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The Bridge Between AI and Learning Difficulties

By: Sai Srihaas Potu

In today’s research environment, children’s diet, physical activity, and other lifestyle factors are commonly studied in the context of health, independent of their effect on cognition and learning. Although it is reasonable to expect that the lifestyle factors explored in health-focused research are intertwined with cognition and learning processes. This thematic review provides an overview of knowledge connecting the selected lifestyle factors of diet, physical activity, and sleep hygiene to children’s cognition and learning. Research from studies of diet and nutrition, physical activity and fitness, sleep, and broader influences of cultural and socioeconomic factors related to health and learning, show that these factors play an important role in the developmental process of children’s cognition and learning stages.

Learning is defined as acquiring new knowledge and skills. It is a critical yet complex process in human development and is ubiquitous in early childhood. Young children learn everyday behaviors, skills, and other knowledge and functions at a pace unparalleled by any other lifecycle stage. Both social skills and academic performance predict children’s probability to be gainfully employed later in life. Therefore, it is in society’s best interest that learning should be optimized for all children. The foundational knowledge and skills acquired during early childhood set the trajectory for learning in the subsequent decades, if not for the entire life.

Consequently, learning difficulties like dyslexia are affecting many kids in our society today. Learning difficulties affect how a person learns to read, write, and speak. They are caused by differences in the brain, most often in how it functions but also sometimes in its structure. These differences affect the way the brain processes information. Subsequently, children with learning disabilities may be at greater risk for certain conditions compared to other kids. Scientific research on this topic is key in order to recognize and create possible treatment solutions for these conditions.

Conventional neuroscience suggests that various regions of the brain serve as predictors for different disorders associated with learning disabilities. However, a new neuroscience study has findings that go against existing views on developmental disorders. Researchers in England were able to prove that the brain’s neural hubs, rather than brain regions, are a strong predictor of learning-related cognitive problems in children by using artificial intelligence (AI) machine learning.

Learning disabilities in children is not uncommon. Seven million students, or 14 percent of all public-school students in the U.S. between the ages of 3 and 21, received special education services under the Individuals with Disabilities Education Act (IDEA) in 2017-18, according to a National Center for Education Statistics. In England, 14.9 percent of all pupils have special-education needs and 3.1 percent have an Education, Health, and Care Plan in January 2019 according to the British Department for Education.

In the study, the researchers used data from cognitive, behavioral, and learning tests, as well as magnetic resonance imaging (MRI), scans of 479 children for an artificial neural network that uses an unsupervised machine learning algorithm. Unsupervised machine learning can capture complex, non-linear relationships, which is useful in transdiagnostic studies.

The team used a growing hierarchical self-organizing map (GHSOM), a type of artificial neural network that is a variant of a self-organizing map (SOM) that is typically used for data visualization of high dimensional data space into a two-dimensional representation. GHSOMs have multiple layers arranged hierarchically, in which each layer has several independent SOMs. The algorithm spotted different brain profiles in the data. The researchers then created whole-brain white-matter connectomes using diffusion-weighted neuroimaging in order to test what would happen when disconnecting hubs.

The researchers discovered that children with poorly connected brain hubs had severe and widespread cognitive impairments, and those with well-connected brain hubs either had no cognitive issues or had select cognitive deficits. The findings emphasize the importance of focusing on the areas of cognitive issues when it comes to targeted interventions and less on the diagnostic classification itself—opening the door for novel therapeutics that target the connectivity of the brain’s hub in the future.

Learning difficulties have evolved throughout history and have been influenced by the social, political, and educational context at the time. The impact on children and adults depends on several factors, such as the severity of the diagnosis, its early detection, and the support received. Therefore it is essential to know what difficulties your child has and whenever there are doubts, consult the team of professionals dedicated to this area, psychologists, and psychologists. Follow your own pace, be aware that your emotions influence your child’s emotions, and above all understand that the learning process is going to be a challenge but it’s not impossible.


1. Chung Yen Looi, Jenny Lim, Francesco Sella. Transcranial random noise stimulation and cognitive training to improve learning and cognition of the atypically developing brain: A pilot study. Scientific Reports. 2017.

2. Hoyland A., Dye L., Lawton C.L. A systematic review of the effect of breakfast on the cognitive performance of children and adolescents. Nutritional Research Reviews. 2009.

3. Kelsey C., Dreisbach C., Alhusen J., Grossmann T. A primer on investigating the role of the microbiome in the brain and cognitive development. Developmental Psychobiology. 2018.

4. Roma Siugzdaite, Joe Bathelt, Joni Holmes, Duncan E. Astle. Transdiagnostic Brain Mapping in Developmental Disorders. Current Biology. 2020.


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