Arielle Crestol

image showing a figure that illustrates the brain age prediction framework, whereby a machine learning algorithm is trained with multiple brain MRI images and then tested with new brain data. The new brain data is without an age tag, meaning the machine learning algorithm has to estimate age based on its training. The resulting brain predicted age (here 11) is compared to the actual age in the last panel (here 9), and a difference between them is calculated, known as the brain age gap (here 2).

New preprint! 🗞️

Lucy Whitmore and I discuss the many potential challenges in using the brain age prediction framework in children and adolescents, and make recommendations for future directions.

🔗: osf.io/preprints/ps...

Here I was thinking Selena only had eyes for me

After 5 years of data collection (179 first-time mothers + controls), the first bemother.eu/en/ paper is out! We uncovered a U-shaped trajectory in maternal GM volume, resolving a long-standing puzzle in the maternal brain field. www.nature.com/articles/s41...

me getting ready to join three new social media sites a year until I die

Very cool paper I got to work on highlighted here 👇

Hi 👋 I'm a PhD candidate in Oslo studying how genetics, mental disorders, and sex hormones impact brain health. I am also cat mom to Cosmo 🐈‍⬛.