ago | 11.2MiB | ||
elife-72904.xml | 2 years, 9 months ago | 157.0KiB |
|
|
elife-72904.xml.media | 2 years, 9 months ago | 17.0MiB | ||
elife-72904_article_code.ipynb Main | 2 years, 9 months ago | 2.3MiB |
|
|
index.html | 2 years, 9 months ago | 462.4KiB |
|
|
index.html.media | 2 years, 9 months ago | 6.6MiB | ||
models | 2 years, 9 months ago | 121.2MiB | ||
nm_utils.py | 2 years, 9 months ago | 8.5KiB |
|
|
requirements.txt | 2 years, 9 months ago | 30.0B |
|