MRI Predict Intelligence Levels in Children?

A group of researchers from the Skoltech Center for Computational and Data-Intensive Science and Engineering (CDISE) took 4th place in the international MRI-based adolescent intelligence prediction competition. For the first time ever, the Skoltech scientists used ensemble methods based on deep learning 3D networks to deal with this challenging prediction task. The results of their study were published in the journal Adolescent Brain Cognitive Development Neurocognitive Prediction.

In 2013, the US National Institutes of Health (NIH) launched the first grand-scale study of its kind in adolescent brain research, Adolescent Brain Cognitive Development (ABCD, https://abcdstudy.org/), to see if and how teenagers' hobbies and habits affect their further brain development.

Magnetic Resonance Imaging (MRI) 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.

NIH scientists 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.

In their recent study, Skoltech researchers 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.

"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 3D 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," explains CDISE PhD student, Ekaterina Kondratyeva.

The results 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 Skoltech team was invited to present their new method at one of the world's most prestigious medical imaging conferences, MICCAI 2019, in Shenzhen, China.

Marina Pominova, Anna Kuzina, Ekaterina Kondrateva, Svetlana Sushchinskaya, Evgeny Burnaev, Vyacheslav Yarkin, Maxim Sharaev.
Ensemble of 3D CNN Regressors with Data Fusion for Fluid Intelligence Prediction.
ABCD-NP 2019. Lecture Notes in Computer Science, vol 11791, 2019. doi: 10.1007/978-3-030-31901-4_19.

Most Popular Now

MEDICA 2024 + COMPAMED 2024: Adapted Hal…

11 - 14 November 2024, Düsseldorf, Germany. The final preparations for MEDICA 2024 and COMPAMED 2024 in Düsseldorf have begun. A total of more than 5,500 exhibitors from approximately 70 countries...

AI does Not Necessarily Lead to more Eff…

The use of artificial intelligence (AI) in hospitals and patient care is steadily increasing. Especially in specialist areas with a high proportion of imaging, such as radiology, AI has long...

Commission Joins Forces with Venture Cap…

The Commission has launched a Trusted Investors Network bringing together a group of investors ready to co-invest in innovative deep-tech companies in Europe together with the EU. The Union's investment...

Why the NHS is Seeking to Make Media Ser…

Opinion Article by Dean Moody, Healthcare Services Director, Airwave Healthcare. Tim Kelsey and Martha Lane Fox called for WiFi to be made available free of charge throughout the NHS back in...

An AI-Powered Pipeline for Personalized …

Ludwig Cancer Research scientists have developed a full, start-to-finish computational pipeline that integrates multiple molecular and genetic analyses of tumors and the specific molecular targets of T cells and harnesses...

Wearable Cameras Allow AI to Detect Medi…

A team of researchers says it has developed the first wearable camera system that, with the help of artificial intelligence (AI), detects potential errors in medication delivery. In a test whose...

Philips and Medtronic Advocacy Partnersh…

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, and Medtronic Neurovascular, a leading innovator in neurovascular therapies, today announced a strategic advocacy partnership. Delivering timely stroke...

AI could Transform How Hospitals Produce…

A pilot study led by researchers at University of California San Diego School of Medicine found that advanced artificial intelligence (AI) could potentially lead to easier, faster and more efficient...

New AI Tool Predicts Protein-Protein Int…

Scientists from Cleveland Clinic and Cornell University have designed a publicly-available software and web database to break down barriers to identifying key protein-protein interactions to treat with medication. The computational tool...

AI for Real-Rime, Patient-Focused Insigh…

A picture may be worth a thousand words, but still... they both have a lot of work to do to catch up to BiomedGPT. Covered recently in the prestigious journal Nature...

Start-Ups will Once Again Have a Starrin…

11 - 14 November 2024, Düsseldorf, Germany. The finalists in the 16th Healthcare Innovation World Cup and the 13th MEDICA START-UP COMPETITION have advanced from around 550 candidates based in 62...

New Research Shows Promise and Limitatio…

Published in JAMA Network Open, a collaborative team of researchers from the University of Minnesota Medical School, Stanford University, Beth Israel Deaconess Medical Center and the University of Virginia studied...