Researchers From Russia Develop MRI Platform That Can Predict Intelligence Levels In Children
Source: Thailand Medical News Dec 28, 2019 4 years, 11 months, 3 weeks, 3 days, 21 hours, 54 minutes ago
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 development.
MRI 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 data.
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 level and the target variable independent from age, gender,
brain size or
MRI scanner used.
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
