MathDevBML · Prediction of Children's Math Learning Disability Using Longitudinal Brain Data and Machine Learning
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2022-05-12 → 2024-05-11
- EU contribution
- €184,708
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
Prediction of Children's Math Learning Disability Using Longitudinal Brain Data and Machine Learning
Mathematics is the fundamental basis of modern science and technology. However, mathematical skills differ among individuals, and 5-7% of the population suffers from mathematical learning difficulty (MLD). MLD may result in poor professional and personal outcomes, including severe financial difficulties. Early detection of potential MLD in young children is thus essential for providing appropriate support, preventing the worsening of difficulties, and achieving financial well-being. In addition, research on the biological basis associated with mathematical difficulties and the development of diagnostic techniques for these difficulties would be beneficial to society, as recent developments in the field of artificial intelligence have brought more attention to the importance of mathematical skills. Previous studies used noninvasive brain measurement techniques such as functional magnetic resonance imaging (fMRI) to reveal brain activity differences between MLD and typically developing children. Other studies further applied machine learning techniques to neuroimaging data to predict MLD. However, participating children in most of these studies had already experienced math education in elementary school. Moreover, previous studies used model-free methods such as support vector classification, and model-based machine learning approach, which facilitates interpretation of brain representations based on latent features, has been largely limited. The project aims to address these gaps based on three objectives. The first objective is to evaluate the neural basis of MLD using longitudinal functional magnetic resonance imaging (fMRI) data collected from preschool children. The second objective is to apply model-based machine learning methods to classify MLD. The third objective is to evaluate the developmental relationship between mathematical difficulty and other cognitive abilities.
Data: CORDIS, © European Union
Project objective
Mathematics is the fundamental basis of modern science and technology. However, individuals differ in mathematical ability, and 5%–7% of the population suffers from a math learning disability (MLD). To provide appropriate support for children with MLD, detecting MLD before entering the formal education system is essential. Previous studies have identified some of the neural correlates of MLD; however, computational approaches to predict MLD have been limited. Also, most studies recruited children who were enrolled in elementary school, which is problematic because negative math experience may worsen the difficulties. This research project aims to address these gaps. By combining brain data of preschoolers with state-of-the-art machine learning techniques, I will construct a computational model aiming at predicting MLD before children enter elementary school. The host laboratory of Dr. Jérôme Prado is currently conducting magnetic resonance imaging (MRI) experiments in 5-year-old preschoolers. Participants are presented with visual stimuli consisting of dot patterns, and their brain activity is measured using functional MRI. I will repeat the same MRI task two years later (when children are 7). The math skills of participants will be measured at the age of 7. Multiple algorithms (model-based and model-free approaches) will be applied to the brain data at the age of 5 to predict the occurrence of MLD at the age of 7. Computational models will be applied to other cognitive abilities (language, reasoning), and the influence on atypical math development will be examined. I will benefit from the strong administrative support and advanced neuroimaging resources at the Lyon Neuroscience Research Center, where I will receive training in technical and leadership skills. This research project is an excellent opportunity for me and the host to contribute to the growth of an innovative research field combining developmental neuroscience and machine learning techniques.
Original text from CORDIS.
Participants
- INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE · ParisCoordinatorFrance
Links
Data: CORDIS, © European Union