Researchers From Russia Develop MRI Platform That Can Predict Intelligence Levels In Children
Researchers from the Skolkovo Institute of Science and Technology (Skoltech) Russia, Center for Computational and Data-Intensive Science and Engineering (CDISE) have recently developed a MRI-
based adolescent intelligence prediction
platform. For the first time ever, the Skoltech scientists used ensemble methods based on deep learning 3-D networks to deal with this challenging prediction task. The results of their study were published in the journal Adolescent Brain Cognitive Development Neurocognitive Prediction
Credit: Skolkovo Institute of Science and Technology
The US National Institutes of Health (NIH) in 2013, launched the first grand-scale study of its kind in adolescent brain
research, Adolescent Brain
Cognitive Development (ABCD, abcdstudy.org/), to see if and how teenagers
' hobbies and habits affect their further brain
or Magnetic Resonance Imaging is a common technique used to obtain images of human internal organs and tissues. Scientists wondered whether the intelligence
level can be predicted from an MRI brain
image. The NIH database contains a total of over 11,000 structural and functional MRI
images of children aged 9-10.
Scientists from NIH launched an international competition, making the enormous NIH database available to a broad community for the first time ever. The participants were given a task of building a predictive model based on brain images. As part of the competition, the Skoltech team applied neural networks for MRI
image processing. To do this, they built a network architecture enabling several mathematical models to be applied to the same data in order to increase the prediction accuracy, and used a novel ensemble method to analyze the MRI
The Skoltech researchers in their recent study, focused on predicting the intelligence
level, or the so called "fluid intelligence
," which characterizes the biological abilities of the nervous system and has little to do with acquired knowledge or skills. Importantly, they made predictions for both the fluid intelligence l
evel and the target variable independent from age, gender, brain
size or MRI
Dr Ekaterina Kondratyeva from Skoltech’s CDISe told Thailand Medical
News via a phone interview, "Our team develops deep learning methods for computer vision tasks in MRI
data analysis, amongst other things. In this study, we applied ensembles of classifiers to 3-D of super precision neural networks: with this approach, one can classify
an image as it is, without first reducing its dimension and, therefore, without losing valuable information."
The findings of the study helped find the correlation between the child's "fluid intelligence
" and brain anatomy. Although the prediction accuracy is less than perfect, the models produced during this competition will help shed light on various aspects of cognitive, social, emotional and physical development of adolescents
. This line of research will definitely continue to expand.
The team from Skoltech was invited to present their new method recently at one of the world's most prestigious medical imaging conferences, MICCAI 2019, in Shenzhen, China.
Reference : Marina Pominova et al, Ensemble of 3D CNN Regressors with Data Fusion for Fluid Intelligence Prediction, Adolescent Brain Cognitive Development Neurocognitive Prediction (2019). DOI: 10.1007/978-3-030-31901-4_19